DIGITAL HEALTH最新文献

{"title":"Machine learning-based prediction of in-hospital mortality in patients with chronic respiratory disease exacerbations.","authors":"Seung Yeob Ryu, Seon Min Lee, Young Jae Kim, Kwang Gi Kim","doi":"10.1177/20552076251326703","DOIUrl":"10.1177/20552076251326703","url":null,"abstract":"<p><strong>Objective: </strong>Exacerbation of chronic respiratory diseases leads to poor prognosis and a significant socioeconomic burden. To address this issue, an artificial intelligence model must assess patient prognosis early and classify patients into high- and low-risk groups. This study aimed to develop a model to predict in-hospital mortality in patients with chronic respiratory disease using demographic, clinical, and environmental factors, specifically air pollution exposure levels.</p><p><strong>Methods: </strong>This study included 6272 patients diagnosed with chronic respiratory diseases comprising 39 risk factors. Air pollution indicators such as particulate matter (PM10), fine particulate matter (PM2.5), CO, NO₂, O₃, and SO₂ were used based on long-term and short-term exposure levels. Logistic regression, support vector machine, random forest, and extreme gradient boost were used to develop prediction models.</p><p><strong>Results: </strong>The AUCs for the four models were 0.932, 0.935, 0.933, and 0.944. The key risk factors that significantly influenced predictions included blood urea nitrogen, red blood cell distribution width, respiratory rate, and age, which were positively correlated with mortality prediction. In contrast, albumin, lymphocyte count, diastolic blood pressure, and SpO2 were negatively correlated with mortality prediction.</p><p><strong>Conclusion: </strong>This study developed a prediction model for in-hospital mortality in patients with chronic respiratory disease and demonstrated a relatively high predictive performance. By incorporating environmental factors, such as air pollution exposure levels, the model with the best performance suggested that 365 days of exposure to air pollution was a key risk factor in mortality prediction.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251326703"},"PeriodicalIF":2.9,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11970076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797008","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":"Risk factors and a predictive model for mixed urinary incontinence among parous women: Insights from a large-scale multicenter epidemiological investigation.","authors":"Qi Wang, Stefano Manodoro, Huifang Lin, Xiaofang Li, Chaoqin Lin, Xiaoxiang Jiang","doi":"10.1177/20552076251333661","DOIUrl":"10.1177/20552076251333661","url":null,"abstract":"<p><strong>Purpose: </strong>This study aims to identify independent risk factors for mixed urinary incontinence (MUI) in parous women using a multicenter epidemiological study and to establish and validate a predictive nomogram.</p><p><strong>Methods: </strong>A large-scale survey was conducted from June 2022 to September 2023, including parous women aged over 20 selected through stratified random sampling. Data encompassed sociodemographic and obstetric histories, comorbidities, and standardized questionnaires. The primary goal was to identify high-risk factors for MUI, while the secondary was to develop a nomogram. Risk factors were determined using univariable and multivariable analyses. The nomogram's performance was assessed via concordance index (C-index) and calibration plots through internal and external validation.</p><p><strong>Results: </strong>A total of 7709 women participated, with an MUI prevalence of 6.8%. Independent risk factors included higher body mass index, urban residence, postmenopausal status, multiple vaginal deliveries, history of pelvic surgery and macrosomia, family history of pelvic floor dysfunction, hypertension, and constipation. The area under the curve for the nomogram model was 0.717 in the training set, 0.714 for internal validation, and 0.725 for external validation. The calibration plots showed a good agreement between the predicted and observed outcomes.</p><p><strong>Conclusion: </strong>This study identifies key risk factors for MUI in parous women and introduces a validated nomogram with high but not perfect predictive accuracy. The model enables early identification and management of MUI, though further refinement could enhance accuracy.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251333661"},"PeriodicalIF":2.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11970075/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797014","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":"The relationship between age and physical activity as objectively measured by accelerometers in older adults with and without dementia.","authors":"Karl Brown, Andrew Shutes-David, Sarah Payne, Adrienne Jankowski, Katie Wilson, Edmund Seto, Debby W Tsuang","doi":"10.1177/20552076251330808","DOIUrl":"10.1177/20552076251330808","url":null,"abstract":"<p><strong>Objective: </strong>This study sought to investigate differences in physical activity and activity fragmentation between older adults with and without dementia and between older adults with dementia with Lewy bodies (DLB) and older adults with Alzheimer's disease (AD). The study also sought to investigate how these differences vary in magnitude at different ages.