scholarly journals A Novel Machine Learning-Based Radiomic Model for Diagnosing High Bleeding Risk Esophageal Varices in Cirrhotic Patients

2021 ◽  
Author(s):  
Yijie Yan ◽  
Yue Li ◽  
Chunlei Fan ◽  
Yuening Zhang ◽  
Shibin Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sensitivity And Specificity ◽  
Esophageal Varices ◽  
External Validation ◽  
Bleeding Risk ◽  
Internal Validation ◽  
Portal Venous Phase ◽  
Net Reclassification Improvement ◽  
Cirrhotic Patients ◽  
High Bleeding Risk

Abstract Background & aims: To develop and validate a novel machine learning-based radiomic model (RM) for diagnosing high bleeding risk esophageal varices (HREV) in cirrhosis. Methods: In training cohort, total 218 cirrhotic patients for mild esophageal varices (EV) and 240 for HREV RM were enrolled for training and internal validation. In external validation cohort, 159 and 340 cirrhotic patients were respectively used for mild EV and HREV RM validation. Interesting regions of liver, spleen, and esophagus were labeled on the portal venous-phase enhanced CT images. RM was assessed by area under the receiver operating characteristic curves (AUROC), sensitivity, specificity, calibration and decision curve analysis (DCA). Results: The AUROC of mild EV RM in training and internal validation was 0.943 and 0.732, sensitivity and specificity was 0.863, 0.773 and 0.763, 0.763. The AUROC, sensitivity and specificity was 0.654, 0.773 and 0.632 in external validation. Interestingly, the AUROC of HREV RM in training and internal validation was 0.983 and 0.834, sensitivity and specificity was 0.948, 0.916 and 0.977, 0.969. The AUROC, sensitivity and specificity was 0.736, 0.690 and 0.762 in external validation. Calibration and DCA indicated RM had good performance in clinical practice. Compared with Baveno VI and its expanded criteria, HREV RM had a higher accuracy and net reclassification improvement reached 49.0% and 32.8%. Conclusion: A novel non-invasive RM for diagnosing HREV in cirrhotic patients with highly accuracy was developed. However, this RM still needs to be validated by a multi-center large cohort.

Development of a prediction score to avoid confirmatory testing in patients with suspected primary aldosteronism

2020 ◽  
Author(s):  
Jacopo Burrello ◽  
Martina Amongero ◽  
Fabrizio Buffolo ◽  
Elisa Sconfienza ◽  
Vittorio Forestiero ◽  
...  
Keyword(s):  
Machine Learning ◽  
Primary Aldosteronism ◽  
External Validation ◽  
Plasma Renin ◽  
Organ Damage ◽  
Internal Validation ◽  
Confirmatory Testing ◽  
Diagnostic Models ◽  
Confirmatory Tests ◽  
Diagnostic Work

Abstract Context The diagnostic work-up of primary aldosteronism (PA) includes screening and confirmation steps. Case confirmation is time-consuming, expensive, and there is no consensus on tests and thresholds to be used. Diagnostic algorithms to avoid confirmatory testing may be useful for the management of patients with PA. Objective Development and validation of diagnostic models to confirm or exclude PA diagnosis in patients with a positive screening test. Design, Patients and Setting We evaluated 1,024 patients who underwent confirmatory testing for PA. The diagnostic models were developed in a training cohort (n=522), and then tested on an internal validation cohort (n=174) and on an independent external prospective cohort (n=328). Main outcome measure Different diagnostic models and a 16-point score were developed by machine learning and regression analysis to discriminate patients with a confirmed diagnosis of PA. Results Male sex, antihypertensive medication, plasma renin activity, aldosterone, potassium levels and presence of organ damage were associated with a confirmed diagnosis of PA. Machine learning based models displayed an accuracy of 72.9-83.9%. The Primary Aldosteronism Confirmatory Testing (PACT) score correctly classified 84.1% at training and 83.9% or 81.1% at internal and external validation, respectively. A flow chart employing the PACT score to select patients for confirmatory testing, correctly managed all patients, and resulted in a 22.8% reduction in the number of confirmatory tests. Conclusions The integration of diagnostic modelling algorithms in clinical practice may improve the management of patients with PA by circumventing unnecessary confirmatory testing.

