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. 2020 Jul;220(1):114-119.
doi: 10.1016/j.amjsurg.2019.10.009. Epub 2019 Oct 9.

Identification of postoperative complications using electronic health record data and machine learning

Michael Bronsert  1 , Abhinav B Singh  2 , William G Henderson  3 , Karl Hammermeister  4 , Robert A Meguid  5 , Kathryn L Colborn  6
Affiliations

Identification of postoperative complications using electronic health record data and machine learning

Michael Bronsert et al. Am J Surg. 2020 Jul.

Abstract

Background: Using the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) complication status of patients who underwent an operation at the University of Colorado Hospital, we developed a machine learning algorithm for identifying patients with one or more complications using data from the electronic health record (EHR).

Methods: We used an elastic-net model to estimate regression coefficients and carry out variable selection. International classification of disease codes (ICD-9), common procedural terminology (CPT) codes, medications, and CPT-specific complication event rate were included as predictors.

Results: Of 6840 patients, 922 (13.5%) had at least one of the 18 complications tracked by NSQIP. The model achieved 88% specificity, 83% sensitivity, 97% negative predictive value, 52% positive predictive value, and an area under the curve of 0.93.

Conclusions: Using machine learning on EHR postoperative data linked to NSQIP outcomes data, a model with 163 predictors from the EHR identified complications well at our institution.

Keywords: Elastic-net; Machine learning; NSQIP; Postoperative complications.

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Conflict of interest statement

Declaration of competing interest The authors report no proprietary or commercial interest in any product mentioned or concept discussed in this article.

Figures

Figure 1.
Figure 1.
a) Discrimination plot. Values on the x-axis are postoperative complication status from the NSQIP test set and values on the y-axis are predicted probabilities from the model fit to the test set. b) Hosmer-Lemeshow Calibration plot. Values on the x-axis are deciles of predicted risk of any complication and values on the y-axis are rates of complications for each decile. The two different lines are observed and expected rates.
Figure 1.
Figure 1.
a) Discrimination plot. Values on the x-axis are postoperative complication status from the NSQIP test set and values on the y-axis are predicted probabilities from the model fit to the test set. b) Hosmer-Lemeshow Calibration plot. Values on the x-axis are deciles of predicted risk of any complication and values on the y-axis are rates of complications for each decile. The two different lines are observed and expected rates.
See this image and copyright information in PMC

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References

    1. Branch-Elliman W, Strymish J, Itani KM, et al. Using clinical variables to guide surgical site infection detection: a novel surveillance strategy. Am J Infect Control 2014; 42: 1291–1295. 2014/12/04 DOI: 10.1016/j.ajic.2014.08.013. - DOI - PubMed
    1. Goto M, Ohl ME, Schweizer ML, et al. Accuracy of administrative code data for the surveillance of healthcare-associated infections: a systematic review and meta-analysis. Clin Infect Dis 2014; 58: 688–696. 2013/11/13 DOI: 10.1093/cid/cit737. - DOI - PubMed
    1. Hu Z, Simon GJ, Arsoniadis EG, et al. Automated Detection of Postoperative Surgical Site Infections Using Supervised Methods with Electronic Health Record Data. Studies in health technology and informatics 2015; 216: 706–710. 2015/08/12. - PMC - PubMed
    1. Ju MH, Ko CY, Hall BL, et al. A comparison of 2 surgical site infection monitoring systems. JAMA surgery 2015; 150: 51–57. 2014/11/27 DOI: 10.1001/jamasurg.2014.2891. - DOI - PubMed
    1. Branch-Elliman W, Strymish J, Kudesia V, et al. Natural Language Processing for Real-Time Catheter-Associated Urinary Tract Infection Surveillance: Results of a Pilot Implementation Trial. Infection control and hospital epidemiology 2015; 36: 1004–1010. 2015/05/30 DOI: 10.1017/ice.2015.122. - DOI - PubMed

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