Risk Factors and Machine Learning-Based Prediction Models for Early Readmission after Thoracoabdominal Aortic Dissection

Introduction: Heart failure (HF) risk and the underlying biological risk factors vary by race. Machine learning (ML) may improve race-specific HF risk prediction but this has not been fully evaluated. Methods: The study included participants from 4 cohorts (ARIC, DHS, JHS, and MESA) aged > 40 years, free of baseline HF, and with adjudicated HF event follow-up. Black adults from JHS and white adults from ARIC were used to derive race-specific ML models to predict 10-year HF risk. The ML models were externally validated in subgroups of black and white adults from ARIC (excluding JHS participants) and pooled MESA/DHS cohorts and compared to prior established HF risk scores developed in ARIC and MESA. Harrell’s C-index and Greenwood-Nam-D’Agostino chi-square were used to assess discrimination and calibration, respectively. Results: In the derivation cohorts, 288 of 4141 (7.0%) black and 391 of 8242 (4.7%) white adults developed HF over 10 years. The ML models had excellent discrimination in both black and white participants (C-indices = 0.88 and 0.89). In the external validation cohorts for black participants from ARIC (excluding JHS, N = 1072) and MESA/DHS pooled cohorts (N = 2821), 131 (12.2%) and 115 (4.1%) developed HF. The ML model had adequate calibration and demonstrated superior discrimination compared to established HF risk models (Fig A). A consistent pattern was also observed in the external validation cohorts of white participants from the MESA/DHS pooled cohorts (N=3236; 100 [3.1%] HF events) (Fig A). The most important predictors of HF in both races were NP levels. Cardiac biomarkers and glycemic parameters were most important among blacks while LV hypertrophy and prevalent CVD and traditional CV risk factors were the strongest predictors among whites (Fig B). Conclusions: Race-specific and ML-based HF risk models that integrate clinical, laboratory, and biomarker data demonstrated superior performance when compared to traditional risk prediction models.

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Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques

BMC Medical Research Methodology ◽

10.1186/s12874-020-01153-1 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Georgios Kantidakis ◽

Hein Putter ◽

Carlo Lancia ◽

Jacob de Boer ◽

Andries E. Braat ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Neural Networks ◽

Liver Transplantation ◽

Prediction Models ◽

Machine Learning Techniques ◽

Brier Score ◽

Cox Models ◽

Learning Techniques ◽

Random Survival Forest

Abstract Background Predicting survival of recipients after liver transplantation is regarded as one of the most important challenges in contemporary medicine. Hence, improving on current prediction models is of great interest.Nowadays, there is a strong discussion in the medical field about machine learning (ML) and whether it has greater potential than traditional regression models when dealing with complex data. Criticism to ML is related to unsuitable performance measures and lack of interpretability which is important for clinicians. Methods In this paper, ML techniques such as random forests and neural networks are applied to large data of 62294 patients from the United States with 97 predictors selected on clinical/statistical grounds, over more than 600, to predict survival from transplantation. Of particular interest is also the identification of potential risk factors. A comparison is performed between 3 different Cox models (with all variables, backward selection and LASSO) and 3 machine learning techniques: a random survival forest and 2 partial logistic artificial neural networks (PLANNs). For PLANNs, novel extensions to their original specification are tested. Emphasis is given on the advantages and pitfalls of each method and on the interpretability of the ML techniques. Results Well-established predictive measures are employed from the survival field (C-index, Brier score and Integrated Brier Score) and the strongest prognostic factors are identified for each model. Clinical endpoint is overall graft-survival defined as the time between transplantation and the date of graft-failure or death. The random survival forest shows slightly better predictive performance than Cox models based on the C-index. Neural networks show better performance than both Cox models and random survival forest based on the Integrated Brier Score at 10 years. Conclusion In this work, it is shown that machine learning techniques can be a useful tool for both prediction and interpretation in the survival context. From the ML techniques examined here, PLANN with 1 hidden layer predicts survival probabilities the most accurately, being as calibrated as the Cox model with all variables. Trial registration Retrospective data were provided by the Scientific Registry of Transplant Recipients under Data Use Agreement number 9477 for analysis of risk factors after liver transplantation.

