Prediction of Postoperative Deterioration in Cardiac Surgery Patients Using Electronic Health Record and Physiologic Waveform Data

Author:

Mathis Michael R.1ORCID,Engoren Milo C.2,Williams Aaron M.3,Biesterveld Ben E.3,Croteau Alfred J.4,Cai Lingrui5,Kim Renaid B.5,Liu Gang5,Ward Kevin R.6,Najarian Kayvan7,Gryak Jonathan8,

Affiliation:

1. Department of Anesthesiology, University of Michigan Health System, Ann Arbor, Michigan; Department of Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, Michigan; Michigan Integrated Center for Health Analytics and Medical Prediction, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan; and Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, Michigan.

2. Department of Anesthesiology, University of Michigan Health System, Ann Arbor, Michigan.

3. Department of General Surgery, University of Michigan Health System, Ann Arbor, Michigan.

4. Department of General Surgery, Hartford HealthCare Medical Group, Hartford, Connecticut.

5. Department of Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, Michigan.

6. Michigan Integrated Center for Health Analytics and Medical Prediction, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan; Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, Michigan; and Department of Emergency Medicine, University of Michigan Health System, Ann Arbor, Michigan.

7. Department of Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, Michigan; Michigan Integrated Center for Health Analytics and Medical Prediction, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan; and Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, Michigan.

8. Department of Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, Michigan; and Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, Michigan.

Abstract

Background Postoperative hemodynamic deterioration among cardiac surgical patients can indicate or lead to adverse outcomes. Whereas prediction models for such events using electronic health records or physiologic waveform data are previously described, their combined value remains incompletely defined. The authors hypothesized that models incorporating electronic health record and processed waveform signal data (electrocardiogram lead II, pulse plethysmography, arterial catheter tracing) would yield improved performance versus either modality alone. Methods Intensive care unit data were reviewed after elective adult cardiac surgical procedures at an academic center between 2013 and 2020. Model features included electronic health record features and physiologic waveforms. Tensor decomposition was used for waveform feature reduction. Machine learning–based prediction models included a 2013 to 2017 training set and a 2017 to 2020 temporal holdout test set. The primary outcome was a postoperative deterioration event, defined as a composite of low cardiac index of less than 2.0 ml min˗1 m˗2, mean arterial pressure of less than 55 mmHg sustained for 120 min or longer, new or escalated inotrope/vasopressor infusion, epinephrine bolus of 1 mg or more, or intensive care unit mortality. Prediction models analyzed data 8 h before events. Results Among 1,555 cases, 185 (12%) experienced 276 deterioration events, most commonly including low cardiac index (7.0% of patients), new inotrope (1.9%), and sustained hypotension (1.4%). The best performing model on the 2013 to 2017 training set yielded a C-statistic of 0.803 (95% CI, 0.799 to 0.807), although performance was substantially lower in the 2017 to 2020 test set (0.709, 0.705 to 0.712). Test set performance of the combined model was greater than corresponding models limited to solely electronic health record features (0.641; 95% CI, 0.637 to 0.646) or waveform features (0.697; 95% CI, 0.693 to 0.701). Conclusions Clinical deterioration prediction models combining electronic health record data and waveform data were superior to either modality alone, and performance of combined models was primarily driven by waveform data. Decreased performance of prediction models during temporal validation may be explained by data set shift, a core challenge of healthcare prediction modeling. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Anesthesiology and Pain Medicine

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