Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality

• URL: http://arxiv.org/abs/2205.03242v1
• Date: Sat, 30 Apr 2022 05:14:53 GMT
• Title: Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality
• Authors: David Ouyang, John Theurer, Nathan R. Stein, J. Weston Hughes, Pierre Elias, Bryan He, Neal Yuan, Grant Duffy, Roopinder K. Sandhu, Joseph Ebinger, Patrick Botting, Melvin Jujjavarapu, Brian Claggett, James E. Tooley, Tim Poterucha, Jonathan H. Chen, Michael Nurok, Marco Perez, Adler Perotte, James Y. Zou, Nancy R. Cook, Sumeet S. Chugh, Susan Cheng and Christine M. Albert
• Abstract summary: Deep learning algorithm was developed to leverage waveform signals from pre-operative ECGs to discriminate post-operative mortality. Patients determined to be high risk by the deep learning model's risk prediction had an unadjusted odds ratio (OR) of 8.83 (5.57-13.20) for post-operative mortality. Findings demonstrate how a novel deep learning algorithm, applied to pre-operative ECGs, can improve discrimination of post-operative mortality.
• Score: 9.192239774090208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background. Pre-operative risk assessments used in clinical practice are limited in their ability to identify risk for post-operative mortality. We hypothesize that electrocardiograms contain hidden risk markers that can help prognosticate post-operative mortality. Methods. In a derivation cohort of 45,969 pre-operative patients (age 59+- 19 years, 55 percent women), a deep learning algorithm was developed to leverage waveform signals from pre-operative ECGs to discriminate post-operative mortality. Model performance was assessed in a holdout internal test dataset and in two external hospital cohorts and compared with the Revised Cardiac Risk Index (RCRI) score. Results. In the derivation cohort, there were 1,452 deaths. The algorithm discriminates mortality with an AUC of 0.83 (95% CI 0.79-0.87) surpassing the discrimination of the RCRI score with an AUC of 0.67 (CI 0.61-0.72) in the held out test cohort. Patients determined to be high risk by the deep learning model's risk prediction had an unadjusted odds ratio (OR) of 8.83 (5.57-13.20) for post-operative mortality as compared to an unadjusted OR of 2.08 (CI 0.77-3.50) for post-operative mortality for RCRI greater than 2. The deep learning algorithm performed similarly for patients undergoing cardiac surgery with an AUC of 0.85 (CI 0.77-0.92), non-cardiac surgery with an AUC of 0.83 (0.79-0.88), and catherization or endoscopy suite procedures with an AUC of 0.76 (0.72-0.81). The algorithm similarly discriminated risk for mortality in two separate external validation cohorts from independent healthcare systems with AUCs of 0.79 (0.75-0.83) and 0.75 (0.74-0.76) respectively. Conclusion. The findings demonstrate how a novel deep learning algorithm, applied to pre-operative ECGs, can improve discrimination of post-operative mortality.

Related papers

• A Novel Generative Multi-Task Representation Learning Approach for Predicting Postoperative Complications in Cardiac Surgery Patients [7.42249589630227]
  Machine learning can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder. surgVAE uncovers intrinsic patterns via cross-task and cross-cohort presentation learning.
  arXiv  Detail & Related papers  (2024-12-02T20:24:02Z)
• Integrating Deep Learning with Fundus and Optical Coherence Tomography for Cardiovascular Disease Prediction [47.7045293755736]
  Early identification of patients at risk of cardiovascular diseases (CVD) is crucial for effective preventive care, reducing healthcare burden, and improving patients' quality of life. This study demonstrates the potential of retinal optical coherence tomography ( OCT) imaging combined with fundus photographs for identifying future adverse cardiac events. We propose a novel binary classification network based on a Multi-channel Variational Autoencoder (MCVAE), which learns a latent embedding of patients' fundus and OCT images to classify individuals into two groups: those likely to develop CVD in the future and those who are not.
  arXiv  Detail & Related papers  (2024-10-18T12:37:51Z)
• Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray [86.38767955626179]
  Deep-learning algorithm to predict coronary artery calcium (CAC) score was developed on 460 chest x-ray. The diagnostic accuracy of the AICAC model assessed by the area under the curve (AUC) was the primary outcome.
  arXiv  Detail & Related papers  (2024-03-27T16:56:14Z)
• Predicting risk of cardiovascular disease using retinal OCT imaging [40.71667870702634]
  Cardiovascular diseases (CVD) are the leading cause of death globally. Optical coherence tomography ( OCT) has gained recognition as a potential tool for early CVD risk prediction. We investigated the potential of OCT as an additional imaging technique to predict future CVD events.
  arXiv  Detail & Related papers  (2024-03-26T14:42:46Z)
• Penalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patients [42.09584755334577]
  Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective therapies. The National Lung Screening Trial (NLST) employed computed tomography texture analysis to quantify the mortality risks of lung cancer patients. We propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the SCAD penalty to select important texture features and employs a deep neural network to estimate the nonparametric component of the model.
  arXiv  Detail & Related papers  (2023-03-09T15:38:16Z)
• Deep-Learning Tool for Early Identifying Non-Traumatic Intracranial Hemorrhage Etiology based on CT Scan [40.51754649947294]
  The deep learning model was developed with 1868 eligible NCCT scans with non-traumatic ICH collected between January 2011 and April 2018. The model's diagnostic performance was compared with clinicians's performance. The clinicians achieve significant improvements in the sensitivity, specificity, and accuracy of diagnoses of certain hemorrhage etiologies with proposed system augmentation.
  arXiv  Detail & Related papers  (2023-02-02T08:45:17Z)
• BIO-CXRNET: A Robust Multimodal Stacking Machine Learning Technique for Mortality Risk Prediction of COVID-19 Patients using Chest X-Ray Images and Clinical Data [0.0]
  This study uses 25 biomarkers and CXR images in predicting the risk in 930 COVID-19 patients admitted in Italy. The proposed multimodal stacking technique produced the precision, sensitivity, and F1-score, of 89.03%, 90.44%, and 89.03%, respectively. The nomogram-based scoring technique was able to predict the death probability of high-risk patients with an F1 score of 92.88 %.
  arXiv  Detail & Related papers  (2022-06-15T15:23:43Z)
• Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation [22.45448597986172]
  Multilabel gradient boosting machine provided the best discriminant power for stroke, major bleeding, and death. Models identified additional risk features (such as hemoglobin level, renal function, etc.) for each outcome.
  arXiv  Detail & Related papers  (2022-02-02T15:15:03Z)
• Deep Learning Predicts Cardiovascular Disease Risks from Lung Cancer Screening Low Dose Computed Tomography [23.614559487371935]
  Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population. LDCT for lung cancer screening offers an opportunity for simultaneous CVD risk estimation in at-risk patients. Deep learning can convert LDCT for lung cancer screening into a dual-screening quantitative tool for CVD risk estimation.
  arXiv  Detail & Related papers  (2020-08-16T21:07:01Z)
• An early warning tool for predicting mortality risk of COVID-19 patients using machine learning [0.0]
  A retrospective study was conducted on 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. A nomogram was developed for predicting the mortality risk among COVID-19 patients. An integrated score (LNLCA) was calculated with the corresponding death probability.
  arXiv  Detail & Related papers  (2020-07-29T15:16:09Z)
• Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT [48.785596536318884]
  The proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States.
  arXiv  Detail & Related papers  (2020-04-02T21:49:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.