The Best Time to Take Your Blood Pressure Pills

Majority of blood pressure medicines are one daily and people take their blood pressure medicines often in the morning. There have been some data based on our understanding of the diurnal variations or circadian rhythms that taking medications at bedtime may work better. For example, peak activity of the renin–angiotensin–aldosterone system (RAAS) occurs during sleep. Further, data from some uncontrolled studies have suggested that taking blood pressure medications at evening (or bedtime) may be beneficial. However, a randomized controlled clinical trial (the gold standard for scientific evidence) has been lacking. That is until now!

A recent study published in the European Heart Journal, by Hermida et al, addressed this very same question. Investigators enrolled 19 084 hypertensive patients (10 614 men and 8470 women) and assigned patients to take the entire dose of their blood pressure pills at bedtime or in the morning. Of all the patients, 1752 experienced either a death due to heart attack, heart attack without death, had stent placed in their heart blood vessels, developed new heart failure, or a stroke. Patients who were taking their medications had 45% lower risk of all outcomes combined, 56% lower risk of heart attach death, 34% lower risk of heart attack, 40% lower risk of stents in their heart blood vessels, 42% lower risk of heart failure, and 49% lower risk of stroke.

Note, these are large differences – these patients were taking their blood pressure pills but just taking pills at a different time made a huge difference. The difference is larger than many other pills that are often prescribed by physicians. If you, or your loved one, takes blood pressure pills, consider talking to your doctor if you should take your pills at bed-time.

Tele-Health and Liver Transplant

Liver transplantation is the only treatment that increases survival times of patients with decompensated cirrhosis. Patients who live further away from a transplant center are disadvantaged. Health care delivery via telehealth is an effective way to remotely manage patients with decompensated cirrhosis. Authors investigated the effects of telehealth on the liver transplant evaluation process.

Using regression models, authors evaluated the differential effects of telehealth vs. usual care on placement on the liver transplant waitlist. We also investigated the effects of telehealth on time from referral to initial evaluation by a transplant hepatologist, liver transplantation, and mortality.

Authors found that the use of telehealth was associated with a substantial reduction in time from referral to initial evaluation by a hepatologist and placement on the liver transplant waitlist-especially for patients with low MELD scores, with no changes in time to transplantation or pre-transplant mortality.

Maintaining Competency and Physician Responsibility

Santen et al wrote a viewpoint in JAMA and propose the following 5 considerations to address competency decline with age and with introduction of new technology and/or procedures.

1. Lifelong learning and mechanisms to ensure maintenance of certification.
2. Responsibility to keep training.
3. Choosing practices that involve less exposure to procedures or content for which physicians are no longer expert
4. Self-assessment and reflection.
5. Responsibility of the health care system to ensure competency of physicians and surgeons for performing procedures.

These proposals are common sense or a no-brainer. However, the devil is always in the details. For example, how to assess lifelong learning? What is the evidence behind certain methods of lifelong learning? Is it possible that the physicians who self-reflect and self-assess are the ones who need it the least? If so, how to encourage physicians to self-reflect? One can go on and on but I hope I made the point. But I want to make a larger point. Medical education obsessively teaches evidence-based medicine, however, very few, if any, medical education methods have even a weak evidence to support them (as long as we do not include expert advice into evidence). It will be rare to see any medical education methodology that has been rigorously studied like we study medical interventions. No wonder we have increasing burnout among medical students, residents, and physicians; it is time we face the fact that our prescriptions for teaching medicine lack scientific evidence.

Fragility Index for Randomized Controlled Trials

An interesting question – how ‘fragile’ are the results of a randomized controlled trial (RCT)?

RCTs are gold-standard for determining the efficacy of a treatment. RCTs randomly assigned patients to an intervention arm or to a placebo (or standard of care or active treatment) arm. Because patients are randomly assigned, we expect that all patient characteristics (genetic, environmental etc.) would be balanced in the two groups. RCTs are often published with significant results – small RCTs without significant results are generally either not published or published in low-tier journals and don’t get prominence

Even when we randomly assign patients to a treatment arm or control arm, there is always a possibility that the two arm may be imbalanced by chance on measured or unmeasured variables. Further, it is also possible that just by chance one group may have statistically significant result than the other group without any true benefit (or harm) of the treatment. While we generally use a p-value of less than 0.05 (which says that there is a 1 in 20 probability that the results are observed due to chance only – not the right definition but its simple!), there remains a small chance that results will be not significant.

In comes ‘Fragility Index’, a measure of determining the robustness of results. Fragility index is the minimum number of patients who must be moved from the nonevent group to the event group to turn a significant result nonsignificant. The fragility index is an easy to calculate index that provides an intuitive way to understand the precision of trial results. Knowing the fragility index and comparing it to the number of patients lost to follow-up can help to understand the uncertainty in evidence even when a study has positive results.

Khan et al, examined cardiovascular clinical trials published between 2007-17 in major Cardiology journals. All trials were large with >500 patient enrollment. Among the 123 RCTs the median fragility index was 13.In almost 1/3rd of trials, the number of patients lost to follow-up was more than the fragility index.

In another study Gaudino et al examined the clinical trials used to support evidence in cardiology guidelines and determined the fragility index. They found that more than a quarter of RCTs supporting current guidelines on myocardial revascularization have a fragility index 3 or lower. Further over 40% of trials had a fragility index which was lower than the number of patients lost to follow-up.

Vaso-Occlusive Crisis in Sickle Cell Patients

Hemoglobin S (HbS) is an abnormal form of hemoglobin and is transmitted through genetically from parents to a child. Normal hemoglobin is mostly made up of 4 globin chains; 2 alpha chains and 2 beta chains. The underlying problem is a single mutation in the beta-chain of the hemoglobin where adenine nucleotide is replaced with a thymidine nucleotide resulting in a missense mutation and changing the amino acid glutamate at no. 6 position with valine. This change in amino acids from glutamate to valine results in changes in the 3-dimensional structure of the beta-chain of hemoglobin. These changes include 1) lower affinity for oxygen, 2) ability to join with other hemoglobin molecules and form polymers, 3) increase oxidation of the RBC cell membrane proteins.

Normal hemoglobin gets oxygenated in the lungs and carries oxygen to the tissues. Oxygen is released in the tissues and hemoglobin gets deoxygenated and returns back to the lungs for oxygenation. Normal hemoglobin remains soluble within the RBC throughout this process. On the other hand, deoxygenated HbS starts forming long filamentous through polymerization of hemoglobin molecules. Importantly, the process takes several seconds in fresh red blood cells (RBC) before polymerizations occurs and there is sufficient time for RBCs to return back to the lungs and get oxygenated.

However, some cells continue to get stuck in blood vessels resulting in completion of polymerization and breakdown of RBCs within the vessels (called intravascular hemolysis). This chronic, slow process of premature destruction of RBC in sickle cell patients is the reason for chronic anemia as well as other slowly developing manifestations of sickle cell disease.

In sickle cell vaso-occlusive crisis, large number of blood vessels are blocked resulting in marked pain and morbidity. Typically, the crisis is precipitated by some event, such as dehydration or an infection. The event stimulates the vascular endothelium making them sticky through expression of cell-binding receptors on the surface. These receptors then bind with other receptors on the surface of RBCs, white blood cells, and platelets resulting in blockage of the vessel lumen. The red blood cells with HbS are already primed to binding with other cells through expression of certain proteins as well as exposure of certain lipid products on the surface. Thus, the vaso-occlusive crisis results from the interaction of HbS containing RBC, endothelium, white blood cells, and platelets.

Physician Burnout & Patient Satisfaction/Experience

There have been concerns that physician burnout leads to lower patient satisfaction. In fact, some studies have found that patient satisfaction is lower among physicians with higher levels of burnout. A meta-analysis published in 2018 assimilated data and reached to the same conclusion. However, the quality of data, and studies, is questionable and there is a need for better conducted studies to examine this relationship.

A recently published article, by Howell et al., in the Journal of Patient Experience found no relationship between patient satisfaction and physician burnout domains of exhaustion and disengagement. This is an interesting finding and has strong implications. What it shows is that despite having high levels of burnout, physicians are able to function in a way that their patients don’t see a difference based on their burnout level. The brunt of burnout is faced by physicians and they shield their patients from its effects, likely at high personal cost.

Obviously, there may be other explanations – for example, the tools used to measure physician burnout are not reliably measuring burnout or that the tools to measure patient satisfaction with physicians are inaccurate and have large measurement bias. Both of these two explanations are possible but less plausible.

