Assessment of ST2 for risk of death following graft-versus-host disease in pediatric and adult age groups.

Assessment of prognostic biomarkers of nonrelapse mortality (NRM) after allogeneic hematopoietic cell transplantation (HCT) in the pediatric age group is lacking. To address this need, we conducted a prospective cohort study with 415 patients at 6 centers: 170 were children age 10 years or younger and 245 were patients older than age 10 years (both children and adults were accrued from 2013 to 2018). The following 4 plasma biomarkers were assessed pre-HCT and at days +7, +14, and +21 post-HCT: stimulation-2 (ST2), tumor necrosis factor receptor 1 (TNFR1), regenerating islet-derived protein 3α (REG3α), and interleukin-6 (IL-6). We performed landmark analyses for NRM, dichotomizing the cohort at age 10 years or younger and using each biomarker median as a cutoff for high- and low-risk groups. Post-HCT biomarker analysis showed that ST2 (>26 ng/mL), TNFR1 (>3441 pg/mL), and REG3α (>25 ng/mL) are associated with NRM in children age 10 years or younger (ST2: hazard ratio [HR], 9.13; 95% confidence interval [CI], 2.74-30.38; P = .0003; TNFR1: HR, 4.29; 95% CI, 1.48-12.48; P = .0073; REG3α: HR, 7.28; 95% CI, 2.05-25.93; P = .0022); and in children and adults older than age 10 years (ST2: HR, 2.60; 95% CI, 1.15-5.86; P = .021; TNFR1: HR, 2.09; 95% CI, 0.96-4.58; P = .06; and REG3α: HR, 2.57; 95% CI, 1.19-5.55; P = .016). When pre-HCT biomarkers were included, only ST2 remained significant in both cohorts. After adjustment for significant covariates (race/ethnicity, malignant disease, graft, and graft-versus-host-disease prophylaxis), ST2 remained associated with NRM only in recipients age 10 years or younger (HR, 4.82; 95% CI, 1.89-14.66; P = .0056). Assays of ST2, TNFR1, and REG3α in the first 3 weeks after HCT have prognostic value for NRM in both children and adults. The presence of ST2 before HCT is a prognostic biomarker for NRM in children age 10 years or younger allowing for additional stratification. This trial was registered at www.clinicaltrials.gov as #NCT02194439.

Early, Goal-Directed Therapy for Septic Shock - A Patient-Level Meta-Analysis.

BACKGROUND: After a single-center trial and observational studies suggesting that early, goal-directed therapy (EGDT) reduced mortality from septic shock, three multicenter trials (ProCESS, ARISE, and ProMISe) showed no benefit. This meta-analysis of individual patient data from the three recent trials was designed prospectively to improve statistical power and explore heterogeneity of treatment effect of EGDT. METHODS: We harmonized entry criteria, intervention protocols, outcomes, resource-use measures, and data collection across the trials and specified all analyses before unblinding. After completion of the trials, we pooled data, excluding the protocol-based standard-therapy group from the ProCESS trial, and resolved residual differences. The primary outcome was 90-day mortality. Secondary outcomes included 1-year survival, organ support, and hospitalization costs. We tested for treatment-by-subgroup interactions for 16 patient characteristics and 6 care-delivery characteristics. RESULTS: We studied 3723 patients at 138 hospitals in seven countries. Mortality at 90 days was similar for EGDT (462 of 1852 patients [24.9%]) and usual care (475 of 1871 patients [25.4%]); the adjusted odds ratio was 0.97 (95% confidence interval, 0.82 to 1.14; P=0.68). EGDT was associated with greater mean (±SD) use of intensive care (5.3±7.1 vs. 4.9±7.0 days, P=0.04) and cardiovascular support (1.9±3.7 vs. 1.6±2.9 days, P=0.01) than was usual care; other outcomes did not differ significantly, although average costs were higher with EGDT. Subgroup analyses showed no benefit from EGDT for patients with worse shock (higher serum lactate level, combined hypotension and hyperlactatemia, or higher predicted risk of death) or for hospitals with a lower propensity to use vasopressors or fluids during usual resuscitation. CONCLUSIONS: In this meta-analysis of individual patient data, EGDT did not result in better outcomes than usual care and was associated with higher hospitalization costs across a broad range of patient and hospital characteristics. (Funded by the National Institute of General Medical Sciences and others; PRISM ClinicalTrials.gov number, NCT02030158 .).