</p><p><strong>Methods: </strong>Accelerometry data were analyzed from individuals with dementia (<i>n</i> = 94) and individuals without dementia (<i>n</i> = 613) who participated in the National Health and Aging Trends Study (NHATS), as well as from individuals with DLB (<i>n</i> = 12) and AD (<i>n</i> = 10) who participated in a pilot study.</p><p><strong>Results: </strong>In the NHATS cohort, individuals without dementia had more activity counts (0.325 million [95% CI 0.162 million, 0.487 million]) and a longer active bout length (0.631 minutes [95% CI 0.311, 0.952]) at the mean age of 79 than individuals with dementia at the same age. There was also suggestive evidence that individuals without dementia had a shorter resting bout length (-2.196 minutes [95% CI -4.996, 0.605]) than individuals with dementia. Differences in data collection and processing prevented direct comparisons between the cohorts, and the parallel analyses in the smaller cohort were underpowered to detect statistically significant differences between DLB and AD.</p><p><strong>Conclusion: </strong>This work shows that objectively measured accelerometry data differ between individuals with and without dementia; future studies with larger samples should investigate whether accelerometry data can be used to aid in the early identification of dementia and differentiation of dementia subtypes.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251330808"},"PeriodicalIF":2.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11970097/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797018","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":"Beyond the STI clinic: Use of administrative claims data and machine learning to develop and validate patient-level prediction models for gonorrhea.","authors":"Lorenzo Argante, Germain Lonnet, Emmanuel Aris, Jane Whelan","doi":"10.1177/20552076251331895","DOIUrl":"10.1177/20552076251331895","url":null,"abstract":"<p><strong>Background: </strong>Gonorrhea is a sexually transmitted infection (STI) that, untreated, can result in debilitating complications such as pelvic inflammatory disease, pain, and infertility. A minority of cases are diagnosed in STI clinics in the United States. Gonorrhea is often asymptomatic and presumed to be substantially underdiagnosed and/or undertreated.</p><p><strong>Objectives: </strong>To generate and compare predictive machine learning (ML) models using administrative claims data to characterize young women in the general United States population who would be most likely to contract gonorrhea.</p><p><strong>Methods: </strong>Data were extracted from the Merative™ MarketScan^® Commercial and Medicaid databases containing routinely collected administrative claims data. Women aged 16-35 years with two years of continuous observation between 1 January 2017 and 31 December 2018 were included. ML classification models were constructed based on logistic regression and tree-based algorithms.</p><p><strong>Results: </strong>Models constructed using tree-based algorithms such as XGBoost provided the best discriminatory results, but simpler ridge regressions models with splines also achieved reasonable discrimination, allowing for the identification of population subsets at increased risk of gonorrhea infection. A subset of 0.1% of the population identified by the XGBoost model had a 70-fold higher risk of gonorrhea than the general population. External validation applying the different models on a Medicaid dataset that was not included in developing the original models was checked and deemed acceptable.</p><p><strong>Conclusions: </strong>The models and methods presented here could facilitate the identification of women at high risk of contracting gonorrhea for whom targeted preventive measures may be most beneficial.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251331895"},"PeriodicalIF":2.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11970062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797002","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":"Predicting 30-day readmissions in pneumonia patients using machine learning and residential greenness.","authors":"Seohyun Choi, Young Jae Kim, Seon Min Lee, Kwang Gi Kim","doi":"10.1177/20552076251325990","DOIUrl":"10.1177/20552076251325990","url":null,"abstract":"<p><strong>Introduction: </strong>Identifying factors that increase the risk of hospital readmission will help determine high-risk patients and decrease the socioeconomic burden. Pneumonia is associated with high readmission rates. Although residential greenness has been reported to have beneficial health effects, no studies have investigated its importance in predicting readmission in patients with pneumonia. This study aimed to build prediction models for 30-day readmission in patients with pneumonia and to analyze the importance of risk factors for readmission, mainly residential greenness.