Hospital Discharge Disposition of Stroke Patients in Tennessee

Abstract

Objectives Early determination of hospital discharge disposition status at an acute admission is extremely important for stroke management and the eventual outcomes of patients with stroke. We investigated the hospital discharge disposition of patients with stroke residing in Tennessee and developed a predictive tool for clinical adoption. Our investigational aims were to evaluate the association of selected patient characteristics with hospital discharge disposition status and predict such status at the time of an acute stroke admission. Methods We analyzed 127,581 records of patients with stroke hospitalized between 2010 and 2014. Logistic regression was used to generate odds ratios and 95% confidence intervals to examine the factor outcome association. An easy-to-use clinical predictive tool was built by using integer-based risk scores derived from coefficients of multivariable logistic regression. Results Among the 127,581 records of patients with stroke, 86,114 (67.5%) indicated home discharge and 41,467 (32.5%) corresponded to facility discharge. All considered patient characteristics had significant correlations with hospital discharge disposition status. Patients were at greater odds of being discharged to another facility if they were women; older; black; patients with a subarachnoid or intracerebral hemorrhage; those with the comorbidities of diabetes mellitus, heart disease, hypertension, chronic kidney disease, arrhythmia, or depression; those transferred from another hospital; or patients with Medicare as the primary payer. A predictive tool had a discriminatory capability with area under the curve estimates of 0.737 and 0.724 for derivation and validation cohorts, respectively. Conclusions Our investigation revealed that the hospital discharge disposition pattern of patients with stroke in Tennessee was associated with the key patient characteristics of selected demographics, clinical indicators, and insurance status. These analyses resulted in the development of an easy-to-use predictive tool for early determination of hospital discharge disposition status.

Subarachnoid Hemorrhage “Fast Track”: A Health Economics and Health Care Redesign Approach for Early Selected Hospital Discharge

“…Another article noted that insurance status was often a determinant of discharge status and ability but did not compare early discharge with current standard and incorporated all stroke patient discharges. 11 One article focused on the economic cost savings of early aneurysm treatment compared with delayed treatment. 12 A study by Alaraj et al 13 investigating reduction of LOS and costs associated with SAH found that LOS has been declining from about 21 days to about 14 days, with similar reduction in costs.…”

Effect of early nutritional initiation on post‐cerebral infarction discharge destination: A propensity‐matched analysis using machine learning

“…Numerous studies have been conducted on the destination patterns of discharged patients. Cho et al 17 reported that their destinations are associated with several patient characteristics, such as selected demographics, clinical indicators, and insurance status. Thorpe et al 18 conducted a meta‐analysis and found that for each one‐point increase on a functional independence measure, a patient is approximately 1.08 times more likely to be discharged home than to institutionalised care.…”

Developing a Predictive Tool for Hospital Discharge Disposition of Patients Poststroke with 30-Day Readmission Validation

“…Both unadjusted and adjusted ORs with 95% confidence intervals were considered. After that, coefficients (beta) from the multivariate logistic regression model were utilized to derive risk scores [ 5 , 20 , 21 ]. A total risk score was calculated for each patient by taking the sum of corresponding risk scores (see example in Table 2 ).…”

“…Early prediction of discharge destination may optimize poststroke care and improve outcomes by mitigating these delays. While many have attempted to predict discharge disposition after stroke [ 1 , 5 – 7 ], outcomes are limited to validate whether the prediction was truly appropriate for the patient in a clinically meaningful way.…”