Faculty Bibliography

BACKGROUND:The Medicare Hospital Readmissions Reduction Program has led to fewer readmissions following hospitalizations with a principal diagnosis of heart failure (HF). Patients with HF are frequently hospitalized for other causes. OBJECTIVES/OBJECTIVE:This study sought to compare trends in Medicare risk-adjusted, 30-day readmissions following principal HF hospitalizations and other hospitalizations with HF. METHODS:This was a retrospective study of 12,973,853 Medicare hospitalizations with a principal or secondary diagnosis of HF between January 2008 and June 2015. Hospitalizations were categorized as follows: principal HF hospitalizations; principal acute myocardial infarction or pneumonia hospitalizations with secondary HF; and other hospitalizations with secondary HF. The study examined trends in risk-adjusted, 30-day, all-cause readmission rates for each cohort and trends in differences in readmission rates among cohorts by using linear spline regression models. RESULTS:Before passage of the Affordable Care Act in March 2010, risk-adjusted, 30-day readmission rates were stable for all 3 cohorts, with mean monthly rates of 26.1%, 24.9%, and 24.4%, respectively. Risk-adjusted readmission rates started declining after passage of the Affordable Care Act by 1.09% (95% confidence interval [CI]: 0.51% to 1.68%), 1.24% (95% CI: 0.92% to 1.57%), and 1.05% (95% CI: 0.52% to 1.58%) per year, respectively, until implementation of the Hospital Readmissions Reduction Program in October 2012 and then stabilized for all 3 cohorts. CONCLUSIONS:Patients with HF are often hospitalized for other causes, and these hospitalizations have high readmission rates. Policy changes led to decreases in readmission rates for both principal and secondary HF hospitalizations. Readmission rates in both groups remain high, suggesting that initiatives targeting all hospitalized patients with HF continue to be warranted.

OBJECTIVES/SPECIFIC AIMS: Determine timing of risk of readmissions within 30 days among patients first discharged to a skilled nursing facilities (SNF) after heart failure hospitalization and subsequently discharged home. METHODS/STUDY POPULATION: This was a retrospective cohort study of patients with SNF stays of 30 days or less following discharge from a heart failure hospitalization. Patients were followed for 30 days following discharge from SNF. We categorized patients based on SNF length of stay (LOS): 1-6 days, 7-13 days, 14-30 days. We then fit a piecewise exponential Bayesian model with the outcome as time to readmission after discharge from SNF for each group. Our event of interest was unplanned readmission; death and planned readmissions were considered as competing risks. Our model examined 2 different time intervals following discharge from SNF: 0-3 days post SNF discharge and 4-30 days post SNF discharge. We reported the hazard rate (credible interval) of readmission for each time interval. We examined all Medicare fee-for-service (FFS) patients 65 and older admitted from July 2012 to June 2015 with a principal discharge diagnosis of HF, based on methods adopted by the Centers for Medicare and Medicaid Services (CMS) for hospital quality measurement. RESULTS/ANTICIPATED RESULTS: Our study included 67,585 HF hospitalizations discharged to SNF and subsequently discharged home [median age, 84 years (IQR; 78-89); female, 61.0%]; 13,257 (19.2%) were discharged with home care, 54,328 (80.4%) without. Median length of SNF admission was 17 days (IQR; 11-22). In total, 16,333 (24.2%) SNF discharges to home were readmitted within 30 days of SNF discharge; median time to readmission was 9 days (IQR; 3-18). The hazard rate of readmission for each group was significantly increased on days 0-3 after discharge from SNF compared with days 4-30 after discharge from SNF. In addition, the hazard rate of readmission during the first 0-3 days after discharge from SNF decreased as the LOS in SNF increased. DISCUSSION/SIGNIFICANCE OF IMPACT: The hazard rate of readmission after SNF discharge following heart failure hospitalization is highest during the first 6 days home. Length of stay at SNF also has an effect on risk of readmission immediately after discharge from SNF; patients with a longer length of stay in SNF were less likely to be readmitted in the first 3 days after discharge from SNF.