</p><p><strong>Methods: </strong>Data on 47 risk factors were collected from 22,600 patients diagnosed with pneumonia. Residential greenness was quantified as the mean of normalized difference vegetation index of the district in which the patient resides. Prediction models were built using logistic regression, support vector machine, random forest, and extreme gradient boosting.</p><p><strong>Results: </strong>Residential greenness was selected from the top 21 risk factors after feature selection. The area under the curves of the four models were 0.6919, 0.6931, 0.7117, and 0.7044. Age, red blood cell distribution width, and history of cancer were the top three risk factors affecting readmission prediction. Residential greenness was the 15th important factor.</p><p><strong>Discussion: </strong>We constructed prediction models for 30-day readmission of patients with pneumonia by incorporating residential greenness as a risk factor. The models demonstrated sufficient performance, and residential greenness was significant in predicting readmission. Incorporating residential greenness into the identification of groups at high risk for readmission can complement the possible loss of information when using data from electronic health records.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251325990"},"PeriodicalIF":2.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11970095/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797010","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":"Understanding public trust in national electronic health record systems: A multi-national qualitative research study.","authors":"Kimon Papadopoulos, Elske Ammenwerth, Guillaume Lame, Nina Stahl, Verena Struckmann, Viktor von Wyl, Felix Gille","doi":"10.1177/20552076251333576","DOIUrl":"10.1177/20552076251333576","url":null,"abstract":"<p><strong>Objective: </strong>Having public trust in national electronic health record systems (NEHRs) is crucial for the successful implementation and participation of NEHRs within a nations healthcare system. Yet, a lack of conceptual clarity precludes healthcare policymakers from incorporating trust to the fullest extent possible. In response, this study seeks to validate an existing framework for public trust in the healthcare system, which will help provide a clearer understanding of what constitutes public trust in NEHRs across members of the public in different countries, cultures, and contexts.</p><p><strong>Methods: </strong>Twenty-four focus groups were conducted in Austria, Germany, France, Italy, the Netherlands, and Switzerland with residents of each respective country to discuss their viewpoints on our public trust in NEHRs framework in order to validate said framework.</p><p><strong>Results: </strong>Frameworks describing the causes and effects of public trust in NEHRs were created for each country studied. Across all countries, the frameworks remained similar to our base framework, highlighting our frameworks' robustness. Data security, privacy, and autonomy were consistently described as the most important aspects of public trust in NEHRs. Concurrently, health system actors, such as doctors, were found to have significant influence on NEHR implementation. Their influence, however, can either be beneficial or detrimental to public trust in NEHRs, depending on their actions and how the public perceives those actions. Additional results detail contextual insights into country-specific viewpoints and the role of healthcare stakeholders in public trust in NEHRs. The results showcase the differences and similarities in which different populations across Europe view trust in NEHRs in the context of our framework.</p><p><strong>Conclusions: </strong>These findings present public trust frameworks in the context of NEHRs for the study countries. These frameworks can assist stakeholders in obtaining a comprehensive understanding of the complexity of public trust in implementing and promoting their NEHRs, including measurements of public trust.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251333576"},"PeriodicalIF":2.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11970066/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797022","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":"Integrated brain tumor segmentation and MGMT promoter methylation status classification from multimodal MRI data using deep learning.","authors":"Muhammad Sohaib Iqbal, Usama Ijaz Bajwa, Rehan Raza, Muhammad Waqas Anwar","doi":"10.1177/20552076251332018","DOIUrl":"10.1177/20552076251332018","url":null,"abstract":"<p><strong>Objective: </strong>Glioblastoma multiforme (GBM) is the most aggressive and prevalent type of brain tumor, with a median survival time of approximately 15 months despite treatment advancements. Determining the O(6)-methylguanine-DNA-methyltransferase (MGMT) promoter status, specifically its methylation, is crucial for treatment planning as it provides valuable prognostic information and indicates chemosensitivity. However, current methods require invasive tissue sampling and genetic testing, resulting in time-consuming processes. The non-invasive technique of assessing MGMT status in GBM patients may offer valuable insights to neuro-oncologists, aiding in precise treatment and surgical planning.