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Development and Validation of Machine Learning-Based Race-Specific Models to Predict 10-Year Risk of Heart Failure: A Multi-Cohort Analysis

Circulation ◽

10.1161/circulationaha.120.053134 ◽

2021 ◽

Author(s):

Matthew W. Segar ◽

Byron C. Jaeger ◽

Kershaw V. Patel ◽

Vijay Nambi ◽

Chiadi E. Ndumele ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Heart Failure ◽

Risk Prediction ◽

Prediction Models ◽

Cohort Analysis ◽

Specific Model ◽

Superior Performance ◽

Risk Models ◽

White Adults

Background: Heart failure (HF) risk and the underlying risk factors vary by race. Traditional models for HF risk prediction treat race as a covariate in risk prediction and do not account for significant parameters such as cardiac biomarkers. Machine learning (ML) may offer advantages over traditional modeling techniques to develop race-specific HF risk prediction models and elucidate important contributors of HF development across races. Methods: We performed a retrospective analysis of four large, community cohort studies (ARIC, DHS, JHS, and MESA) with adjudicated HF events. Participants were aged >40 years and free of HF at baseline. Race-specific ML models for HF risk prediction were developed in the JHS cohort (for Black race-specific model) and White adults from ARIC (for White rate-specific model). The models included 39 candidate variables across demographic, anthropometric, medical history, laboratory, and electrocardiographic domains. The ML models were externally validated and compared with prior established traditional and non-race specific ML models in race-specific subgroups of the pooled MESA/DHS cohort and Black participants of ARIC. Harrell's C-index and Greenwood-Nam-D'Agostino chi-square tests were used to assess discrimination and calibration, respectively. Results: The ML models had excellent discrimination in the derivation cohorts for Black (N=4,141 in JHS, C-index=0.88) and White (N=7,858 in ARIC, C-index=0.89) participants. In the external validation cohorts, the race-specific ML model demonstrated adequate calibration and superior discrimination (C-indices=0.80-0.83 [for Black individuals] and 0.82 [for White individuals]) compared with established HF risk models or with non-race specific ML models derived using race as a covariate. Among the risk factors, natriuretic peptide levels were the most important predictor of HF risk across both races, followed by troponin levels in Black and EKG-based Cornell voltage in White individuals. Other key predictors of HF risk among Black individuals were glycemic parameters and socioeconomic factors. In contrast, prevalent cardiovascular (CV) disease and traditional CV risk factors were stronger predictors of HF risk in White adults. Conclusions: Race-specific and ML-based HF risk models that integrate clinical, laboratory, and biomarker data demonstrated superior performance when compared with traditional HF risk and non-race specific ML models. This approach identifies distinct race-specific contributors of HF.

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A Machine Learning Model to Predict Intravenous Immunoglobulin-Resistant Kawasaki Disease Patients: A Retrospective Study Based on the Chongqing Population

Frontiers in Pediatrics ◽

10.3389/fped.2021.756095 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jie Liu ◽

Jian Zhang ◽

Haodong Huang ◽

Yunting Wang ◽

Zuyue Zhang ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Logistic Regression ◽

