Dataset Information

Epidemiology and Predictors of all-cause 30-Day readmission in patients with sickle cell crisis.

ABSTRACT: The 30-day readmission rate after hospitalization for a sickle cell crisis (SCC) is extremely high. Accurate information on readmission diagnoses, total readmission costs and factors associated with readmission is required to effectively plan resource allocation and to plan interventions to reduce readmission rates. The present study aimed to examine readmission diagnoses and factors associated with all-cause 30-day readmission after hospitalization for SCC. We analyzed 2016 nationwide readmission database (NRD) to identify patterns of 30-day readmission by patient demographic characteristics and time after hospitalization for SCC. We estimated the percentage and most common readmission diagnoses for 30-day and 7-day readmissions after discharge. We studied the relationship between risk factors and readmission and the impact of readmission on patient outcomes and resulting financial burden on health care in dollars. In 2016, of 67,887 discharges after index hospitalizations, 18099 (26.9%) were readmitted within 30-days. Of all readmissions, 5166 (7.6%) were readmitted within 7 days. The spectrum of readmission diagnoses was largely similar in both 30-day and 7-day readmission with more than 80% patients in both time periods readmitted with diagnoses related to SCC. The mean length of stay for readmitted patients was significantly longer than the index hospitalization (5.3 days (5.1-5.5) vs 4.9 days (CI 4.8-5.1, p?

SUBMITTER: Kumar V

PROVIDER: S-EPMC7005718 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Epidemiology and Predictors of all-cause 30-Day readmission in patients with sickle cell crisis.

Kumar Vivek V Chaudhary Neha N Achebe Maureen M MM

Scientific reports 20200207 1

The 30-day readmission rate after hospitalization for a sickle cell crisis (SCC) is extremely high. Accurate information on readmission diagnoses, total readmission costs and factors associated with readmission is required to effectively plan resource allocation and to plan interventions to reduce readmission rates. The present study aimed to examine readmission diagnoses and factors associated with all-cause 30-day readmission after hospitalization for SCC. We analyzed 2016 nationwide readmissi ...[more]

PMID: 32034210

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Project description:Background and Purpose: Hospital readmissions impose a substantial burden on the healthcare system. Reducing readmissions after stroke could lead to improved quality of care especially since stroke is associated with a high rate of readmission. The goal of this study is to enhance our understanding of the predictors of 30-day readmission after ischemic stroke and develop models to identify high-risk individuals for targeted interventions. Methods: We used patient-level data from electronic health records (EHR), five machine learning algorithms (random forest, gradient boosting machine, extreme gradient boosting-XGBoost, support vector machine, and logistic regression-LR), data-driven feature selection strategy, and adaptive sampling to develop 15 models of 30-day readmission after ischemic stroke. We further identified important clinical variables. Results: We included 3,184 patients with ischemic stroke (mean age: 71 ± 13.90 years, men: 51.06%). Among the 61 clinical variables included in the model, the National Institutes of Health Stroke Scale score above 24, insert indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy had the highest importance score. The Model's AUC (area under the curve) for predicting 30-day readmission was 0.74 (95%CI: 0.64-0.78) with PPV of 0.43 when the XGBoost algorithm was used with ROSE-sampling. The balance between specificity and sensitivity improved through the sampling strategy. The best sensitivity was achieved with LR when optimized with feature selection and ROSE-sampling (AUC: 0.64, sensitivity: 0.53, specificity: 0.69). Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems' resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.

Dataset Information

Epidemiology and Predictors of all-cause 30-Day readmission in patients with sickle cell crisis.

Publications

Epidemiology and Predictors of all-cause 30-Day readmission in patients with sickle cell crisis.

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