</p><p><strong>Methods: </strong>This research study utilizes two benchmark datasets-BraTS2021 brain tumor segmentation dataset and MGMT promoter status classification dataset-and proposes a pipeline of segmentation-based classification of MGMT promoter status utilizing all modalities of magnetic resonance imaging (MRI) scans by stacking them. The pipeline consists of two phases: in the first phase, a 3D Residual U-Net (3D ResU-Net) architecture is used to segment the brain tumor into sub-regions using a stack of MRI modalities. In the second phase, the segmented tumor voxel obtained from the first phase is input into a 3D ResNet10 model to predict MGMT promoter status.</p><p><strong>Results: </strong>The segmentation phase of the pipeline achieves promising results with average dice scores of 0.81, 0.84, and 0.80 for tumor core (TC), whole tumor (WT), and enhancing tumor (ET) regions, respectively, on the internal validation set. The classification phase obtains a ROC-AUC score of 0.66 on the internal validation set.</p><p><strong>Conclusion: </strong>This pipeline demonstrates the potential of a non-invasive approach to support neuro-oncologists in brain tumor diagnosis and treatment planning. While still at the research stage, it provides insights into tumor sub-regions and MGMT promoter status, highlighting the role of AI-driven methods in assessing molecular data. Future studies and clinical validation are needed to further explore its applicability in real-world clinical settings.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251332018"},"PeriodicalIF":2.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11970068/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797006","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 potential of deep learning models integrating transformer and LSTM in predicting blood glucose levels for T1D patients.","authors":"Xin Xiong, XinLiang Yang, Yunying Cai, Yuxin Xue, JianFeng He, Heng Su","doi":"10.1177/20552076251328980","DOIUrl":"10.1177/20552076251328980","url":null,"abstract":"<p><strong>Objective: </strong>Diabetes mellitus is a chronic condition that requires constant blood glucose monitoring to prevent serious health risks. Accurate blood glucose prediction is essential for managing glucose fluctuations and reducing the risk of hypo- and hyperglycemic events. However, existing models often face limitations in prediction horizon and accuracy. This study aims to develop a hybrid deep learning model combining Transformer and Long Short-Term Memory (LSTM) networks to improve prediction accuracy and extend the prediction horizon, using personalized patient information and continuous glucose monitoring data to support better real-time diabetes management.</p><p><strong>Methods: </strong>In this study, we propose a hybrid deep learning model combining Transformer and LSTM networks to predict blood glucose levels for up to 120 min. The Transformer Encoder captures long-range dependencies, while the LSTM models short-term patterns. To improve feature extraction, we integrate Bidirectional LSTM and Transformer Encoder layers at multiple stages. We also use positional encoding, dropout layers, and a sliding window technique to reduce noise and manage temporal dependencies. Richer features, including meal composition and insulin dosage, are incorporated to enhance prediction accuracy. The model's performance is validated using real-world clinical data and error grid analysis.</p><p><strong>Results: </strong>On clinical data, the model achieved root mean square error/mean absolute error of 10.157/6.377 (30-min), 10.645/6.417 (60-min), 13.537/7.283 (90-min), and 13.986/6.986 (120-min). On simulated data, the results were 1.793/1.376 (15-min), 2.049/1.311 (30-min), and 3.477/1.668 (60-min). Clark Grid Analysis showed that over 96% of predictions fell within the clinical safety zone up to 120 min, confirming its clinical feasibility.</p><p><strong>Conclusion: </strong>This study demonstrates that the combined Transformer and LSTM model can effectively predict blood glucose concentration in type 1 diabetes patients with high accuracy and clinical applicability. The model provides a promising solution for personalized blood glucose management, contributing to the advancement of artificial intelligence technology in diabetes care.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251328980"},"PeriodicalIF":2.