scholarly journals Machine Learning Prediction Model for Acute Renal Failure After Acute Aortic Syndrome Surgery

2022 ◽  
Vol 8 ◽  
Author(s):  
Jinzhang Li ◽  
Ming Gong ◽  
Yashutosh Joshi ◽  
Lizhong Sun ◽  
Lianjun Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Renal Failure ◽  
Prediction Model ◽  
Prediction Models ◽  
External Validation ◽  
Scoring Systems ◽  
Acute Aortic Syndrome ◽  
Internal Validation ◽  
Medical Centers ◽  
Better Than

BackgroundAcute renal failure (ARF) is the most common major complication following cardiac surgery for acute aortic syndrome (AAS) and worsens the postoperative prognosis. Our aim was to establish a machine learning prediction model for ARF occurrence in AAS patients.MethodsWe included AAS patient data from nine medical centers (n = 1,637) and analyzed the incidence of ARF and the risk factors for postoperative ARF. We used data from six medical centers to compare the performance of four machine learning models and performed internal validation to identify AAS patients who developed postoperative ARF. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to compare the performance of the predictive models. We compared the performance of the optimal machine learning prediction model with that of traditional prediction models. Data from three medical centers were used for external validation.ResultsThe eXtreme Gradient Boosting (XGBoost) algorithm performed best in the internal validation process (AUC = 0.82), which was better than both the logistic regression (LR) prediction model (AUC = 0.77, p < 0.001) and the traditional scoring systems. Upon external validation, the XGBoost prediction model (AUC =0.81) also performed better than both the LR prediction model (AUC = 0.75, p = 0.03) and the traditional scoring systems. We created an online application based on the XGBoost prediction model.ConclusionsWe have developed a machine learning model that has better predictive performance than traditional LR prediction models as well as other existing risk scoring systems for postoperative ARF. This model can be utilized to provide early warnings when high-risk patients are found, enabling clinicians to take prompt measures.

scholarly journals Prediction of Masked Hypertension and Masked Uncontrolled Hypertension Using Machine Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Ming-Hui Hung ◽  
Ling-Chieh Shih ◽  
Yu-Ching Wang ◽  
Hsin-Bang Leu ◽  
Po-Hsun Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Characteristics ◽  
Prediction Models ◽  
External Validation ◽  
Uncontrolled Hypertension ◽  
Gradient Boosting ◽  
Masked Hypertension ◽  
Internal Validation ◽  
Hypertensive Patients ◽  
Extreme Gradient Boosting

Objective: This study aimed to develop machine learning-based prediction models to predict masked hypertension and masked uncontrolled hypertension using the clinical characteristics of patients at a single outpatient visit.Methods: Data were derived from two cohorts in Taiwan. The first cohort included 970 hypertensive patients recruited from six medical centers between 2004 and 2005, which were split into a training set (n = 679), a validation set (n = 146), and a test set (n = 145) for model development and internal validation. The second cohort included 416 hypertensive patients recruited from a single medical center between 2012 and 2020, which was used for external validation. We used 33 clinical characteristics as candidate variables to develop models based on logistic regression (LR), random forest (RF), eXtreme Gradient Boosting (XGboost), and artificial neural network (ANN).Results: The four models featured high sensitivity and high negative predictive value (NPV) in internal validation (sensitivity = 0.914–1.000; NPV = 0.853–1.000) and external validation (sensitivity = 0.950–1.000; NPV = 0.875–1.000). The RF, XGboost, and ANN models showed much higher area under the receiver operating characteristic curve (AUC) (0.799–0.851 in internal validation, 0.672–0.837 in external validation) than the LR model. Among the models, the RF model, composed of 6 predictor variables, had the best overall performance in both internal and external validation (AUC = 0.851 and 0.837; sensitivity = 1.000 and 1.000; specificity = 0.609 and 0.580; NPV = 1.000 and 1.000; accuracy = 0.766 and 0.721, respectively).Conclusion: An effective machine learning-based predictive model that requires data from a single clinic visit may help to identify masked hypertension and masked uncontrolled hypertension.