AI in Health Care–National Academy of Medicine’s Perspective

Here is the full report

Here are some highlights:

1. Promoting population-representative data with accessibility, standardization, and quality is imperative
2. Prioritize ethical, equitable, and inclusive health care AI while addressing explicit and implicit bias
3. Contextualizing the dialogue of transparency and trust requires accepting differential needs.
4. Near-term focus is needed on augmented intelligence vs AI autonomous agents
5. Develop and deploy appropriate training and educational programs to support health care AI.
6. Leverage frameworks and best practices for learning health care systems, human factors, and implementation science to address the challenges in operationalizing health care AI
7. Balance innovation with safety via regulation and legislation to promote trust.

Vasodilatation in Acute Heart Failure

Acute heart failure is a condition when the cardiac output (the amount of blood pumped by heart every minutes) is not sufficient to meet the needs of the body. Cardiac output depends on how much blood is in the heart before it starts contracting (cardiac pre-load), how strongly hear contracts (cardiac contractility), how much resistance heart faces when pumping blood into the arteries (cardiac after-load, or peripheral vasoconstriction), and how many times heart beats in a minutes.

For a failing heart, increasing the force of contraction and decreasing the pressure against which it pumps blood (after-load or vasoconstriction) are important factors. In fact, peripheral vasodilators such as ACE inhibitors or ARB are standards of care for heart failure patients. One may ask, what if we decrease the pressure against which heart pumps blood really low, in other words, if we cause high (intensive) vasodilation with drugs. This particular hypothesis was tested in The GALACTIC Study recently published in JAMA.

Interestingly, authors found no benefit of intensive vasodilatation on composite endpoint of death or rehospitalization. Further, the intensive vasodilation arm had higher risk of adverse effects such as worsening renal function, hypokalemia, dizziness, and hypotension.

The results are important because they suggest that while we focus on cardiac output, blood flow to individual organ (or fraction of cardiac output received by various organs may be as important, if not more important. Intensive vasodilatation likely results in poor perfusion to various organ resulting in increased adverse effects.

Sleepiness after work, Burnout, and Empathy

Tiredness after work, particularly shift work is common. How this relates to burnout and empathy is poorly explored. This study assessed the effects of shift (Day, Night), time of day (AM, PM), and gender (Male, Female) on sleepiness, empathy, and burnout in medical students. Working a 12 h night shift resulted in increased sleepiness as compared to a 12 h day shift. Sleepiness after a night shift resulted in differences in empathy. The sleepier the participant after working the 12 h shift, the lower their emotional empathy score.Similarly, sleepiness was associated with higher levels of burnout and females were affected more than men.

Artificial Intelligence in a Smartwatch for Atrial Fibrillation

An interesting study by Perez et al in NEJM found some very interesting things:

1. A large number of people opted-in to be part of the study (over 400K)
2. In about 4 months, about 0.5% participants were noted to have irregular heart rate – a relatively small percentage than one would expect.
3. Of the people who returned 7-day monitoring after being notified of irregular heart beat, about one-third had atrial fibrillation. This is a significant number of atrial fibrillation in otherwise asymptomatic individuals.

The study highlights several important things, some noted above. However, what it does not tell us if the use of smartwatch to identify atrial fibrillation in otherwise healthy people results in improved health outcomes or not. A future study should be able to evaluate this question. For now, at least we know we can depend on smartwatch to identify atrial fibrillation.

Burnout among Nurses

Dyrbye et al conducted a national survey of the US nurses (response rate 26.2%) and found that 35.3% had burnout symptoms. Interestingly, 30.7% of the respondents also had symptoms of depression. Quite interestingly, authors found that nurses who had higher burnout were more likely to have been absent 1 or more days during the last month and had poor work-performance.

Critique: Authors need to be commended for conducting a national study of nurses. However, this study has the same issue that we find in other burnout literature; person who is burnout is also the one who determines if they have poor performance or not. At least theoretically, one would assume that a person who is burnout will look more negatively towards themselves and their work than a person who is not burned out; we would expect a high correlation between the two. On the other hand, a study in which nurses’ superiors could have been asked to comment on performance would have generated more reliable data, however, such study would have been quite expensive requiring significant external funding.

Burnout in Healthcare–A systems approach

Interesting and thought-provoking analytical piece by Montgomery et al which correctly points out that the responsibility of mitigating burnout lies with the institution and that the practice of putting the responsibility on healthcare workers should be abandoned. Currently, most places try to focus on the healthcare worker – asking them to take better care of themselves, be resilient, cope with stressors on their own, etc. They propose the following:

1. Provider burnout should be added to the current assessment of healthcare quality
2. Burnout should be assessed at the departmental/unit level (and I would add that it should be measured at least annually).
3. Development of health workplaces should be a major goal
4. Key questions concerning research and practice should not be coming from researchers alone.

Personally, I do believe that there are not enough researchers examining the interventions needed to address burnout. There is lack of funding from national funding agencies and from locally from institutions.

Some Recent Physician Burnout Literature

An abstract, presented at the the annual meeting and published in the Journal of American College of Surgeons’ supplement examined relationship between burnout and involvement of surgeons in medical device development/innovation.

As is often found in medical students across the world, New Zealand’s medical students also appear to have high levels of burnout. Farrell et al surveyed one of the two medical school in New Zealand and found that the prevalence of exhaustion was 77% while of disengagement was 68%. Another proof that burnout in medical profession is a worldwide phenomenon.

A study examined burnout among Egyptian ICU physicians and found interesting results. The level of exhaustion was 38.8%, somewhat better than generally found in the West. However, the level of depersonalization was 70.1%. This is really surprising; do Egyptian physicians really feel that much disconnected or disengaged from their patients?

Should Older Patients with Subclinical Hypothyroidism be Treated with Thyroid Replacement?

With increasing age, many patients develop subclinical hypothyroidism. Subclinical hypothyroidism is defined by the presence of elevated thyroid stimulating hormone (TSH) but normal free T4 levels. Some patients with subclinical hypothyroidism also have symptoms that are not uncommon with increasing age such as constipation, tiredness, mental slowness, and fatigue.

Prior studies have found no association between treating patients with subclinical hypothyroidism with thyroid replacement therapy and resolution of symptoms. However, very old individuals (older than 80 years) are often not included in the studies.

To examine if treatment of subclinical hypothyroidism with thyroid replacement therapy results in resolution of such symptoms, Moojiraat et al. combined data from two randomized controlled trials. Combining data from two trials results in increased number of patients above 80 (and hence power of the study to detect a difference).

Contrary to expectations, but consistent with previous findings, thyroid replacement therapy does not result in resolution of symptoms in patients with subclinical hypothyroidism. Instead, there may be some increase in adverse events.

Prediction of COPD Exacerbations – ACCEPT Tool

Preventing exacerbations in patients with COPD is a major goal. If we can identify patients who are at higher risk of exacerbations, we may be able to tailor more aggressive therapy to such patients. This will result in better utilization of resources, improved risk/benefit ratio, and will not expose low-risk patients to the adverse effects of aggressive therapies. Thus, predicting exacerbation risk in individual patients can guide these clinical decisions. Unfortunately, there are no externally validated and implementable tools to predict COPD exacerbation.

That is until now: Adibi and colleagues have uploaded their manuscript to a preprint server (bioRxiv) which used data from three randomized trials to develop ACCEPT, a clinical prediction tool based on routinely available predictors for COPD exacerbations. Authors externally validated ACCEPT in a large, multinational prospective cohort. ACCEPT appears to be the first COPD exacerbation prediction tool that jointly estimates the individualized rate and severity of exacerbations. The tool is designed to be easily applicable in clinical practice and is accessible as a web application.

Issues with Current Physician Burnout Research

The relationship of physician burnout with patient outcomes has been often reported but the studies have been of poor quality and/or report the outcomes as perceived by physicians. Obviously, using physician’s perception of burnout and physician’s perception of patient outcomes is circular in nature; a burnout physician may be likely to think that the patient care provided by her is of lower quality.

A systemic review, in Annals of Internal Medicine highlights the similar issue. Moreover, it also shows that where a relationship is shown, the effect sizes tend to be larger suggesting that publication bias may be contributing to preponderance of studies showing an effect (because studies showing no effect are not getting published.

The physician burnout research (and researchers) should start using rigorous scientific methods to define outcomes, predictors, associations, and evaluation of interventions. Otherwise, such an important topic will get a poor reputation due to low-quality research.

National Academy of Medicine Talks about Physician Burnout

In response to concerning rates of depression, stress, and burnout among US medical students and clinicians, the National Academy of Medicine (formerly Institute of Medicine) launched the Action Collaborative on Clinician Well-Being and Resilience in 2017.

Incorporating input from experts in human factors and systems engineering and health informatics, as well as medical, nursing, pharmacy, and dentistry experts and educators, the committee took a systems approach to clinician burnout.

The committee’s systems model for professional well-being and clinician burnout has 3 levels: frontline care delivery, health care organization, and external environment, which together influence the work system factors that contribute to clinician burnout and professional well-being. The work system factors often extend across more than 1 system level (care delivery, health care organization, and external environment), and improvement can occur at every level to relieve workplace stress. The recommendations from the report, organized under 6 overarching goals, reflect the crosscutting nature of the identified factors contributing to clinician burnout and professional well-being.

