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Machine-learning prediction of unplanned 30-day rehospitalization using the French hospital medico-administrative database.

ABSTRACT: Predicting unplanned rehospitalizations has traditionally employed logistic regression models. Machine learning (ML) methods have been introduced in health service research and may improve the prediction of health outcomes. The objective of this work was to develop a ML model to predict 30-day all-cause rehospitalizations based on the French hospital medico-administrative database.This was a retrospective cohort study of all discharges in the year 2015 from acute-care inpatient hospitalizations in a tertiary-care university center comprising 4 French hospitals. The study endpoint was unplanned 30-day all-cause rehospitalization. Logistic regression (LR), classification and regression trees (CART), random forest (RF), gradient boosting (GB), and neural networks (NN) were applied to the collected data. The predictive performance of the models was evaluated using the H-measure and the area under the ROC curve (AUC).Our analysis included 118,650 hospitalizations, of which 4127 (3.5%) led to rehospitalizations via emergency departments. The RF model was the most performant model according to the H-measure (0.29) and the AUC (0.79). The performances of the RF, GB and NN models (H-measures ranged from 0.18 to 0. 29, AUC ranged from 0.74 to 0.79) were better than those of the LR model (H-measure?=?0.18, AUC?=?0.74); all P values <.001. In contrast, LR was superior to CART (H-measure?=?0.16, AUC?=?0.70), P?

SUBMITTER: Jaotombo F

PROVIDER: S-EPMC7717815 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Machine-learning prediction of unplanned 30-day rehospitalization using the French hospital medico-administrative database.

Jaotombo Franck F Pauly Vanessa V Auquier Pascal P Orleans Veronica V Boucekine Mohamed M Fond Guillaume G Ghattas Badih B Boyer Laurent L

Medicine 20201201 49

Predicting unplanned rehospitalizations has traditionally employed logistic regression models. Machine learning (ML) methods have been introduced in health service research and may improve the prediction of health outcomes. The objective of this work was to develop a ML model to predict 30-day all-cause rehospitalizations based on the French hospital medico-administrative database.This was a retrospective cohort study of all discharges in the year 2015 from acute-care inpatient hospitalizations ...[more]

PMID: 33285668

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Project description:BackgroundReducing unplanned rehospitalizations is one of the priorities of health care policies in France and other Western countries. An easy-to-use algorithm for identifying patients at higher risk of rehospitalizations would help clinicians prioritize actions and care concerning discharge transitions. Our objective was to develop a predictive unplanned 30-day all-cause rehospitalization risk score based on the French hospital medico-administrative database.MethodsThis was a retrospective cohort study of all 2015 discharges from acute-care inpatient hospitalizations in a tertiary-care university center comprising four hospitals. The study endpoint was unplanned 30-day all-cause rehospitalization via emergency departments, and we collected sociodemographic, clinical, and hospital characteristics based on hospitalization database computed for reimbursement of fees. We derived a predictive rehospitalization risk score using a split-sample design and multivariate logistic regression, and we compared the discriminative properties with the LACE index risk-score.ResultOur analysis included 118,650 hospitalizations, of which 4,127 (3.5%) led to rehospitalizations via emergency departments. Variables independently associated with rehospitalization were age, gender, state-funded medical assistance, as well as disease category and severity, Charlson comorbidity index, hospitalization via emergency departments, length of stay (LOS), and previous hospitalizations 6 months before. The predictive rehospitalization risk score yielded satisfactory discriminant properties (C statistic: 0.74) exceeding the LACE index (0.66).ConclusionOur findings indicate that the possibility of unplanned rehospitalization remains high for some patient characteristics, indicating that targeted interventions could be beneficial for patients at the greatest risk. We developed an easy-to-use predictive rehospitalization risk-score of unplanned 30-day all-cause rehospitalizations with satisfactory discriminant properties. Future works should, however, explore if other data from electronic medical records and other databases could improve the accuracy of our predictive rehospitalization risk score based on medico-administrative data.

