Developing Machine Learning Algorithms to Predict Pulmonary Complications After Emergency Gastrointestinal Surgery.
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ABSTRACT:
SUBMITTER: Xue Q
PROVIDER: S-EPMC8365303 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundPulmonary complications, including infections, are highly prevalent in patients after hematopoietic cell transplantation with chronic graft-vs-host disease. These comorbid diseases can make the diagnosis of early lung graft-vs-host disease (bronchiolitis obliterans syndrome) challenging. A quantitative method to differentiate among these pulmonary diseases can address diagnostic challenges and facilitate earlier and more targeted therapy.Study design and methodsWe conducted a single-center study of 66 patients with CT chest scans analyzed with a quantitative imaging tool known as parametric response mapping. Parametric response mapping results were correlated with pulmonary function tests and clinical characteristics. Five parametric response mapping metrics were applied to K-means clustering and support vector machine models to distinguish among posttransplantation lung complications solely from quantitative output.ResultsCompared with parametric response mapping, spirometry showed a moderate correlation with radiographic air trapping, and total lung capacity and residual volume showed a strong correlation with radiographic lung volumes. K-means clustering analysis distinguished four unique clusters. Clusters 2 and 3 represented obstructive physiology (encompassing 81% of patients with bronchiolitis obliterans syndrome) in increasing severity (percentage air trapping 15.6% and 43.0%, respectively). Cluster 1 was dominated by normal lung, and cluster 4 was characterized by patients with parenchymal opacities. A support vector machine algorithm differentiated bronchiolitis obliterans syndrome with a specificity of 88%, sensitivity of 83%, accuracy of 86%, and an area under the receiver operating characteristic curve of 0.85.InterpretationOur machine learning models offer a quantitative approach for the identification of bronchiolitis obliterans syndrome vs other lung diseases, including late pulmonary complications after hematopoietic cell transplantation.
| S-EPMC8097633 | biostudies-literature
Project description:BackgroundCarpal tunnel syndrome (CTS) is extremely common and typically treated with carpal tunnel decompression (CTD). Although generally an effective treatment, up to 25% of patients do not experience meaningful benefit. Given the prevalence, this amounts to considerable morbidity and cost without return. Being able to reliably predict which patients would benefit from CTD preoperatively would support more patient-centered and value-based care.MethodsWe used registry data from 1916 consecutive patients undergoing CTD for CTS at a regional hand center between 2010 and 2019. Improvement was defined as change exceeding the respective QuickDASH subscale's minimal important change estimate. Predictors included a range of clinical, demographic and patient-reported variables. Data were split into training (75%) and test (25%) sets. A range of machine learning algorithms was developed using the training data and evaluated with the test data. We also used a machine learning technique called chi-squared automatic interaction detection to develop flowcharts that could help clinicians and patients to understand the chances of a patient improving with surgery.ResultsThe top performing models predicted functional and symptomatic improvement with accuracies of 0.718 (95% confidence interval 0.660, 0.771) and 0.759 (95% confidence interval 0.708, 0.810), respectively. The chi-squared automatic interaction detection flowcharts could provide valuable clinical insights from as little as two preoperative questions.ConclusionsPatient-reported outcome measures and machine learning can support patient-centered and value-based healthcare. Our algorithms can be used for expectation management and to rationalize treatment risks and costs associated with CTD.
| S-EPMC9015194 | biostudies-literature
Project description:BackgroundThe fluid balance associated with a better outcome following emergency surgery is unknown. The aim of this study was to explore the association of the perioperative fluid balance and postoperative complications during emergency gastrointestinal surgery.MethodsWe retrospectively included patients undergoing emergency surgery for gastrointestinal obstruction or perforation. A perioperative fluid balance of 2.5 L divided the cohort in a conservative and liberal group. Outcome was Clavien-Dindo graded complications registered 90 days postoperatively. We used logistic regression adjusted for age, sex, American Society of Anesthesiologists' classification, use of epidural analgesia, use of vasopressor, type of surgery, intraabdominal pathology, and hospital. Predicted risk of complications was demonstrated on a continuous scale of the fluid balance.ResultsWe included 342 patients operated between July 2014 and July 2015 from three centers. The perioperative fluid balance was 1.6 L IQR [1.0 to 2.0] in the conservative vs. 3.6 L IQR [3.0 to 5.3] in the liberal group. Odds ratio of overall 2.6 (95% CI 1.5 to 4.4), p < 0.001, and cardiopulmonary complications 3.2 (95% CI 1.9 to 5.7), p < 0.001, were increased in the liberal group. A perioperative fluid balance of 0-2 L was associated with minimal risk of cardiopulmonary complications compared to 1.5-3.5 L for renal complications.ConclusionWe found a perioperative fluid balance above 2.5 L to be associated with an increased risk of overall and cardiopulmonary complications following emergency surgery for gastrointestinal obstruction or perforation. A perioperative fluid balance of 0-2 L was associated with the lowest risk of cardiopulmonary complications and 1.5-3.5 L for renal complications.
