Facility profiling under competing risks using multivariate prognostic scores: Application to kidneytransplant centers

The performance of health care facilities (e.g. hospitals, transplant centers, etc.) is often evaluated through time-to-event outcomes. In this paper, we consider the case where, for each subject, the failure event is due to one of several mutually exclusive causes (competing risks). Since the distribution of patient characteristics may differ greatly by the center, some form of covariate adjustment is generally necessary in order for center-specific outcomes to be accurately compared (to each other or to an overall average). We propose a weighting method for comparing facility-specific cumulative incidence functions to an overall average. The method directly standardizes each facility’s non-parametric cumulative incidence function through a weight function constructed from a multivariate prognostic score. We formally define the center effects and derive large-sample properties of the proposed estimator. We evaluate the finite sample performance of the estimator through simulation. The proposed method is applied to the end-stage renal disease setting to evaluate the center-specific pre-transplant mortality and transplant cumulative incidence functions from the Scientific Registry of Transplant Recipients.

Competing-risk analysis of coronavirus disease 2019 in-hospital mortality in a Northern Italian centre from SMAtteo COvid19 REgistry (SMACORE)

An accurate prediction of the clinical outcomes of European patients requiring hospitalisation for Coronavirus Disease 2019 (COVID-19) is lacking. The aim of the study is to identify predictors of in-hospital mortality and discharge in a cohort of Lombardy patients with COVID-19. All consecutive hospitalised patients from February 21st to March 30th, 2020, with confirmed COVID-19 from the IRCCS Policlinico San Matteo, Pavia, Lombardy, Italy, were included. In-hospital mortality and discharge were evaluated by competing risk analysis. The Fine and Gray model was fitted in order to estimate the effect of covariates on the cumulative incidence functions (CIFs) for in-hospital mortality and discharge. 426 adult patients [median age 68 (IQR 56 to 77 years)] were admitted with confirmed COVID-19 over a 5-week period; 292 (69%) were male. By 21 April 2020, 141 (33%) of these patients had died, 239 (56%) patients had been discharged and 46 (11%) were still hospitalised. Among these 46 patients, updated as of 30 May, 2020, 5 (10.9%) had died, 8 (17.4%) were still in ICU, 12 (26.1%) were transferred to lower intensity care units and 21 (45.7%) were discharged. Regression on the CIFs for in-hospital mortality showed that older age, male sex, number of comorbidities and hospital admission after March 4th were independent risk factors associated with in-hospital mortality. Older age, male sex and number of comorbidities definitively predicted in-hospital mortality in hospitalised patients with COVID-19.

Anemia at Intensive Care Unit Admission and Hospital Mortality Among Patients at a Referral Hospital in Malawi

Background Anemia is associated with intensive care unit (ICU) outcomes, but data describing this association in sub-Saharan Africa are scarce. Patients in this region are at risk for anemia due to endemic conditions like malaria and because transfusion services are limited. Methods This was a prospective cohort study of ICU patients at Kamuzu Central Hospital (KCH) in Malawi. Exclusion criteria included age <5 years, pregnancy, ICU readmission, or admission for head injury. Cumulative incidence functions and Fine-Gray competing risk models were used to evaluate hemoglobin (Hgb) at ICU admission and hospital mortality. Results Of 499 patients admitted to ICU, 359 were included. The median age was 28 years (interquartile ranges (IQRs) 20-40) and 37.5% were men. Median Hgb at ICU admission was 9.9 g/dL (IQR 7.5-11.4 g/dL; range 1.8-18.1 g/dL). There were 61 (19%) patients with Hgb < 7.0 g/dL, 59 (19%) with Hgb 7.0-8.9 g/dL, and 195 (62%) with Hgb ≥ 9.0 g/dL. Hospital mortality was 51%, 59%, and 54%, respectively. In adjusted analyses, anemia was associated with hospital mortality but was not statistically significant. Conclusions This study provides preliminary evidence that anemia at ICU admission may be an independent predictor of hospital mortality in Malawi. Larger studies are needed to confirm this association.