Effects of Physician-targeted Pay for Performance on Use of Spontaneous Breathing Trials in Mechanically Ventilated Patients.

RATIONALE: Pay for performance is an increasingly common quality improvement strategy despite the absence of robust supporting evidence. OBJECTIVES: To determine the impact of a financial incentive program rewarding physicians for the completion of daily spontaneous breathing trials (SBTs) in three academic hospitals. METHODS: We compared data from mechanically ventilated patients from 6 months before to 2 years after introduction of a financial incentive program that provided annual payments to critical care physicians contingent on unit-level SBT completion rates. We used Poisson regression to compare the frequency of days on which SBTs were completed among eligible patients and days on which patients were excluded from SBT eligibility among all mechanically ventilated patients. We used multivariate regression to compare risk-adjusted duration of mechanical ventilation and in-hospital mortality. MEASUREMENTS AND MAIN RESULTS: The cohort included 7,291 mechanically ventilated patients with 75,621 ventilator days. Baseline daily SBT rates were 96.8% (hospital A), 16.4% (hospital B), and 74.7% (hospital C). In hospital A, with the best baseline performance, there was no change in SBT rates, exclusion rates, or duration of mechanical ventilation across time periods. In hospitals B and C, with lower SBT completion rates at baseline, there was an increase in daily SBT completion rates and a concomitant increase in exclusions from eligibility. Duration of mechanical ventilation decreased in hospital C but not in hospital B. Mortality was unchanged for all hospitals. CONCLUSIONS: In hospitals with low baseline SBT completion, physician-targeted financial incentives were associated with increased SBT rates driven in part by increased exclusion rates, without consistent improvements in outcome.

Differences in Hospital Risk-standardized Mortality Rates for Acute Myocardial Infarction When Assessed Using Transferred and Nontransferred Patients.

BACKGROUND: One in 5 patients with acute myocardial infarction (AMI) are transferred between hospitals. However, current hospital performance measures based on AMI mortality exclude these patients from the evaluation of referral hospitals. OBJECTIVE: To determine the relationship between risk-standardized mortality for transferred and nontransferred patients at referral hospitals. RESEARCH DESIGN: This is a retrospective cohort study. SUBJECTS: Fee-for-service Medicare claims from 2011 for patients hospitalized with a primary diagnosis of AMI, at hospitals admitting at least 15 patients in transfer. MEASURES: Hospital-specific risk-standardized 30-day mortality rates (RSMRs) for 2 groups of patients: those admitted through transfer from another hospital, and those natively admitted without a preceding or subsequent interhospital transfer. RESULTS: There were 304 hospitals admitting at least 15 patients in transfer. These hospitals cared for 77,711 natively admitted patients (median, 254; interquartile range, 162-321), and 11,829 patients admitted in transfer (median, 26; interquartile range, 19-46). Risk-standardized mortality rates were higher for natively admitted patients than for those admitted in transfer (mean, 11.5%±1.2% vs. 7.2%±1.1%). There was weak correlation between hospital performance as assessed by RSMR for patients natively admitted versus those admitted in transfer (Pearson r=0.24, P<0.001); when performance was arrayed by quartile, 102 hospitals (33.6%) differed at least 2 quartiles of performance across the 2 patient groups. CONCLUSIONS: For Medicare patients with AMI, hospital-specific RSMRs for natively admitted patients are only weakly associated with RSMRs for patients transferred in from another hospital. Current AMI performance metrics may fail to provide guidance about hospital quality for transferred patients.

ICU Telemedicine and Critical Care Mortality: A National Effectiveness Study.

BACKGROUND: Intensive care unit (ICU) telemedicine is an increasingly common strategy for improving the outcome of critical care, but its overall impact is uncertain. OBJECTIVES: To determine the effectiveness of ICU telemedicine in a national sample of hospitals and quantify variation in effectiveness across hospitals. RESEARCH DESIGN: We performed a multicenter retrospective case-control study using 2001-2010 Medicare claims data linked to a national survey identifying US hospitals adopting ICU telemedicine. We matched each adopting hospital (cases) to up to 3 nonadopting hospitals (controls) based on size, case-mix, and geographic proximity during the year of adoption. Using ICU admissions from 2 years before and after the adoption date, we compared outcomes between case and control hospitals using a difference-in-differences approach. RESULTS: A total of 132 adopting case hospitals were matched to 389 similar nonadopting control hospitals. The preadoption and postadoption unadjusted 90-day mortality was similar in both case hospitals (24.0% vs. 24.3%, P=0.07) and control hospitals (23.5% vs. 23.7%, P<0.01). In the difference-in-differences analysis, ICU telemedicine adoption was associated with a small relative reduction in 90-day mortality (ratio of odds ratios=0.96; 95% CI, 0.95-0.98; P<0.001). However, there was wide variation in the ICU telemedicine effect across individual hospitals (median ratio of odds ratios=1.01; interquartile range, 0.85-1.12; range, 0.45-2.54). Only 16 case hospitals (12.2%) experienced statistically significant mortality reductions postadoption. Hospitals with a significant mortality reduction were more likely to have large annual admission volumes (P<0.001) and be located in urban areas (P=0.04) compared with other hospitals. CONCLUSIONS: Although ICU telemedicine adoption resulted in a small relative overall mortality reduction, there was heterogeneity in effect across adopting hospitals, with large-volume urban hospitals experiencing the greatest mortality reductions.

