Subject(s)
COVID-19/mortality , Heart Failure/mortality , Multiple Organ Failure/mortality , Respiratory Insufficiency/mortality , Shock, Septic/mortality , Terminal Care/statistics & numerical data , Aged , Aged, 80 and over , Cause of Death , Cohort Studies , Coinfection/epidemiology , Female , Heart Failure/epidemiology , Hospitalization , Humans , Intensive Care Units , Male , Middle Aged , Multiple Organ Failure/epidemiology , Nervous System Diseases/epidemiology , Nervous System Diseases/mortality , Patient Care Planning , Patients' Rooms , Renal Insufficiency/epidemiology , Retrospective Studies , SARS-CoV-2 , Shock, Septic/epidemiology , United States/epidemiology , Withholding Treatment/statistics & numerical dataSubject(s)
Coronary Care Units , Heart Diseases/mortality , Lung Diseases/mortality , Multiple Organ Failure/mortality , Patient Admission , Sepsis/mortality , Aged , Cause of Death , Comorbidity , Female , Heart Diseases/diagnosis , Heart Diseases/therapy , Hospital Mortality , Humans , Lung Diseases/diagnosis , Lung Diseases/therapy , Male , Michigan/epidemiology , Multiple Organ Failure/diagnosis , Multiple Organ Failure/therapy , Prognosis , Retrospective Studies , Risk Assessment , Risk Factors , Sepsis/diagnosis , Sepsis/therapy , Time FactorsABSTRACT
BACKGROUND: Little is known about the prevalence, predictors, and outcomes of late vasopressor administration which evolves after admission to the ICU. RESEARCH QUESTION: What is the epidemiology of late vasopressor administration in the ICU? STUDY DESIGN AND METHODS: We retrospectively studied a cohort of veterans admitted to the Veterans Administration ICUs for ≥ 4 days from 2014 to 2017. The timing of vasopressor administration was categorized as early (only within the initial 3 days), late (on day 4 or later and none on day 3), and continuous (within the initial 2 days through at least day 4). Regressions were performed to identify patient factors associated with late vasopressor administration and the timing of vasopressor administration with posthospitalization discharge mortality. RESULTS: Among the 62,206 hospitalizations with at least 4 ICU days, late vasopressor administration occurred in 5.5% (3,429 of 62,206). Patients with more comorbidities (adjusted OR [aOR], 1.02 per van Walraven point; 95% CI, 1.02-1.03) and worse severity of illness on admission (aOR, 1.01 per percentage point risk of death; 95% CI, 1.01-1.02) were more likely to receive late vasopressor therapy. Nearly 50% of patients started a new antibiotic within 24 h of receiving late vasopressor therapy. One-year mortality after survival to discharge was higher for patients with continuous (adjusted hazard ratio [aHR], 1.48; 95% CI, 1.33-1.65) and late vasopressor administration (aHR, 1.26; 95% CI, 1.15-1.38) compared with only early vasopressor administration. INTERPRETATION: Late vasopressor administration was modestly associated with comorbidities and admission illness severity. One-year mortality was higher among those who received late vasopressor administration compared with only early vasopressor administration. Research to understand optimization of late vasopressor therapy administration may improve long-term mortality.
Subject(s)
Critical Care , Vasoconstrictor Agents/administration & dosage , Aged , Drug Administration Schedule , Female , Hospital Mortality , Hospitalization , Humans , Male , Middle Aged , Retrospective Studies , Survival RateABSTRACT
Comparing hospital performance in a health system is traditionally done with multilevel regression models that adjust for differences in hospitals' patient case-mix. In contrast, "template matching" compares outcomes of similar patients at different hospitals but has been used only in limited patient settings.Our objective was to test a basic template matching approach in the nationwide Veterans Affairs healthcare system (VA), compared with a more standard regression approach.We performed various simulations using observational data from VA electronic health records whereby we randomly assigned patients to "pseudo hospitals," eliminating true hospital level effects. We randomly selected a representative template of 240 patients and matched 240 patients on demographic and physiological factors from each pseudo hospital to the template. We varied hospital performance for different simulations such that some pseudo hospitals negatively impacted patient mortality.Electronic health record data of 460,213 hospitalizations at 111 VA hospitals across the United States in 2015.We assessed 30-day mortality at each pseudo hospital and identified lowest quintile hospitals by template matching and regression. The regression model adjusted for predicted 30-day mortality (as a measure of illness severity).Regression identified the lowest quintile hospitals with 100% accuracy compared with 80.3% to 82.0% for template matching when systematic differences in 30-day mortality existed.The current standard practice of risk-adjusted regression incorporating patient-level illness severity was better able to identify lower-performing hospitals than the simplistic template matching algorithm.
