Abstract
BackgroundRisks prediction models of 30-day all-cause hospital readmissions are multi-factorial. Severity of illness (SOI) and risk of mortality (ROM) categorized by All Patient Refined Diagnosis Related Groups (APR-DRG) seem to predict hospital readmission but lack large sample validation. Effects of risk reduction interventions including providing post-discharge outpatient visits remain uncertain. We aim to determine the accuracy of using SOI and ROM to predict readmission and further investigate the role of outpatient visits in association with hospital readmission.MethodsHospital readmission data were reviewed retrospectively from September 2012 through June 2015. Patient demographics and clinical variables including insurance type, homeless status, substance abuse, psychiatric problems, length of stay, SOI, ROM, ICD-10 diagnoses and medications prescribed at discharge, and prescription ratio at discharge (number of medications prescribed divided by number of ICD-10 diagnoses) were analyzed using logistic regression. Relationships among SOI, type of hospital visits, time between hospital visits, and readmissions were also investigated.ResultsA total of 6011 readmissions occurred from 55,532 index admissions. The adjusted odds ratios of SOI and ROM predicting readmissions were 1.31 (SOI: 95 % CI 1.25–1.38) and 1.09 (ROM: 95 % CI 1.05–1.14) separately. Ninety percent (5381/6011) of patients were readmitted from the Emergency Department (ED) or Urgent Care Center (UCC). Average time interval from index discharge date to ED/UCC visit was 9 days in both the no readmission and readmission groups (p > 0.05). Similar hospital readmission rates were noted during the first 10 days from index discharge regardless of whether post-index discharge patient clinic visits occurred when time-to-event analysis was performed.ConclusionsSOI and ROM significantly predict hospital readmission risk in general. Most readmissions occurred among patients presenting for ED/UCC visits after index discharge. Simply providing early post-discharge follow-up clinic visits does not seem to prevent hospital readmissions.
Highlights
Risks prediction models of 30-day all-cause hospital readmissions are multi-factorial
Since this study focused on identifying independent risk factors associated with 30-day all-cause unplanned hospital readmissions after the index discharge, patients were either followed up 30 days after the index discharge or until they reached the endpoint of this study (i.e., June 30, 2015)
Among the 55,887 hospital admissions reviewed, 355 patients were excluded from this study including 83 patients who upon reaching the endpoint of this study, were still hospitalized
Summary
Risks prediction models of 30-day all-cause hospital readmissions are multi-factorial. Available data suggest inpatient length of stay, age, and lack of post-hospital follow-up visits are independent risk factors predicting hospital readmissions [6,7,8]. Other risks such as gender, patient psychosocial status, and history of substance abuse have been reported albeit these indices have provided inconsistent results [9,10,11,12]. The LACE index has been used to predict the risk of readmission in both medical and surgical patients by calculating a composite score that includes length of stay (“L”), acuity of admission (“A”), comorbidities (“C”), and the number of ED visits over the past 6 months (“E”) [15]. While this method had fair discriminatory power [16], equipoise exists regarding the accuracy of using these scoring systems to predict unplanned readmissions in other studies [17, 18]
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