1. Create positive work environments

2. Create positive learning environments

3. Reduce administrative burden

4. Enable technology solutions

5. Provide support to clinicians and students

6. Invest in research

Angiotensin Receptor Blockers and Suicide Risk

Interesting conclusions in a study reported in the JAMA Network Open:

The use of ARBs may be associated with an increased risk of suicide compared with ACEIs.

Investigators matched 964 cases to 3856 controls. Compared to ACE inhibitors, ARBs were associated with 63% higher risk of death by suicide. The results remained significant (60% increase) when individuals with history of self-harm were excluded.

Proliferation of Risk Factors for Physician Burnout

Several authors have promoted various risk factors for physician burnout. Below are some that I noticed in my email:

Moral Injury:

Kopacz MS, Ames D, Koenig HG. It's time to talk about physician burnout and moral injury. Lancet Psychiatry. 2019 Nov;6(11):e28. doi:10.1016/S2215-0366(19)30385-2. PubMed PMID: 31631880.

Low or lack of Mindfulness:

Lebares CC et al. Key factors for implementing mindfulness-based burnout interventions in surgery. The American Journal of Surgery (in Press)

Lack of Resilience and Grit

Shakir HJ, Cappuzzo JM, Shallwani H, Kwasnicki A, Bullis C, Wang J, Hess RM,Levy EI. Relationship of Grit and Resilience to Burnout Among US NeurosurgeryResidents. World Neurosurg. 2019 Oct 16. pii: S1878- 750(19)32658-0. doi:10.1016/j.wneu.2019.10.043. [Epub ahead of print]  PubMed PMID: 31629138.

Attacks on the Calling of Medicine

Stewart MT, Serwint JR. Burning without burning out: A call to protect thecalling of medicine. Curr Probl Pediatr Adolesc Health Care. 2019 Oct 17:100655. doi: 10.1016/j.cppeds.2019.100655. [Epub ahead of print] PubMed PMID: 31631025

Above are some of the examples of risk factors cited in literature and the list continues to grow with each passing day. There are folks who have developed ‘Burnout Prevention Programs’ around these risk factors. Some even market these programs and make money.

My concern stems from the fact that while there may be strong conceptual underpinnings for at least some of these risk factors, rigorous studies examining the relationship are missing. The current state of evidence is so poor that a clinician will not take seriously risk factors with such limited evidence when managing a patient. Then, why, physicians doing this to themselves.

In particular, the worrisome part is the so-called prevention programs. Where is the evidence to support that such interventions work? If we don’t accept evidence for our patients without randomized clinical trials or at least strong observational studies, why we accept such intervention programs without evidence of efficacy?

Waste in US Healthcare System

US healthcare system is quite unique; it is the costliest system in the world but the outcomes are poor. In other words, the value of healthcare (outcomes/cost) is low and a significant amount expense goes in waste. There have been efforts to reduce waste in the healthcare system but it is unclear how effective such efforts have been.

In one estimate, the overall annual cost of waste in the healthcare is between $760 to $935 billion or 25% of the total healthcare spending. Authors also tried to estimate the amount of waste in different domains. Of interest is that fact that administrative complexity and pricing failure are the largest ticket items but there are only meager attempts to address these.

Below are the numbers:

Domain	Waste (in billions)
Failure of Care Delivery	$102.4 to $165.7
Failure of Care Coordination	$27.2 to $78.2
Low-value Care	$75.7 to $101.2
Pricing Failure	$230.7 to $240.5
Fraud and Abuse	$58.5 to $83.9
Administrative Complexity	$265.6

Selepressin: Not so useful in Septic Shock

Septic shock is an advanced stage of body’s response to an infection and manifests as marked decrease in blood pressure with resulting decrease blood flow (and hence nutrients and oxygen) to the tissues. The decreased blood pressure is due to vasodilatation and increased capillary permeability (leaky capillaries).

The treatment of septic shock includes antibiotics to treat the infection, intravenous fluids to replenish fluid that has seeped out into the tissues from leaky capillaries, and vasopressors (such as norepinephrine) to counter vasodilation. Not uncommonly, norepinephrine is not sufficient to raise blood pressure and increase blood flow to the tissues. Vasopressin, another vasopressor, is sometimes used in addition to norepinephrine to support blood pressure. However, vasopressin has other adverse effects. These adverse effects are due to the fact that vasopressin stimulates three types of vasopressin receptors (V1a, V1b, and V2). Stimulation of V1a has vasopressor effect while stimulation of V1b and V2 results in increased coagulation activity, nitric oxide release, corticosteroid secretion, and excessive water retention.

Selepressin is a selective V1a receptor agonist and has only vasopressor effect. One would assume that selective stimulation of V1a receptors with selepressin will result in beneficial outcome. However, in a clinical trial (N=868), selepressin was not found to be effective in reducing ventilator-free days, norepinephrine-free days, mortality, lower ICU days, or lower need for kidney replacement therapy.

High-Flow Nasal Cannula vs. Noninvasive Ventilation

In patients who are being mechanically ventilated and are at high risk of extubation failure, this randomized controlled trial (N=641) found non-invasive ventilation with high-flow oxygen superior to high-flow nasal cannula alone. Here is the study.

Hospital Readmission Penalty Might be Increasing Mortality

Hospital Readmission Reduction Program and association penalties for higher than average/expected readmissions have resulted in a significant decrease in 30-day readmissions after hospital discharge. This also appears to have saved money to the CMS. When looking at the readmissions only, this programs appears to be a resounding success. However, its unintended consequences are becoming clear only now.

Using a national database of almost all hospitals, this study found that hospitals that were able to decrease readmission rates for patients with acute exacerbation of COPD, also had an increase in mortality for such patients within 30-days after discharge. While the underlying mechanisms are open for speculation, this association needs to be taken seriously and possibility of a casual relationship needs to be explored.

Stacked ICU Admissions and Mortality

This interesting study shows that when ICU admissions are stacked, that is two or more admissions come too close to each other, there is an increased risk of patient mortality, longer hospital stay, and higher odds of nursing home discharge.

Investigators enrolled 13,234 consecutive ICU admissions of which 1/4rth had an elapsed time since the last admission (ETLA) of < 55 min. Stacked admissions had on average, a higher unadjusted [1.16 (95% CI 1–1.35, P = 0.05)] and adjusted [1.23 (95% CI 1.04–1.44, P = 0.01)] odds ratio of ICU death, higher unadjusted [1.11 (95% CI 0.99–1.24, P = 0.06)] and adjusted [1.20 (95% 1.05–1.35, P = 0.004)]  odds ratio of hospital death, and a lower adjusted OR of home discharge of 0.91 (95% CI 0.84–0.99, P = 0.04).

Log-transformed Predictor in Regression Model

It is not uncommon in a regression model that a predictor is log-transformed to meet the normality assumption of the residuals. Below is an example where our goal is to examine a relationship between urinary arsenic concentration and white blood cell (WBC) count (in thousands). Urinary arsenic distribution had right-skew and hence the predictor was log-transformed for this regression. The output is below and the coefficient is highlighted in yellow.

The interpretation of regression coefficients can be sometimes confusing. However, when the predictor variable is a continuous variable (here it is LNUARS), it is easy to visualize it graphically. Simply, think that the coefficient is slope for a line on a graph where Y-axis has outcome (WBC count here) and X-axis has predictor. Now, we can interpret it as ‘change in Y (WBC here) for each unit change in X (LNUARS here)’. Note, we are saying a unit change and this unit can be any unit depending on a given variable.

Now, we have our predictor (urinary arsenic) log-transformed due to its skewed distribution. The coefficient (or slope of the graph) here means change in WBC count (unit is in thousands for this output) for one unit change in log of total normalized urinary arsenic. While this is an accurate interpretation of the coefficient, we don’t use log-scale measurements in our regular life. Further, we may find it difficult to communicate with others when describing results. Hence, it makes much more sense to convert total arsenic from log-scale to our usual scale.

As a general rule, and without going into mathematical details, the interpretation of a log-transformed variable is slightly different than usual interpretation that we would do otherwise. A simplest way is to multiply the coefficient with 0.01; the resulting value will be change in Y for 1% change in X. Note, it is not one unit change bur rather one percent change. Here, the coefficient is -0.195. Multiplying it with 0.01 gives us -0.00195. The Y = WBC here has unit in 1000 cells and X here is total urinary arsenic. Hence, we will say that for each 1% increase in total urinary arsenic, the WBC decreases by 0.00195 (in thousands). We can multiply 0.00195 by 1000 (=1.95) and then each 1% increase in normalized total urinary arsenic decreases WBC by about 2 cells.

