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Ann Surg. 2019 Apr;269(4):652-662. doi: 10.1097/SLA.0000000000002706.

MySurgeryRisk: Development and Validation of a Machine-learning Risk Algorithm for Major Complications and Death After Surgery.

Author information

1
Department of Medicine, College of Medicine, University of Florida, Gainesville, FL.
2
Precision and Intelligent Systems in Medicine (PRISMA), University of Florida, Gainesville, FL.
3
Department of Industrial and Systems Engineering, College of Engineering, UF.
4
University of Florida Health, UF.
5
Department of Health Outcomes and Policy, UF.
6
Department of Surgery, College of Medicine, UF.
7
Department of Computer and Information Science and Engineering, College of Engineering, UF.
8
Department of Health Services Research, Management and Policy, UF.
9
Department of Surgery, Malcom Randall VAMC, Gainesville, FL.
10
Department of Biomedical Engineering, College of Engineering, UF.
11
Department of Electrical and Computer Engineering, College of Engineering, UF.

Abstract

OBJECTIVE:

To accurately calculate the risk for postoperative complications and death after surgery in the preoperative period using machine-learning modeling of clinical data.

BACKGROUND:

Postoperative complications cause a 2-fold increase in the 30-day mortality and cost, and are associated with long-term consequences. The ability to precisely forecast the risk for major complications before surgery is limited.

METHODS:

In a single-center cohort of 51,457 surgical patients undergoing major inpatient surgery, we have developed and validated an automated analytics framework for a preoperative risk algorithm (MySurgeryRisk) that uses existing clinical data in electronic health records to forecast patient-level probabilistic risk scores for 8 major postoperative complications (acute kidney injury, sepsis, venous thromboembolism, intensive care unit admission >48 hours, mechanical ventilation >48 hours, wound, neurologic, and cardiovascular complications) and death up to 24 months after surgery. We used the area under the receiver characteristic curve (AUC) and predictiveness curves to evaluate model performance.

RESULTS:

MySurgeryRisk calculates probabilistic risk scores for 8 postoperative complications with AUC values ranging between 0.82 and 0.94 [99% confidence intervals (CIs) 0.81-0.94]. The model predicts the risk for death at 1, 3, 6, 12, and 24 months with AUC values ranging between 0.77 and 0.83 (99% CI 0.76-0.85).

CONCLUSIONS:

We constructed an automated predictive analytics framework for machine-learning algorithm with high discriminatory ability for assessing the risk of surgical complications and death using readily available preoperative electronic health records data. The feasibility of this novel algorithm implemented in real time clinical workflow requires further testing.

PMID:
29489489
PMCID:
PMC6110979
[Available on 2020-04-01]
DOI:
10.1097/SLA.0000000000002706

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