Khera et al. “Evaluation of 30-Day Hospital Readmission and Mortality Rates Using Regression-Discontinuity Framework ”

Online Supplement

Supplemental figures

Online Figure 1: Hospital selection flowsheet.A total of 3256 hospitals that were eligible and had at least target condition under the Hospital Readmissions Reduction Program (HRRP) with 25 or more discharges.

Online Figure 2: Temporal trends in readmission rates.The plots present median (interquartile range) of hospital-level post-discharge readmission and mortality rates for heart failure (HF), myocardial infarction (MI), and pneumonia for each study year during days 1-30 and days 31-60 in the post-discharge period.

Online Figure 3: Distribution of readmission rates for each post-discharge day across hospitals.For each post-discharge day, solid box represents the median and interquartile range for the distribution of readmission rates across hospitals with whiskers representing marginals of this distribution. The distributions are presented for heart failure (HF), myocardial infarction (MI) and pneumonia.

A) HF, readmissions

B) MI, readmissions

C) Pneumonia, readmission

Online Figure 4: Changes in Readmission Rates from Day 1 Through 60 Post-Discharge by Cause of Readmission for PneumoniaReadmission rates from days 1 through 60 post-discharge during the first year (July 2008-June 2009) and final year (July 2015-June 2016) of the study. Readmissions were classified as cardiovascular readmissions, non-cardiovascular readmissions, and readmissions for the same condition as the index diagnosis, and were defined based on the primary diagnosis of the readmission. Lines represent the smooth curves based on generalized additive models with a 4th order polynomial. Red: 2008-09, blue: 2015-16.

Online Figure 5: Distribution of post-discharge mortality rates for each post-discharge day across hospitals.For each post-discharge day, solid box represents the median and interquartile range for the distribution of post-discharge mortality rates across hospitals with whiskers representing marginals of this distribution. The distributions are presented for heart failure (HF), myocardial infarction (MI) and pneumonia.A) HF, mortality

B) MI, mortality

C) Pneumonia, mortality

Supplemental Tables

Online Table 1: Agency for Healthcare Research and Quality (AHRQ) - Clinical classification software (CCS) codes to identify cardiovascular hospitalizations.

Condition CCS code

Heart valve disorders 96

Peri-; endo-; and myocarditis; cardiomyopathy 97

Essential hypertension 98

Hypertension with complications and secondary hypertension 99

Acute myocardial infarction 100

Coronary atherosclerosis and other heart disease 101

Nonspecific chest pain 102

Pulmonary heart disease 103

Other and ill-defined heart disease 104

Conduction disorders 105

Cardiac dysrhythmias 106

Cardiac arrest and ventricular fibrillation 107

Congestive heart failure; nonhypertensive 108

Acute cerebrovascular disease 109

Occlusion or stenosis of precerebral arteries 110

Other and ill-defined cerebrovascular disease 111

Transient cerebral ischemia 112

Late effects of cerebrovascular disease 113

Peripheral and visceral atherosclerosis 114

Aortic; peripheral; and visceral artery aneurysms 115

Aortic and peripheral arterial embolism or thrombosis 116

Other circulatory disease 117

Phlebitis; thrombophlebitis and thromboembolism 118

Online Table 2: Statistical power

The power to detect a difference of 0.1% and 0.05% in the outcome at day 30 for an alpha of 0.05.

Condition/Outcome Statistical Power to detect differences of

0.10% 0.05%

Acute myocardial infarction

Readmission >99.9% 81.2%

Mortality >99.9% 92.9%

Heart failure

Readmission >99.9% 97.5%

Mortality >99.9% >99.9%

Pneumonia

Readmission >99.9% >99.9%

Mortality >99.9% >99.9%

Online Table 3: Coefficient for discontinuities and their 95% confidence intervals for local polynomial regression with a cut point at post-discharge day 30.

Changes in readmissions based on models evaluating data between post-discharge days 10 to 50, using local polynomial and linear models. Data presented for 4th order local polynomial regression but a similar effect estimates were observed for 2nd order and 3rd order local polynomial regression as well.

Change across day-30

P-valueRegression

discontinuity

estimate

Lower

confidence

limit

Upper

confidence

limit

Acute myocardial infarction

(day 10 to 50)

Non-linear -0.03 -0.08 0.00 0.07

Linear 0.01 -0.02 0.02 0.87

Heart failure

(day 10 to 50)

Non-linear 0.00 -0.02 0.04 0.68

Linear 0.01 -0.01 0.02 0.33

Pneumonia

(day 10 to 50)

Non-linear 0.01 -0.02 0.03 0.59

Linear 0.01 -0.01 0.01 0.51