Abstract 261: Predicting Inpatient Hospital Length of Stay for Acute Health Failure Patients

Abstract

Background: Hospital length of stay (LOS) is a primary driver of costs of care for hospitalized Acute Health Failure (AHF). A predictive model of LOS associated with AHF hospitalized patients would be beneficial in identifying potential high risk patients that may benefit from more intensive interventions. Objective: To develop predictive models for the length of inpatient hospital stay based on information available upon admission for AHF. Methods: A retrospective study using the MarketScan Hospital Drug Database identified patients with an index admission event with an acute heart failure primary discharge diagnosis (ICD-9 code 428.XX) and diagnosis reference group (DRG),(DRG 127, 291,292 or 293) for hospital admissions between 2006 and 2010. Index hospitalization was identified as the first AHF admission with no admissions in the preceding 90 days. Patient demographics, admission source, hospital characteristics (total number of beds, teaching status, urban-rural), geographic location, Charlson Comorbidity Score (CCI) and select comorbidities were identified within the index admission and served as potential input variables to model the index admission LOS. Variable selection was informed by univariate negative binomial regression models and regression tree models. Final predictive models took the form of full and stepwise negative binomial regression models (predicting LOS) and logistic regression models to predict short (≤ 2 days, approximately 20th percentile) and long (≥8 days, approximately 80th percentile) LOS. Results: 23,629 distinct admissions were identified with mean LOS 5.5 days. The three strongest predictors of LOS in both the univariate models and the regression tree model were CCI score, nephropathy and cardiac dysrhythmia. The negative binomial model shows that comorbid cardiac dysrhythmias, anemia, nephropathy, thromboembolic disease, COPD and high CCI score were associated with increased LOS. For the logistic regression models predicting the probability of long LOS (concordance=67.3-67.5%), the most important variables were cardiac dysrhythmia, index year, and CCI score. For the logistic regression models predicting the probability of short LOS (concordance=66.3-66.5%), patients with cardiac dysrhythmia, anemia, increasing age and high CCI score were less likely to have LOS≤2 days. Conclusion: Across multiple modeling methods and different outcome variable definitions, it was consistently observed that AHF patients with comorbid cardiac dysrhythmia, nephropathy, anemia, higher CCI scores and older patients tended to have increased lengths of inpatient stay for their index hospitalization. Clinicians may use this information in identifying patients with higher risk of longer hospitalizations to optimize inpatient healthcare decisions and cost management strategies.