The p-value is significant but the change of 2-cells for 1% change in urinary arsenic may not be large enough to be clinically meaningful; however, that is another topic of discussion – difference between statistically significant and clinically meaningful – for another day.

Anti-Mullerian Hormone in Men

Anti-Müllerian hormone (AMH) is a Sertoli cell-secreted protein that plays a major role in the development of internal male genitalia during embryonic life. Around the 7th week of gestation, AMH causes regression of the Mullerian duct and hence it is also known as Müllerian-inhibiting substance (MIS). Persistent Mullerian duct leads to formation of female internal sex organs. During adult life, AMH continues to be produced by the Sertoli cells in the testis in men although its functional relevance remains unclear.

In 2016, an very strong association of AMH with all-cause mortality was reported in men.

“In unadjusted analysis, each unit increase in serum anti-mullerian hormone level was associated with a 13 % lower risk of death (HR = 0.87; 95 %CI = 0.83-0.92). In multivariable models, the inverse association between serum anti-mullerian hormone levels and mortality remained significant (HR = 0.94; 95 %CI = 0.90-0.98) and was independent of confounding variables. Similarly, individuals in the highest quartile had significantly lower risk of death as compared to individuals in the lowest quartile (unadjusted HR = 0.13, 95 %CI = 0.07-0.25; adjusted HR = 0.36, 95 %CI = 0.16-0.81).”

While the study showed an association the underlying mechanistic pathways remained unclear.

Recently, AMH has been shown to be associated with serum C-reactive protein (CRP) levels in men raising the possibility that the underlying mechanism may include modulation of inflammatory response. It is a potentially an exciting area of research and new discoveries in future may highlight important relationships between AMH and health, morbidity, and mortality in humans.

Soft Drink Consumption and Mortality

In this population-based cohort study of 451,743 individuals from 10 countries in Europe, greater consumption of total, sugar-sweetened, and artificially sweetened soft drinks was associated with a higher risk of all-cause mortality.

1. 17% higher all-cause mortality was found among participants who consumed 2 or more glasses per day (vs consumers of <1 glass per month) of total soft drinks (hazard ratio [HR], 1.17; 95% CI, 1.11-1.22; P < .001),

2. 8% higher mortality in participants who consumed sugar-sweetened soft drinks (HR, 1.08; 95% CI, 1.01-1.16; P = .004), and

3. 26% higher mortality in participants who drank artificially sweetened soft drinks (HR, 1.26; 95% CI, 1.16-1.35; P < .001).

Consumption of artificially sweetened soft drinks was positively associated with deaths from circulatory diseases, and sugar-sweetened soft drinks were associated with deaths from digestive diseases.

Direct Oral Anticoagulants vs Warfarin in Older Patients With Atrial Fibrillation and Ischemic Stroke

This observational study examined 11,662 patients with atrial fibrillation who had had an ischemic stroke and were anticoagulation naïve, patients discharged while receiving:

1. Direct oral anticoagulants (DOACs) had more days at home post-discharge
2. Were less likely to experience major adverse cardiovascular events, all-cause mortality, all-cause readmissions, cardiovascular readmissions, or hemorrhagic strokes
3. Had a small but significant increase in gastrointestinal bleeding.

Overall, DOACs appear to be an effective and safe treatment option compared with warfarin for patients with atrial fibrillation who have ischemic stroke and may be even more beneficial despite a small in crease in gastrointestinal bleeding.

Perioperative Direct Oral Anticoagulant Use

This study of 3007 patients with atrial fibrillation who were undergoing surgery enrolled patients to have:

1. Direct Oral Anticoagulants (DOACs) were omitted 1-day before a low-bleeding-risk procedure
2. DOACs were omitted 2 days before a high-bleeding-risk procedure
3. Patients with renal impairment had the pre-operative omission duration readjusted

Study investigators found that the perioperative risk of bleeding and arterial thromboembolism were low with this simplified strategy. The 30-day postoperative rate of major bleeding was 1.35% (95% CI, 0%-2.00%) in the apixaban cohort, 0.90% (95% CI, 0%-1.73%) in the dabigatran cohort, and 1.85% (95% CI, 0%-2.65%) in the rivaroxaban cohort. The rate of arterial thromboembolism was 0.16% (95% CI, 0%-0.48%) in the apixaban cohort, 0.60% (95% CI, 0%-1.33%) in the dabigatran cohort, and 0.37% (95% CI, 0%-0.82%) in the rivaroxaban cohort.

DOACs	Bleeding Risk	Arterial Thromboembolism
Apixaban	1.35	0.16
Dabigatran	0.90	0.60
Rivaroxaban	1.85	0.37

Hospital Visits After Discharge in Readmission Reduction Program Era

This study raises interesting questions about the effectiveness of the Hospital Readmission Reduction Program. While readmissions are going down, the hospital ED visits and observation admits are going up. Are hospitals gaming the system?

A Case For More Vacations!

Vacationing more may reduce your risk of metabolic syndrome. So, don’t bypass your vacations and give sometime to yourself.

Here is the link

Longitudinal Study of Physician Burnout

Here is the study

The most interesting part is “burnout trends among staff tended to move in the opposite direction from trends among clinicians.”

Delirium in Hospitalized Patients and Family Visitation

Often elderly patients develop delirium during hospitalization. Delirium is waxing and waning cognitive function and often manifests as memory lapses, agitation, unable to understand care being provided, and limited cooperation in the provision of care.

One of the reasons offered for the possible causation of delirium is an unfamiliar environment and people (in addition to the underlying illness). However, while this is plausible, it has not been rigorously studied.

A recent study published in the JAMA is trying to address the ‘casualty question’ in addition to the therapeutic effect of family presence. What investigators wanted to see was that if there is a difference in the incidence of delirium between patients who get frequent family visitation versus those who did not. To increase family visitation, investigators relaxed family visitation hours to some intensive care units while other units continue to have their usual visitation hours. Overall, the number of hours a family member visited patient increased significantly in the intervention arm (4.8 hours versus 1.4 hours); almost three times increase.

Investigators did not find a statistically significant difference between the two groups although there appears to be a somewhat decreased incidence of delirium in patients admitted to ICUs in the intervention arm (18.9 vs 20.1%).

These findings are important and while do not reject the hypothesis that unfamiliar environment may play a causal role in delirium, it does question its validity. Future studies are likely to address this question further.

Suing Patients for Unpaid Hospital Medical Bills

Although published as a research letter, this study highlights a very important aspect of the business side of medicine in healthcare. Healthcare is very costly in the United States when compared to other countries. To collect revenue for the services provided, should hospitals pursue unpaid bills through all means including garnishing wages? If hospitals don’t do that then are they at risk of being in financial distress, particularly hospitals that are in poor areas? A larger question, that as a society needs an answer, is whether healthcare is a privilege or a right? Should an individuals be health responsible for healthcare expenses or society should collectively support healthcare costs of everyone? Blaming hospitals for pursuing unpaid bills is not going to fix the problem but may actually make it worse.

Anti-platelet Drugs after Coronary Intervention

The type and duration of anti-platelet therapy after cardiac catheterization and stent placement (also called percutaneous coronary intervention or PCI) is an ongoing debate.

There are two main groups of drugs. One is aspirin which is under use for over 100 years and we know a lot about it. The other group is P2Y12 blockers. P2Y12 is a receptor on the surface of platelets and these drugs block this receptor. The most well-known drug in this group is clopidogrel or Plavix.

A Japanese study examined the role of aspirin + clopidogrel for one month followed by 12-months of clopidogrel versus aspirin + clopidogrel for 12 months showed that the earlier strategy was superior. In contrast, a Korean study, also published in JAMA, found that there is no statistically significant difference between the two.

The results may appear different and the reason my be that the two studies were somewhat different. Below are some of the differences:

1. Korean study allowed the use of other P2Y12 inhibitors/blockers such as prasugrel or ticagrelor while Japanese study allowed on clopidogrel.

2. Korean study had lower adherence to drug therapy in the P2Y12 group as compared to combined group (79% vs. 95%).

3. P2Y12 inhibitors were given for 3-months in the first group in Korean study while Japanese study had for 1-month only.

Despite these somewhat difference results, the overall message is the same. Patients may have similar outcomes when given clopidogrel (or other P2Y12 inhibitors) alone after an initial period of combined therapy with aspirin. This may be a particularly good news for those patients who are at higher risk of bleeding.

Anti-platelet Therapy after Cardiac Stent Placement

The type and duration of anti-platelet therapy after cardiac catheterization and stent placement (also called percutaneous coronary intervention or PCI) is an ongoing debate. Aspirin is under use for over 100 years and we know a lot about it. Plus, it is a cheaper drug. Clopidogrel, better known by its market name Plavix, is now in marker for many years and its generic versions are available. Almost all studies have examined the use of additional anti-platelet drugs after PCI on top of aspirin use. What if we examine the use of anti-platelet drugs on top of clopidogrel use?

