Predicting and Surviving Prolonged Critical Illness After Congenital Heart Surgery

Abstract

Objectives: Prolonged critical illness after congenital heart surgery disproportionately harms patients and the healthcare system, yet much remains unknown. We aimed to define prolonged critical illness, delineate between nonmodifiable and potentially preventable predictors of prolonged critical illness and prolonged critical illness mortality, and understand the interhospital variation in prolonged critical illness.

Design: Observational analysis.

Setting: Pediatric Cardiac Critical Care Consortium clinical registry.

Patients: All patients, stratified into neonates (≤28 d) and nonneonates (29 d to 18 yr), admitted to the pediatric cardiac ICU after congenital heart surgery at Pediatric Cardiac Critical Care Consortium hospitals.

Interventions: None.

Measurements and main results: There were 2,419 neonates and 10,687 nonneonates from 22 hospitals. The prolonged critical illness cutoff (90th percentile length of stay) was greater than or equal to 35 and greater than or equal to 10 days for neonates and nonneonates, respectively. Cardiac ICU prolonged critical illness mortality was 24% in neonates and 8% in nonneonates (vs 5% and 0.4%, respectively, in nonprolonged critical illness patients). Multivariable logistic regression identified 10 neonatal and 19 nonneonatal prolonged critical illness predictors within strata and eight predictors of mortality. Only mechanical ventilation days and acute renal failure requiring renal replacement therapy predicted prolonged critical illness and prolonged critical illness mortality in both strata. Approximately 40% of the prolonged critical illness predictors were nonmodifiable (preoperative/patient and operative factors), whereas only one of eight prolonged critical illness mortality predictors was nonmodifiable. The remainders were potentially preventable (postoperative critical care delivery variables and complications). Case-mix-adjusted prolonged critical illness rates were compared across hospitals; six hospitals each had lower- and higher-than-expected prolonged critical illness frequency.

Conclusions: Although many prolonged critical illness predictors are nonmodifiable, we identified several predictors to target for improvement. Furthermore, we observed that complications and prolonged critical care therapy drive prolonged critical illness mortality. Wide variation of prolonged critical illness frequency suggests that identifying practices at hospitals with lower-than-expected prolonged critical illness could lead to broader quality improvement initiatives.

Figure 1:

Survival after PCI. This Kaplan-Meier curve shows survival over time with PCI-cutoff serving as day 0 (post-operative day 35 and for neonates and non-neonates day 10 respectively).

Figure 2:

Predictors of PCI and PCI- mortality. This Venn diagram includes predictors of PCI (large circle), PCI-mortality (small circle), and both (overlap of circles). Predictors unique to neonatal stratum are in the upper section, predictors unique to non-neonatal strata are in the lower section, and predictors for both strata are in the middle section. Italics indicates factor appears in diagram twice. STS = The Society of Thoracic Surgeons, STAT = The Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery ^a, ^b, and ^c Denote similar factors that are measured uniquely in each stratum where ^a is preterm in neonates and age <6 months for non-neonates, ^b is as any extracardiac abnormality in neonates and chromosomal anomaly or syndrome in non-neonates, and ^c is pre-operative CICU length of stay (in days) in neonates and pre-operative hospitalization >1 day in non-neonates

Figure 3:

Variation in PCI-incidence by hospital. These graphs show the observed-to expected ratio (O:E) and 95% confidence interval for the overall cohort (A), neonatal stratum (B), and non-neonatal stratum (C) for 22 hospitals. Hospitals with lower-than-expected (*) and higher-than-expected (^) incidence of PCI are denoted.