Association of Days Alive and Out of the Hospital After Ventricular Assist Device Implantation With Adverse Events and Quality of Life.

Importance: There is a need to better assess the cumulative effect on morbidity and mortality in patients undergoing durable left ventricular assist device (LVAD) implantation. This study evaluates a patient-centered performance metric (days alive and out of hospital [DAOH]) for durable LVAD therapy. Objective: To determine the incidence of percent of DAOH before and after LVAD implantation and (2) explore its association with established quality metrics (death, adverse events [AEs], quality of life). Design, Settings, and Participants: This was a retrospective national cohort study of Medicare beneficiaries implanted with a durable continuous-flow LVAD between April 2012 and December 2016. The data were analyzed from December 2021 to May 2022. Follow-up was 100% complete at 1 year. Data from The Society of Thoracic Surgeons Intermacs registry were linked to Medicare claims. Main Outcomes and Measures: The number of DAOH 180 days before and 365 days after LVAD implantation and daily patient location (home, index hospital, nonindex hospital, skilled nursing facility, rehabilitation center, hospice) were calculated. Percent of DAOH was indexed to each beneficiary's pre- (percent DAOH-BF) and postimplantation (percentage of DAOH-AF) follow-up time. The cohort was stratified by terciles of percentage of DAOH-AF. Results: Among the 3387 patients included (median [IQR] age: 66.3 [57.9-70.9] years), 80.9% were male, 33.6% and 37.1% were Interfaces Patient Profile 2 and 3, respectively, and 61.1% received implants as destination therapy. Median (IQR) percent of DAOH-BF was 88.8% (82.7%-93.8%) and 84.6% (62.1-91.5%) for percent of DAOH-AF. While DAOH-BF was not associated with post-LVAD outcomes, patients in the low tercile of percentage of DAOH-AF had a longer index hospitalization stay (mean, 44 days; 95% CI, 16-77), were less likely to be discharged home (mean. -46.4 days; 95% CI, 44.2-49.1), and spent more time in a skilled nursing facility (mean, 27 days; 95% CI, 24-29), rehabilitation center (mean, 10 days; 95% CI, 8-12), or hospice (mean, 6 days; 95% CI, 4-8). Increasing percentage of DAOH-AF was associated with patient risk, AEs, and indices of HRQoL. Patients experiencing non-LVAD-related AEs had the lowest percentage of DAOH-AF. Conclusions and Relevance: Significant variability existed in the percentage of DAOH within a 1-year time horizon and was associated with the cumulative AEs burden. This patient-centered measure may assist clinicians in informing patients about expectations after durable LVAD implantation. Validation of percentage DAOH as a quality metric for LVAD therapy across centers should be explored.

Care fragmentation predicts 90-day durable ventricular assist device outcomes.

OBJECTIVES: To examine whether fragmentation of care is associated with worse in-hospital and 90-day outcomes following durable ventricular assist device (VAD) implant. STUDY DESIGN: Cohort study. METHODS: This study was conducted using Medicare claims linked to the Society of Thoracic Surgeons (STS) Interagency Registry for Mechanically Assisted Circulatory Support (Intermacs) among patients undergoing VAD implant between July 2009 and April 2017. Medicare data were used to measure fragmentation of the multidisciplinary care delivery network for the treating hospital, based on providers' history of shared patients within the previous year. STS Intermacs data were used for risk adjustment and outcomes ascertainment. Hospitals were sorted into terciles based on the degree of network fragmentation, measured as the mean number of links separating providers in the network. Multivariable regression was used to associate network fragmentation with 90-day death or infection risk. RESULTS: The cohort included 5159 patients who underwent VAD implant, with 11.2% dying and 27.6% experiencing an infection within 90 days after implant. After adjustment, a 1-unit increase in network fragmentation was associated with an increase of 0.179 in the probability of in-hospital infection and an increase of 0.183 in the probability of 90-day infection (both P < .05). Similar results were observed in models of the numbers of in-hospital and 90-day infections. Network fragmentation was predictive of the probability of 90-day mortality, although this relationship was not significant after adjustment. CONCLUSIONS: Care delivery network fragmentation is associated with higher in-hospital and 90-day infection rates following durable VAD implant. These networks may serve as novel targets for enhancing outcomes for patients undergoing VAD implant.

Association Between Care Fragmentation and Total Spending After Durable Left Ventricular Device Implant: A Mediation Analysis of Health Care-Associated Infections Within a National Medicare-Society of Thoracic Surgeons Intermacs Linked Dataset.

BACKGROUND: Care fragmentation is associated with higher rates of infection after durable left ventricular assist device (LVAD) implant. Less is known about the relationship between care fragmentation and total spending, and whether this relationship is mediated by infections. METHODS: Total payments were captured from admission to 180 days post-discharge. Drawing on network theory, a measure of care fragmentation was developed based on the number of shared patients among providers (ie, anesthesiologists, cardiac surgeons, cardiologists, critical care specialists, nurse practitioners, physician assistants) caring for 4,987 Medicare beneficiaries undergoing LVAD implantation between July 2009 - April 2017. Care fragmentation was measured using average path length, which describes how efficiently information flows among network members; longer path length indicates greater fragmentation. Terciles based on the level of care fragmentation and multivariable regression were used to analyze the relationship between care fragmentation and LVAD payments and mediation analysis was used to evaluate the role of post-implant infections. RESULTS: The patient cohort was 81% male, 73% white, 11% Intermacs Profile 1 with mean (SD) age of 63.1 years (11.1). The mean (SD) level of care fragmentation in provider networks was 1.7 (0.2) and mean (SD) payment from admission to 180 days post-discharge was $246,905 ($109,872). Mean (SD) total payments at the lower, middle, and upper terciles of care fragmentation were $250,135 ($111,924), $243,288 ($109,376), and $247,290 ($108,241), respectively. In mediation analysis, the indirect effect of care fragmentation on total payments, through infections, was positive and statistically significant (ß=16032.5, p=0.008). CONCLUSIONS: Greater care fragmentation in the delivery of care surrounding durable LVAD implantation is associated with a higher incidence of infections, and consequently, higher payments for Medicare beneficiaries. Interventions to reduce care fragmentation may reduce the incidence of infections and in turn enhance the value of care for patients undergoing durable LVAD implantation.

Variability of subspecialty-specific anesthesia-controlled times at two academic institutions.

Realistic scheduling of operating room cases decreases costs, optimizes utilization and improves staff and patient satisfaction. Currently limited data exists to establish anesthesia-controlled time benchmarks based on specific subspecialty service. In this multicenter retrospective analysis of cases performed during a 53 month period at two large multispecialty academic institutions, data were retrieved from the perioperative information systems at each center. Both induction and emergence times were calculated. We then determined mean and median anesthesia controlled times based on each subspecialty service and compared them to previously published anesthesia-controlled time data. We obtained data on 104,184 cases at hospital A, and 122,560 cases at Hospital B. For all specialties at hospital A and hospital B, median induction time was 16.0 min and 17.0 min, emergence time was 14.0 and 8.0 min, and total anesthesia controlled time was 31.0 min and 27.0 min respectively. There was considerable variability among different surgical specialties deviating from the previously established 30 min benchmark. Subspecialties with lower total anesthesia controlled times in both centers were pain, general surgery, gynecology, plastic surgery and urology. Subspecialties with higher total anesthesia controlled times in both centers included cardiac surgery, neurosurgery, transplant and vascular. Cardiac surgery had the highest total time of 60 min and 50 min at Hospital A and B respectively. Individual specialty-specific anesthesia controlled times should be used for case scheduling and to benchmark anesthesia performance.