BMC Medical Informatics and Decision Making最新文献

{"title":"Sharing reliable information worldwide: healthcare strategies based on artificial intelligence need external validation. Position paper.","authors":"F Pennestrì, F Cabitza, N Picerno, G Banfi","doi":"10.1186/s12911-025-02883-2","DOIUrl":"10.1186/s12911-025-02883-2","url":null,"abstract":"<p><p>Training machine learning models using data from severe COVID-19 patients admitted to a central hospital, where entire wards are specifically dedicated to COVID-19, may yield predictions that differ significantly from those generated using data collected from patients admitted to a high-volume specialized hospital for orthopedic surgery, where COVID-19 is only a secondary diagnosis. This disparity arises despite the two hospitals being geographically close (within20 kilometers). While machine learning can facilitate rapid public health responses, rigorous external validation and continuous monitoring are essential to ensure reliability and safety.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"56"},"PeriodicalIF":3.3,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11796012/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143187771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decision experiences in joint replacement surgery for patients with haemophilic arthritis: A qualitative study.","authors":"YaNan Kan, YunChun Bao, Fuying Ye","doi":"10.1186/s12911-025-02901-3","DOIUrl":"10.1186/s12911-025-02901-3","url":null,"abstract":"<p><strong>Background: </strong>Patients with end-stage haemophilic arthritis (HA) are often hesitant about joint replacement surgery, yet little is known about the decision experiences faced by these patients. The aim of this study was to better understand the experiences faced by patients with HA when making decisions about joint replacement surgery and to provide a reference for health care professionals in the development of decision-making aids.</p><p><strong>Methods: </strong>Fifteen HA patients who were candidates for joint replacement surgery at a tertiary and first-class hospital in Zhejiang Province were interviewed using a semistructured in-depth interview from January to December 2023. Colaizzi's seven-step method was used to analyse the data and refine the themes.</p><p><strong>Results: </strong>The decision experience for patients with HA regarding joint replacement surgery can be summarized into four themes: decision information conflict, decision support conflict, self-perceived conflict, and self-developmental conflict.</p><p><strong>Conclusion: </strong>Patients with HA face numerous decision conflicts. Health care professionals should develop joint replacement surgery decision-making aids suitable for patients with HA as soon as possible to reduce the decision conflict.</p><p><strong>Trial registration: </strong>The study received approval from the Ethics Committee of Zhejiang Provincial Hospital of Traditional Chinese Medicine, China (registration date: September 22, 2023; registration number: 2023-KLS-294-01).</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"55"},"PeriodicalIF":3.3,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11792258/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143187665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing clinical prognostic models to predict graft survival after renal transplantation: comparison of statistical and machine learning models.","authors":"Getahun Mulugeta, Temesgen Zewotir, Awoke Seyoum Tegegne, Mahteme Bekele Muleta, Leja Hamza Juhar","doi":"10.1186/s12911-025-02906-y","DOIUrl":"10.1186/s12911-025-02906-y","url":null,"abstract":"<p><strong>Introduction: </strong>Renal transplantation is a critical treatment for end-stage renal disease, but graft failure remains a significant concern. Accurate prediction of graft survival is crucial to identify high-risk patients. This study aimed to develop prognostic models for predicting renal graft survival and compare the performance of statistical and machine learning models.</p><p><strong>Methodology: </strong>The study utilized data from 278 renal transplant recipients at the Ethiopian National Kidney Transplantation Center between September 2015 and February 2022. To address the class imbalance of the data, SMOTE resampling was applied. Various models were evaluated, including Standard and penalized Cox models, Random Survival Forest, and Stochastic Gradient Boosting. Prognostic predictors were selected based on statistical significance and variable importance.</p><p><strong>Results: </strong>The median graft survival time was 33 months, and the mean hazard of graft failure was 0.0755. The 3-month, 1-year, and 3-year graft survival rates were found to be 0.979, 0.953, and 0.911, respectively. The Stochastic Gradient Boosting (SGB) model demonstrated the best discrimination and calibration performance, with a C-index of 0.943 and a Brier score of 0.000351. The Ridge-based Cox model closely followed the SGB model's prediction performance with better interpretability. The key prognostic predictors of graft survival included an episode of acute and chronic rejections, post-transplant urological complications, post-transplant nonadherence, blood urea nitrogen level, post-transplant regular exercise, and marital status.