A study published in JAMA examined this question in a multi-center, open-label, randomized clinical trial conducted in Japan. They enrolled 3045 patients to either 1 month of aspirin + clopidogrel therapy followed by clopidogrel therapy alone versus 12 months of aspirin + clopidogrel therapy. The outcome they were looking at was a combined outcome of cardiovascular death, myocardial infarction, stroke (ischemic or hemorrhagic), stent thrombosis, or bleeding.

I am certain that authors were hoping for finding that the two treatments were similar. To their surprise, not only 1-month of aspirin + clopidogrel followed by clopidogrel alone was similar in efficacy but was superior to the 12-months of aspirin + clopidogrel (2.35% vs. 3.70%; P<0.001). In other words, a shorter duration of combined therapy followed by clopidogrel therapy is as effective, if not better, than 12-months of combined therapy. These findings are important especially for patients who are at high risk of bleeding.

Patient Satisfaction and Patient Psychological Well-being

Patient satisfaction with physician communication is part of the HCAHPS patient satisfaction survey. HCAHPS is a mandated survey of a sample of patients discharged from hospitals to assess their satsifaction with hospital, physicians, nurses, and discharge process. While CMS has advised not to use the individual domains of the survey as those individual domains are not tested (as compared to the whole survey which is tested), hospital administrators continue to use individuals domains to reward/punish physicians.

This recently published study shows that patient satisfaction strongly depends on patient’s psychological well-being. It is unlikely that a physician has any strong influence on a patient’s psychological well-being and hence on the patient satisfaction. Interesting study! We need more such studies to tease out what factors (patient level or healthcare level) that affect patient satisfaction so that we can adjust healthcare delivery accordingly.

Academic Center–Too Big to Fail?

A very interesting article published online in JAMA with a provacative title “Academic Medical Centers: Too Large for Their Own Health?”. The following few sentences are very telling “…. many of today’s AMCs are similar to huge tankers loaded with health care services, and research and education are merely passengers ……. the margins on clinical revenues are often used to cover deficits in budgets for research and education and from providing subsidized care ………. any changes threatening the margins from clinical care will affect the entire mission of an AMC. The AMC missions are not self-funded; the margins on clinical care are required at most AMCs …” (emphasis added).

Something for leaders in healthcare to think about and consider when planning for the future.

Plasma Volume Estimates and Mortality

Measuring plasma volume is quite difficult and is an involved process. Therefore, equations have been developed that estimated plasma volume by some easily measured variables. One such equation is Straus formula. Using this formula, Marawan et al found that estimated plasma volume is not only related all-cause mortality but also to cardiovascular, cancer, and other-cause mortality.

These findings beg the question about the underlying mechanism of this association. It is difficult to say how this works except that perhaps one of the common final pathway of most diseases is to increase plasma volume.

Socioeconomic Status and Readmission Penalties

CMS will be including socioeconomic status when penalizing hospitals for excess readmissions within 30-days of discharge from hospital. the socioeconomic status will be determined by calculating the percentage of population seen at a hospital that are dual eligible (both Medicaid and Medicare).  See here for the details.

There is hope that this adjustment will allow hospitals that take care of very sick patients but also socioeconomically disadvantage populations will find a more even playing field when competing with hospitals that serve relatively wealthier populations.

It is a no brainer that patients with strong support systems, easy and quick access to their primary care provider, and high health literacy are less likely to be readmitted as compared to patients who lack these factors. Often, poor support system, poverty, lack of primary care access, and limited health literacy go hand in hand. These disadvantaged patients are most difficult to treat and are higher risk of disease-related and treatment-related complications.

Now Maddox et al has calculated which hospitals are likely to benefit and how large the benefit is going to be. They found that hospitals in the lowest quintile of dual enrollment (that is hospitals in the relatively wealthier neighborhoods) saw an increase of $12.3 million in penalties, while those in the highest quintile of dual enrollment (serving disadvantaged populations) saw a decrease of $22.4 million. Large hospitals, teaching hospitals, hospitals in the most disadvantaged neighborhoods, and those with the highest proportion of beneficiaries with disabilities were markedly more likely to see a reduction in penalties, as were hospitals in states with higher Medicaid eligibility cutoffs.

Shared Decision Making in Medicine

I was reading this ‘viewpoint’ in JAMA; an interesting read. I try to practice shared decision making as much as possible, perhaps more than what my patients would like.

However, I do feel that there is lack of data supporting shared decision making or ‘informed decision making’. I would perhaps define ‘informed decision making’ as decision making process in which patients are informed of their options and then given a recommendation by their treating physicians. Of course, patients have a right to say no to the recommended option, give reasons for refusing the recommended option, and then give their preferred option to the physicians.

Having said that, I am unaware of good data supporting any decision making strategy. There are, of course, tons of arguments supporting why shared decision making is good. But when, in medicine, we started depending on arguments without trying to develop evidence?

To develop evidence, first we have to define what is it that we want to achieve (an outcome). Some may argue that ‘shared decision making’ is in itself an outcome. I am not sure how this can be an outcome of choice. Perhaps a better outcome could be one that is patient-focused, such as mortality, morbidity, readmissions, quality of life etc. Things that matter are more meaningful. Or outcome that is physician-focused: such as number of law-suits by patients (or on behalf of patients). But there need to be reasonable outcome(s).

Then, we need to examine how shared-decision making affects those outcomes. My worry is that we are adding ‘must-do’ things in our workflow without determining their efficacy.

One way I look at the seriousness, or lack of it, by the proponents of a given ‘thought’ or ‘idea’ is their lack of desire to investigate their proposed remedy using scientific methods. We don’t accept a medicine, procedure, or treatment without strong evidence. Why we should accept other things using a lesser proof of evidence (unless we think it is not as important)?

One area where we find such a lack of seriousness in developing evidence is medical education itself. Even when we occasionally study a medical education intervention correctly, we find that our thinking was flawed. While we may claim that we understand the complexity of human behavior, we are often wrong. I doubt that we are right when it comes to shared decision making. When properly studied, I doubt we will find any effect on any important outcome.

Physician Burnout is Rising

Physicians have higher levels of burnout than the general US population. There are several proposed factors for this higher prevalence although none have been proven beyond doubt.

In addition to higher prevalence, the prevalence is increasing raising concerns for the health of some of the highly educated and skilled professionals in our society. A recent study found that the prevalence of burnout in a large academic medical center faculty increased from 40.6% to 45.6%. Both domains of burnout increased; exhaustion increased from 52.9% to 57.7% while depersonalization increased from 44.8% to 51.1%.

Other things that changed were that fewer physicians in 2017 felt that they had control over their schedules than in 2014 (71.6% vs. 64.3%), felt that they had could impact decision making that affects day to day practice (58.7% vs. 55.4%)  and were satisfied with their workload (55% vs. 50.5%).

Authors also validated a finding from prior studies that early-career physicians are more susceptible to burnout than mid-career physicians. However, they did not find a difference in burnout by gender or by race.

Readmission Rate and Insurance Type of the Patient

One of the programs instituted through the Affordable Care Act (ACA), also known as Obamacare, is the Hospital Readmissions Reduction Program (HRRP). This program focuses on the readmission of the patients within 30-days of discharge. Hospitals that perform worse than the national average are penalized up to 3% of their Medicare payments. While the program targets Medicare beneficiaries, it is likely that changes hospitals make to reduce readmissions will benefit all patients.

An article published in Health Affairs examines this question. Authors find that the readmission rates declined after the HRRP went into effect (not surprising as it has been shown by us and many others). In addition, they also report that readmissions declined for both Medicare and Medicaid patients. However, readmission rate of Medicaid patients remained higher than Medicare patients. Readmission rate was lowest for patients who had insurance throughout the study period and declined at a similar rate compared to others.

This study highlights an important but often neglected area when examining readmissions. Readmissions depend on three main factors:

1) Hospital Discharge Practices: These are factors that are under the control of the hospital (physicians and hospital employees). These include adequate discharge instructions, adequate hand-offs to the outpatient provider, adequate education about patient illness during hospitalization, making followup appointments, etc.

2) Patient factors: These includes the severity of the underlying illness of the patient. Some patients are far sicker and even when they are discharged from hospitals, they are just beyond the edge of the need for hospitalization. A small change in patient’s condition leads to a visit (and admission) back to the hospital.

3) Social support factors: These include support from family, friends, community, and other resources.

Of these three, very little attention has been paid to the third main factor, social support. Patients with private insurance are likely to have more resources and stronger support. On the other hand, patients on Medicaid are likely to be poor with an inadequate social support system. I believe that we will start hitting the wall with regard to the readmission rate reduction and the rate of decline in readmissions will start slowing down (if it is already not slowing). Ultimately, as a society, we will have to focus on the social support aspect of readmissions to keep patients healthy and out of the hospitals.