BACKGROUND:Temporal changes in the readmission rates for patient groups and conditions that were not directly under the purview of Hospital Readmissions Reduction Program (HRRP) can help assess whether efforts to lower readmissions extended beyond targeted patients and conditions. METHODS:Using Nationwide Readmissions Database (2010-2015), we assessed trends in all-cause readmission rates for one of the 3 HRRP conditions (acute myocardial infarction, heart failure, pneumonia) or conditions not targeted by HRRP in 6 age-insurance groups defined by age-groups (≥65 or <65 years) and payer (Medicare, Medicaid, or private insurance). RESULTS:In the ≥65-year age-group, readmission rates for those covered by Medicare, private-insurance, and Medicaid decreased annually for acute myocardial infarction (risk-adjusted odds ratio, OR [95%CI], Medicare 0.94 [0.94-0.95], private-insurance 0.95 [0.93-0.97], and Medicaid 0.93 [0.90-0.97]), heart failure (ORs, 0.96 [0.96-0.97], 0.97 [0.96-0.99], and 0.96 [0.94-0.98], for the 3 payers, respectively), and pneumonia (ORs, 0.96 [0.96-0.97), 0.96 [0.95-0.97], and 0.94 [0.92-0.96], respectively). In the <65-year age-group, there was a similar decline in 30-day readmission rates for acute myocardial infarction (risk-adjusted ORs for yearly decrease, Medicare 0.97 [0.96-0.98], private-insurance 0.93 [0.92-0.94], and Medicaid 0.94 [0.92-0.95]), heart failure (ORs, 0.98 [0.97-0.98], 0.97 [0.95-0.98], and 0.96 [0.96-0.97], for the 3 payers, respectively), and pneumonia (ORs, 0.98 [0.97-0.99], 0.98 [0.97-1.00], and 0.98 [0.97-0.99], respectively). In comparison to the targeted conditions, there was a relatively small, but significant, decrease in readmission rates for non-target conditions across all age-insurance groups. CONCLUSION/CONCLUSIONS:There was a significant decline in readmission rates across patient age-insurance groups for the 3 target conditions under the HRRP, as well as a decline in readmission rates for non-target conditions. There appears to be a systematic improvement in readmission rates for patient groups beyond the population of fee-for-service, older, Medicare beneficiaries included in the HRRP.

BACKGROUND:The assessment of clinical guideline adherence for the evaluation of pulmonary embolism (PE) via computed tomography pulmonary angiography (CTPA) currently requires either labor-intensive, retrospective chart review or prospective collection of PE risk scores at the time of CTPA order. The recording of clinical data in a structured manner in the electronic health record (EHR) may make it possible to automate the calculation of a patient's PE risk classification and determine whether the CTPA order was guideline concordant. OBJECTIVES/OBJECTIVE:The objective of this study was to measure the performance of automated, structured-data-only versions of the Wells and revised Geneva risk scores in emergency department encounters during which a CTPA was ordered. The hypothesis was that such an automated method would classify a patient's PE risk with high accuracy compared to manual chart review. METHODS:We developed automated, structured-data-only versions of the Wells and revised Geneva risk scores to classify 212 emergency department (ED) encounters during which a CTPA was performed as "PE Likely" or "PE Unlikely." We then combined these classifications with D-dimer ordering data to assess each encounter as guideline concordant or discordant. The accuracy of these automated classifications and assessments of guideline concordance were determined by comparing them to classifications and concordance based on the complete Wells and revised Geneva scores derived via abstractor manual chart review. RESULTS:The automatically derived Wells and revised Geneva risk classifications were 91.5% and 92% accurate compared to the manually determined classifications, respectively. There was no statistically significant difference between guideline adherence calculated by the automated scores as compared to manual chart review (Wells: 70.8 vs. 75%, p = 0.33 | Revised Geneva: 65.6 vs. 66%, p = 0.92). CONCLUSION/CONCLUSIONS:The Wells and revised Geneva score risk classifications can be approximated with high accuracy using automated extraction of structured EHR data elements in patients who received a CTPA. Combining these automated scores with D-dimer ordering data allows for the automated assessment of clinical guideline adherence for CTPA ordering in the emergency department, without the burden of manual chart review.

OBJECTIVE:To examine predictors for understanding reason for hospitalization. METHODS:This was a retrospective analysis of a prospective, observational cohort study of patients 65 years or older admitted for acute coronary syndrome, heart failure, or pneumonia and discharged home. Primary outcome was complete understanding of diagnosis, based on post-discharge patient interview. Predictors assessed were the following: jargon on discharge instructions, type of medical team, whether outpatient provider knew if the patient was admitted, and whether the patient reported more than one day notice before discharge. RESULTS:Among 377 patients, 59.8% of patients completely understood their diagnosis. Bivariate analyses demonstrated that outpatient provider being aware of admission and having more than a day notice prior to discharge were not associated with patient understanding diagnosis. Presence of jargon was not associated with increased likelihood of understanding in a multivariable analysis. Patients on housestaff and cardiology teams were more likely to understand diagnosis compared to non-teaching teams (OR 2.45, 95% CI 1.30-4.61, p<0.01 and OR 3.83, 95% CI 1.92-7.63, p<0.01, respectively). CONCLUSIONS:Non-teaching team patients were less likely to understand their diagnosis. Further investigation of how provider-patient interaction differs among teams may aid in development of tools to improve hospital to community transitions.