</p><p><strong>Conclusions: </strong>The Stochastic Gradient Boosting model demonstrated the highest predictive performance, while the Ridge-Cox model offered better interpretability with a comparable performance. Clinicians should consider the trade-off between prediction accuracy and interpretability when selecting a model. Incorporating these findings into the clinical practice can improve risk stratification and personalized management strategies for kidney transplant recipients.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"54"},"PeriodicalIF":3.3,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11792663/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143122284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning.","authors":"Xiyu Cao, Jianli Ma, Xiaoyi He, Yufei Liu, Yang Yang, Yaqi Wang, Chuantao Zhang","doi":"10.1186/s12911-025-02885-0","DOIUrl":"10.1186/s12911-025-02885-0","url":null,"abstract":"<p><strong>Background: </strong>Respiratory diseases and Cardiovascular Diseases (CVD) often coexist, with airflow obstruction (AO) severity closely linked to CVD incidence and mortality. As both conditions rise, early identification and intervention in risk populations are crucial. However, current CVD risk models inadequately consider AO as an independent risk factor. Therefore, developing an accurate risk prediction model can help identify and intervene early.</p><p><strong>Methods: </strong>This study used the National Health and Nutrition Examination Survey (NHANES) III (1988-1994) and NHANES 2007-2012 datasets. Inclusion criteria were participants aged over 40 with complete AO and CVD data; exclusions were those with missing key data. Analysis included 12 variables: age, gender, race, PIR, education, smoking, alcohol, BMI, hyperlipidemia, hypertension, diabetes, and AO. Logistic regression analyzed the association between AO and CVD, with sensitivity and subgroup analyses. Six ML models predicted CVD risk for the general population, using AO as a predictor. RandomizedSearchCV with 5-fold cross-validation was used for hyperparameter optimization. Models were evaluated by AUC, accuracy, precision, recall, F1 score, and Brier score, with the SHapley Additive exPlanations (SHAP) enhancing explainability. A separate ML model was built for the subpopulation with AO, evaluated similarly.</p><p><strong>Results: </strong>The cross-sectional analysis showed that there was a significant positive correlation between AO occurrence and CVD prevalence, indicating that AO is an important risk factor for CVD (all P < 0.05). For the general population, the XGBoost model was selected as the optimal model for predicting CVD risk (AUC = 0.7508, AP = 0.3186). The top three features in terms of importance were age, hypertension, and PIR. For the subpopulation with airflow obstruction, the XGBoost model was also selected as the optimal model for predicting CVD risk (AUC = 0.6645, AP = 0.3545). SHAP shows that education level has the greatest impact on predicting CVD risk, followed by gender and race.</p><p><strong>Conclusion: </strong>AO correlates positively with CVD. Age, hypertension, PIR affect CVD risk most in general. For AO patients, education, gender, ethnicity are key CVD risk factors.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"50"},"PeriodicalIF":3.3,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11792416/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143122292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine learning-based model for predicting paroxysmal and persistent atrial fibrillation based on EHR.","authors":"Yuqi Zhang, Sijin Li, Peibiao Mai, Yanqi Yang, Niansang Luo, Chao Tong, Kuan Zeng, Kun Zhang","doi":"10.1186/s12911-025-02880-5","DOIUrl":"10.1186/s12911-025-02880-5","url":null,"abstract":"<p><strong>Background: </strong>There is no effective way to accurately predict paroxysmal and persistent atrial fibrillation (AF) subtypes unless electrocardiogram (ECG) observation is obtained. We aim to develop a predictive model using a machine learning algorithm for identification of paroxysmal and persistent AF, and investigate the influencing factors.</p><p><strong>Methods: </strong>We collected demographic data, medication use, serological indicators, and baseline cardiac ultrasound data of all included subjects, totaling 50 variables. The diagnosis of AF subtypes is confirmed by ECG observation for at least more than 7 days. Variable selection was performed by spearman correlation analysis, recursive feature elimination, and least absolute shrinkage and selection operator regression. We built a prediction model for AF using three machine learning methods. Finally, the significance of each variable was analyzed by Shapley additive explanations method.