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

Tabbutt, Sarah, MD, PhD1; Schuette, Jennifer, MD, MS2; Zhang, Wenying, MS3; Alten, Jeffrey, MD4; Donohue, Janet, MPH5; Gaynor, J. William, MD6; Ghanayem, Nancy, MD7; Jacobs, Jeffrey, MD8; Pasquali, Sara K., MD, MHS5; Thiagarajan, Ravi, MD9; Dimick, Justin B., MD, MPH3; Banerjee, Mousumi, PhD3; Cooper, David, MD, MPH10; Gaies, Michael, MD, MPH, MSc5

Pediatric Critical Care Medicine: February 2019 - Volume 20 - Issue 2 - p 136–142
doi: 10.1097/PCC.0000000000001776
Cardiac Intensive Care

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.

1Benioff Children’s Hospital and the University of California San Francisco Medical School, San Francisco, CA.

2Johns Hopkins Children’s Center and Johns Hopkins School of Medicine, Baltimore, MD.

3University of Michigan, Ann Arbor, MI.

4Children’s Hospital of Alabama and the University of Alabama School of Medicine, Birmingham, AL.

5CS Mott Children’s Hospital and the University of Michigan, Ann Arbor, MI.

6Children’s Hospital of Philadelphia and the University of Pennsylvania Medical School, Philadelphia, PA.

7Texas Children’s Hospital and the Baylor College of Medicine, Houston, TX.

8Johns Hopkins All Children’s Heart Institute, Saint Petersburg, FL.

9Children’s Hospital Boston and the Harvard Medical School, Boston, MA.

10Cincinnati Children’s Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, OH.

*See also p. 194.

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Mr. Zhang disclosed work for hire. Dr. Pasquali receives funding from the Janette Ferrantino Professorship. Dr. Thiagarajan’s institution received funding from Bristol Myers Squibb and Pfizer. Dr. Dimick received funding from ArborMetrix. Dr. Gaies receives support from the National Heart, Lung, and Blood Institute (NHLBI) (K08HL116639; Principal Investigator [PI]) that indirectly supports this research. His institution received grant support from the National Institutes of Health (K08 award from NHLBI [PI: Dr. Gaies]). The remaining authors have disclosed that they do not have any potential conflicts of interest.

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©2019The Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies