Predicting 30-Day Readmissions in an Asian Population: Building a Predictive Model by Incorporating Markers of Hospitalization Severity

Abstract

BackgroundTo reduce readmissions, it may be cost-effective to consider risk stratification, with targeting intervention programs to patients at high risk of readmissions. In this study, we aimed to derive and validate a prediction model including several novel markers of hospitalization severity, and compare the model with the LACE index (Length of stay, Acuity of admission, Charlson comorbidity index, Emergency department visits in past 6 months), an established risk stratification tool.MethodThis was a retrospective cohort study of all patients ≥ 21 years of age, who were admitted to a tertiary hospital in Singapore from January 1, 2013 through May 31, 2015. Data were extracted from the hospital’s electronic health records. The outcome was defined as unplanned readmissions within 30 days of discharge from the index hospitalization. Candidate predictive variables were broadly grouped into five categories: Patient demographics, social determinants of health, past healthcare utilization, medical comorbidities, and markers of hospitalization severity. Multivariable logistic regression was used to predict the outcome, and receiver operating characteristic analysis was performed to compare our model with the LACE index.Results74,102 cases were enrolled for analysis. Of these, 11,492 patient cases (15.5%) were readmitted within 30 days of discharge. A total of fifteen predictive variables were strongly associated with the risk of 30-day readmissions, including number of emergency department visits in the past 6 months, Charlson Comorbidity Index, markers of hospitalization severity such as ‘requiring inpatient dialysis during index admission, and ‘treatment with intravenous furosemide 40 milligrams or more’ during index admission. Our predictive model outperformed the LACE index by achieving larger area under the curve values: 0.78 (95% confidence interval [CI]: 0.77–0.79) versus 0.70 (95% CI: 0.69–0.71).ConclusionSeveral factors are important for the risk of 30-day readmissions, including proxy markers of hospitalization severity.

Can we understand population healthcare needs using electronic medical records?

“…It is imperative that important variables are routinely captured as part of a holistic biopsychosocial approach to patient assessment. For instance, physical function, (12,13) social support (14)(15)(16)(17)(18) and patient activation (19) are well-known predictors of re-admission risk and can be used to identify care needs that require intervention. A comprehensive literature review is needed to locate variables with discriminatory and predictive value that can be included in the EMR.…”

FAM-FACE-SG: a score for risk stratification of frequent hospital admitters

“…Intravenous furosemide was chosen as one of candidate variables because it has been used in acute treatment of heart failure and therefore a proxy marker of heart disease severity. Moreover, we found it a significant predictor of frequent hospital admission risk [2] and 30-day readmission risk [26]. …”

Evaluating a novel Integrated Community of Care (ICoC) for patients from an urbanised low-income community in Singapore using the participatory action research (PAR) methodology: a study protocol

“…Similar demographic, SES and medical comorbidities data (predictors used for propensity scores calculation listed in(Online supplementary appendix A ) will be collected for both groups to allow calculation of propensity scores as a basis for comparability. We have shown in our previous study 31 that these data can be extracted from our data warehouse for inclusion in a propensity score model. Information such as abbreviated mental test, modified Barthel index, instrumental activities of daily living and health related quality of life will not be available for the control group.…”