</p><p><strong>Results: </strong>After screening, we found the optimal variable set consisting of 10 variables. The model we built achieved good predictive performance (AUC = 0.870, 95%CI 0.858 to 0.882), and had specificity of 0.851 (95%CI 0.844 to 0.858) and sensitivity of 0.716 (95%CI 0.676 to 0.755). Good predictive performance was stably achieved in different age subgroups and different gender subgroups. LA and NT-proBNP were the two most important variables for predicting paroxysmal and persistent AF in all models, except for the female subgroup aged less than 60 years.</p><p><strong>Conclusions: </strong>Our model makes it possible to predict paroxysmal and persistent AF based on baseline data at admission. Early and individualized intervention strategies based on our model may help to improve clinical outcomes in AF patients.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"51"},"PeriodicalIF":3.3,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11792530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143122167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accounting for racial bias and social determinants of health in a model of hypertension control.","authors":"Yang Hu, Nicholas Cordella, Rebecca G Mishuris, Ioannis Ch Paschalidis","doi":"10.1186/s12911-025-02873-4","DOIUrl":"10.1186/s12911-025-02873-4","url":null,"abstract":"<p><strong>Background: </strong>Hypertension control remains a critical problem and most of the existing literature views it from a clinical perspective, overlooking the role of sociodemographic factors. This study aims to identify patients with not well-controlled hypertension using readily available demographic and socioeconomic features and elucidate important predictive variables.</p><p><strong>Methods: </strong>In this retrospective cohort study, records from 1/1/2012 to 1/1/2020 at the Boston Medical Center were used. Patients with either a hypertension diagnosis or related records (≥ 130 mmHg systolic or ≥ 90 mmHg diastolic, n = 164,041) were selected. Models were developed to predict which patients had uncontrolled hypertension defined as systolic blood pressure (SBP) records exceeding 160 mmHg.</p><p><strong>Results: </strong>The predictive model of high SBP reached an Area Under the Receiver Operating Characteristic Curve of 74.49% ± 0.23%. Age, race, Social Determinants of Health (SDoH), mental health, and cigarette use were predictive of high SBP. Being Black or having critical social needs led to higher probability of uncontrolled SBP. To mitigate model bias and elucidate differences in predictive variables, two separate models were trained for Black and White patients. Black patients face a 4.7 <math><mo>×</mo></math> higher False Positive Rate (FPR) and a 0.58 <math><mo>×</mo></math> lower False Negative Rate (FNR) compared to White patients. Decision threshold differentiation was implemented to equalize FNR. Race-specific models revealed different sets of social variables predicting high SBP, with Black patients being affected by structural barriers (e.g., food and transportation) and White patients by personal and demographic factors (e.g., marital status).</p><p><strong>Conclusions: </strong>Models using non-clinical factors can predict which patients exhibit poorly controlled hypertension. Racial and SDoH variables are significant predictors but lead to biased predictive models. Race-specific models are not sufficient to resolve such biases and require further decision threshold tuning. A host of structural socioeconomic factors are identified to be targeted to reduce disparities in hypertension control.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"53"},"PeriodicalIF":3.3,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11792567/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143122280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Safety and accuracy of digitally supported primary and secondary urgent care telephone triage in England: an observational study using routine data.","authors":"Vanashree Sexton, Catherine Grimley, Jeremy Dale, Helen Atherton, Gary Abel","doi":"10.1186/s12911-025-02888-x","DOIUrl":"10.1186/s12911-025-02888-x","url":null,"abstract":"<p><strong>Background: </strong>England's urgent care telephone triage system comprises non-clinician-led primary triage (NHS111) assessment followed, for approximately 50% patients, by clinician-led secondary triage. Digital decision support is utilised by both. We explore the system's safety and accuracy relative to patients' use of emergency departments (EDs) and in-patient care in the subsequent 24 h.</p><p><strong>Methods: </strong>Descriptive analyses were used to investigate outcomes of 98,946 calls that underwent primary and secondary triage. We investigated sensitivity (safety) and specificity (efficiency/accuracy) in relation to subsequent ED attendance and in-patient hospital admission. Mixed effects regression models were used to explore potential under-estimation of clinical risk (under-triage).</p><p><strong>Results: </strong>Sensitivity was greater in primary triage, whilst specificity was greater in secondary triage. The positive predictive value for attending ED after being assigned a triage urgency level of within 2 h was 46.0% for secondary triage compared to 20.7% for primary triage; for inpatient admission it was 18.0% and 9.2% respectively. 