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11970073/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797004","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":"Machine-learning models for differentiating benign and malignant breast masses: Integrating automated breast volume scanning intra-tumoral, peri-tumoral features, and clinical information.","authors":"Meixue Dai, Yueqiong Yan, Zhong Li, Jidong Xiao","doi":"10.1177/20552076251332738","DOIUrl":"10.1177/20552076251332738","url":null,"abstract":"<p><strong>Background: </strong>Differentiating between benign and malignant breast masses is critical for clinical decision-making. Automated breast volume scanning (ABVS) provides high-resolution three-dimensional imaging, addressing the limitations of conventional ultrasound. However, the impact of peritumoral region size on predictive performance has not been systematically studied. This study aims to optimize diagnostic performance by integrating radiomics features and clinical data using multiple machine-learning models.</p><p><strong>Methods: </strong>This retrospective study included ABVS images and clinical data from 250 patients with breast masses. Radiomics features were extracted from both intratumoral and peritumoral regions (5, 10, and 20 mm). These features, combined with clinical data, were used to develop models based on four algorithms: Support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LGBM). Model performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration curves, and decision curves, with SHapley Additive exPlanations (SHAP) analysis employed for interpretability.</p><p><strong>Results: </strong>The inclusion of peritumoral features improved the diagnostic performance to varying degrees, with the model incorporating a 10 mm peritumoral region achieving the highest overall accuracy. Combining radiomics with clinical features further enhanced predictive performance. The LGBM model outperformed the other algorithms across subgroups, achieving a maximum AUC of 0.909, an accuracy of 0.878, and an F1-score of 0.971. SHAP analysis revealed the contribution of key features, improving model interpretability.</p><p><strong>Conclusion: </strong>This study demonstrates the value of integrating radiomics and clinical features for breast mass diagnosis, with optimized peritumoral regions enhancing model performance. The LGBM model emerged as the preferred algorithm due to its superior performance. These findings provide strong support for the clinical application of ABVS imaging and future multicenter studies, highlighting the importance of microenvironmental features in diagnosis.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251332738"},"PeriodicalIF":2.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11963789/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774762","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":"Interactive decision aid on therapy decision making for patients with chronic kidney disease: A prospective exploratory pilot study.","authors":"Jun-Ming Su, Huey-Liang Kuo, Kai-Ling Yang, Chih-Jung Wu, Ya-Fang Ho","doi":"10.1177/20552076251332832","DOIUrl":"10.1177/20552076251332832","url":null,"abstract":"<p><strong>Objective: </strong>Patients with advanced chronic kidney disease (CKD) face challenging decisions about kidney replacement therapy (KRT) options. This study aimed to evaluate the effectiveness of an Interactive Patient-Decision-Aid App for Kidney Replacement Therapy (iPtDA-KRT App) in improving decision self-efficacy, knowledge, and readiness while reducing decisional conflict and regret among patients with advanced CKD.</p><p><strong>Methods: </strong>A prospective, two-arm clinical trial was conducted with 70 patients who used either the mobile app-based iPtDA-KRT or a paper-based PtDA for 4 weeks. Key measures included decision self-efficacy (DSES), decisional conflict (DCS), KRT knowledge (KRTKS), preparation for decision-making (PrepDM), and decision regret (DRS). These measures were assessed immediately post-intervention and at 4 weeks post-intervention. Additionally, outcomes such as DSES, DCS, and DRS were tracked at 8 weeks post-intervention. Data were analyzed using generalized estimating equations.</p><p><strong>Results: </strong>The experimental group included 19 male and 16 female patients with a mean age of 66.4 ± 14.0 years, while the control group included 22 male and 13 female patients with a mean age of 59.7 ± 16.1 years. The experimental group demonstrated significantly greater and sustained improvements in self-efficacy, knowledge, and preparation for decision-making compared to the control group. Reductions in decisional conflict and regret were also maintained over time.</p><p><strong>Conclusions: </strong>The iPtDA-KRT App demonstrated substantial and lasting benefits over paper-based PtDAs in supporting patients' complex decision-making. These findings suggest that digital, interactive PtDAs may enhance informed decision-making, reduce long-term decisional conflict, and improve patient confidence in clinical settings.</p><p><strong>Trial registration: </strong>This study was registered at ClinicalTrials.gov (Identifier: NCT05666544).</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"11 ","pages":"20552076251332832"},"PeriodicalIF":2.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11963785/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774761","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}