Regression Analysis ◽

Retrospective Study ◽

Kawasaki Disease ◽

Prediction Model ◽

Prediction Models ◽

Ivig Resistance ◽

Independent Risk Factors

Objective: We explored the risk factors for intravenous immunoglobulin (IVIG) resistance in children with Kawasaki disease (KD) and constructed a prediction model based on machine learning algorithms.Methods: A retrospective study including 1,398 KD patients hospitalized in 7 affiliated hospitals of Chongqing Medical University from January 2015 to August 2020 was conducted. All patients were divided into IVIG-responsive and IVIG-resistant groups, which were randomly divided into training and validation sets. The independent risk factors were determined using logistic regression analysis. Logistic regression nomograms, support vector machine (SVM), XGBoost and LightGBM prediction models were constructed and compared with the previous models.Results: In total, 1,240 out of 1,398 patients were IVIG responders, while 158 were resistant to IVIG. According to the results of logistic regression analysis of the training set, four independent risk factors were identified, including total bilirubin (TBIL) (OR = 1.115, 95% CI 1.067–1.165), procalcitonin (PCT) (OR = 1.511, 95% CI 1.270–1.798), alanine aminotransferase (ALT) (OR = 1.013, 95% CI 1.008–1.018) and platelet count (PLT) (OR = 0.998, 95% CI 0.996–1). Logistic regression nomogram, SVM, XGBoost, and LightGBM prediction models were constructed based on the above independent risk factors. The sensitivity was 0.617, 0.681, 0.638, and 0.702, the specificity was 0.712, 0.841, 0.967, and 0.903, and the area under curve (AUC) was 0.731, 0.814, 0.804, and 0.874, respectively. Among the prediction models, the LightGBM model displayed the best ability for comprehensive prediction, with an AUC of 0.874, which surpassed the previous classic models of Egami (AUC = 0.581), Kobayashi (AUC = 0.524), Sano (AUC = 0.519), Fu (AUC = 0.578), and Formosa (AUC = 0.575).Conclusion: The machine learning LightGBM prediction model for IVIG-resistant KD patients was superior to previous models. Our findings may help to accomplish early identification of the risk of IVIG resistance and improve their outcomes.

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Machine learning improves the prediction of febrile neutropenia in Korean inpatients undergoing chemotherapy for breast cancer

Scientific Reports ◽

10.1038/s41598-020-71927-6 ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 1

Author(s):

Bum-Joo Cho ◽

Kyoung Min Kim ◽

Sanchir-Erdene Bilegsaikhan ◽

Yong Joon Suh

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Risk Factors ◽

Febrile Neutropenia ◽

Prediction Models ◽

Learning Algorithms ◽

Area Under The Curve ◽

Primary Prophylaxis ◽

Machine Learning Algorithms ◽

Significant Difference

Abstract Febrile neutropenia (FN) is one of the most concerning complications of chemotherapy, and its prediction remains difficult. This study aimed to reveal the risk factors for and build the prediction models of FN using machine learning algorithms. Medical records of hospitalized patients who underwent chemotherapy after surgery for breast cancer between May 2002 and September 2018 were selectively reviewed for development of models. Demographic, clinical, pathological, and therapeutic data were analyzed to identify risk factors for FN. Using machine learning algorithms, prediction models were developed and evaluated for performance. Of 933 selected inpatients with a mean age of 51.8 ± 10.7 years, FN developed in 409 (43.8%) patients. There was a significant difference in FN incidence according to age, staging, taxane-based regimen, and blood count 5 days after chemotherapy. The area under the curve (AUC) built based on these findings was 0.870 on the basis of logistic regression. The AUC improved by machine learning was 0.908. Machine learning improves the prediction of FN in patients undergoing chemotherapy for breast cancer compared to the conventional statistical model. In these high-risk patients, primary prophylaxis with granulocyte colony-stimulating factor could be considered.

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Using Machine Learning to Predict Five-Year Reintervention Risk in Type B Aortic Dissection Patients After Thoracic Endovascular Aortic Repair

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3813 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1560-1567

Author(s):