Project description:BackgroundExisting readmission reduction solutions tend to focus on complementing inpatient care with enhanced care transition and postdischarge interventions. These solutions are initiated near or after discharge, when clinicians' impact on inpatient care is ending. Preventive intervention during hospitalization is an underexplored area that holds potential for reducing readmission risk. However, it is challenging to predict readmission risk at the early stage of hospitalization because few data are available.ObjectiveThe objective of this study was to build an early prediction model of unplanned 30-day hospital readmission using a large and diverse sample. We were also interested in identifying novel readmission risk factors and protective factors.MethodsWe extracted the medical records of 96,550 patients in 205 participating Cerner client hospitals across four US census regions in 2016 from the Health Facts database. The model was built with index admission data that can become available within 24 hours and data from previous encounters up to 1 year before the index admission. The candidate models were evaluated for performance, timeliness, and generalizability. Multivariate logistic regression analysis was used to identify readmission risk factors and protective factors.ResultsWe developed six candidate readmission models with different machine learning algorithms. The best performing model of extreme gradient boosting (XGBoost) achieved an area under the receiver operating characteristic curve of 0.753 on the development data set and 0.742 on the validation data set. By multivariate logistic regression analysis, we identified 14 risk factors and 2 protective factors of readmission that have never been reported.ConclusionsThe performance of our model is better than that of the most widely used models in US health care settings. This model can help clinicians identify readmission risk at the early stage of hospitalization so that they can pay extra attention during the care process of high-risk patients. The 14 novel risk factors and 2 novel protective factors can aid understanding of the factors associated with readmission.

Project description:BackgroundCurrent guidelines for adult patients with pneumococcal meningitis (PM) recommend initial management in intermediate or intensive care units (ICU), but evidence to support these recommendations is limited. We aimed to describe ICU admission practices of patients with PM.MethodsWe conducted a retrospective analysis of the French medico administrative database of consecutive adult patients with PM and sepsis criteria hospitalized between 2011 and 2020. We defined two groups, "Direct ICU" corresponding to a direct ICU admission and "Delayed ICU" corresponding to a secondary ICU admission.ResultsWe identified 4052 patients hospitalized for a first episode of PM, including 2006 "Direct ICU" patients (50%) and 2046 "delayed ICU" patients (50%). The patients were mainly males [n = 2260 (56%)] with median age of 61 years [IQR 50-71] and a median Charlson index of 1 [0-3]. Among them, median SAPS II on admission was 46 [33-62], 2173 (54%) had a neurological failure on admission with 2133 (53%) in coma, 654 (16%) with brainstem failure, 488 (12%) with seizures and 779 (19%) with focal signs without coma. PM was frequently associated with pneumonia [n = 1411 (35%)], and less frequently with endocarditis [n = 317 (8%)]. The median ICU length of stay and hospital length of stay were 6 days [2-14] and 21 days [13-38], respectively. In-hospital mortality was 27% (n = 1100) and 640 (16%) patients were secondarily transferred to rehabilitation care unit. Direct ICU group was significantly more severe but after adjustment for age, sex, comorbidities, organ failures on admission and admission from home, direct ICU admission was significantly associated with a lower mortality (Odds ratio 0.67 [0.56-0.80], p < 0.01). This corresponded to one death avoided for 11 PM directly admitted in ICU.ConclusionsAmong patients with PM and sepsis, direct ICU admission was associated with lower mortality rates when compared to delayed admission.

Project description:BackgroundUnplanned hospital readmissions are a major source of morbidity among dialysis patients, in whom the risk of hospital readmission is exceptionally high. The contribution of dialysis facility staffing to hospital readmission has been largely overlooked.MethodsUsing annual data of dialysis patients from the United States Renal Data System from 2010 to 2013, we assessed dialysis facilities with a significantly worse (SW) and facilities with a nonsignificant (NS) standardized readmission ratio (SRR). SRR estimates were risk adjusted for patient factors, past year comorbidities, and index hospitalization characteristics. Facility staffing variables were compared between 2 exposure groups: facilities with SW and NS SRRs. Four measures of staffing, including patient-to-staffing ratio, were compared between SW and matched NS facilities.ResultsAbout 136,000-148,000 dialysis patients with 269,000-319,000 index hospital discharges were used to identify facilities with SW and facilities with NS SRR annually. Approximately 3-4% of facilities were identified as having SW SRR among > 5,000 facilities annually. The percent of nurses-to-total staff was significantly lower in 2010 for SW facilities than in matched NS facilities (42.5 vs. 45.6%, p = 0.012), but this disparity was attenuated by 2013 (44.8 vs. 44.7%, p = 0.949). There was a higher patient-to-nurse ratio for SW facilities than for NS facilities (mean 16.4 vs. 15.2, p = 0.038) in 2010 as well, and the disparity was reduced by 2013. The trends were similar for patient-to-total staff and patient-to-registered nurse, but not statistically significant.ConclusionsThis study found that dialysis facilities with SW 30-day readmission rates had lower proportions of nurses-to-total staff and higher patient-to-nurse ratios, but this disparity improved in recent years. Additional research is warranted focusing on how evidence-based staffing at dialysis facilities can contribute to reduction of hospital readmission, and this knowledge is needed to inform clinical practice guidelines and policy decisions regarding optimal dialysis patient staffing.

