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

Sarah Tabbutt, Jennifer Schuette, Wenying Zhang, Jeffrey Alten, Janet Donohue, J. William Gaynor, Nancy Ghanayem, Jeffrey Jacobs, Sara K. Pasquali, Ravi Thiagarajan, Justin B. Dimick, Mousumi Banerjee, David Cooper, Michael Gaies

Research output: Contribution to journalArticle

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.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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Pediatrics
Mortality
Critical Care
Hospital Mortality
Thoracic Surgery
Risk Adjustment
Artificial Respiration
Extracorporeal Membrane Oxygenation
Sternum
Pediatric Hospitals
Quality of Health Care
Chromosome Aberrations
Calibration
Cluster Analysis
Registries
Logistic Models

ASJC Scopus subject areas

  • Pediatrics, Perinatology, and Child Health
  • Critical Care and Intensive Care Medicine

Cite this

A Novel Model Demonstrates Variation in Risk-Adjusted Mortality Across Pediatric Cardiac ICUs After Surgery. / Tabbutt, Sarah; Schuette, Jennifer; Zhang, Wenying; Alten, Jeffrey; Donohue, Janet; Gaynor, J. William; Ghanayem, Nancy; Jacobs, Jeffrey; Pasquali, Sara K.; Thiagarajan, Ravi; Dimick, Justin B.; Banerjee, Mousumi; Cooper, David; Gaies, Michael.

In: Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies, Vol. 20, No. 2, 01.02.2019, p. 136-142.

Research output: Contribution to journalArticle

Tabbutt, Sarah ; Schuette, Jennifer ; Zhang, Wenying ; Alten, Jeffrey ; Donohue, Janet ; Gaynor, J. William ; Ghanayem, Nancy ; Jacobs, Jeffrey ; Pasquali, Sara K. ; Thiagarajan, Ravi ; Dimick, Justin B. ; Banerjee, Mousumi ; Cooper, David ; Gaies, Michael. / A Novel Model Demonstrates Variation in Risk-Adjusted Mortality Across Pediatric Cardiac ICUs After Surgery. In: Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies. 2019 ; Vol. 20, No. 2. pp. 136-142.
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author = "Sarah Tabbutt and Jennifer Schuette and Wenying Zhang and Jeffrey Alten and Janet Donohue and Gaynor, {J. William} and Nancy Ghanayem and Jeffrey Jacobs and Pasquali, {Sara K.} and Ravi Thiagarajan and Dimick, {Justin B.} and Mousumi Banerjee and David Cooper and Michael Gaies",
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AU - Tabbutt, Sarah

AU - Schuette, Jennifer

AU - Zhang, Wenying

AU - Alten, Jeffrey

AU - Donohue, Janet

AU - Gaynor, J. William

AU - Ghanayem, Nancy

AU - Jacobs, Jeffrey

AU - Pasquali, Sara K.

AU - Thiagarajan, Ravi

AU - Dimick, Justin B.

AU - Banerjee, Mousumi

AU - Cooper, David

AU - Gaies, Michael

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N2 - 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.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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