Abstain from Alcohol to Decrease Risk of Atrial Fibrillation

Here is a press release about a study that was presented by has not yet been published. Perhaps the best study design for evaluating the benefits/harms of alcohol.

Lead Exposure in Childhood can lead to Personality Changes

Here is the link to a simplified version

Readmissions and Mortality–Any Difference by Race?

Since the introduction of Hospital Readmission Reduction Program by the CMS, there has been a significant decrease in readmissions for all targetted diseases. However, studies have raised concern that although readmissions are decreasing, there may be an increase in mortality.

A recent study showed that there is no difference in mortality between white and black patients. Authors used interrupted time-series analysis. I enjoyed statistical modelling but wonder if the within-hospital and between hosptial effects were properly accounted for. In other words, the assumption in their modeling is that both effects are the same (unlikely to be true). It is possible that the within and between effects are different and may shed a better light on what is happening at individual hospital level and what is happening across hospitals. Having said that, it is an interesting study with important implications.

Are Machine Learning Tools Better than Standard Tools in Predicting Readmissions

Just saw this study where authors compared standard readmission tools (HOSPITAL score, modified LACE score, and Maxim/RightCare score) with a model developed using machine learning. Authors found that machine learning score (they called it Baltimore Score or B-score) performed much better than standard tools.

While I agree that machine learning tools will likely outperform standard methods. Standard methods are quite a bit of oversimplification of the real life, machine learning tools less so. However, I doubt that authors have got their model right. Two reasons: One, their sample size is relatively small. Two, they have not validated their tool in a new dataset.

Social risk factors adjustment for readmission penalties

I have shown, in my previous work, that social risk factors affect hospital performance. CMS, at th eurging of Congress, will be including including social risk factors in their patient risk models. Here is an interesting paper that examined retrospectively the effect of adjustment for social risk factors on readmission penalties and found that the penalties will drop by almost half for safety-net hospitals. I am certain that these adjustment will bring more fairness in hospital comparisons and will decrease the amount of penalty these safety-net hospitals have to face. 

Obesity and Hospital Readmissions

The link between obesity and readmissions is not as clear cut, despite the fact that almost 40% of older adults in the US are obese. In this relatively large study examining the readmission rates of obese nursinghome patients, authors found that the relationship is perhaps complex and islikely affected by age and co-morbid conditions.

Rebate on Prescription Drugs

Illuminating article; must read. Here are two sentences that capture the essence of this issue: “In 2016, leading products in these classes had total pre-rebate spending in Part D of $2.5 billion (Lantus Solostar) and $4.4 billion (Harvoni). For an individual patient, this would reduce list prices on which their deductibles and co-insurance are calculated from $417 to $142 for patients filling a 1-month supply for Lantus Solostar and from $92 648 to $35 206 for patients filling an 84-day course of treatment with Harvoni, assuming class-level rebates applied to these products.”

Failure of Patient-centerd Transitions of Care Program to Improve Outcomes in Heart Failure Patients

Improving patient care through focus on transitions of care is hard! There are very few studies that have evaluated strategies that work in a randomized controlled trial (RCT) fashion and none has been able to show any benefit. Here is a large RCT that failed to show a benefit.

What is that works? And works consistently?

Number Needed to Treat

Number needed to treat is an important concept as it simplifies the communication of the effect size from clinical studies. It tells us the number of patients needed to be treated to get the desired outcomes (or an undesireable adverse event). Here is a much longer description from JAMA.

Physician Organizations’ contribution to political Candidates and NRA ratings

Must read article in JAMA. Here is a graph that says is all; full article is here.

Quick Overview of Mediation Analysis

JAMA recently published this overview of Mediation Analysis; often a misunderstood statistical method (and some may argue a method for causal modeling), mediation analysis is a powerful technique that can help to delineate biological or psychological mechanisms.

Physicians Support Tighter FDA Control on New Drug Approval

An interesting research letter published in JAMA Internal Medicine reports that 80% of physicians agree with the strict FDA approval process to protect public from ineffective or dangerous drugs. Further, 60% thought that FDA should not allow off-label promotion of drugs to physicians.

Genistein, a Phytoestrogen, and Mortality

Several studies in medical literature and countless lay-media articles promote the benefits of phytoestrogens. In this large study, using data from NHANES, Marcelo and colleagues showed that that high levels of genistein in urine are associated with lower mortality. In particular, it decreases mortality from all causes.

Apixaban in ESRD and Atrial Fibrillation

Patients with ESRD (end-stage renal disease) are usually prescribed coumadin when they need anticoagulation; the reason being that we know very little (or not at all) about the effect of new oral anticoagulants in patients with ESRD.

In this study, Siontis et al performed a retrospective cohort study of Medicare beneficiaries with ESRD and atrial fibrillation, the later an indication for anticoagulation. Outcomes were compared between patients who were treated with apixaban versus patients who were treated with warfarin. Authors examined the survival difference between the two groups for stroke or systemic embolism, major bleeding, gastrointestinal bleeding, intracranial bleeding, and death using Kaplan–Meier analyses and hazard ratios (HRs) and 95% CIs were obtained using Cox regression analyses.

Authors found that there was no difference in stroke and systemic embolism between the two groups. However, the group taking apixaban had 28% lower risk of major bleed.

While this is retrospective data, and clinical trial data is lacking, this study will support the use of new oral anticoagulants in ESRD patients.

Is Science Getting Less Bang for its Buck?

Here is an interesting article published in The Atlantic which claims that the productivity in science is declining. The investment in science is increasing but the pace of novel or truly important discoveries is decreasing. They give various reasons; I have few additional thoughts that I assume authors thought of but were not included in this piece.

1. The age of scientist at discovery is increasing due to the large volume of knowledge we ‘force’ young scientist to learn. We have formalized ‘learning’ to an extent that we feel it is obligatory for anyone to ‘know’ it all before moving forward. Just as an example, the ‘required’ courses to take are often not directly relevant to the learner’s interest or the to the topic. WE have added those without any scientific evidence that such additional courses help with discovery. We preach evidence, we prefer not to teach by science. And when we try to accumulate evidence, we do it using irrelevant outcomes. This is the problem with education in general and not with science education.

2. Truly novel discoveries happen in spurts. Discovery of zero was truly important, one may argue more important than general relativity. Perhaps discovery of algebra and calculus were also more important, and fundamental, than general relativity. But these discoveries are not ‘novel’ anymore for us. I believe new discoveries happen after we have utilized the older discoveries to close to the fullest extent. Imagine a new discovery as a new house; once we build a new house, we start filling it, decorating it, accumulating useful (and some junk) ‘stuff’. This is perhaps what happens with truly important discoveries. A truly important discovery is followed by many small discoveries over next many years until the potential of new discovery has been fully realized and then another new discovery happens.

Serum creatinine in critically ill patient

An interesting, short and sweet article in JAMA highlighting that serum creatinine increase in critically-ill patients may underestimate the degree of AKI.

They give following reasons for this underestimation:
1. Creatinine production decreases in sepsis, hence there is decreased release of creatinine and slow rise in serum creatinine
2. Critically-ill patients receive large-volume resuscitation and hence dilute serum creatinine. Of note, the volume of distribution of creatinine is about 65% of body weight.

Having said that, there does not appear to be any good alternatives for determining renal function ad GFR estimation in such population.

Vitamin D and Cardiorespiratory Fitness

Here is a study just published showing relationship between cardiorespiratory fitness and vitamin D levels. The study adjusted for various confounders and the relationship was independent of these confounders. The study is cross-sectional and hence the direction of causal effect is difficult to determine. That is, whether greater cardiorespiratory fitness is associated with more time spent outside and hence more sunlight exposure and vitamin D formation or whether vitamin D, through its effects on muscles, nerves, bones, results in higher cardiorespiratory fitness

Deep Learning in Pathology

Here is an intersting article, showing the role of AI in pathological diagnoses. Medical speciliaties most dependent on pattern recognition, such a pathology, dermatology, or radiology, are more likely to get AI assistance sooner. With time, it is quite possible that AI can replace such physicians. For now, it appears that AI will help them work faster. Remember, increasing efficiency means need for fewer physicians in such specilaties and possibly decliing salary.

Incentives and Work

An interesting article which is also very germane to academic physicians. In particular the sentence “what drives most academics to the university on a given day (including evenings and weekends) is not the money (otherwise we would work in the private sector) or the stability of the income stream (because the probability of losing a job is close to zero for a tenured academic). In fact, Stern (2004) shows that “scientists pay to be scientists.”  is right on the spot. One can say about physicians in academia that they “pay to be in academics”. In almost every case, they can earn quite a bit more in private practice. It is the meaning in their work that motivates them.