Prediction of Genotype Positivity in Patients with Hypertrophic Cardiomyopathy Using Machine Learning

2021 ◽  
Author(s):  
Lusha W. Liang ◽  
Michael A. Fifer ◽  
Kohei Hasegawa ◽  
Mathew S. Maurer ◽  
Muredach P. Reilly ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Testing ◽  
Hypertrophic Cardiomyopathy ◽  
Predictive Value ◽  
External Validation ◽  
Scoring Systems ◽  
Training Set ◽  
Test Set ◽  
Net Reclassification Improvement ◽  
Mayo Score

Background - Genetic testing can determine family screening strategies and has prognostic and diagnostic value in hypertrophic cardiomyopathy (HCM). However, it can also pose a significant psychosocial burden. Conventional scoring systems offer modest ability to predict genotype positivity. The aim of our study was to develop a novel prediction model for genotype positivity in patients with HCM by applying machine learning (ML) algorithms. Methods - We constructed three ML models using readily available clinical and cardiac imaging data of 102 patients from Columbia University with HCM who had undergone genetic testing (the training set). We validated model performance on 76 patients with HCM from Massachusetts General Hospital (the test set). Within the test set, we compared the area under the receiver operating characteristic curves (AUCs) for the ML models against the AUCs generated by the Toronto HCM Genotype Score ("the Toronto score") and Mayo HCM Genotype Predictor ("the Mayo score") using the Delong test and net reclassification improvement (NRI). Results - Overall, 63 of the 178 patients (35%) were genotype positive. The random forest ML model developed in the training set demonstrated an AUC of 0.92 (95% CI 0.85-0.99) in predicting genotype positivity in the test set, significantly outperforming the Toronto score (AUC 0.77, 95% CI 0.65-0.90, p=0.004, NRI: p<0.001) and the Mayo score (AUC 0.79, 95% CI 0.67-0.92, p=0.01, NRI: p=0.001). The gradient boosted decision tree ML model also achieved significant NRI over the Toronto score (p<0.001) and the Mayo score (p=0.03), with an AUC of 0.87 (95% CI 0.75-0.99). Compared to the Toronto and Mayo scores, all three ML models had higher sensitivity, positive predictive value, and negative predictive value. Conclusions - Our ML models demonstrated a superior ability to predict genotype positivity in patients with HCM compared to conventional scoring systems in an external validation test set.

The velocity of volume increase of esophageal varices has a predictive value of the bleeding risk in cirrhotic patients

1991 ◽  
Vol 13 ◽  
pp. S163 ◽  
Author(s):  
G.P. Rigo ◽  
G. Zanasi ◽  
A. Pirani ◽  
A. Merighi ◽  
N.J. Chahin ◽  
...  
Keyword(s):  
Esophageal Varices ◽  
Predictive Value ◽  
Bleeding Risk ◽  
Volume Increase ◽  
Cirrhotic Patients

scholarly journals Establishing Machine Learning Models to Predict Curative Resection in Early Gastric Cancer with Undifferentiated Histology: Development and Usability Study (Preprint)

2020 ◽  
Author(s):  
Chang Seok Bang ◽  
Ji Yong Ahn ◽  
Jie-Hyun Kim ◽  
Young-Il Kim ◽  
Il Ju Choi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gastric Cancer ◽  
Early Gastric Cancer ◽  
Curative Resection ◽  
External Validation ◽  
Data Set ◽  
Internal Validation ◽  
Size And Shape ◽  
Extreme Gradient Boosting ◽  
Undifferentiated Histology

