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| Hauptseite > Publikationsdatenbank > Analysis of Cerebral CT Based on Supervised Machine Learning as a Predictor of Outcome After Out-of-Hospital Cardiac Arrest > print |
| Hauptseite > Publikationsdatenbank > Analysis of Cerebral CT Based on Supervised Machine Learning as a Predictor of Outcome After Out-of-Hospital Cardiac Arrest > print |
| 001 | 1028208 | ||
| 005 | 20250204113906.0 | ||
| 024 | 7 | _ | |a 10.1212/WNL.0000000000209583 |2 doi |
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| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Gramespacher, Hannes |0 0000-0002-9980-4978 |b 0 |e First author |
| 245 | _ | _ | |a Analysis of Cerebral CT Based on Supervised Machine Learning as a Predictor of Outcome After Out-of-Hospital Cardiac Arrest |
| 260 | _ | _ | |a [Erscheinungsort nicht ermittelbar] |c 2024 |b Ovid |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1719489142_24926 |2 PUB:(DE-HGF) |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a H. Gramespacher was supported by the Cologne Clinician Scientist Program (CCSP)/Faculty of Medicine/University of Cologne. Funded by the German Research Foundation (DFG, FI 773/15-1). |
| 520 | _ | _ | |a AbstractBackground and ObjectivesIn light of limited intensive care capacities and a lack of accurate prognostic tools to advise caregivers and family members responsibly, this study aims to determine whether automated cerebral CT (CCT) analysis allows prognostication after out-of-hospital cardiac arrest.MethodsIn this monocentric, retrospective cohort study, a supervised machine learning classifier based on an elastic net regularized logistic regression model for gray matter alterations on nonenhanced CCT obtained after cardiac arrest was trained using 10-fold cross-validation and tested on a hold-out sample (random split 75%/25%) for outcome prediction. Following the literature, a favorable outcome was defined as a cerebral performance category of 1–2 and a poor outcome of 3–5. The diagnostic accuracy was compared with established and guideline-recommended prognostic measures within the sample, that is, gray matter-white matter ratio (GWR), neuron-specific enolase (NSE), and neurofilament light chain (NfL) in serum.ResultsOf 279 adult patients, 132 who underwent CCT within 14 days of cardiac arrest with good imaging quality were identified. Our approach discriminated between favorable and poor outcomes with an area under the curve (AUC) of 0.73 (95% CI 0.59–0.82). Thus, the prognostic power outperformed the GWR (AUC 0.66, 95% CI 0.56–0.76). The biomarkers NfL, measured at days 1 and 2, and NSE, measured at day 2, exceeded the reliability of the imaging markers derived from CT (AUC NfL day 1: 0.87, 95% CI 0.75–0.99; AUC NfL day 2: 0.90, 95% CI 0.79–1.00; AUC NSE day: 2 0.78, 95% CI 0.62–0.94).DiscussionOur data show that machine learning–assisted gray matter analysis of CCT images offers prognostic information after out-of-hospital cardiac arrest. Thus, CCT gray matter analysis could become a reliable and time-independent addition to the standard workup with serum biomarkers sampled at predefined time points. Prospective studies are warranted to replicate these findings. |
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| 700 | 1 | _ | |a Schmieschek, Maximilian H. T. |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Warnke, Clemens |0 0000-0002-3510-9255 |b 2 |
| 700 | 1 | _ | |a Adler, Christoph |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Bittner, Stefan |0 0000-0003-2179-3655 |b 4 |
| 700 | 1 | _ | |a Dronse, Julian |0 P:(DE-Juel1)162382 |b 5 |
| 700 | 1 | _ | |a Richter, Nils |b 6 |
| 700 | 1 | _ | |a Zaeske, Charlotte |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Gietzen, Carsten |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Schlamann, Marc |0 P:(DE-HGF)0 |b 9 |
| 700 | 1 | _ | |a Baldus, Stephan |0 P:(DE-HGF)0 |b 10 |
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| 700 | 1 | _ | |a Onur, Oezguer A. |0 P:(DE-HGF)0 |b 12 |e Corresponding author |
| 773 | _ | _ | |a 10.1212/WNL.0000000000209583 |g Vol. 103, no. 1, p. e209583 |0 PERI:(DE-600)1491874-2 |n 1 |p e209583 |t Neurology |v 103 |y 2024 |x 0028-3878 |
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