scholarly journals Machine learning-based in-hospital mortality prediction models for patients with acute coronary syndrome

2022 ◽  
Author(s):  
Jun Ke ◽  
Yiwei Chen ◽  
Xiaoping Wang ◽  
Zhiyong Wu ◽  
Qiongyao Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Coronary Syndrome ◽  
Hospital Mortality ◽  
Prediction Models ◽  
Mortality Prediction ◽  
Coronary Syndrome ◽  
Mortality Prediction Models

scholarly journals Machine Learning-based in-hospital Mortality Prediction Models for Patients With Acute Coronary Syndrome

2020 ◽  
Author(s):  
Jun Ke ◽  
Yiwei Chen ◽  
Xiaoping Wang ◽  
Zhiyong Wu ◽  
qiongyao Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Hospital Mortality ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Hdl Cholesterol ◽  
Coronary Syndrome

Abstract BackgroundThe purpose of this study is to identify the risk factors of in-hospital mortality in patients with acute coronary syndrome (ACS) and to evaluate the performance of traditional regression and machine learning prediction models.MethodsThe data of ACS patients who entered the emergency department of Fujian Provincial Hospital from January 1, 2017 to March 31, 2020 for chest pain were retrospectively collected. The study used univariate and multivariate logistic regression analysis to identify risk factors for in-hospital mortality of ACS patients. The traditional regression and machine learning algorithms were used to develop predictive models, and the sensitivity, specificity, and receiver operating characteristic curve were used to evaluate the performance of each model.ResultsA total of 7810 ACS patients were included in the study, and the in-hospital mortality rate was 1.75%. Multivariate logistic regression analysis found that age and levels of D-dimer, cardiac troponin I, N-terminal pro-B-type natriuretic peptide (NT-proBNP), lactate dehydrogenase (LDH), high-density lipoprotein (HDL) cholesterol, and calcium channel blockers were independent predictors of in-hospital mortality. The study found that the area under the receiver operating characteristic curve of the models developed by logistic regression, gradient boosting decision tree (GBDT), random forest, and support vector machine (SVM) for predicting the risk of in-hospital mortality were 0.963, 0.960, 0.963, and 0.959, respectively. Feature importance evaluation found that NT-proBNP, LDH, and HDL cholesterol were top three variables that contribute the most to the prediction performance of the GBDT model and random forest model.ConclusionsThe predictive model developed using logistic regression, GBDT, random forest, and SVM algorithms can be used to predict the risk of in-hospital death of ACS patients. Based on our findings, we recommend that clinicians focus on monitoring the changes of NT-proBNP, LDH, and HDL cholesterol, as this may improve the clinical outcomes of ACS patients.

scholarly journals Clinical and Laboratory Predictors of In-hospital Mortality in Patients With Coronavirus Disease-2019: A Cohort Study in Wuhan, China

2020 ◽  
Vol 71 (16) ◽  
pp. 2079-2088 ◽  
Author(s):  
Kun Wang ◽  
Peiyuan Zuo ◽  
Yuwei Liu ◽  
Meng Zhang ◽  
Xiaofang Zhao ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Mortality Prediction ◽  
Gradient Boosting ◽  
Laboratory Model ◽  
Training Cohort ◽  
Clinical Model ◽  
Extreme Gradient Boosting ◽  
Mortality Prediction Models

Abstract Background This study aimed to develop mortality-prediction models for patients with coronavirus disease-2019 (COVID-19). Methods The training cohort included consecutive COVID-19 patients at the First People’s Hospital of Jiangxia District in Wuhan, China, from 7 January 2020 to 11 February 2020. We selected baseline data through the stepwise Akaike information criterion and ensemble XGBoost (extreme gradient boosting) model to build mortality-prediction models. We then validated these models by randomly collected COVID-19 patients in Union Hospital, Wuhan, from 1 January 2020 to 20 February 2020. Results A total of 296 COVID-19 patients were enrolled in the training cohort; 19 died during hospitalization and 277 discharged from the hospital. The clinical model developed using age, history of hypertension, and coronary heart disease showed area under the curve (AUC), 0.88 (95% confidence interval [CI], .80–.95); threshold, −2.6551; sensitivity, 92.31%; specificity, 77.44%; and negative predictive value (NPV), 99.34%. The laboratory model developed using age, high-sensitivity C-reactive protein, peripheral capillary oxygen saturation, neutrophil and lymphocyte count, d-dimer, aspartate aminotransferase, and glomerular filtration rate had a significantly stronger discriminatory power than the clinical model (P = .0157), with AUC, 0.98 (95% CI, .92–.99); threshold, −2.998; sensitivity, 100.00%; specificity, 92.82%; and NPV, 100.00%. In the subsequent validation cohort (N = 44), the AUC (95% CI) was 0.83 (.68–.93) and 0.88 (.75–.96) for the clinical model and laboratory model, respectively. Conclusions We developed 2 predictive models for the in-hospital mortality of patients with COVID-19 in Wuhan that were validated in patients from another center.