BACKGROUND Undifferentiated type of early gastric cancer (U-EGC) is included among the expanded indications of endoscopic submucosal dissection (ESD); however, the rate of curative resection remains unsatisfactory. Endoscopists predict the probability of curative resection by considering the size and shape of the lesion and whether ulcers are present or not. The location of the lesion, indicating the likely technical difficulty, is also considered. OBJECTIVE The aim of this study was to establish machine learning (ML) models to better predict the possibility of curative resection in U-EGC prior to ESD. METHODS A nationwide cohort of 2703 U-EGCs treated by ESD or surgery were adopted for the training and internal validation cohorts. Separately, an independent data set of the Korean ESD registry (n=275) and an Asan medical center data set (n=127) treated by ESD were chosen for external validation. Eighteen ML classifiers were selected to establish prediction models of curative resection with the following variables: age; sex; location, size, and shape of the lesion; and whether ulcers were present or not. RESULTS Among the 18 models, the extreme gradient boosting classifier showed the best performance (internal validation accuracy 93.4%, 95% CI 90.4%-96.4%; precision 92.6%, 95% CI 89.5%-95.7%; recall 99.0%, 95% CI 97.8%-99.9%; and F1 score 95.7%, 95% CI 93.3%-98.1%). Attempts at external validation showed substantial accuracy (first external validation 81.5%, 95% CI 76.9%-86.1% and second external validation 89.8%, 95% CI 84.5%-95.1%). Lesion size was the most important feature in each explainable artificial intelligence analysis. CONCLUSIONS We established an ML model capable of accurately predicting the curative resection of U-EGC before ESD by considering the morphological and ecological characteristics of the lesions.

scholarly journals Predicting Prolonged Length of ICU Stay through Machine Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2242
Author(s):  
Jingyi Wu ◽  
Yu Lin ◽  
Pengfei Li ◽  
Yonghua Hu ◽  
Luxia Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
External Validation ◽  
Prediction Performance ◽  
Brier Score ◽  
Support Vector ◽  
Internal Validation ◽  
Prolonged Length ◽  
Saps Ii ◽  
Overall Performance

This study aimed to construct machine learning (ML) models for predicting prolonged length of stay (pLOS) in intensive care units (ICU) among general ICU patients. A multicenter database called eICU (Collaborative Research Database) was used for model derivation and internal validation, and the Medical Information Mart for Intensive Care (MIMIC) III database was used for external validation. We used four different ML methods (random forest, support vector machine, deep learning, and gradient boosting decision tree (GBDT)) to develop prediction models. The prediction performance of the four models were compared with the customized simplified acute physiology score (SAPS) II. The area under the receiver operation characteristic curve (AUROC), area under the precision-recall curve (AUPRC), estimated calibration index (ECI), and Brier score were used to measure performance. In internal validation, the GBDT model achieved the best overall performance (Brier score, 0.164), discrimination (AUROC, 0.742; AUPRC, 0.537), and calibration (ECI, 8.224). In external validation, the GBDT model also achieved the best overall performance (Brier score, 0.166), discrimination (AUROC, 0.747; AUPRC, 0.536), and calibration (ECI, 8.294). External validation showed that the calibration curve of the GBDT model was an optimal fit, and four ML models outperformed the customized SAPS II model. The GBDT-based pLOS-ICU prediction model had the best prediction performance among the five models on both internal and external datasets. Furthermore, it has the potential to assist ICU physicians to identify patients with pLOS-ICU risk and provide appropriate clinical interventions to improve patient outcomes.

Estimating Bleeding Risk in Patients with Cancer-Associated Thrombosis: Evaluation of Existing Risk Scores and Development of a New Risk Score

2021 ◽  
Author(s):  
Maria A. de Winter ◽  
Jannick A. N. Dorresteijn ◽  
Walter Ageno ◽  
Cihan Ay ◽  
Jan Beyer-Westendorf ◽  
...  
Keyword(s):  
Prediction Model ◽  
External Validation ◽  
Predictive Performance ◽  
Bleeding Risk ◽  
Risk Scores ◽  
Cancer Type ◽  
Clinical Predictors ◽  
Internal Validation ◽  
Patients With Cancer ◽  
Cancer Associated Thrombosis