Weiyuan Lin ◽

Lifeng Que ◽

Guisen Lin ◽

Rui Chen ◽

Qiyang Lu ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

High Risk ◽

Aortic Dissection ◽

Thoracic Endovascular Aortic Repair ◽

Type B ◽

Imaging Data ◽

Type B Aortic Dissection ◽

Medical Guidelines

Purpose: Type B aortic dissection (TBAD) is a high-risk disease, commonly treated with thoracic endovascular aortic repair (TEVAR). However, for the long-term follow-up, it is associated with a high 5-year reintervention rate for patients after TEVAR. There is no accurate definition of prognostic risk factors for TBAD in medical guidelines, and there is no scientific judgment standard for patients’ quality of life or survival outcome in the next five years in clinical practice. A large amount of medical data features makes prognostic analysis difficult. However, machine learning (ML) permits lots of objective data features to be considered for clinical risk stratification and patient management. We aimed to predict the 5-year prognosis in TBAD after TEVAR by Ml, based on baseline, stent characteristics and computed tomography angiography (CTA) imaging data, and provided a certain degree of scientific basis for prognostic risk score and stratification in medical guidelines. Materials and Methods: Dataset we recorded was obtained from 172 TBAD patients undergoing TEVAR. Totally 40 features were recorded, including 14 baseline, 5 stent characteristics and 21 CTA imaging data. Information gain (IG) was used to select features highly associated with adverse outcome. Then, the Gradient Boost classifier was trained using grid search and stratified 5-fold cross-validation, and Its predictive performance was evaluated by the area under the curve (AUC) in the receiver operating characteristic (ROC). Results: Totally 60 patients underwent reintervention during follow-up. Combing 24 features selected by IG, ML model predicted prognosis well in TBAD after TEVAR, with an AUC of 0.816 and a 95% confidence interval of 0.797 to 0.837. Reintervention rate of prediction was slightly higher than the actual (48.2% vs. 34.8%). Conclusion: Machine learning, which combined with baseline, stent characteristics and CTA imaging data for personalized risk computations, effectively predicted reintervention risk in TBAD patients after TEVAR in 5-year follow-up. The model could be used to efficiently assist the clinical management of TBAD patients and prompt high-risk factors.

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Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning

Scientific Reports ◽

10.1038/s41598-021-82492-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Wan Xu ◽

Nan-Nan Sun ◽

Hai-Nv Gao ◽

Zhi-Yuan Chen ◽

Ya Yang ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Clinical Characteristics ◽

Prediction Models ◽

Clinical Manifestations ◽

Diagnostic System ◽

Machine Learning Algorithms ◽

Human Beings ◽

Factors Analysis ◽

Modeling And Prediction

AbstractCOVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the ARDS group and no-ARDS group of COVID-19 patients were elaborately compared and both traditional machine learning algorithms and deep learning-based method were used to build the prediction models. Results indicated that the median age of ARDS patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male and patients with BMI > 25 were more likely to develop ARDS. The clinical features of ARDS patients included cough (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection (30.3%), and comorbidities such as hypertension (48.7%). Abnormal biochemical indicators such as lymphocyte count, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, accuracy, sensitivity and specificity in identifying the mild patients who were easy to develop ARDS, which undoubtedly helped to deliver proper care and optimize use of limited resources.

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Incidence, Risk Factors and Outcomes of Postoperative Headache After Stanford Type a Acute Aortic Dissection Surgery

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2021.781137 ◽

2021 ◽

Vol 8 ◽

Author(s):

Dashuai Wang ◽

Sheng Le ◽

Jingjing Luo ◽

Xing Chen ◽

Rui Li ◽

...

Keyword(s):

Risk Factors ◽

Clinical Practice ◽

Aortic Dissection ◽

Acute Aortic Dissection ◽

Prediction Models ◽

Multivariate Logistic Regression Analysis ◽

Type A ◽

Smoking History ◽

Incidence Risk ◽

Postoperative Headache

Background: Postoperative headache (POH) is common in clinical practice, however, no studies about POH after Stanford type A acute aortic dissection surgery (AADS) exist. This study aims to describe the incidence, risk factors and outcomes of POH after AADS, and to construct two prediction models.Methods: Adults who underwent AADS from 2016 to 2020 in four tertiary hospitals were enrolled. Training and validation sets were randomly assigned according to a 7:3 ratio. Risk factors were identified by univariate and multivariate logistic regression analysis. Nomograms were constructed and validated on the basis of independent predictors.Results: POH developed in 380 of the 1,476 included patients (25.7%). Poorer outcomes were observed in patients with POH. Eight independent predictors for POH after AADS were identified when both preoperative and intraoperative variables were analyzed, including younger age, female sex, smoking history, chronic headache history, cerebrovascular disease, use of deep hypothermic circulatory arrest, more blood transfusion, and longer cardiopulmonary bypass time. White blood cell and platelet count were also identified as significant predictors when intraoperative variables were excluded from the multivariate analysis. A full nomogram and a preoperative nomogram were constructed based on these independent predictors, both demonstrating good discrimination, calibration, clinical usefulness, and were well validated. Risk stratification was performed and three risk intervals were defined based on the full nomogram and clinical practice.Conclusions: POH was common after AADS, portending poorer outcomes. Two nomograms predicting POH were developed and validated, which may have clinical utility in risk evaluation, early prevention, and doctor-patient communication.

