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Ann Thorac Surg. 2018 May;105(5):1419-1428. doi: 10.1016/j.athoracsur.2018.03.003. Epub 2018 Mar 22.

The Society of Thoracic Surgeons 2018 Adult Cardiac Surgery Risk Models: Part 2-Statistical Methods and Results.

Author information

1
Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina.
2
Division of Cardiac Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland; Division of Cardiovascular Surgery, Johns Hopkins All Children's Heart Institute, St. Petersburg, Florida.
3
Department of Cardiovascular and Thoracic Surgery, West Virginia University, Morgantown, West Virginia.
4
Division of Cardiac Surgery, Columbia University, New York, New York.
5
Starr-Wood Cardiothoracic Group, Portland, Oregon.
6
Division of Cardiothoracic Surgery, University of Colorado Anschutz School of Medicine, Aurora, Colorado.
7
Atrium Health, Cardiovascular and Thoracic Surgery, Charlotte, North Carolina.
8
Division of Cardiac Surgery, University of Massachusetts Medical School, Worcester, Massachusetts.
9
Houston Methodist DeBakey Heart and Vascular Center, Houston, Texas.
10
Department of Cardiac Surgery, MedStar Heart and Vascular Institute, Georgetown University, Washington, DC.
11
The Heart Hospital Baylor Plano, Plano, Texas.
12
Division of Thoracic and Cardiovascular Surgery, Lahey Hospital and Medical Center, Burlington, Massachusetts.
13
Division of Cardiothoracic Surgery, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
14
Department of Surgery, University of Florida, Gainesville, Florida.
15
Department of Surgery and Center for Quality and Safety, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts. Electronic address: dshahian@partners.org.

Abstract

BACKGROUND:

The Society of Thoracic Surgeons (STS) uses statistical models to create risk-adjusted performance metrics for Adult Cardiac Surgery Database (ACSD) participants. Because of temporal changes in patient characteristics and outcomes, evolution of surgical practice, and additional risk factors available in recent ACSD versions, completely new risk models have been developed.

METHODS:

Using July 2011 to June 2014 ACSD data, risk models were developed for operative mortality, stroke, renal failure, prolonged ventilation, mediastinitis/deep sternal wound infection, reoperation, major morbidity or mortality composite, prolonged postoperative length of stay, and short postoperative length of stay among patients who underwent isolated coronary artery bypass grafting surgery (n = 439,092), aortic or mitral valve surgery (n = 150,150), or combined valve plus coronary artery bypass grafting surgery (n = 81,588). Separate models were developed for each procedure and endpoint except mediastinitis/deep sternal wound infection, which was analyzed in a combined model because of its infrequency. A surgeon panel selected predictors by assessing model performance and clinical face validity of full and progressively more parsimonious models. The ACSD data (July 2014 to December 2016) were used to assess model calibration and to compare discrimination with previous STS risk models.

RESULTS:

Calibration in the validation sample was excellent for all models except mediastinitis/deep sternal wound infection, which slightly underestimated risk and will be recalibrated in feedback reports. The c-indices of new models exceeded those of the last published STS models for all populations and endpoints except stroke in valve patients.

CONCLUSIONS:

New STS ACSD risk models have generally excellent calibration and discrimination and are well suited for risk adjustment of STS performance metrics.

[Indexed for MEDLINE]

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