scholarly journals Predicting mortality in hemodialysis patients using machine learning analysis

2020 ◽  
Author(s):  
Victoria Garcia-Montemayor ◽  
Alejandro Martin-Malo ◽  
Carlo Barbieri ◽  
Francesco Bellocchio ◽  
Sagrario Soriano ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Prediction Models ◽  
Initial Period ◽  
Area Under The Curve ◽  
Mortality Prediction ◽  
Machine Learning Techniques ◽  
Prediction Of Mortality ◽  
Mortality Prediction Models

Abstract Background Besides the classic logistic regression analysis, non-parametric methods based on machine learning techniques such as random forest are presently used to generate predictive models. The aim of this study was to evaluate random forest mortality prediction models in haemodialysis patients. Methods Data were acquired from incident haemodialysis patients between 1995 and 2015. Prediction of mortality at 6 months, 1 year and 2 years of haemodialysis was calculated using random forest and the accuracy was compared with logistic regression. Baseline data were constructed with the information obtained during the initial period of regular haemodialysis. Aiming to increase accuracy concerning baseline information of each patient, the period of time used to collect data was set at 30, 60 and 90 days after the first haemodialysis session. Results There were 1571 incident haemodialysis patients included. The mean age was 62.3 years and the average Charlson comorbidity index was 5.99. The mortality prediction models obtained by random forest appear to be adequate in terms of accuracy [area under the curve (AUC) 0.68–0.73] and superior to logistic regression models (ΔAUC 0.007–0.046). Results indicate that both random forest and logistic regression develop mortality prediction models using different variables. Conclusions Random forest is an adequate method, and superior to logistic regression, to generate mortality prediction models in haemodialysis patients.

scholarly journals Evaluation of crowdsourced mortality prediction models as a framework for assessing AI in medicine

2021 ◽  
Author(s):  
Timothy Bergquist ◽  
Thomas Schaffter ◽  
Yao Yan ◽  
Thomas Yu ◽  
Justin Prosser ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Improve Patient Care ◽  
Mortality Prediction ◽  
Direct Access ◽  
Healthcare Outcomes ◽  
Patient Privacy ◽  
Mortality Prediction Models ◽  
And Performance ◽  
Similar Accuracy

AbstractApplications of machine learning in healthcare are of high interest and have the potential to significantly improve patient care. Yet, the real-world accuracy and performance of these models on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate different methods that predict healthcare outcomes. To overcome patient privacy concerns, we employed a Model-to-Data approach, allowing citizen scientists and researchers to train and evaluate machine learning models on private health data without direct access to that data. We focused on the prediction of all-cause mortality as the community challenge question. In total, we had 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries. The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI 0.942, 0.951) and an area under the precision-recall curve of 0.487 (95% CI 0.458, 0.499) on patients prospectively collected over a one year observation of a large health system. Post-hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data and have similar accuracy on the population. This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI.

Abstract 238: Validation of In-hospital Mortality Prediction Models for Patients with Heart Failure

2015 ◽  
Vol 8 (suppl_2) ◽  
Author(s):  
Tara Lagu ◽  
Mihaela Stefan ◽  
Quinn Pack ◽  
Auras Atreya ◽  
Mohammad A Kashef ◽  
...  
Keyword(s):  
Heart Failure ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Clinical Data ◽  
Prediction Models ◽  
Mortality Prediction ◽  
Mortality Prediction Model ◽  
Mortality Prediction Models ◽  
Clinical Models ◽  
Administrative Model

Background: Mortality prediction models, developed with the goal of improving risk stratification in hospitalized heart failure (HF) patients, show good performance characteristics in the datasets in which they were developed but have not been validated in external populations. Methods: We used a novel multi-hospital dataset [HealthFacts (Cerner Corp)] derived from the electronic health record (years 2010-2012). We examined the performance of four published HF inpatient mortality prediction models developed using data from: the Acute Decompensated Heart Failure National Registry (ADHERE), the Enhanced Feedback for Effective Cardiac Treatment (EFFECT) study, and the Get With the Guidelines-Heart Failure (GWTG-HF) registry. We compared to an administrative HF mortality prediction model (Premier model) that includes selected patient demographics, comorbidities, prior heart failure admissions, and therapies administered (e.g., inotropes, mechanical ventilation) in the first 2 hospital days. We also compared to a model that uses clinical data but is not heart failure-specific: the Laboratory-Based Acute Physiology Score (LAPS2). We included patients aged ≥18 years admitted with HF to one of 62 hospitals in the database. We applied all 6 models to the data and calculated the c-statistics. Results: We identified 13,163 patients ≥18 years old with a diagnosis of heart failure. Median age was 74 years; approximately half were women; 65% of patients were white and 27% were black. In-hospital mortality was 4.3%. Bland-Altman plots revealed that, at higher predicted mortality, the Premier model outperformed the clinical models. Discrimination of the models varied: ADHERE model (0.68); EFFECT (0.70); GWTG-HF, Peterson (0.69); GWTG-HF, Eapen (0.70); LAPS2 (0.74); Premier (0.81) (Figure). Conclusions: Clinically-derived inpatient heart failure mortality models exhibited similar performance with c statistics hovering around 0.70. A generic clinical mortality prediction model (LAPS2) had slightly better performance, as did a detailed administrative model. Any of these models may be useful for severity adjustment in comparative effectiveness studies of heart failure patients. When clinical data are not available, the administrative model performs similarly to clinical models.

