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.