Accurate preoperative prediction of unplanned 30-day postoperative readmission using 8 predictor variables

被引:16
|
作者
Gibula, Douglas R. [1 ,2 ]
Singh, Abhinav B. [2 ]
Bronsert, Michael R. [2 ,3 ]
Henderson, William G. [2 ,3 ,4 ]
Battaglia, Catherine [5 ,6 ]
Hammermeister, Karl E. [2 ,3 ,7 ]
Glebova, Natalia O. [2 ,8 ]
Meguid, Robert A. [2 ,3 ]
机构
[1] Univ Utah, Dept Neurosurg, Salt Lake City, UT USA
[2] Univ Colorado, Dept Surg, Surg Outcomes & Appl Res Program, Sch Med, Aurora, CO USA
[3] Univ Colorado, Adult & Child Consortium Hlth Outcomes Res & Deli, Sch Med, Aurora, CO USA
[4] Colorado Sch Publ Hlth, Dept Biostat & Informat, Aurora, CO USA
[5] Univ Colorado, Colorado Sch Publ Hlth, Dept Hlth Syst Management & Policy, Aurora, CO USA
[6] Eastern Colorado Hlth Care Syst, Dept Vet Affairs, Aurora, CO USA
[7] Univ Colorado, Dept Med, Div Cardiol, Sch Med, Aurora, CO USA
[8] Midatlantic Permanente Med Grp, Dept Vasc Surg, Rockville, MD USA
基金
美国国家卫生研究院; 美国医疗保健研究与质量局;
关键词
SURGICAL SITE INFECTIONS; HOSPITAL READMISSION; RISK-FACTORS; SURGERY; MORTALITY; OUTCOMES; QUALITY;
D O I
10.1016/j.surg.2019.05.022
中图分类号
R61 [外科手术学];
学科分类号
摘要
Background: Unplanned postoperative readmissions are associated with high costs, may indicate poor care quality, and present a substantial opportunity for healthcare quality improvement. Patients want to know their risk of unplanned readmission, and surgeons need to know the risk to adequately counsel their patients. The Surgical Risk Preoperative Assessment System tool was developed from the American College of Surgeons National Surgical Quality Improvement Program dataset and is a parsimonious model using 8 predictor variables. Surgical Risk Preoperative Assessment System is applicable to >3,000 operations in 9 surgical specialties, predicts 30-day postoperative mortality and morbidity, and is incorporated into our electronic health record. Methods: A Surgical Risk Preoperative Assessment System model was developed using logistic regression. It was compared to the 28 nonlaboratory variables model from the American College of Surgeons National Surgical Quality Improvement Program 2012 to 2017 dataset using the c-index as a measure of discrimination, the Hosmer-Lemeshow observed-to-expected plots testing calibration, and the Brier score, a combined metric of discrimination and calibration. Results: Of 4,861,370 patients, 188,150 (3.98%) experienced unplanned readmission related to the index operation. The Surgical Risk Preoperative Assessment System model's c-index, 0.728, was 99.3% of that of the full model's, 0.733; the Hosmer-Lemeshow plots indicated good calibration; and the Brier score was 0.0372 for Surgical Risk Preoperative Assessment System and 0.0371 for the full model. Conclusion: The 8 variable Surgical Risk Preoperative Assessment System model detects patients at risk for postoperative unplanned, related readmission as accurately as the full model developed from all 28 nonlaboratory preoperative variables in the American College of Surgeons National Surgical Quality Improvement Program dataset. Therefore, unplanned readmission can be integrated into the existing Surgical Risk Preoperative Assessment System tool providing moderately accurate prediction of postoperative readmission. (C) 2019 Elsevier Inc. All rights reserved.
引用
收藏
页码:812 / 819
页数:8
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