In-hospital mortality from Coronavirus Disease 2019 in a northern Italian centre:results of a competing risk analysis from SMAtteo COvid19 REgistry (SMACORE)

Objectives An accurate prediction of the clinical outcomes of European patients requiring hospitalisation for Coronavirus Disease 2019 (COVID-19) is lacking. The aim of the study is to identify predictors of in-hospital mortality and discharge in a cohort of Lombardy patients with COVID-19. MethodsAll consecutive hospitalised patients from February 21stto March 30th, 2020, with confirmed COVID-19 from the IRCCS Policlinico San Matteo, Pavia, Lombardy, Italy, were included. In-hospital mortality and discharge were evaluated by competing risk analysis. The Fine and Gray model was fitted in order to estimate the effect of covariates on the cumulative incidence functions (CIFs) for in-hospital mortality and discharge.Results 426 adult patients (median age 68 (IQR, 56 to 77 years) were admitted with confirmed COVID-19 over a 5-week period; 292 (69%) were male. By 21 April 2020, 141 (33%) of these patients had died, 239 (56%) patients had been discharged and 46 (11%) were still hospitalised. Regression on the CIFs for in-hospital mortality showed that older age, male sex, number of comorbidities and hospital admission after March 4thwere independent risk factors associated with in-hospital mortality.Conclusions Olderage, male sex and number of comorbidities definitively predicted in-hospital mortality in hospitalised patients with COVID-19

Controlled Release Dinoprostone Insert and Foley Compared to Foley Alone: A Randomized Pilot Trial

Objective The aim of study is to compare, in a pilot study, combined dinoprostone vaginal insert and Foley catheter (DVI + Foley) with Foley alone (Foley) for cervical ripening and labor induction at term. Study Design In this open-label pilot randomized controlled trial, women not in labor, with intact membranes, no prior uterine incision, an unfavorable cervix, gestational age ≥37 weeks, and a live, nonanomalous singleton fetus in cephalic presentation were randomly assigned, stratified by parity, to DVI + Foley or Foley. Oxytocin was used in both groups after cervical ripening. Primary outcome was time to vaginal delivery. Results From April 2017 to January 2018, 100 women were randomized. Median (25–75th percentile) time to vaginal delivery for nulliparous women was 21.2 (16.6–38.0) hours with DVI + Foley (n = 26) compared with 31.3 (23.3–46.9) hours with Foley (n = 24) (Wilcoxon p = 0.05). Median time to vaginal delivery for parous women was 17.1 (13.6–21.9) hours with DVI + Foley (n = 25) compared with 14.8 (12.7–19.5) hours with Foley (n = 25) (Wilcoxon p = 0.21). Results were also analyzed to consider the competing risk of cesarean using cumulative incidence functions. Conclusion Compared with Foley alone, combined use of the dinoprostone vaginal insert and Foley for cervical ripening may shorten time to vaginal delivery for nulliparous but not parous women.

Semiparametric regression and risk prediction with competing risks data under missing cause of failure

The cause of failure in cohort studies that involve competing risks is frequently incompletely observed. To address this, several methods have been proposed for the semiparametric proportional cause-specific hazards model under a missing at random assumption. However, these proposals provide inference for the regression coefficients only, and do not consider the infinite dimensional parameters, such as the covariate-specific cumulative incidence function. Nevertheless, the latter quantity is essential for risk prediction in modern medicine. In this paper we propose a unified framework for inference about both the regression coefficients of the proportional cause-specific hazards model and the covariate-specific cumulative incidence functions under missing at random cause of failure. Our approach is based on a novel computationally efficient maximum pseudo-partial-likelihood estimation method for the semiparametric proportional cause-specific hazards model. Using modern empirical process theory we derive the asymptotic properties of the proposed estimators for the regression coefficients and the covariate-specific cumulative incidence functions, and provide methodology for constructing simultaneous confidence bands for the latter. Simulation studies show that our estimators perform well even in the presence of a large fraction of missing cause of failures, and that the regression coefficient estimator can be substantially more efficient compared to the previously proposed augmented inverse probability weighting estimator. The method is applied using data from an HIV cohort study and a bladder cancer clinical trial.