Aspirin and Body Weight

Low dose aspirin has been shown to reduce cardiovascular events but the observed effect may vary by weight. In fact, in one of our studies, we have shown that not only obese individuals had greater baseline platelet reactivity, they also had greater residual platelet reactivity after low-dose aspirin therapy. Comparing obese and nonobese individuals after aspirin therapy, results for aggregometry to collagen were 6.7 vs 6.1 ohms, P=.008; aggregometry to adenosine diphosphate were 13.1 vs 11.8 ohms,P<.0001; aggregometry to arachidonic acid (AA) were 4.9% vs 8.3% nonzero aggregation, P=.002; urinary excretion of 11-dehydro-thromboxane B2 (Tx-M) were 4.9% vs 8.3% nonzero aggregation, P=.002; and aspirin resistance were 26.% vs 20.5%, P=.002; respectively.

Now an individual-patient meta-analysis of primary prevention randomized controlled trials with low-dose aspirin have shown that clinical outcomes are also different in patients with weight>70Kg than in patients with weight<70Kg. On the other hand, when a higher dose of aspirin was examined, individuals with weight>70Kg obtained clinical benefits. Clinical benefits were not limited to cardiovascular outcomes but also included colorectal outcomes.

An obvious implication of such studies is that individuals with weight>70Kg should take perhaps more than one baby aspirin to receive benefit from aspirin.

Artificial Intelligence & Medicine

Some recent articles ……

Goldberg JE, Rosenkrantz AB. Artificial Intelligence and Radiology: A Social Media Perspective. Current Problems in Diagnostic Radiology. 2018 Jul 23.
An interesting study examining the types of conversations on Twitter about the role of artificial intelligence in radiology. It appears that most tweets (or linked websites) were upbeat and wanted radiologists to continue supervision of AI-run diagnostics.

Meskó B, Hetényi G, Győrffy Z. Will artificial intelligence solve the human resource crisis in healthcare?. BMC health services research. 2018 Dec;18(1):545.
This article expresses the hope that artificial intelligence may be able to help in mitigating human resource crisis in health sector.

dos Santos DP, Giese D, Brodehl S, Chon SH, Staab W, Kleinert R, Maintz D, Baeßler B. Medical students' attitude towards artificial intelligence: a multicentre survey. European radiology. 2018 Jul 6:1-7.
This article examines the attitudes of undergraduate medical students towards artificial intelligence in radiology and medicine. Students think that artificial intelligence will revolutionize the practice of medicine, in particular radiological diagnosis.

Struggling Healthcare Organizations

A systematic review identified five characteristics of healthcare organizations that are struggling to improve quality.

1.  Poor organizational culture: Such as limited ownership, not collaborative, hierarchical, with disconnected leadership

2. Inadequate infrastructure: Limited quality improvement, staffing, information technology or resources

3. Lack of a cohesive mission: Mission conflicts with other missions, is externally motivated, poorly defined or promotes mediocrity

4. System shocks: Events such as leadership turnover, new electronic health record system or organizational scandals that detract from daily operations

5. Dysfunctional external relations with other hospitals, stakeholders, or governing bodies.

Physician Competition Effects

An interesting study examined the impact of competition among cardiologists on service provision and patients' health outcomes in the United States. Authors found that cardiologists in more concentrated markets perform more diagnostic procedures. On the other hand, higher concentration result in fewer readmissions but has no effect on mortality.

Medical Bill Collections Decrease with Patient’s Age

An interesting study that finds that the average size of medical debt decreased nearly 40 percent from when patient was twenty-seven old to when sixty-four years old. Authors thought that this was with an increase in health insurance coverage and incomes.

Well-being–some readings

Well-being has been a focus of philosophers for centuries. While philosophers tend to question how we should live, the very answer to this question begs the question how living that way will make us better, that is, how living in a certain way will make us live well.

A little self-reflection will help you to realize that there are many things that make you live well, make you happy, excited, or content, or conversely make you feel sad. Think of the things that make you feel happy. Often these things include relationships, friends, money, accomplishments. Now think of things that make you feel sad; these may include anxiety, worry, illness, poverty. Lists of both things, things that make us happy and things that make us sad, can be long and likely will vary from person to person (at least to some extent). The question one may ask is what is it that one thing (or a small group of things) that is fundamentally deterministic of feeling well.

Hedonism is a theory of well-being which focuses on individual’s pleasure or pain. Thus, pleasure is associated with high well-being and pain is associated with poor well-being. On the other hand, perfectionism focuses on our ability to develop certain virtues or characteristics. Perfectionism is similar to eudaimonia; well-being is associated with developing virtues that are human nature. Desire theory proposes that well-being is present when one gets what one desires; in other words, fulfillment of desires is associated with well-being. Objective list theories are a set of theories that have in common a list of things that make one happy; list from one theory may not overlap with another theory. This group of theory highlight the fact that it is not easy to define what constitute well-being.

Machine Learning Predicts Clinical Deterioration

Edelson and her colleagues trained a gradient boosted machine model on a dataset of almost 300K patients and were able to predict clinical deterioration better than the modified early warning (MEWS) system. Here is the link for the abstract that was presented at ATS 2018.

The simplicity of the tool which uses only age, heart rate, and respiratory is really impressive. Interestingly, respiratory rate is often the least accurately measured vital sign in the hospitals despite its simplicity. A more accurate measurement of respiratory rate can potentially help better predictive models to predict clinical worsening in a patient’s condition. Better prediction can not only help to patients by focusing on the cause of clinical worsening but will also help to more optimally utilize resources within the hospital.

Some Interesting Recent Articles from bioRxiv

1. The Subtype Specificity of Genetic Loci Associated with Stroke in 16,664 cases and 32,792 controls: Using Bayesian multinomial regression in 16,664 stroke cases and 32,792 controls of European ancestry, investigators wanted to determine the most likely combination of stroke subtypes affected for loci with published genome-wide stroke associations. Of the 16 loci, seven influenced both ischemic and hemorrhagic stroke, an EDNRA locus demonstrated opposing effects on ischemic and hemorrhagic stroke. No loci were predicted to influence all stroke subtypes in the same direction.

2. Sensitivity to Affective Touch Depends on Adult Attachment Style: Higher scores on an attachment anxiety dimension (but not an attachment avoidance) were associated with reduced pleasantness discrimination between affective vs. non-affective, neutral touch.

3. The GRE Over the Entire Range of Scores Lacks Predictive Ability for PhD Outcomes in the Biomedical Sciences: GRE scores are often assumed to be predictive of student success in graduate school; however, authors found no association in admission data.

MIMIC Database

I ran across this publicly available dataset which looks great for someone interested in data-analysis. The dataset appears to be extensive. I have not worked with its analysis yet but this will be something that will be on my ‘things-to-do” list. According to its website “MIMIC is an openly available dataset developed by the MIT Lab for Computational Physiology, comprising deidentified health data associated with ~40,000 critical care patients. It includes demographics, vital signs, laboratory tests, medications, and more.”

Proposed Changes to Medicare Physician Fee Schedule 2018

Last week, the Centers for Medicare & Medicaid Services (CMS) proposed a rule to update payment policies, payment rates, and quality provisions for services furnished under the Medicare Physician Fee Schedule (PFS) on or after January 1, 2019. Some of the proposed changes include:

1. Streamlining E&M payment with reduction in clinical documentation burden

2 .Recognition of services based on the use of communication technology

3. Creation of a bundled episode of care for substance abuse disorders

4. Flexibility on the use of radiology assistants

5. Removing functional status reporting requirement for outpatient treatment

6. A slight increase of $0.06 per RVU

7. Updating the direct practice expenses based on recent survey

8. Medicare telehealth services

9. Request for information on price transparency

10. Changes to the Quality Payment Program

Here is the link for those interested in learning more about these proposed changes

Physician White Coats and Patient Preference

An interesting study published in the BMJ Open and an interesting overview here by Brad Flansbaum.

Bottom line, formal physician attire with a white coat was rated significantly higher by patients than any other attire. Just an re-emphasis on the fact that ‘packing’ matters. One may want to to look at this that those physicians who take their profession seriously also try to wear a better representative attire than others; I am sure there a large number of physicians who may strongly disagree with this assessment.

Vitamin D Levels in African Americans

Vitamin D is associated with bone and calcium metabolism. However, more recently, studies have increasingly reported the effect of vitamin D on other organs and systems. For example, vitamin D is associated with all-cause mortality and cardiovascular mortality. Vitamin D is also associated with peripheral arterial diseases. Further, vitamin D has been associated with serum C-reactive protein levels (a biomarker of systemic inflammation). Vitamin D has also been associated with liver function.

