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.

Geographic Rounding and Patient Satisfaction

Geographic rounding is often touted as a way by which healthcare team can communicate better among themselves and thus patient will get similar messages from all team members. Further, team members will appear to patient have strong team-work . Lastly, this will also allow healthcare team to spend more time with patients. Hence, patient satisfaction will increase. While it sounds reasonable in theory, there is little evidence supporting this logical sequence. Siddiqui et al examined this question in their recent study published in the Journal of Patient Experience.

Interestingly, they found that the patients cared for by geographically localized teams did not have better patient experience. They suggest that other factors such as physician communication skills may overshadow the impact of having localized teams. Obviously, further research is needed to better understand organizational, team, and individual factors impacting patient experience.

Phytoestrogens and Liver

Genistein is a phytoestrogen with similarities to female sex hormones and, has been shown to prevent nonalcoholic fatty liver injury in animal studies but human studies are lacking.

In this study, authors, analyzed data from the National Health and Nutrition Examination Survey from 1999 to 2010 and examined the relationship between normalized urinary genistein (nUG) and serum ALT using linear regression models. Investigators further examined the differential effect of sex using an interaction term.

In almost 10K patients, authors found a statistically significant association between genistein and liver function in males, but not in females. At least for me, the sex-specific role of genistein in mitigating liver disease is very interesting.

Sepsis and qSOFA, SIRS, NEWS

Sepsis is a rather common admitting diagnosis to hospital with high mortality rate. Identifying patients who are at a higher risk of adverse outcomes may help to optimize resource allocation. To this end, initially SIRS definition was recommended. More recently, qSOFA is being promoted as a predictor of adverse events during hospitalization due to sepsis. However, there is a lesser known NEWS score that is also being used at few hospitals.
This study compared the three scores in a retrospective sample and found that NEWS may be similar or superior to qSOFA. Note that qSOFA (3 variables) is the quickest and simplest of the three while NEWS is the most complex (20 variables).

A Fantastic Weekly Update on Artificial Intelligence

I have been enjoying this weekly update by Azeem Azhar. And I invite you to try it too. Here is the link

http://www.exponentialview.co

SES Adjustment for Readmission Penalty

CMS started implementing the Hospital Readmissions Reduction Program (HRRP) on October 1, 2013, penalizing hospitals with higher readmission rates. While readmission rates were adjusted for several factors, socioeconomic status (SES) was missing leading to protests and lobbying by hospitals caring for poor people. Hence, in 2016 the US Congress directed the CMS to to adjust for low SES. In response, CMS has made following adjustments to HRRP.

1. Make five peer groups of hospitals based on SES. SES will be determined by the proportion of “dual eligible” Medicare beneficiaries (those who are also eligible for full Medicaid benefits).
2. Calculate the median excess readmission ratios (ERRs) within each peer group to adjust for penalties
3. Scale penalties using the budget neutrality factor.

Fuller and colleagues analyzed the effect of SES adjustments (as outlined above) and examined the effect on the number and distribution of penalized hospitals. An interesting read!