The number of discharge medications predicts thirty-day hospital readmission: a cohort study

Abstract

BackgroundHospital readmission occurs often and is difficult to predict. Polypharmacy has been identified as a potential risk factor for hospital readmission. However, the overall impact of the number of discharge medications on hospital readmission is still undefined.MethodsTo determine whether the number of discharge medications is predictive of thirty-day readmission using a retrospective cohort study design performed at Barnes-Jewish Hospital from January 15, 2013 to May 9, 2013. The primary outcome assessed was thirty-day hospital readmission. We also assessed potential predictors of thirty-day readmission to include the number of discharge medications.ResultsThe final cohort had 5507 patients of which 1147 (20.8 %) were readmitted within thirty days of their hospital discharge date. The number of discharge medications was significantly greater for patients having a thirty-day readmission compared to those without a thirty-day readmission (7.2 ± 4.1 medications [7.0 medications (4.0 medications, 10.0 medications)] versus 6.0 ± 3.9 medications [6.0 medications (3.0 medications, 9.0 medications)]; P < 0.001). There was a statistically significant association between increasing numbers of discharge medications and the prevalence of thirty-day hospital readmission (P < 0.001). Multiple logistic regression identified more than six discharge medications to be independently associated with thirty-day readmission (OR, 1.26; 95 % CI, 1.17–1.36; P = 0.003). Other independent predictors of thirty-day readmission were: more than one emergency department visit in the previous six months, a minimum hemoglobin value less than or equal to 9 g/dL, presence of congestive heart failure, peripheral vascular disease, cirrhosis, and metastatic cancer. A risk score for thirty-day readmission derived from the logistic regression model had good predictive accuracy (AUROC = 0.661 [95 % CI, 0.643–0.679]).ConclusionsThe number of discharge medications is associated with the prevalence of thirty-day hospital readmission. A risk score, that includes the number of discharge medications, accurately predicts patients at risk for thirty-day readmission. Our findings suggest that relatively simple and accessible parameters can identify patients at high risk for hospital readmission potentially distinguishing such individuals for interventions to minimize readmissions.

Association Between Functional Impairment and Medication Burden in Adults with Heart Failure

“…The attendant risks of taking many medications are well described in the Geriatrics literature, with known associations with a number of adverse outcomes, including falls, disability, and hospitalizations . Consequently, there is an inherent tension between this observation and providing guideline‐concordant care to adults with HF, which frequently includes concurrently prescribing various medications for HF, as well as for other common comorbid cardiovascular and noncardiovascular conditions.…”

Thirty‐day hospital readmission rate amongst older adults correlates with an increased number of medications, but not with Beers medications

“…Increases in the number of medications as well as polypharmacy have been recognized as the most important risk factors for adverse drug reactions linked to rehospitalization. . Picker et al found that the number of discharge medications was significantly associated with the risk of hospital readmission within 30 days .…”

The HOSPITAL score and LACE index as predictors of 30 day readmission in a retrospective study at a university-affiliated community hospital

“…Studies have identified age, race, having a regular health care provider, major surgery, medical comorbidities, length of hospital stay, previous admissions in the last year, failure to transfer important information to the outpatient setting, discharging patients too soon, the number of medications at discharge, and many other risk factors for hospital readmission within 30 days (Auerbach et al, 2016; Picker et al, 2015; Hasan et al, 2010; Silverstein et al, 2008). Despite identifying with these risk factors, healthcare providers have poor accuracy in predicting which patients are at high risk of hospital readmission without a risk assessment tool (Allaudeen et al, 2011).…”