A Comparison of Free-Standing versus Co-Located Long-Term Acute Care Hospitals.

BACKGROUND: Long-term acute care hospitals (LTACs) provide specialized treatment for patients with chronic critical illness. Increasingly LTACs are co-located within traditional short-stay hospitals rather than operated as free-standing facilities, which may affect LTAC utilization patterns and outcomes. METHODS: We compared free-standing and co-located LTACs using 2005 data from the United States Centers for Medicare & Medicaid Services. We used bivariate analyses to examine patient characteristics and timing of LTAC transfer, and used propensity matching and multivariable regression to examine mortality, readmissions, and costs after transfer. RESULTS: Of 379 LTACs in our sample, 192 (50.7%) were free-standing and 187 (49.3%) were co-located in a short-stay hospital. Co-located LTACs were smaller (median bed size: 34 vs. 66, p <0.001) and more likely to be for-profit (72.2% v. 68.8%, p = 0.001) than freestanding LTACs. Co-located LTACs admitted patients later in their hospital course (average time prior to transfer: 15.5 days vs. 14.0 days) and were more likely to admit patients for ventilator weaning (15.9% vs. 12.4%). In the multivariate propensity-matched analysis, patients in co-located LTACs experienced higher 180-day mortality (adjusted relative risk: 1.05, 95% CI: 1.00-1.11, p = 0.04) but lower readmission rates (adjusted relative risk: 0.86, 95% CI: 0.75-0.98, p = 0.02). Costs were similar between the two hospital types (mean difference in costs within 180 days of transfer: -$3,580, 95% CI: -$8,720 -$1,550, p = 0.17). CONCLUSIONS: Compared to patients in free-standing LTACs, patients in co-located LTACs experience slightly higher mortality but lower readmission rates, with no change in overall resource use as measured by 180 day costs.

Hospital factors associated with discharge bias in ICU performance measurement.

OBJECTIVE: Performance assessments based on in-hospital mortality for ICU patients can be affected by discharge practices such that differences in mortality may reflect variation in discharge patterns rather than quality of care. Time-specific mortality rates, such as 30-day mortality, are preferred but are harder to measure. The degree to which the difference between 30-day and in-hospital ICU mortality rates-or "discharge bias"-varies by hospital type is unknown. The aim of this study was to quantify variation in discharge bias across hospitals and determine the hospital characteristics associated with greater discharge bias. DESIGN: Retrospective cohort study. SETTING: Nonfederal Pennsylvania hospital discharges in 2008. PATIENTS: Eligible patients were 18 years old or older and admitted to an ICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We used logistic regression with hospital-level random effects to calculate hospital-specific risk-adjusted 30-day and in-hospital mortality rates. We then calculated discharge bias, defined as the difference between 30-day and in-hospital mortality rates, and used multivariable linear regression to compare discharge bias across hospital types. A total of 43,830 patients and 134 hospitals were included in the analysis. Mean (SD) risk-adjusted hospital-specific in-hospital and 30-day ICU mortality rates were 9.6% (1.3) and 12.7% (1.5), respectively. Hospital-specific discharge biases ranged from -1.3% to 6.6%. Discharge bias was smaller in large hospitals compared with small hospitals, making large hospitals appear comparatively worse from a benchmarking standpoint when using in-hospital mortality instead of 30-day mortality. CONCLUSIONS: Discharge practices bias in-hospital ICU mortality measures in a way that disadvantages large hospitals. Accounting for discharge bias will prevent these hospitals from being unfairly disadvantaged in public reporting and pay-for-performance.