scholarly journals GRACE risk score: Sex-based validity of in-hospital mortality prediction in Canadian patients with acute coronary syndrome

2017 ◽  
Vol 244 ◽  
pp. 24-29 ◽  
Author(s):  
Inna Y. Gong ◽  
Shaun G. Goodman ◽  
David Brieger ◽  
Chris P. Gale ◽  
Derek P. Chew ◽  
...  
Keyword(s):  
Acute Coronary Syndrome ◽  
Hospital Mortality ◽  
Risk Score ◽  
Mortality Prediction ◽  
Coronary Syndrome ◽  
Grace Risk Score

scholarly journals Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gregor Lichtner ◽  
Felix Balzer ◽  
Stefan Haufe ◽  
Niklas Giesa ◽  
Fridtjof Schiefenhövel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Critically Ill ◽  
Prediction Models ◽  
Predictive Performance ◽  
Learning Model ◽  
Mortality Prediction ◽  
Viral Pneumonia ◽  
Machine Learning Model ◽  
Mortality Prediction Models ◽  
Time Courses

AbstractIn a pandemic with a novel disease, disease-specific prognosis models are available only with a delay. To bridge the critical early phase, models built for similar diseases might be applied. To test the accuracy of such a knowledge transfer, we investigated how precise lethal courses in critically ill COVID-19 patients can be predicted by a model trained on critically ill non-COVID-19 viral pneumonia patients. We trained gradient boosted decision tree models on 718 (245 deceased) non-COVID-19 viral pneumonia patients to predict individual ICU mortality and applied it to 1054 (369 deceased) COVID-19 patients. Our model showed a significantly better predictive performance (AUROC 0.86 [95% CI 0.86–0.87]) than the clinical scores APACHE2 (0.63 [95% CI 0.61–0.65]), SAPS2 (0.72 [95% CI 0.71–0.74]) and SOFA (0.76 [95% CI 0.75–0.77]), the COVID-19-specific mortality prediction models of Zhou (0.76 [95% CI 0.73–0.78]) and Wang (laboratory: 0.62 [95% CI 0.59–0.65]; clinical: 0.56 [95% CI 0.55–0.58]) and the 4C COVID-19 Mortality score (0.71 [95% CI 0.70–0.72]). We conclude that lethal courses in critically ill COVID-19 patients can be predicted by a machine learning model trained on non-COVID-19 patients. Our results suggest that in a pandemic with a novel disease, prognosis models built for similar diseases can be applied, even when the diseases differ in time courses and in rates of critical and lethal courses.

scholarly journals A machine learning–based 1-year mortality prediction model after hospital discharge for clinical patients with acute coronary syndrome

2019 ◽  
Vol 26 (2) ◽  
pp. 1289-1304 ◽  
Author(s):  
Syed Waseem Abbas Sherazi ◽  
Yu Jun Jeong ◽  
Moon Hyun Jae ◽  
Jang-Whan Bae ◽  
Jong Yun Lee
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Acute Coronary Syndrome ◽  
Prediction Model ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Mortality Prediction ◽  
Coronary Syndrome ◽  
Mortality Prediction Model ◽  
Operating Characteristic Curve

Cardiovascular disease is the leading cause of death worldwide so, early prediction and diagnosis of cardiovascular disease is essential for patients affected by this fatal disease. The goal of this article is to propose a machine learning–based 1-year mortality prediction model after discharge in clinical patients with acute coronary syndrome. We used the Korea Acute Myocardial Infarction Registry data set, a cardiovascular disease database registered in 52 hospitals in Korea for 1 November 2005–30 January 2008 and selected 10,813 subjects with 1-year follow-up traceability. The ranges of hyperparameters to find the best prediction model were selected from four different machine learning models. Then, we generated each machine learning–based mortality prediction model with hyperparameters completed the range fitness via grid search using training data and was evaluated by fourfold stratified cross-validation. The best prediction model with the highest performance was found, and its hyperparameters were extracted. Finally, we compared the performance of machine learning–based mortality prediction models with GRACE in area under the receiver operating characteristic curve, precision, recall, accuracy, and F-score. The area under the receiver operating characteristic curve in applied machine learning algorithms was averagely improved up to 0.08 than in GRACE, and their major prognostic factors were different. This implementation would be beneficial for prediction and early detection of major adverse cardiovascular events in acute coronary syndrome patients.

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