| S-EPMC8862386 | biostudies-literature
Project description:BackgroundPulmonary embolisms (PE) are life-threatening medical events, and early identification of patients experiencing a PE is essential to optimizing patient outcomes. Current tools for risk stratification of PE patients are limited and unable to predict PE events before their occurrence.ObjectiveWe developed a machine learning algorithm (MLA) designed to identify patients at risk of PE before the clinical detection of onset in an inpatient population.Materials and methodsThree machine learning (ML) models were developed on electronic health record data from 63,798 medical and surgical inpatients in a large US medical center. These models included logistic regression, neural network, and gradient boosted tree (XGBoost) models. All models used only routinely collected demographic, clinical, and laboratory information as inputs. All were evaluated for their ability to predict PE at the first time patient vital signs and lab measures required for the MLA to run were available. Performance was assessed with regard to the area under the receiver operating characteristic (AUROC), sensitivity, and specificity.ResultsThe model trained using XGBoost demonstrated the strongest performance for predicting PEs. The XGBoost model obtained an AUROC of 0.85, a sensitivity of 81%, and a specificity of 70%. The neural network and logistic regression models obtained AUROCs of 0.74 and 0.67, sensitivity of 81% and 81%, and specificity of 44% and 35%, respectively.ConclusionsThis algorithm may improve patient outcomes through earlier recognition and prediction of PE, enabling earlier diagnosis and treatment of PE.
| S-EPMC9052977 | biostudies-literature
Project description:A growing literature is utilizing machine learning methods to develop psychopathology risk algorithms that can be used to inform preventive intervention. However, efforts to develop algorithms for internalizing disorder onset have been limited. The goal of this study was to utilize prospective survey data and ensemble machine learning to develop algorithms predicting adult onset internalizing disorders. The data were from Waves 1-2 of the National Epidemiological Survey on Alcohol and Related Conditions (n = 34,653). Outcomes were incident occurrence of DSM-IV generalized anxiety, panic, social phobia, depression, and mania between Waves 1-2. In total, 213 risk factors (features) were operationalized based on their presence/occurrence at the time of or before Wave 1. For each of the five internalizing disorder outcomes, super learning was used to generate a composite algorithm from several linear and non-linear classifiers (e.g., random forests, k-nearest neighbors). AUCs achieved by the cross-validated super learner ensembles were in the range of 0.76 (depression) to 0.83 (mania), and were higher than AUCs achieved by the individual algorithms. Individuals in the top 10% of super learner predicted risk accounted for 37.97% (depression) to 53.39% (social anxiety) of all incident cases. Thus, the algorithms achieved acceptable-to-excellent prediction accuracy with a high concentration of incident cases observed among individuals predicted to be highest risk. In parallel with the development of effective preventive interventions, further validation, expansion, and dissemination of algorithms predicting internalizing disorder onset/trajectory could be of great value.
| S-EPMC7027595 | biostudies-literature
Project description:The quality of treatment and prognosis after pediatric congenital heart surgery remains unsatisfactory. A reliable prediction model for postoperative complications of congenital heart surgery patients is essential to enable prompt initiation of therapy and improve the quality of prognosis. Here, we develop an interpretable machine-learning-based model that integrates patient demographics, surgery-specific features and intraoperative blood pressure data for accurately predicting complications after pediatric congenital heart surgery. We used blood pressure variability and the k-means algorithm combined with a smoothed formulation of dynamic time wrapping to extract features from time-series data. In addition, SHAP framework was used to provide explanations of the prediction. Our model achieved the best performance both in binary and multi-label classification compared with other consensus-based risk models. In addition, this explainable model explains why a prediction was made to help improve the clinical understanding of complication risk and generate actionable knowledge in practice. The combination of model performance and interpretability is easy for clinicians to trust and provide insight into how they should respond before the condition worsens after pediatric congenital heart surgery.
| S-EPMC8390484 | biostudies-literature
Project description:BackgroundIn this paper, we examine whether machine learning and deep learning can be used to predict difficult airway intubation in patients undergoing thyroid surgery.MethodsWe used 10 machine learning and deep learning algorithms to establish a corresponding model through a training group, and then verify the results in a test group. We used R for the statistical analysis and constructed the machine learning prediction model in Python.ResultsThe top 5 weighting factors for difficult airways identified by the average algorithm in machine learning were age, sex, weight, height, and BMI. In the training group, the AUC values and accuracy and the Gradient Boosting precision were 0.932, 0.929, and 100%, respectively. As for the modeled effects of predicting difficult airways in test groups, among the models constructed by the 10 algorithms, the three algorithms with the highest AUC values were Gradient Boosting, CNN, and LGBM, with values of 0.848, 0.836, and 0.812, respectively; In addition, among the algorithms, Gradient Boosting had the highest accuracy with a value of 0.913; Additionally, among the algorithms, the Gradient Boosting algorithm had the highest precision with a value of 100%.ConclusionAccording to our results, Gradient Boosting performed best overall, with an AUC >0.8, an accuracy >90%, and a precision of 100%. Besides, the top 5 weighting factors identified by the average algorithm in machine learning for difficult airways were age, sex, weight, height, and BMI.
