Date Published: March 12, 2019
Publisher: Public Library of Science
Author(s): Vanessa Pauly, Hélène Mendizabal, Stéphanie Gentile, Pascal Auquier, Laurent Boyer, Nan Liu.
Reducing 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.
This 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.
Our 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).
Our 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.
Reducing 30-day rehospitalizations is a priority of health care policies in Western countries [1,2]. Unplanned rehospitalizations are common [3,4] and costly [3,5], reflecting poor quality inpatient care [6–8] and poorly coordinated transitions between hospitals and homes . Despite the growing literature on this issue, unplanned rehospitalizations are still poorly understood and controlled . We need to better understand its determinants and be able to identify patients at high risk of rehospitalization in order to improve quality of care and reduce rehospitalizations and associated health care costs . To date, the majority of studies have focused on particular conditions, e.g., patients with specific diagnoses [11–14] or socio-demographic characteristics like old age [4,15,16], limiting their generalizability [17,18]. Despite the need to target patients at high risk of rehospitalizations in order to propose cost-effective interventions at hospital level [3,19], there is very limited research addressing all-cause unplanned rehospitalizations.
The principal findings of this study can be summarized as follows. In a large sample of acute-care inpatients (82,862 patients and 118,650 hospitalizations), the rate of unplanned 30-day all-cause rehospitalization in four French university hospitals proved to be low (3.5%). Several factors predicted these rehospitalizations (i.e., age, gender, state-funded medical assistance, disease category and severity, Charlson comorbidity index, hospitalization via emergency departments, LOS, and previous hospitalization 6 months before), which could be targeted in a French national rehospitalization reduction program. Finally, we developed and internally validated an easy-to-use predictive rehospitalization risk score of unplanned 30-day all-cause rehospitalization with satisfactory discriminatory properties that can help physicians identify patients at high risk then propose adapted transitional care interventions.
The 3.5% unplanned rehospitalization rate was substantially lower in our study than that of studies performed in other countries, suggesting that universal insurance coverage may be a key factor for controlling rehospitalization. Despite this low unplanned rehospitalization rate, our findings likewise indicate that the possibility of unplanned rehospitalization remains high for patients with certain characteristics, suggesting the interest of proposing targeted interventions for patients at the greatest risk. We also 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 available in electronic medical records and other databases could improve the accuracy of our predictive rehospitalization risk score based on medico-administrative data. Finally, further research is required to determine whether such quantification risk modifies in fine in real-life patient care and outcomes.