Fully Automated Cardiac Assessment for Diagnostic and Prognostic Stratification Following Myocardial Infarction

Author:

Schuster Andreas12ORCID,Lange Torben12ORCID,Backhaus Sören J.12,Strohmeyer Carolin12,Boom Patricia C.12,Matz Jonas12,Kowallick Johannes T.23,Lotz Joachim23,Steinmetz Michael24,Kutty Shelby5,Bigalke Boris6,Gutberlet Matthias7,de Waha‐Thiele Suzanne89,Desch Steffen10,Hasenfuß Gerd12,Thiele Holger10,Stiermaier Thomas89ORCID,Eitel Ingo89ORCID

Affiliation:

1. Department of Cardiology and Pneumology University Medical Center GöttingenGeorg‐August University Göttingen Germany

2. German Centre for Cardiovascular Research (DZHK), partner site Göttingen Göttingen Germany

3. Institute for Diagnostic and Interventional Radiology University Medical Center GöttingenGeorg‐August University Göttingen Germany

4. Department of Pediatric Cardiology University Medical Center GöttingenGeorg‐August University Göttingen Germany

5. Helen B. Taussig Heart Center The Johns Hopkins Hospital and School of Medicine Baltimore MD

6. Department of Cardiology Charité Campus Benjamin FranklinUniversity Medical Center Berlin Berlin Germany

7. Institute of Diagnostic and Interventional Radiology Heart Center Leipzig at University of Leipzig Germany

8. Medical Clinic II (Cardiology/Angiology/Intensive Care Medicine) University Heart Center LübeckUniversity Hospital Schleswig‐Holstein Lübeck Germany

9. German Centre for Cardiovascular Research (DZHK), partner site Hamburg/Kiel/Lübeck Lübeck Germany

10. Department of Internal Medicine/Cardiology and Leipzig Heart Institute Heart Center Leipzig at University of Leipzig Germany

Abstract

Background Cardiovascular magnetic resonance imaging is considered the reference methodology for cardiac morphology and function but requires manual postprocessing. Whether novel artificial intelligence–based automated analyses deliver similar information for risk stratification is unknown. Therefore, this study aimed to investigate feasibility and prognostic implications of artificial intelligence–based, commercially available software analyses. Methods and Results Cardiovascular magnetic resonance data (n=1017 patients) from 2 myocardial infarction multicenter trials were included. Analyses of biventricular parameters including ejection fraction (EF) were manually and automatically assessed using conventional and artificial intelligence–based software. Obtained parameters entered regression analyses for prediction of major adverse cardiac events, defined as death, reinfarction, or congestive heart failure, within 1 year after the acute event. Both manual and uncorrected automated volumetric assessments showed similar impact on outcome in univariate analyses (left ventricular EF, manual: hazard ratio [HR], 0.93 [95% CI 0.91–0.95]; P <0.001; automated: HR, 0.94 [95% CI, 0.92–0.96]; P <0.001) and multivariable analyses (left ventricular EF, manual: HR, 0.95 [95% CI, 0.92–0.98]; P =0.001; automated: HR, 0.95 [95% CI, 0.92–0.98]; P =0.001). Manual correction of the automated contours did not lead to improved risk prediction (left ventricular EF, area under the curve: 0.67 automated versus 0.68 automated corrected; P =0.49). There was acceptable agreement (left ventricular EF: bias, 2.6%; 95% limits of agreement, −9.1% to 14.2%; intraclass correlation coefficient, 0.88 [95% CI, 0.77–0.93]) of manual and automated volumetric assessments. Conclusions User‐independent volumetric analyses performed by fully automated software are feasible, and results are equally predictive of major adverse cardiac events compared with conventional analyses in patients following myocardial infarction. Registration URL: https://www.clinicaltrials.gov ; Unique identifiers: NCT00712101 and NCT01612312.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Cardiology and Cardiovascular Medicine

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