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

BACKGROUND: Hospital 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. METHODS: To 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. RESULTS: The 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]). CONCLUSIONS: The 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.

A randomized trial of real-time automated clinical deterioration alerts sent to a rapid response team.

BACKGROUND: Episodes of patient deterioration on hospital units are expected to increasingly contribute to morbidity and healthcare costs. OBJECTIVE: To determine if real-time alerts sent to the rapid response team (RRT) improved patient care. DESIGN: Randomized, controlled trial. SETTING: Eight medicine units (Barnes-Jewish Hospital). PATIENTS: Five hundred seventy-one patients. INTERVENTION: Real-time alerts generated by a validated deterioration algorithm were sent real-time to the RRT (intervention) or hidden (control). MEASUREMENTS: Intensive care unit (ICU) transfer, hospital mortality, hospital duration. RESULTS: ICU transfer (17.8% vs 18.2%; odds ratio: 0.972; 95% confidence interval [CI]: 0.635-1.490) and hospital mortality (7.3% vs 7.7%; odds ratio: 0.947; 95% CI: 0.509-1.764) were similar for the intervention and control groups. The number of patients requiring transfer to a nursing home or long-term acute care hospital was similar for patients in the intervention and control groups (26.9% vs 26.3%; odds ratio: 1.032; 95% CI: 0.712-1.495). Hospital duration (8.4 ± 9.5 days vs 9.4 ± 11.1 days; P = 0.038) was statistically shorter for the intervention group. The number of RRT calls initiated by the primary care team was similar for the intervention and control groups (19.9% vs 16.5%; odds ratio: 1.260; 95% CI: 0.823-1.931). CONCLUSIONS: Real-time alerts sent to the RRT did not reduce ICU transfers, hospital mortality, or the need for subsequent long term care. However, hospital length of stay was modestly reduced.

Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Abeta42 in humans.

OBJECTIVES: Amyloid-beta(42) (Abeta(42)) appears central to Alzheimer's disease (AD) pathogenesis and is a major component of amyloid plaques. Mean cerebrospinal fluid (CSF) Abeta(42) is decreased in dementia of the Alzheimer's type. This decrease may reflect plaques acting as an Abeta(42) "sink," hindering transport of soluble Abeta(42) between brain and CSF. We investigated this hypothesis. METHODS: We compared the in vivo brain amyloid load (via positron emission tomography imaging of the amyloid-binding agent, Pittsburgh Compound-B [PIB]) with CSF Abeta(42) and other measures (via enzyme-linked immunosorbent assay) in clinically characterized research subjects. RESULTS: Subjects fell into two nonoverlapping groups: those with positive PIB binding had the lowest CSF Abeta(42) level, and those with negative PIB binding had the highest CSF Abeta(42) level. No relation was observed between PIB binding and CSF Abeta(40), tau, phospho-tau(181), plasma Abeta(40), or plasma Abeta(42). Importantly, PIB binding and CSF Abeta(42) did not consistently correspond with clinical diagnosis; three cognitively normal subjects were PIB-positive with low CSF Abeta(42), suggesting the presence of amyloid in the absence of cognitive impairment (ie, preclinical AD). INTERPRETATION: These observations suggest that brain amyloid deposition results in low CSF Abeta(42), and that amyloid imaging and CSF Abeta(42) may potentially serve as antecedent biomarkers of (preclinical) AD.