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Pediatr Crit Care Med. 2018 Nov 26. doi: 10.1097/PCC.0000000000001776. [Epub ahead of print]

A Novel Model Demonstrates Variation in Risk-Adjusted Mortality Across Pediatric Cardiac ICUs After Surgery.

Author information

1
Benioff Children's Hospital and the University of California San Francisco Medical School, San Francisco, CA.
2
Johns Hopkins Children's Center and Johns Hopkins School of Medicine, Baltimore, MD.
3
University of Michigan, Ann Arbor, MI.
4
Children's Hospital of Alabama and the University of Alabama School of Medicine, Birmingham, AL.
5
CS Mott Children's Hospital and the University of Michigan, Ann Arbor, MI.
6
Children's Hospital of Philadelphia and the University of Pennsylvania Medical School, Philadelphia, PA.
7
Texas Children's Hospital and the Baylor College of Medicine, Houston, TX.
8
Johns Hopkins All Children's Heart Institute, Saint Petersburg, FL.
9
Children's Hospital Boston and the Harvard Medical School, Boston, MA.
10
Cincinnati Children's Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, OH.

Abstract

OBJECTIVE:

To develop a postoperative mortality case-mix adjustment model to facilitate assessment of cardiac ICU quality of care, and to describe variation in adjusted cardiac ICU mortality across hospitals within the Pediatric Cardiac Critical Care Consortium.

DESIGN:

Observational analysis.

SETTING:

Multicenter Pediatric Cardiac Critical Care Consortium clinical registry.

PARTICIPANTS:

All surgical cardiac ICU admissions between August 2014 and May 2016. The analysis included 8,543 admissions from 23 dedicated cardiac ICUs.

INTERVENTIONS:

None.

MEASUREMENTS AND MAIN RESULTS:

We developed a novel case-mix adjustment model to measure postoperative cardiac ICU mortality after congenital heart surgery. Multivariable logistic regression was performed to assess preoperative, intraoperative, and immediate postoperative severity of illness variables as candidate predictors. We used generalized estimating equations to account for clustering of patients within hospital and obtain robust SEs. Bootstrap resampling (1,000 samples) was used to derive bias-corrected 95% CIs around each predictor and validate the model. The final model was used to calculate expected mortality at each hospital. We calculated a standardized mortality ratio (observed-to-expected mortality) for each hospital and derived 95% CIs around the standardized mortality ratio estimate. Hospital standardized mortality ratio was considered a statistically significant outlier if the 95% CI did not include 1. Significant preoperative predictors of mortality in the final model included age, chromosomal abnormality/syndrome, previous cardiac surgeries, preoperative mechanical ventilation, and surgical complexity. Significant early postoperative risk factors included open sternum, mechanical ventilation, maximum vasoactive inotropic score, and extracorporeal membrane oxygenation. The model demonstrated excellent discrimination (C statistic, 0.92) and adequate calibration. Comparison across Pediatric Cardiac Critical Care Consortium hospitals revealed five-fold difference in standardized mortality ratio (0.4-1.9). Two hospitals had significantly better-than-expected and two had significantly worse-than-expected mortality.

CONCLUSIONS:

For the first time, we have demonstrated that variation in mortality as a quality metric exists across dedicated cardiac ICUs. These findings can guide efforts to reduce mortality after cardiac surgery.

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