Dataset Information

Machine Learning to Predict Long-Term Cardiac-Relative Prognosis in Patients With Extra-Cardiac Vascular Disease.

ABSTRACT: Aim: Patients with ischemic stroke (IS), transient ischemic attack (TIA), and/or peripheral artery disease (PAD) represent a population with an increased risk of coronary artery disease. Prognostic risk assessment to identify those with the highest risk that may benefit from more intensified treatment remains challenging. To explore the feasibility and capability of machine learning (ML) to predict long-term adverse cardiac-related prognosis in patients with IS, TIA, and/or PAD. Methods: We analyzed 636 consecutive patients with a history of IS, TIA, and/or PAD. All patients underwent a coronary CT angiography (CCTA) scan. Thirty-five clinical data and 34 CCTA metrics underwent automated feature selection for ML model boosting. The clinical outcome included all-cause mortality (ACM) and major adverse cardiac events (MACE) (ACM, unstable angina requiring hospitalization, non-fatal myocardial infarction (MI), and revascularization 90 days after the index CCTA). Results: During the follow-up of 3.9 ± 1.6 years, 21 patients had unstable angina requiring hospitalization, eight had a MI, 23 had revascularization and 13 deaths. ML demonstrated a significant higher area-under-curve compared with the modified Duke index (MDI), segment stenosis score (SSS), segment involvement score (SIS), and Framingham risk score (FRS) for the prediction of ACM (ML:0.92 vs. MDI:0.66, SSS:0.68, SIS:0.67, FRS:0.51, all P < 0.001) and MACE (ML:0.84 vs. MDI:0.82, SSS:0.76, SIS:0.73, FRS:0.53, all P < 0.05). Conclusion: Among the patients with IS, TIA, and/or PAD, ML demonstrated a better capability of predicting ACM and MCAE than clinical scores and CCTA metrics.

SUBMITTER: Lin G

PROVIDER: S-EPMC8655836 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Machine Learning to Predict Long-Term Cardiac-Relative Prognosis in Patients With Extra-Cardiac Vascular Disease.

Lin Guisen G Liu Qile Q Chen Yuchen Y Zong Xiaodan X Xi Yue Y Li Tingyu T Yang Yuelong Y Zeng An A Chen Minglei M Liu Chen C Liang Yanting Y Xu Xiaowei X Huang Meiping M

Frontiers in cardiovascular medicine 20211125

<b>Aim:</b> Patients with ischemic stroke (IS), transient ischemic attack (TIA), and/or peripheral artery disease (PAD) represent a population with an increased risk of coronary artery disease. Prognostic risk assessment to identify those with the highest risk that may benefit from more intensified treatment remains challenging. To explore the feasibility and capability of machine learning (ML) to predict long-term adverse cardiac-related prognosis in patients with IS, TIA, and/or PAD. <b>Method ...[more]

PMID: 34901231

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Project description:BackgroundThere have been few longitudinal studies assessing the effect of preoperative phenotypic severity on long-term esthetic outcomes in metopic craniosynostosis. This study evaluates the relationship between metopic severity and long-term esthetic outcomes using interfrontal angle (IFA) and CranioRate, a novel metopic synostosis severity measure.MethodsPatients with metopic craniosynostosis who underwent bifrontal orbital advancement and remodeling between 2012 and 2017 were reviewed. Preoperative computed tomography head scans were analyzed for IFA and CranioRate, a machine learning algorithm which generates quantitative severity ratings including metopic severity score (MSS) and cranial morphology deviation (CMD). Long-term esthetic outcomes were assessed by craniofacial surgeons using blinded 3-rater esthetic grading of clinical photos. Raters assessed Whitaker score and the presence of temporal hollowing, lateral orbital retrusion, frontal bone irregularities and/or "any visible irregularities."ResultsPreoperative scans were performed at a mean age of 7.7±3.4 months, with average MSS of 6/10, CMD of 200/300, and IFA of 116.8±13.8 degrees. Patients underwent bifrontal orbital advancement and remodeling at mean 9.9±3.1 months. The average time from operation to esthetic assessment was 5.4±1.0 years. Pearson correlation revealed a significant negative correlation between MSS and age at computed tomography ( r =-0.451, P =0.004) and IFA ( r =-0.371, P =0.034) and between IFA and age at surgery ( r =-0.383, P =0.018). In multinomial logistic regression, preoperative MSS was the only independent predictor of visible irregularities (odds ratio=2.18, B =0.780, P =0.024) and preoperative IFA alone significantly predicted Whitaker score, with more acute IFA predicting worse Whitaker score (odds ratio=0.928, B =-0.074, P =0.928).ConclusionsMore severe preoperative phenotypes of metopic craniosynostosis were associated with worse esthetic dysmorphology. Objective measures of preoperative metopic severity predicted long-term esthetic outcomes.

Project description:ObjectiveThis study aims to construct and validate several machine learning (ML) algorithms to predict long-term mortality and identify risk factors in unselected patients post-cardiac surgery.MethodsThe Medical Information Mart for Intensive Care (MIMIC-III) database was used to perform a retrospective administrative database study. Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, scoring systems, and treatment information on the first day of ICU admission. Four-year mortality was set as the study outcome. We used the ML methods of logistic regression (LR), artificial neural network (NNET), naïve bayes (NB), gradient boosting machine (GBM), adapting boosting (Ada), random forest (RF), bagged trees (BT), and eXtreme Gradient Boosting (XGB). The prognostic capacity and clinical utility of these ML models were compared using the area under the receiver operating characteristic curves (AUC), calibration curves, and decision curve analysis (DCA).ResultsOf 7,368 patients in MIMIC-III included in the final cohort, a total of 1,337 (18.15%) patients died during a 4-year follow-up. Among 65 variables extracted from the database, a total of 25 predictors were selected using recursive feature elimination and included in the subsequent analysis. The Ada model performed best among eight models in both discriminatory ability with the highest AUC of 0.801 and goodness of fit (visualized by calibration curve). Moreover, the DCA shows that the net benefit of the RF, Ada, and BT models surpassed that of other ML models for almost all threshold probability values. Additionally, through the Ada technique, we determined that red blood cell distribution width (RDW), blood urea nitrogen (BUN), SAPS II, anion gap (AG), age, urine output, chloride, creatinine, congestive heart failure, and SOFA were the Top 10 predictors in the feature importance rankings.ConclusionsThe Ada model performs best in predicting 4-year mortality after cardiac surgery among the eight ML models, which might have significant application in the development of early warning systems for patients following operations.

Dataset Information

Machine Learning to Predict Long-Term Cardiac-Relative Prognosis in Patients With Extra-Cardiac Vascular Disease.

Publications

Machine Learning to Predict Long-Term Cardiac-Relative Prognosis in Patients With Extra-Cardiac Vascular Disease.

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