1.5% (n = 1468) patients triaged to same-day or less urgent care at secondary triage were subsequently admitted for in-patient care. In relation to in-patient admission within 24 h, there were greater odds of potential under-triage for calls made between midnight and 6am, and for shorter duration calls, respectively OR = 1.71; CI:1.32-2.21 and OR: 1.66, CI: 1.30-2.11. The service provider (e.g., service provider 2, OR = 5.61; CI:3.36-9.36) and individual clinician (OR covering the 95% midrange = 16.15) conducting triage were the characteristics most greatly associated with this potential under-triage; p < 0.001 for all.</p><p><strong>Conclusions: </strong>Clinician-led urgent care triage is more accurate in identifying the likelihood of a need for ED or in-patient care than non-clinician triage. Non-clinician primary triage is risk averse, reflected in its high sensitivity but low specificity. Service and clinician characteristics associated with potential under-triage need further investigation to inform ways of improving the safety and effectiveness of urgent care telephone triage.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"52"},"PeriodicalIF":3.3,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11792721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143122287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing hypoglycaemia prediction in type 1 diabetes with Ensemble Machine Learning modeling.","authors":"Daphne N Katsarou, Eleni I Georga, Maria A Christou, Panagiota A Christou, Stelios Tigas, Costas Papaloukas, Dimitrios I Fotiadis","doi":"10.1186/s12911-025-02867-2","DOIUrl":"10.1186/s12911-025-02867-2","url":null,"abstract":"<p><strong>Background: </strong>Type 1 diabetes (T1D) is a chronic endocrine disorder characterized by high blood glucose levels, impacting millions of people globally. Its management requires intensive insulin therapy, frequent blood glucose monitoring, and lifestyle adjustments. The accurate prediction of the short-term course of glucose levels in the subcutaneous space in T1D people, as measured by a continuous glucose monitoring (CGM) system, is essential for improving glucose control by avoiding harmful hypoglycaemic and hyperglycaemic glucose swings, facilitating precise insulin management and individualized care and, in turn, minimizing long-term vascular complications.</p><p><strong>Methods: </strong>In this study, we propose an ensemble univariate short-term predictive model of the subcutaneous glucose concentration in T1D targeting at improving its error in the hypoglycaemic region. As such, the underlying basis functions are selected to minimize the percentage of erroneous predictions (EP) in the hypoglycaemic region, with EP being evaluated with continuous glucose error grid analysis (CG-EGA). The dataset comprises 29 individuals with T1D, who were monitored for 2 to 4 weeks during the GlucoseML prospective observational clinical study.</p><p><strong>Results: </strong>Among six different basis models (i.e., linear regression (LR), automatic relevance determination (ARD), support vector regression (SVR), Gaussian process regression (GPR), eXtreme gradient boosting (XGBoost), and long short-term memory (LSTM)), XGBoost and SVR showed a dominant performance in the hypoglycaemic region and were selected as the constituent basis models of the ensemble model. The results indicate that the ensemble model significantly reduces the percentage of EP in the hypoglycaemic region for a 30 min prediction horizon to 19% as compared with its individual basis models (i.e., XGBoost and SVR), whilst its errors over the entire glucose range (hypoglycaemia, euglycaemia, and hyperglycaemia) are similar to those of the basis models.</p><p><strong>Conclusions: </strong>The consideration of the performance of the basis functions in the hypoglycaemic region during the construction of the ensemble model contributes to enhancing their joint performance in that specific area. This could lead to more precise insulin management and a reduced risk of short-term hypoglycaemic fluctuations.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"46"},"PeriodicalIF":3.3,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11783934/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143073967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards unbiased skin cancer classification using deep feature fusion.","authors":"Ali Atshan Abdulredah, Mohammed A Fadhel, Laith Alzubaidi, Ye Duan, Monji Kherallah, Faiza Charfi","doi":"10.1186/s12911-025-02889-w","DOIUrl":"10.1186/s12911-025-02889-w","url":null,"abstract":"<p><p>This paper introduces SkinWiseNet (SWNet), a deep convolutional neural network designed for the detection and automatic classification of potentially malignant skin cancer conditions. SWNet optimizes feature extraction through multiple pathways, emphasizing network width augmentation to enhance efficiency. The proposed model addresses