Project description:BackgroundThe Improving Medicare Post-Acute Care Transformation (IMPACT) Act of 2014 stipulates that standardized functional status (self-care and mobility) and cognitive function data will be used for quality reporting in post-acute care settings. Thirty-day post-discharge unplanned rehospitalization is an established quality metric that has recently been extended to post-acute settings. The relationships between the functional domains in the IMPACT Act and 30-day unplanned rehospitalization are poorly understood.ObjectiveTo determine the degree to which discharge mobility, self-care, and cognitive function are associated with 30-day unplanned rehospitalization following discharge from post-acute care.DesignThis was a retrospective cohort study.SettingInpatient rehabilitation facilities submitting claims and assessment data to the Centers for Medicare and Medicaid Services in 2012-2013.ParticipantsMedicare fee-for-service enrollees discharged from post-acute rehabilitation in 2012-2013. The sample included community-dwelling adults admitted for rehabilitation following an acute care stay who survived for 32 days following discharge (N = 252,406).InterventionsNot applicable.Main measuresThirty-day unplanned rehospitalization following post-acute rehabilitation.Key resultsThe unadjusted 30-day unplanned rehospitalization rate was 12.0 % (n = 30,179). Overall, patients dependent at discharge for mobility had a 50 % increased odds of rehospitalization (OR = 1.50, 95 % CI: 1.42-1.59), patients dependent for self-care a 36 % increased odds (OR = 1.36, 95 % CI: 1.27-1.47), and patients dependent for cognition a 19 % increased odds (OR = 1.19, 95 % CI: 1.09-1.29). Patients dependent for both self-care and mobility at discharge (n = 8312, 3.3 %) had a 16.1 % (95 % CI: 15.3-17.0 %) adjusted rehospitalization rate versus 8.5 % (95 % CI: 8.3-8.8 %) for those independent for both (n = 74,641; 29.6 %).ConclusionsThe functional domains identified in the IMPACT Act were associated with 30-day unplanned rehospitalization following post-acute care in this large national sample. Further research is needed to better understand and improve the functional measures, and to determine if their association with rehospitalizations varies across post-acute settings, patient populations, or episodes of care.

Project description:BackgroundEarly unplanned hospital readmissions are associated with increased harm to patients, increased medical costs, and negative hospital reputation. With the identification of at-risk patients, a crucial step toward improving care, appropriate interventions can be adopted to prevent readmission. This study aimed to build machine learning models to predict 14-day unplanned readmissions.MethodsWe conducted a retrospective cohort study on 37,091 consecutive hospitalized adult patients with 55,933 discharges between September 1, 2018, and August 31, 2019, in an 1193-bed university hospital. Patients who were aged < 20 years, were admitted for cancer-related treatment, participated in clinical trial, were discharged against medical advice, died during admission, or lived abroad were excluded. Predictors for analysis included 7 categories of variables extracted from hospital's medical record dataset. In total, four machine learning algorithms, namely logistic regression, random forest, extreme gradient boosting, and categorical boosting, were used to build classifiers for prediction. The performance of prediction models for 14-day unplanned readmission risk was evaluated using precision, recall, F1-score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC).ResultsIn total, 24,722 patients were included for the analysis. The mean age of the cohort was 57.34 ± 18.13 years. The 14-day unplanned readmission rate was 1.22%. Among the 4 machine learning algorithms selected, Catboost had the best average performance in fivefold cross-validation (precision: 0.9377, recall: 0.5333, F1-score: 0.6780, AUROC: 0.9903, and AUPRC: 0.7515). After incorporating 21 most influential features in the Catboost model, its performance improved (precision: 0.9470, recall: 0.5600, F1-score: 0.7010, AUROC: 0.9909, and AUPRC: 0.7711).ConclusionsOur models reliably predicted 14-day unplanned readmissions and were explainable. They can be used to identify patients with a high risk of unplanned readmission based on influential features, particularly features related to diagnoses. The operation of the models with physiological indicators also corresponded to clinical experience and literature. Identifying patients at high risk with these models can enable early discharge planning and transitional care to prevent readmissions. Further studies should include additional features that may enable further sensitivity in identifying patients at a risk of early unplanned readmissions.

Dataset Information

Machine-learning prediction of unplanned 30-day rehospitalization using the French hospital medico-administrative database.

Publications

Machine-learning prediction of unplanned 30-day rehospitalization using the French hospital medico-administrative database.

Similar Datasets

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