Abstract Background Bleeding risk is highly relevant for treatment decisions in cancer-associated thrombosis (CAT). Several risk scores exist, but have never been validated in patients with CAT and are not recommended for practice. Objectives To compare methods of estimating clinically relevant (major and clinically relevant nonmajor) bleeding risk in patients with CAT: (1) existing risk scores for bleeding in venous thromboembolism, (2) pragmatic classification based on cancer type, and (3) new prediction model. Methods In a posthoc analysis of the Hokusai VTE Cancer study, a randomized trial comparing edoxaban with dalteparin for treatment of CAT, seven bleeding risk scores were externally validated (ACCP-VTE, HAS-BLED, Hokusai, Kuijer, Martinez, RIETE, and VTE-BLEED). The predictive performance of these scores was compared with a pragmatic classification based on cancer type (gastrointestinal; genitourinary; other) and a newly derived competing risk-adjusted prediction model based on clinical predictors for clinically relevant bleeding within 6 months after CAT diagnosis with nonbleeding-related mortality as the competing event (“CAT-BLEED”). Results Data of 1,046 patients (149 events) were analyzed. Predictive performance of existing risk scores was poor to moderate (C-statistics: 0.50–0.57; poor calibration). Internal validation of the pragmatic classification and “CAT-BLEED” showed moderate performance (respective C-statistics: 0.61; 95% confidence interval [CI]: 0.56–0.66, and 0.63; 95% CI 0.58–0.68; good calibration). Conclusion Existing risk scores for bleeding perform poorly after CAT. Pragmatic classification based on cancer type provides marginally better estimates of clinically relevant bleeding risk. Further improvement may be achieved with “CAT-BLEED,” but this requires external validation in practice-based settings and with other DOACs and its clinical usefulness is yet to be demonstrated.

A Novel Radiomics-Platelet Nomogram for the Prediction of Gastroesophageal Varices Needing Treatment in Cirrhotic Patients

2021 ◽  
Author(s):  
Yiken Lin ◽  
Lijuan Li ◽  
Dexin Yu ◽  
Zhuyun Liu ◽  
Shuhong Zhang ◽  
...  
Keyword(s):  
External Validation ◽  
Multivariate Logistic Regression Model ◽  
Noninvasive Method ◽  
Training Set ◽  
Noninvasive Methods ◽  
Internal Validation ◽  
Gastroesophageal Varices ◽  
Portal Phase ◽  
Cirrhotic Patients ◽  
Validation Set

Abstract Background and aimsHighly accurate noninvasive methods for predicting gastroesophageal varices needing treatment (VNT) are desired. Radiomics is a newly emerging technology of image analysis. This study aims to develop and validate a novel noninvasive method based on radiomics for predicting VNT in cirrhosis.MethodsIn this retrospective-prospective study, a total of 245 cirrhotic patients were divided as the training set, internal validation set and external validation set. Radiomics features were extracted from portal-phase computed tomography (CT) images of each patient. A radiomics signature (Rad-score) was constructed with the least absolute shrinkage and selection operator algorithm and 10-folds cross-validation in the training set. Combined with independent risk factors, a radiomics nomogram was built with a multivariate logistic regression model. ResultsThe rad-score, consisting of 14 features from the gastroesophageal region and 5 from the splenic hilum region, was effective for VNT classification. The diagnostic performance was further improved by combining the rad-score with platelet counts, achieving an AUC of 0.987(95% CI, 0.969-1.00), 0.973(95% CI, 0.939-1.00) and 0.947(95% CI, 0.876-1.00) in the training set, internal validation set and external validation set respectively. In efficacy and safety assessment, the radiomics nomogram could spare more than 40% of endoscopic examinations with a low risk of missing VNT (<5%), and no more than 8.3% of unnecessary endoscopic examinations still be performed.ConclusionsIn this study, we developed and validated a novel, diagnostic radiomics-based nomogram which is a reliable and noninvasive method to predict VNT in cirrhotic patients.

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