potential biases associated with skin conditions, particularly in individuals with darker skin tones or excessive hair, by incorporating feature fusion to assimilate insights from diverse datasets. Extensive experiments were conducted using publicly accessible datasets to evaluate SWNet's effectiveness.This study utilized four datasets-Mnist-HAM10000, ISIC2019, ISIC2020, and Melanoma Skin Cancer-comprising skin cancer images categorized into benign and malignant classes. Explainable Artificial Intelligence (XAI) techniques, specifically Grad-CAM, were employed to enhance the interpretability of the model's decisions. Comparative analysis was performed with three pre-existing deep learning networks-EfficientNet, MobileNet, and Darknet. The results demonstrate SWNet's superiority, achieving an accuracy of 99.86% and an F1 score of 99.95%, underscoring its efficacy in gradient propagation and feature capture across various levels. This research highlights the significant potential of SWNet in advancing skin cancer detection and classification, providing a robust tool for accurate and early diagnosis. The integration of feature fusion enhances accuracy and mitigates biases associated with hair and skin tones. The outcomes of this study contribute to improved patient outcomes and healthcare practices, showcasing SWNet's exceptional capabilities in skin cancer detection and classification.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"48"},"PeriodicalIF":3.3,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11786435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143073968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the assessment of post-cardiac valve surgery pulmonary complication risks through the integration of wearable continuous physiological and clinical data.","authors":"Lixuan Li, Yuekong Hu, Zhicheng Yang, Zeruxin Luo, Jiachen Wang, Wenqing Wang, Xiaoli Liu, Yuqiang Wang, Yong Fan, Pengming Yu, Zhengbo Zhang","doi":"10.1186/s12911-025-02875-2","DOIUrl":"10.1186/s12911-025-02875-2","url":null,"abstract":"<p><strong>Background: </strong>Postoperative pulmonary complications (PPCs) following cardiac valvular surgery are characterized by high morbidity, mortality, and economic cost. This study leverages wearable technology and machine learning algorithms to preoperatively identify high-risk individuals, thereby enhancing clinical decision-making for the mitigation of PPCs.</p><p><strong>Methods: </strong>A prospective study was conducted at the Department of Cardiovascular Surgery of West China Hospital, Sichuan University, from August 2021 to December 2022. We examined 100 cardiac valvular surgery patients, where wearable technology was utilized to collect and analyze nocturnal physiological data at the 24-hour admission, in conjunction with clinical data extraction from the Hospital Information System's electronic records. We systematically evaluated three different input types (physiological, clinical, and both) and five classifiers (XGB, LR, RF, SVM, KNN) to identify the combination with strong predictive performance for PPCs. Feature selection was conducted using Recursive Feature Elimination with Cross-Validated (RFECV) for each model, yielding an optimal feature subset for each, followed by a grid search to tune hyperparameters. Stratified 5-fold cross-validation was used to evaluate the generalization performance. The significance of AUC differences between models was tested using the DeLong test to determine the optimal prognostic model comprehensively. Additionally, univariate logistic regression analysis was conducted on the features of the best-performing model to understand the impact of individual feature on PPCs.</p><p><strong>Results: </strong>In this study, 22 patients (22%) developed PPCs. Across classifiers, models combining both physiological and clinical features performed better than physiological or clinical features alone. Specifically, including physiological data in the classification model improved AUC, ACC, F1, and precision by an average of 8.32%, 1.80%, 3.28% and 6.06% compared to using clinical data only. The XGB classifier, utilizing both dataset, achieved the highest performance with an AUC of 0.82 (± 0.08) and identified eight significant features. The DeLong test indicated that the XGB model utilizing the both dataset significantly outperformed the XGB models trained on the physiological or clinical datasets alone. Univariate logistic regression analysis suggested that surgical methods, age, nni_50, and min_ven_in_mean are significantly associated with the occurrence of PPCs.</p><p><strong>Conclusion: </strong>The integration of continuous wearable physiological and clinical data significantly improves preoperative risk assessment for PPCs, which helps to optimize surgical management and reduce PPCs morbidity and mortality.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"47"},"PeriodicalIF":3.3,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11786410/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143073966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}