A Randomized Trial of Palliative Care Discussions Linked to an Automated Early Warning System Alert.

OBJECTIVE: To determine whether an Early Warning System could identify patients wishing to focus on palliative care measures. DESIGN: Prospective, randomized, pilot study. SETTING: Barnes-Jewish Hospital, Saint Louis, MO (January 15, 2015, to December 12, 2015). PATIENTS: A total of 206 patients; 89 intervention (43.2%) and 117 controls (56.8%). INTERVENTIONS: Palliative care in high-risk patients targeted by an Early Warning System. MEASUREMENTS AND MAIN RESULTS: Advanced directive documentation was significantly greater prior to discharge in the intervention group (37.1% vs 15.4%; p < 0.001) as were first-time requests for advanced directive documentation (14.6% vs 0.0%; p < 0.001). Documentation of resuscitation status was also greater prior to discharge in the intervention group (36.0% vs 23.1%; p = 0.043). There was no difference in the number of patients requesting a change in resuscitation status between groups (11.2% vs 9.4%; p = 0.666). However, changes in resuscitation status occurred earlier and on the general medicine units for the intervention group compared to the control group. The number of patients transferred to an ICU was significantly lower for intervention patients (12.4% vs 27.4%; p = 0.009). The median (interquartile range) ICU length of stay was significantly less for the intervention group (0 [0-0] vs 0 [0-1] d; p = 0.014). Hospital mortality was similar (12.4% vs 10.3%; p = 0.635). CONCLUSIONS: This study suggests that automated Early Warning System alerts can identify patients potentially benefitting from directed palliative care discussions and reduce the number of ICU transfers.

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.