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Project description:BackgroundMachine learning (ML) has seen an increase in application, and is an important element of a digital evolution. The role of ML within upper gastrointestinal surgery for malignancies has not been evaluated properly in the literature. Therefore, this systematic review aims to provide a comprehensive overview of ML applications within upper gastrointestinal surgery for malignancies.MethodsA systematic search was performed in PubMed, EMBASE, Cochrane, and Web of Science. Studies were only included when they described machine learning in upper gastrointestinal surgery for malignancies. The Cochrane risk-of-bias tool was used to determine the methodological quality of studies. The accuracy and area under the curve were evaluated, representing the predictive performances of ML models.ResultsFrom a total of 1821 articles, 27 studies met the inclusion criteria. Most studies received a moderate risk-of-bias score. The majority of these studies focused on neural networks (n = 9), multiple machine learning (n = 8), and random forests (n = 3). Remaining studies involved radiomics (n = 3), support vector machines (n = 3), and decision trees (n = 1). Purposes of ML included predominantly prediction of metastasis, detection of risk factors, prediction of survival, and prediction of postoperative complications. Other purposes were predictions of TNM staging, chemotherapy response, tumor resectability, and optimal therapy.ConclusionsMachine Learning algorithms seem to contribute to the prediction of postoperative complications and the course of disease after upper gastrointestinal surgery for malignancies. However, due to the retrospective character of ML studies, these results require trials or prospective studies to validate this application of ML.
| S-EPMC9839827 | biostudies-literature
Project description:PurposeSeveral biomarkers of response to immune checkpoint inhibitors (ICI) show potential but are not yet scalable to the clinic. We developed a pipeline that integrates deep learning on histology specimens with clinical data to predict ICI response in advanced melanoma.Experimental designWe used a training cohort from New York University (New York, NY) and a validation cohort from Vanderbilt University (Nashville, TN). We built a multivariable classifier that integrates neural network predictions with clinical data. A ROC curve was generated and the optimal threshold was used to stratify patients as high versus low risk for progression. Kaplan-Meier curves compared progression-free survival (PFS) between the groups. The classifier was validated on two slide scanners (Aperio AT2 and Leica SCN400).ResultsThe multivariable classifier predicted response with AUC 0.800 on images from the Aperio AT2 and AUC 0.805 on images from the Leica SCN400. The classifier accurately stratified patients into high versus low risk for disease progression. Vanderbilt patients classified as high risk for progression had significantly worse PFS than those classified as low risk (P = 0.02 for the Aperio AT2; P = 0.03 for the Leica SCN400).ConclusionsHistology slides and patients' clinicodemographic characteristics are readily available through standard of care and have the potential to predict ICI treatment outcomes. With prospective validation, we believe our approach has potential for integration into clinical practice.
| S-EPMC7785656 | biostudies-literature
Project description:ObjectivesPostoperative major bleeding is a common problem in patients undergoing cardiac surgery and is associated with poor outcomes. We evaluated the performance of machine learning (ML) methods to predict postoperative major bleeding.MethodsA total of 1,045 patients who underwent isolated coronary artery bypass graft surgery (CABG) were enrolled. Their datasets were assigned randomly to training (70%) or a testing set (30%). The primary outcome was major bleeding defined as the universal definition of perioperative bleeding (UDPB) classes 3-4. We constructed a reference logistic regression (LR) model using known predictors. We also developed several modern ML algorithms. In the test set, we compared the area under the receiver operating characteristic curves (AUCs) of these ML algorithms with the reference LR model results, and the TRUST and WILL-BLEED risk score. Calibration analysis was undertaken using the calibration belt method.ResultsThe prevalence of postoperative major bleeding was 7.1% (74/1,045). For major bleeds, the conditional inference random forest (CIRF) model showed the highest AUC [0.831 (0.732-0.930)], and the stochastic gradient boosting (SGBT) and random forest models demonstrated the next best results [0.820 (0.742-0.899) and 0.810 (0.719-0.902)]. The AUCs of all ML models were higher than [0.629 (0.517-0.641) and 0.557 (0.449-0.665)], as achieved by TRUST and WILL-BLEED, respectively.ConclusionML methods successfully predicted major bleeding after cardiac surgery, with greater performance compared with previous scoring models. Modern ML models may enhance the identification of high-risk major bleeding subpopulations.
| S-EPMC9366116 | biostudies-literature