African Americans tend to have significantly lower levels of serum vitamin D and hence may be at a higher risk of low vitamin D related morbidity. The reason perhaps lies in genetics, at least that is what this study suggests. Investigators assessed the association between percentage of European ancestry and serum vitamin D level among 2,183 African-American women. An algorithm implemented through a software was used to estimate the percentage of European ancestry versus African ancestry in each individual. After adjustment for various potential confounders (genotype batch, age, body mass index, supplemental vitamin D use, ultraviolet B radiation flux in the participant’s state of residence, and season of blood draw), investigators reported that each 10% increase in European ancestry was associated with a 0.67-ng/mL increase in serum vitamin D concentration (95% confidence interval: 0.17, 1.17).

Question remains if the low serum levels of vitamin D are clinically meaningful in African Americans. Do low serum vitamin D levels are reflective of low tissue levels (or effective tissue levels)? These type of questions will need a different and more involved study designs for answers.

Diabetes and Risk of Cancer

Patients with diabetes mellitus are at increased risk of cardiovascular complications such as heart attack, stroke, peripheral arterial disease etc. Commonly, the cause of death in diabetes is due to cardiovascular events.

Does diabetes increases risk of cancer in addition of increased risk of cardiovascular diseases? This important question was explored in this interesting study of over half-million Chinese. Investigators enrolled patients with and without diabetes and without any prior diagnosis in the study between 2004-2008 and then followed them until the end of 2013 for development of any cancer. During the follow-up period, they found more than 17,000 cases of cancer. Diabetes was associated with an increased risk of all-cause cancer and site-specific cancer.

Diabetes increased the risk of overall cancer by 13%, of liver cancer by 51%, of the pancreatic cancer by 76% and of the breast cancer by 21%. Investigators further reported that higher random blood glucose levels were associated with increased risk of cancer. Perhaps, it is the elevated blood glucose levels in diabetics that is responsible for cancer.

Medical Procedures and Heart Valve Infection

There are not free lunches! It is not uncommon to think that getting a procedure is better than not getting one, however, all procedures are associated with risks. For each procedure, the risk and benefits need to be carefully assessed before making a decision to undergo a procedure or not. Often having a procedure has greater benefits than risks, or so we assume.

While the benefits of a procedure are often clear, for example detection of coronary diseases when deciding about cardiac catheterization, the risks are not as well-studied. The list of risks associated with a procedure almost always includes risk of death and injury to some organ(s) but the actual incidence of these adverse events is usually poorly known, especially for for adverse events that are not immediately followed by the procedure (that is, there is lag-time between a procedure and associated adverse events). The more time lapses between the procedure and an adverse event, the difficult it is to ascribe adverse event to the procedure; generally so many other things, including additional procedures, happen in-between for diagnostic or therapeutic reasons.

One aspect of this issue is nicely addressed in an article published in the Journal of the American College of Cardiology where authors identified all adult patients treated for endocarditis in hospitals in Sweden and compared the occurrence of invasive medical procedures 12 weeks before endocarditis with a corresponding 12-week time period exactly 1 year earlier.

Of the 7,013 cases of infective endocarditis during the study period, several were strongly associated with cardiovascular procedures, especially coronary artery bypass grafting; procedures of the skin and management of wounds; transfusion; dialysis; bone marrow puncture; and some endoscopies, particularly bronchoscopy. A particularly interesting finding, that makes sense, is that the risk of infective endocarditis was higher if patient had a procedure while hospitalized than when patient had that procedure in outpatient setting. One way to look at this is to think that procedures should be done in outpatient setting but the converse is also possible (and more likely) that patients admitted to the hospital are sicker and hence are at higher risk of adverse effects.

Opioid use and voting Republican

A very provocative study by Goodwin et al have found that the chronic use of prescription opioid use was correlated with support for Republication candidate in 2016 Presidential elections. In case someone is concerned if the observed effect is due to the confounding by age (elderly people voting Republican but also having high need for opiates due to chronic diseases), authors did adjust for age and a broad category of other factors. I will invite everyone to read this study oneself.

Odds Ratio Limitations

Odds ratio is perhaps the most commonly reported effect size for binary outcome variables. The widespread use is likely due to the fact that logistics regression models provide odds ratio. Logistic regression models are relatively easy to implement and hence odds ratio is commonly reported. An article in JAMA recently highlighted some of the limitations of using odds ratio. The first two are well-known; the third one is not as well-known and its consequences are not fully realized. 

1) The interpretation is in odds and not in probabilities

2) Odds ratio approximates relative risk in limited situations only

3) Odds ratio depends on the the amount of unexplained variance; odds ratio may increase if the amount of unexplained variation decreases due to inclusion of strong explanatory variables in the model. Odds ratios obtained from same data set but different model may not be comparable due to the above limitation.

Productivity Paradox and EHR

Bob Wachter and Michael Howell are optimistic about the future improvements in health information technology and its impact on physicians. I share their enthusiasm and I am optimistic about an increase in productivity in healthcare sector. And why not? Its about time.

Productivity in every other sector has resulted in decreasing utilization of human resources and increasing production of goods. However, such is not the case in healthcare. A physicians still sees about the same number of patients in a day as he/she used to see 50 or 100 years ago. It is quite possible that the number patients seen might have dropped due to documentation and other requirements. The result is evident; healthcare sector now accounts for almost one-fifth of the GDP in the US. Much of this has to so with the lack of growth in productivity in healthcare sector as compared to other sectors.

What is promising is that artificial intelligence will likely to increase productivity in the healthcare. I don’t think that this will come through improvements in health information technology (HIT). I believe it will come with better utilization of HIT as data repository for learning algorithms. Technology will also help in data capture, and it does so in many ways even now, but most importantly in data inference. However, it will not be good for an average physician.

Remember, productivity can increase only if more patients can be seen by fewer physicians (or healthcare workers). That is not possible with one physician simultaneously seeing several patients. It is possible only when machines see most patients and physician sees patients only in those situations where algorithms have not gotten enough data to develop a routine.

And what those situations will occur in relatively rare conditions. These will not our usual common diseases, what we might call ‘bread-and-butter’ medicine. Rather these will be either rare diseases or rare manifestations of common diseases. A physician overseeing such a highly productive healthcare delivery system will have to be a master clinician and a superb diagnostician.

In other words, an increase in productivity in healthcare sector will spell the doom for majority of physicians ….. happy to hear what you think……….

Hospital Readmissions and Malnutrition

While it is easy to imagine how malnutrition can be related to excess readmissions, there are not very many studies examining this association. Of note, the CMS adjustment algorithm for expected readmissions takes into account malnutrition.

This retrospective study from Australia, examined this association among 26 253 admissions, representing 19 924 patients with a 6-month readmission rate of 44.8%. They found that malnourished patients had 40% higher risk of readmission within first week after discharge and 23% higher risk of readmission between the second week and 23rd week after discharge. Authors concluded that malnutrition was a strong predictor of unplanned readmissions and suggested examination of targeted nutritional interventions to lower readmission rate. 

Artificial Intelligence versus Human Doctors

While it is relatively easy to imagine artificial intelligence taking over pattern recognition type of physician work (such as reading images like radiologists or identify skin conditions by looking at the skin like dermatologists), the job of a general internist is much more demanding and requires assimilation of data inputs from a large number of sources, hence making it difficult for an artificial intelligence system to accurately predict a disease (or prepare a list of differential diagnoses).

On the other hand, the process is not, and should not be, very difficult for a well-crafted system. Razzaki et al tested such an artificial intelligence algorithm and compared it with physicians in its ability to predict diagnosis. For their study, they adopted a semi-naturalistic, role-play paradigm that simulated a realistic consultation between a patient and either their artificial intelligence system or a human doctor. Their study was designed to assess both the clinical (diagnostic and triage) accuracy and the ability to gather all of the relevant history from the patient. What they found was that their artificial intelligence algorithm was able to provide diagnostic and triage advice with a level of accuracy and safety approaching that of human doctors. If there study results are replicated then such systems may not only reduce costs and improve access to healthcare worldwide but may also provide better standardization of care delivered to patients.

Obesity and Increased Risk of Death

It is now well-known that obesity (BMI>29.9) or weight below the normal range (BMI<18.5) are associated with high risk of mortality. On the other hand, just being a little overweight (between 25 and 29.9) may reduce overall risk of death. These results were mainly based on a study published in 2005. However, there were some questions about possible bias due to incomplete adjustments for some risk factors such as smoking.

An update of these results with analysis for potential bias was recently published and available on NHANES website. Basically, the results remained the same and obesity and underweight both groups remained associated with high risk of death.

Of particular interest is the underweight group; while lay media has been focused on obesity, the other extreme, that is being underweight, is also associated with increased risk of death. In fact, the risk of death may be twice as high as normal weight in underweight individuals between the ages of 25 and 59 years.

Nutrition Data in NHANES

Ahluwalia et al published a review article (now updated) that gives a very interesting and comprehensive overview of dietary data collected in NHANES. The data is available publicly and dataset is really large allowing for examination of a large number of hypotheses.