scholarly journals Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features

2021 ◽  
Vol 11 (6) ◽  
pp. 501
Author(s):  
Simone Schiaffino ◽  
Marina Codari ◽  
Andrea Cozzi ◽  
Domenico Albano ◽  
Marco Alì ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Pulmonary Artery ◽  
Hospital Mortality ◽  
Univariate Analysis ◽  
Mortality Prediction ◽  
Chest Ct ◽  
Multilayer Perceptrons ◽  
Emergency Department Admission ◽  
Pulmonary Artery Diameter

Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann–Whitney U test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset split. Out of 897 patients, the 229 (26%) patients who died during hospitalization had higher median pulmonary artery diameter (29.0 mm) than patients who survived (27.0 mm, p < 0.001) and higher median ascending aortic diameter (36.6 mm versus 34.0 mm, p < 0.001). SVM and MLP best models considered the same ten input features, yielding a 0.747 (precision 0.522, recall 0.800) and 0.844 (precision 0.680, recall 0.567) area under the curve, respectively. In this model integrating clinical and radiological data, pulmonary artery diameter was the third most important predictor after age and parenchymal involvement extent, contributing to reliable in-hospital mortality prediction, highlighting the value of vascular metrics in improving patient stratification.

scholarly journals Mortality Prediction in Cerebral Hemorrhage Patients Using Machine Learning Algorithms in Intensive Care Units

2021 ◽  
Vol 11 ◽  
Author(s):  
Ximing Nie ◽  
Yuan Cai ◽  
Jingyi Liu ◽  
Xiran Liu ◽  
Jiahui Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Random Forest ◽  
Hospital Mortality ◽  
Intensive Care Units ◽  
Cerebral Hemorrhage ◽  
Characteristic Curve ◽  
Learning Algorithms ◽  
Mortality Prediction ◽  
Machine Learning Algorithms

Objectives: This study aims to investigate whether the machine learning algorithms could provide an optimal early mortality prediction method compared with other scoring systems for patients with cerebral hemorrhage in intensive care units in clinical practice.Methods: Between 2008 and 2012, from Intensive Care III (MIMIC-III) database, all cerebral hemorrhage patients monitored with the MetaVision system and admitted to intensive care units were enrolled in this study. The calibration, discrimination, and risk classification of predicted hospital mortality based on machine learning algorithms were assessed. The primary outcome was hospital mortality. Model performance was assessed with accuracy and receiver operating characteristic curve analysis.Results: Of 760 cerebral hemorrhage patients enrolled from MIMIC database [mean age, 68.2 years (SD, ±15.5)], 383 (50.4%) patients died in hospital, and 377 (49.6%) patients survived. The area under the receiver operating characteristic curve (AUC) of six machine learning algorithms was 0.600 (nearest neighbors), 0.617 (decision tree), 0.655 (neural net), 0.671(AdaBoost), 0.819 (random forest), and 0.725 (gcForest). The AUC was 0.423 for Acute Physiology and Chronic Health Evaluation II score. The random forest had the highest specificity and accuracy, as well as the greatest AUC, showing the best ability to predict in-hospital mortality.Conclusions: Compared with conventional scoring system and the other five machine learning algorithms in this study, random forest algorithm had better performance in predicting in-hospital mortality for cerebral hemorrhage patients in intensive care units, and thus further research should be conducted on random forest algorithm.

scholarly journals Pectoralis Muscle Mass on Chest CT at Admission Predicts Prognosis in Patients with Pneumonia

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ryohsuke Yokosuka ◽  
Ryosuke Imai ◽  
Shosei Ro ◽  
Manabu Murakami ◽  
Kohei Okafuji ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Muscle Mass ◽  
Odds Ratio ◽  
Univariate Analysis ◽  
Muscle Thickness ◽  
Chest Ct ◽  
Pectoralis Major ◽  
Pectoralis Muscle ◽  
Pectoralis Minor Muscle ◽  
Area And Volume

