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Objective: Unplanned readmissions following a hospitalization remain common despite significant efforts to curtail these. Wearable devices may offer help identify patients at high risk for an unplanned readmission., Materials and Methods: We conducted a multi-center retrospective cohort study using data from the All of Us data repository. We included subjects with wearable data and developed a baseline Feedforward Neural Network (FNN) model and a Long Short-Term Memory (LSTM) time-series deep learning model to predict daily, unplanned rehospitalizations up to 90 days from discharge. In addition to demographic and laboratory data from subjects, post-discharge data input features include wearable data and multiscale entropy features based on intraday wearable time series. The most significant features in the LSTM model were determined by permutation feature importance testing., Results: In sum, 612 patients met inclusion criteria. The complete LSTM model had a higher area under the receiver operating characteristic curve than the FNN model (0.83 vs 0.795). The 5 most important input features included variables from multiscale entropy (steps) and number of active steps per day., Discussion: Data available from wearable devices can improve ability to predict readmissions. Prior work has focused on predictors available up to discharge or on additional data abstracted from wearable devices. Our results from 35 institutions highlight how multiscale entropy can improve readmission prediction and may impact future work in this domain., Conclusion: Wearable data and multiscale entropy can improve prediction of a deep-learning model to predict unplanned 90-day readmissions. Prospective studies are needed to validate these findings., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objectives: To determine the predictive value of social determinants of health (SDoH) variables on 30-day readmission following a sepsis hospitalization as compared with traditional clinical variables., Design: Multicenter retrospective cohort study using patient-level data, including demographic, clinical, and survey data., Settings: Thirty-five hospitals across the United States from 2017 to 2021., Patients: Two hundred seventy-one thousand four hundred twenty-eight individuals in the AllofUs initiative, of which 8909 had an index sepsis hospitalization., Interventions: None., Measurements and Main Results: Unplanned 30-day readmission to the hospital. Multinomial logistic regression models were constructed to account for survival in determination of variables associate with 30-day readmission and are presented as adjusted odds rations (aORs). Of the 8909 sepsis patients in our cohort, 21% had an unplanned hospital readmission within 30 days. Median age (interquartile range) was 54 years (41-65 yr), 4762 (53.4%) were female, and there were self-reported 1612 (18.09%) Black, 2271 (25.49%) Hispanic, and 4642 (52.1%) White individuals. In multinomial logistic regression models accounting for survival, we identified that change to nonphysician provider type due to economic reasons (aOR, 2.55 [2.35-2.74]), delay of receiving medical care due to lack of transportation (aOR, 1.68 [1.62-1.74]), and inability to afford flow-up care (aOR, 1.59 [1.52-1.66]) were strongly and independently associated with a 30-day readmission when adjusting for survival. Patients who lived in a ZIP code with a high percentage of patients in poverty and without health insurance were also more likely to be readmitted within 30 days (aOR, 1.26 [1.22-1.29] and aOR, 1.28 [1.26-1.29], respectively). Finally, we found that having a primary care provider and health insurance were associated with low odds of an unplanned 30-day readmission., Conclusions: In this multicenter retrospective cohort, several SDoH variables were strongly associated with unplanned 30-day readmission. Models predicting readmission following sepsis hospitalization may benefit from the addition of SDoH factors to traditional clinical variables., Competing Interests: Drs. Shashikumar, Malhotra, and Nemati disclosed that they are cofounders of Healcisio. Dr. Malhotra received funding from Zoll, LivaNova, Jazz, and Eli Lilly; he disclosed that ResMed provided a philanthropic donation from University of California at San Diego (UCSD). Drs. Malhotra, Taylor, Rodriguez, Nemati, Wardi, and Meier received support for article research from the National Institutes of Health (NIH). Drs. Malhotra and Nemati are cofounders of Healciscio, a medical start-up forming in approval with the UCSD and in accordance with the UCSD’s conflicts of interest management policies. Dr. Taylor’s institution received funding from the National Institute of Nursing Research. Dr. Wardi’s institution received funding from the NIH; he received funding from Northwest Anesthesia. Dr. Meier received funding through her institution from the NIH. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Objectives: Although methamphetamine use is common, the scope of methamphetamine use and outcomes for patients admitted to the hospital is unclear. This study aims to identify the prevalence of methamphetamine use from January 2012 to January 2022, coingestions, hospital course, and readmission rate of admitted patients., Methods: This was a retrospective cohort study conducted on patients admitted to our center with the following inclusions: age older than 18 years, positive/"pending confirm" value for methamphetamine on urine drug screen, and/or an International Classification of Diseases , Tenth Revision , code related to stimulant use disorder as an active issue. Urine drug screen data are reported as methamphetamine +/- and polysubstance (PS) +/-. Patient demographics, admission diagnosis, and hospital course were extracted. Statistical tests used included t tests and Mann-Whitney U tests., Results: A total of 19,159 encounters were included, representing 12,057 unique patients. The median (interquartile range) age was 43 (33-54) years. Of all encounters, 35.3% were methamphetamine + and PS -, and 46.3% were methamphetamine + and PS +. Hospitalizations increased from 883 in 2012 to 2532 in 2021. The median (IQR) hospital stay was 48 (48-120) hours. Of all encounters, 16.8% included an intensive care unit (ICU) admission, and the median ICU stay was 42 (21-87) hours. A total of 2988 patients (24.7%) were readmitted within the study period, and 4988 (71.5%) returned within 1 year of the previous encounter. In context of all emergency department admissions from 2013 to 2022, 13.1% had a urine drug screen + for methamphetamine., Conclusions: Hospitalizations with recent methamphetamine use doubled at our institution from 2012 to 2022. In addition, 1 in 4 is readmitted (typically within 1 year), and a minority requires ICU care., Competing Interests: The authors report no conflicts of interest., (Copyright © 2024 American Society of Addiction Medicine.)

Objective: Sepsis has a high rate of 30-day unplanned readmissions. Predictive modeling has been suggested as a tool to identify high-risk patients. However, existing sepsis readmission models have low predictive value and most predictive factors in such models are not actionable., Materials and Methods: Data from patients enrolled in the AllofUs Research Program cohort from 35 hospitals were used to develop a multicenter validated sepsis-related unplanned readmission model that incorporates clinical and social determinants of health (SDH) to predict 30-day unplanned readmissions. Sepsis cases were identified using concepts represented in the Observational Medical Outcomes Partnership. The dataset included over 60 clinical/laboratory features and over 100 SDH features., Results: Incorporation of SDH factors into our model of clinical and demographic features improves model area under the receiver operating characteristic curve (AUC) significantly (from 0.75 to 0.80; P < .001). Model-agnostic interpretability techniques revealed demographics, economic stability, and delay in getting medical care as important SDH predictive features of unplanned hospital readmissions., Discussion: This work represents one of the largest studies of sepsis readmissions using objective clinical data to date (8935 septic index encounters). SDH are important to determine which sepsis patients are more likely to have an unplanned 30-day readmission. The AllofUS dataset provides granular data from a diverse set of individuals, making this model potentially more generalizable than prior models., Conclusion: Use of SDH improves predictive performance of a model to identify which sepsis patients are at high risk of an unplanned 30-day readmission., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)