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Abstract P280: Revisiting CVD Risk Prediction Using Machine Learning Approaches: A Case Study

Circulation ◽

10.1161/circ.141.suppl_1.p280 ◽

2020 ◽

Vol 141 (Suppl_1) ◽

Cited By ~ 1

Author(s):

Hesam Dashti ◽

Yanyan Liu ◽

Robert J Glynn ◽

Paul M Ridker ◽

Samia Mora ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Risk Prediction ◽

Prediction Models ◽

Baseline Risk ◽

Case Group ◽

Ann Model ◽

Biomedical Image Processing ◽

Learning Methods ◽

Machine Learning Methods

Introduction: Applications of machine learning (ML) methods have been demonstrated by the recent FDA approval of new ML-based biomedical image processing methods. In this study, we examine applications of ML, specifically artificial neural networks (ANN), for predicting risk of cardiovascular (CV) events. Hypothesis: We hypothesized that using the same CV risk factors, ML-based CV prediction models can improve the performance of current predictive models. Methods: Justification for the Use of Statins in Prevention: An Intervention Trial Evaluating Rosuvastatin (JUPITER; NCT00239681) is a multi-ethnic trial that randomized non-diabetic participants with LDL-C<130 mg/dL and hsCRP≥2 mg/L to rosuvastatin versus placebo. We restricted the analysis to white and black participants allocated to the placebo arm, and estimated the race- and sex-specific Pooled Cohorts Equations (PCE) 5-year risk score using race, sex, age, HDL-C, total cholesterol, systolic BP, antihypertensive medications, and smoking. A total of 218 incident CV cases occurred (maximum follow-up 5 years). For every participant in the case group, we randomly selected 4 controls from the placebo arm after stratifying for the baseline risk factors (Table 1). The risk factors from a total of n=1,090 participants were used to train and test the ANN model. We used 80% of the participants (n=872) for designing the network and left out 20% of the data (n=218) for testing the predictive model. We used the TensorFlow software to design, train, and evaluate the ANN model. Results: We compared the performances of the ANN and the PCE score on the 218 test subjects (Figure 1). The high AUC of the neural network (0.85; 95% CI 0.78-0.91) on this dataset suggests advantages of machine learning methods compared to the current methods. Conclusions: This result demonstrates the potential of machine learning methods for enhancing and improving the current techniques used in cardiovascular risk prediction and should be evaluated in other cohorts.

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Dense phenotyping from electronic health records enables machine-learning-based prediction of preterm birth

10.1101/2020.07.15.20154864 ◽

2020 ◽

Author(s):

Abin Abraham ◽

Brian L Le ◽

Idit Kosti ◽

Peter Straub ◽

Digna R Velez Edwards ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Preterm Birth ◽

Electronic Health Records ◽

Prediction Models ◽

Mode Of Delivery ◽

Machine Learning Algorithms ◽

Health Records ◽

Electronic Health ◽

Independent Cohort

Abstract: Identifying pregnancies at risk for preterm birth, one of the leading causes of worldwide infant mortality, has the potential to improve prenatal care. However, we lack broadly applicable methods to accurately predict preterm birth risk. The dense longitudinal information present in electronic health records (EHRs) is enabling scalable and cost-efficient risk modeling of many diseases, but EHR resources have been largely untapped in the study of pregnancy. Here, we apply machine learning to diverse data from EHRs to predict singleton preterm birth. Leveraging a large cohort of 35,282 deliveries, we find that a prediction model based on billing codes alone can predict preterm birth at 28 weeks of gestation (ROC-AUC=0.75, PR-AUC=0.40) and outperforms a comparable model trained using known risk factors (ROC-AUC=0.59, PR-AUC=0.21). Our machine learning approach is also able to accurately predict preterm birth sub-types (spontaneous vs. indicated), mode of delivery, and recurrent preterm birth. We demonstrate the portability of our approach by showing that the prediction models maintain their accuracy on a large, independent cohort (5,978 deliveries) with only a modest decrease in performance. Interpreting the features identified by the model as most informative for risk stratification demonstrates that they capture non-linear combinations of known risk factors and patterns of care. The strong performance of our approach across multiple clinical contexts and an independent cohort highlights the potential of machine learning algorithms to improve medical care during pregnancy.

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