Background and Objectives. The concept of sarcopenia has been attracting attention in recent years, but its association with in-hospital mortality of patients with pneumonia is still unclear. Therefore, we investigated the relationship between pectoralis muscle mass on chest computed tomography (CT) and in-hospital mortality in patients with pneumonia. Methods. A retrospective cohort study was performed in patients aged 18 years or older with pneumonia who underwent chest CT within 24 hours of admission between April 2014 and March 2019. We measured the thickness, area, and volume of the pectoralis major and minor muscles at the level of the aortic arch. Factors associated with mortality were examined using logistic regression analysis. Results. A total of 483 patients (mean age 77 ± 14 years, 300 men (62%)) were included, and fifty-one patients (11%) died during admission. In univariate analysis, decreased thickness, area, and volume of the pectoralis major and minor muscles were associated with higher in-hospital mortality. Multivariate analysis with adjustment for age, gender, serum albumin, and A-DROP revealed that thinner pectoralis major and minor muscles were independent factors of poor prognosis (odds ratio: 0.878, 95% confidence interval (CI): 0.783–0.985, P = 0.026 and odds ratio: 0.842, 95% CI: 0.733–0.968, P = 0.016 , respectively). Approximately 25% of the patients died when the pectoralis minor muscle thickness was 5 mm or less, and no patients died when it was 15 mm or more. Conclusion. The pectoralis muscle mass may be an independent prognostic factor in hospitalized patients with pneumonia.

scholarly journals Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19

2020 ◽  
pp. bmjspcare-2020-002602 ◽  
Author(s):  
Prathamesh Parchure ◽  
Himanshu Joshi ◽  
Kavita Dharmarajan ◽  
Robert Freeman ◽  
David L Reich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hospital Mortality ◽  
Characteristic Curve ◽  
Laboratory Data ◽  
Mortality Prediction ◽  
Outcome Variable ◽  
Electronic Health Record Data ◽  
Test Set ◽  
Electronic Health ◽  
Near Term

ObjectivesTo develop and validate a model for prediction of near-term in-hospital mortality among patients with COVID-19 by application of a machine learning (ML) algorithm on time-series inpatient data from electronic health records.MethodsA cohort comprised of 567 patients with COVID-19 at a large acute care healthcare system between 10 February 2020 and 7 April 2020 observed until either death or discharge. Random forest (RF) model was developed on randomly drawn 70% of the cohort (training set) and its performance was evaluated on the rest of 30% (the test set). The outcome variable was in-hospital mortality within 20–84 hours from the time of prediction. Input features included patients’ vital signs, laboratory data and ECG results.ResultsPatients had a median age of 60.2 years (IQR 26.2 years); 54.1% were men. In-hospital mortality rate was 17.0% and overall median time to death was 6.5 days (range 1.3–23.0 days). In the test set, the RF classifier yielded a sensitivity of 87.8% (95% CI: 78.2% to 94.3%), specificity of 60.6% (95% CI: 55.2% to 65.8%), accuracy of 65.5% (95% CI: 60.7% to 70.0%), area under the receiver operating characteristic curve of 85.5% (95% CI: 80.8% to 90.2%) and area under the precision recall curve of 64.4% (95% CI: 53.5% to 75.3%).ConclusionsOur ML-based approach can be used to analyse electronic health record data and reliably predict near-term mortality prediction. Using such a model in hospitals could help improve care, thereby better aligning clinical decisions with prognosis in critically ill patients with COVID-19.

Volume Measurement in the Diagnosis of Mounier Kuhn Syndrome and an Unknown Accompanying Pathology: Pulmonary Artery Enlargement

2019 ◽  
Vol 15 (8) ◽  
pp. 796-801
Author(s):  
Fatma Aktaş ◽  
Turan Aktaş
Keyword(s):  
Computed Tomography ◽  
Pulmonary Artery ◽  
Volume Measurement ◽  
Ct Scans ◽  
Control Group ◽  
Volume Calculation ◽  
Rare Congenital Anomaly ◽  
Tracheal Diverticulum ◽  
Significant Difference ◽  
Pulmonary Artery Diameter

Background: Mounier Kuhn Syndrome (MKS) is a rare congenital anomaly characterized by abnormal dilatation of the trachea and main bronchi. The aim of this study is to discuss tracheal volume measurement in MKS, and the pathologies accompanying MKS, especially pulmonary artery enlargement. Materials and Methods: 38 patients, 18 of whom were diagnosed with MKS and 20 as control group, were included in the study. Trachea volume and pulmonary artery diameter were measured through thorax-computed tomography (CT) images of the patients. Accompanying pathologies were recorded. Results: In the measurements done through the CT scans, the trachea volume was found to be 25.45 cm3 in the control group and 44.17 cm3 in the patient group. The most frequent accompanying pathologies were tracheal diverticulum, bronchiectasis and pulmonary artery enlargement. Conclusion: In patients with MKS, there is a significant difference in volume calculation as in trachea diameter. Though bronchiectasis and tracheal diverticulum are known as pathologies most frequently accompanying MKS, to the knowledge of the researchers, pulmonary artery enlargement due to the increase in pulmonary truncus diameter was first emphasized in this article.

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