Programmatic Palliative Care Consultations in Pediatric Heart Transplant Evaluations.

Guidelines advocate for integrating palliative care into the management of heart failure (HF) and of children with life-limiting disease. The potential impact of palliative care integration into pediatric HF on patient-centered outcomes is poorly understood. The present study sought to assess the association of programmatic implementation of palliative care into the heart transplant evaluation process with hospital-free days (HFD) and end of life (EOL) treatment choices. The study included patients less than 19 years of age who underwent a heart transplant evaluation between February 2012 and April 2020 at a single center. Patients evaluated in the programmatic palliative care (PPC) era (January 2016-April 2020) were compared to patients evaluated in the pre-PPC era (February 2012-December 2015). The study included 188 patients, with 91 (48%) in the PPC era and 97 (52%) in the pre-PCC era. Children < 1 year of age at the time of the evaluation represented 32% of the cohort. 52% of patients had single ventricle physiology. PPC was not significantly associated with increased HFD (IRR 0.94 [95% CI 0.79-1.2]). PPC was however associated with intensity of EOL care with decreased mechanical ventilation (OR 0.12 [95% CI 0.02-0.789], p = 0.03) and decreased use of ionotropic support (OR 0.13 [95% CI 0.02-0.85], p =0.03). PPC in pediatric heart transplant evaluations may be associated with less invasive interventions at EOL.

A "placement of death" approach for studies of treatment effects on ICU length of stay.

Length of stay in the intensive care unit (ICU) is a common outcome measure in randomized trials of ICU interventions. Because many patients die in the ICU, it is difficult to disentangle treatment effects on length of stay from effects on mortality; conventional analyses depend on assumptions that are often unstated and hard to interpret or check. We adapt a proposal from Rosenbaum that addresses concerns about selection bias and makes its assumptions explicit. A composite outcome is constructed that equals ICU length of stay if the patient was discharged alive and indicates death otherwise. Given any preference ordering that compares death with possible lengths of stay, we can estimate the intervention's effects on the composite outcome distribution. Sensitivity analyses can show results for different preference orderings. We discuss methods for constructing approximate confidence intervals for treatment effects on quantiles of the outcome distribution or on proportions of patients with outcomes preferable to various cutoffs. Strengths and weaknesses of possible primary significance tests (including the Wilcoxon-Mann-Whitney rank sum test and a heteroskedasticity-robust variant due to Brunner and Munzel) are reviewed. An illustrative example reanalyzes a randomized trial of an ICU staffing intervention.