Predictors of frequency of 1-year readmission in adult patients with diabetes.

Diabetes mellitus (DM) is the third most common chronic condition associated with frequent hospital readmissions. Predictors of the number of readmissions within 1 year among patients with DM are less often studied compared with those of 30-day readmission. This study aims to identify predictors of number of readmissions within 1 year amongst adult patients with DM and compare different count regression models with respect to model fit. Data from 2008 to 2015 were extracted from the electronic medical records of the National University Hospital, Singapore. Inpatients aged ≥ 18 years at the time of index admission with a hospital stay > 24 h and survived until discharge were included. The zero-inflated negative binomial (ZINB) model was fitted and compared with three other count models (Poisson, zero-inflated Poisson and negative binomial) in terms of predicted probabilities, misclassification proportions and model fit. Adjusted for other variables in the model, the expected number of readmissions was 1.42 (95% confidence interval [CI] 1.07 to 1.90) for peripheral vascular disease, 1.60 (95% CI 1.34 to 1.92) for renal disease and 2.37 (95% CI 1.67 to 3.35) for Singapore residency. Number of emergency visits, number of drugs and age were other significant predictors, with length of stay fitted as a zero-inflated component. Model comparisons suggested that ZINB provides better prediction than the other three count models. The ZINB model identified five patient characteristics and two comorbidities associated with number of readmissions. It outperformed other count regression models but should be validated before clinical adoption.

Predicting and Validating 30-day Hospital Readmission in Adults With Diabetes Whose Index Admission Is Diabetes-related.

OBJECTIVE: The primary objective is to develop a prediction model of 30-day hospital readmission among adults with diabetes mellitus (DM) whose index admission was DM-related. The secondary aims are to internally and externally validate the prediction model and compare its performance with 2 existing models. RESEARCH DESIGN AND SETTING: Data of inpatients aged ≥ 18 years from 2008 to 2015 were extracted from the electronic medical record system of the National University Hospital, Singapore. Unplanned readmission within 30 days was calculated from the discharge date of the index hospitalization. Multivariable logistic regression and 10-fold cross-validation were performed. For external validation, simulations based on prevalence of 30-day readmission, and the regression coefficients provided by referenced papers were conducted. RESULTS: Eleven percent of 2355 patients reported 30-day readmission. The prediction model included 4 predictors: length of stay, ischemic heart disease, peripheral vascular disease, and number of drugs. C-statistics for the prediction model and 10-fold cross-validation were 0.68 (95% CI 0.66, 0.70) and 0.67 (95% CI 0.63 to 0.70), respectively. Those for the 3 simulated external validation data sets ranged from 0.64 to 0.68. CONCLUSION: The prediction model performs well with good internal and external validity for identifying patients with DM at risk of unplanned 30-day readmission.

Factors and experiences associated with unscheduled 30-day hospital readmission: A mixed method study.

INTRODUCTION: Analysis of risk factors can pave the way for reducing unscheduled hospital readmissions and improve resource utilisation. METHODS: This was a concurrent nested, mixed method study. Factors associated with patients readmitted within 30 days between 2011 and 2015 at the National University Hospital, Singapore (N=104,496) were examined. Fifty patients were sampled in 2016 to inform an embedded qualitative study. Narrative interviews explored the periods of readmissions and related experiences, contrasted against those of non-readmitted patients. RESULTS: Neoplastic disease (odds ratio [OR] 1.91, 95% confidence interval [CI] 1.70-2.15), number of discharged medications (5 to 10 medications OR 1.21, 95% CI 1.14-1.29; ≥11 medications OR 1.80, 95% CI 1.66-1.95) and length of stay >7 days (OR 1.46, 95% CI 1.36-1.58) were most significantly associated with readmissions. Other factors including number of surgical operations, subvention class, number of emergency department visits in the previous year, hospital bill size, gender, age, Charlson comorbidity index and ethnicity were also independently associated with hospital readmissions. Although readmitted and non-readmitted patients shared some common experiences, they reported different psychological reactions to their illnesses and viewed hospital care differently. Negative emotions, feeling of being left out by the healthcare team and perception of ineffective or inappropriate treatment were expressed by readmitted patients. CONCLUSION: Patient, hospital and system-related factors were associated with readmissions, which may allow early identification of at-risk patients. Qualitative analysis suggested several areas of improvement in care including greater empowerment and involvement of patients in care and decision making.