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BackgroundReadmissions after hospital discharge are a common occurrence and are costly for both hospitals and patients. Previous attempts to create universal risk prediction models for readmission have not met with success. In this study we leveraged a comprehensive electronic health record to create readmission-risk models that were institution- and patient- specific in an attempt to improve our ability to predict readmission.MethodsThis is a retrospective cohort study performed at a large midwestern tertiary care medical center. All patients with a primary discharge diagnosis of congestive heart failure, acute myocardial infarction or pneumonia over a two-year time period were included in the analysis.The main outcome was 30-day readmission. Demographic, comorbidity, laboratory, and medication data were collected on all patients from a comprehensive information warehouse. Using multivariable analysis with stepwise removal we created three risk disease-specific risk prediction models and a combined model. These models were then validated on separate cohorts.Results3572 patients were included in the derivation cohort. Overall there was a 16.2% readmission rate. The acute myocardial infarction and pneumonia readmission-risk models performed well on a random sample validation cohort (AUC range 0.73 to 0.76) but less well on a historical validation cohort (AUC 0.66 for both). The congestive heart failure model performed poorly on both validation cohorts (AUC 0.63 and 0.64).ConclusionsThe readmission-risk models for acute myocardial infarction and pneumonia validated well on a contemporary cohort, but not as well on a historical cohort, suggesting that models such as these need to be continuously trained and adjusted to respond to local trends. The poor performance of the congestive heart failure model may suggest that for chronic disease conditions social and behavioral variables are of greater importance and improved documentation of these variables within the electronic health record should be encouraged.

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outcomes among opioid-exposed infants is limited, particularly for those not diagnosed with neonatal opioid withdrawal syndrome (NOWS).OBJECTIVES To describe infant mortality among opioid-exposed infants and identify how mortality risk differs in opioid-exposed infants with and without a diagnosis of NOWS compared with infants without opioid exposure. DESIGN, SETTING, AND PARTICIPANTSA retrospective cohort study of maternal-infant dyads was conducted, linking health care claims with vital records for births from January 1, 2010, to December 31, 2014, with follow-up of infants until age 1 year (through 2015). Maternal-infant dyads were included if the infant was born in Texas at 22 to 43 weeks' gestational age to a woman aged 15 to 44 years insured by Texas Medicaid. Data analysis was performed from May 2019 to October 2020. EXPOSURE The primary exposure was prenatal opioid exposure, with infants stratified by the presence or absence of a diagnosis of NOWS during the birth hospitalization.MAIN OUTCOMES AND MEASURES Risk of infant mortality (death at age <365 days) was examined using Kaplan-Meier and log-rank tests. A series of logistic regression models was estimated to determine associations between prenatal opioid exposure and mortality, adjusting for maternal and neonatal characteristics and clustering infants at the maternal level to account for statistical dependence owing to multiple births during the study period.RESULTS Among 1 129 032 maternal-infant dyads, 7207 had prenatal opioid exposure, including 4238 diagnosed with NOWS (mean [SD] birth weight, 2851 [624] g) and 2969 not diagnosed with NOWS (mean [SD] birth weight, 2971 [639] g). Infant mortality was 20 per 1000 live births for opioid-exposed infants not diagnosed with NOWS, 11 per 1000 live births for infants with NOWS, and 6 per 1000 live births in the reference group (P < .001). After adjusting for maternal and neonatal characteristics, mortality in infants with a NOWS diagnosis was not significantly different from the reference population (odds ratio, 0.82; 95% CI, 0.58-1.14). In contrast, the odds of mortality in opioid-exposed infants not diagnosed with NOWS was 72% greater than the reference population (odds ratio, 1.72; 95% CI, 1.25-2.37). CONCLUSIONS AND RELEVANCEIn this study, opioid-exposed infants appeared to be at increased risk of mortality, and the treatments and supports provided to those diagnosed with NOWS may be protective. Interventions to support opioid-exposed maternal-infant dyads are warranted, regardless of the perceived severity of neonatal opioid withdrawal.