Diagnostic and prognostic research最新文献

{"title":"Counterfactual prediction from machine learning models: transportability and joint analysis for model development and evaluation using multi-source data.","authors":"Sarah C Voter, Issa J Dahabreh, Christopher B Boyer, Habib Rahbar, Despina Kontos, Jon A Steingrimsson","doi":"10.1186/s41512-025-00201-y","DOIUrl":"10.1186/s41512-025-00201-y","url":null,"abstract":"<p><strong>Background: </strong>When a machine learning model is developed and evaluated in a setting where the treatment assignment process differs from the setting of intended model deployment, failure to account for this difference can lead to suboptimal model development and biased estimates of model performance.</p><p><strong>Methods: </strong>We consider the setting where data from a randomized trial and an observational study emulating the trial are available for machine learning model development and evaluation. We provide two approaches for estimating the model and assessing model performance under a hypothetical treatment strategy in the target population underlying the observational study. The first approach uses counterfactual predictions from the observational study only and relies on the assumption of conditional exchangeability between treated and untreated individuals (no unmeasured confounding). The second approach leverages the exchangeability between treatment groups in the trial (supported by study design) to \"transport\" estimates from the trial to the population underlying the observational study, relying on an additional assumption of conditional exchangeability between the populations underlying the observational study and the randomized trial.</p><p><strong>Results: </strong>We examine the assumptions underlying both approaches for fitting the model and estimating performance in the target population and provide estimators for both objectives. We then develop a joint estimation strategy that combines data from the trial and the observational study, and discuss benchmarking of the trial and observational results.</p><p><strong>Conclusions: </strong>Both the observational and transportability analyses can be used to fit a model and estimate performance under a counterfactual treatment strategy in the population underlying the observational data, but they rely on different assumptions. In either case, the assumptions are untestable, and deciding which method is more appropriate requires careful contextual consideration. If all assumptions hold, then combining the data from the observational study and the randomized trial can be used for more efficient estimation.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"22"},"PeriodicalIF":2.6,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12490139/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting venous thromboembolism among hospitalized adults: a protocol for development and validation of an implementable real-time prognostic model.","authors":"Henry J Domenico, Benjamin F Tillman, Shari L Just, Yeji Ko, Amanda S Mixon, Asli Weitkamp, Jonathan S Schildcrout, Colin Walsh, Thomas Ortel, Benjamin French","doi":"10.1186/s41512-025-00205-8","DOIUrl":"10.1186/s41512-025-00205-8","url":null,"abstract":"<p><strong>Background: </strong>Hospital-acquired venous thromboembolism (HA-VTE) is a leading cause of morbidity and mortality among hospitalized adults. Numerous prognostic models have been developed to identify those patients with elevated risk of HA-VTE. None, however, has met the necessary criteria to guide clinical decision-making. This study outlines a protocol for refining and validating a general-purpose prognostic model for HA-VTE, designed for real-time automation within the electronic health record (EHR) system.</p><p><strong>Methods: </strong>A retrospective cohort of 132,561 inpatient encounters (89,586 individual patients) at a large academic medical center will be collected, along with clinical and demographic data available as part of routine care. Data for temporal, geographic, and domain external validation cohorts will also be collected. Logistic regression will be used to predict occurrence of HA-VTE during an inpatient encounter. Variables considered for model inclusion will be based on prior demonstrated association with HA-VTE and their availability in both retrospective EHR data and routine clinical care. Least absolute shrinkage and selection operator (LASSO) with tenfold cross-validation will be used for initial variable selection. Variables selected by the LASSO procedure, along with those deemed necessary by clinicians, will be used in an unpenalized multivariable logistic regression model. Discrimination and calibration will be reported for the derivation and validation cohorts. Discrimination will be measured using Harrell's C statistic. Calibration will be measured using calibration intercept, calibration slope, Brier score, integrated calibration index, and visual examination of non-linear calibration curve. Model reporting will adhere to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines for clinical prediction models using machine learning methods (TRIPOD + AI).</p><p><strong>Discussion: </strong>We describe methods for developing, evaluating, and validating a prognostic model for HA-VTE using routinely collected EHR data. By combining best practices in statistical development and validation, knowledge engineering, and clinical domain knowledge, the resulting model should be well suited for real-time clinical implementation. Although this protocol describes our development of a model for HA-VTE, the general approach can be applied to other clinical outcomes.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"19"},"PeriodicalIF":2.6,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12416065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145016698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and internal validation of a prediction model for post-COVID-19 condition 2 years after infection-results of the CORFU study.","authors":"Dorthe Odyl Klein, Nick Wilmes, Sophie F Waardenburg, Gouke J Bonsel, Erwin Birnie, Marieke Sjn Wintjens, Stella Cm Heemskerk, Emma Bnj Janssen, Chahinda Ghossein-Doha, Michiel C Warlé, Lotte Mc Jacobs, Bea Hemmen, Jeanine A Verbunt, Bas Ct van Bussel, Susanne van Santen, Bas Ljh Kietselaer, Gwyneth Jansen, Folkert W Asselbergs, Marijke Linschoten, Juanita A Haagsma, S M J van Kuijk","doi":"10.1186/s41512-025-00203-w","DOIUrl":"10.1186/s41512-025-00203-w","url":null,"abstract":"<p><strong>Background: </strong>A subset of COVID-19 patients develops post-COVID-19 condition (PCC). This condition results in disability in numerous areas of patients' lives and a reduced health-related quality of life, with societal impact including work absences and increased healthcare utilization. There is a scarcity of models predicting PCC, especially those considering the severity of the initial severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and incorporating long-term follow-up data. Therefore, we developed and internally validated a prediction model for PCC 2 years after SARS-CoV-2 infection in a cohort of COVID-19 patients.</p><p><strong>Methods: </strong>Data from the CORona Follow-Up (CORFU) study were used. This research initiative integrated data from multiple Dutch COVID-19 cohort studies. We utilized 2-year follow-up data collected via the questionnaires between October 1st of 2021 and December 31st of 2022. Participants were former COVID-19 patients, approximately 2-year post-SARS-CoV-2 infection. Candidate predictors were selected based on literature and availability across cohorts. The outcome of interest was the prevalence of PCC at 2 years after the initial infection. Logistic regression with backward stepwise elimination identified significant predictors such as sex, BMI and initial disease severity. The model was internally validated using bootstrapping. Model performance was quantified as model fit, discrimination and calibration.</p><p><strong>Results: </strong>In total 904 former COVID-19 patients were included in the analysis. The cohort included 146 (16.2%) non-hospitalized patients, 511 (56.5%) ward admitted patients, and 247 (27.3%) intensive care unit (ICU) admitted patients. Of all participants, 551 (61.0%) participants suffered from PCC. We included 20 candidate predictors in the multivariable analysis. The final model, after backward elimination, identified sex, body mass index (BMI), ward admission, ICU admission, and comorbidities such as arrhythmia, asthma, angina pectoris, previous stroke, hernia, osteoarthritis, and rheumatoid arthritis as predictors of post-COVID-19 condition. Nagelkerke's R-squared value for the model was 0.19. The optimism-adjusted AUC was 71.2%, and calibration was good across predicted probabilities.</p><p><strong>Conclusions: </strong>This internally validated prediction model demonstrated moderate discriminative ability to predict PCC 2 years after COVID-19 based on sex, BMI, initial disease severity, and a collection of comorbidities.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"18"},"PeriodicalIF":2.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12400538/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144980798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Calibrating multiplex serology for Helicobacter pylori.","authors":"Emmanuelle A Dankwa, Martyn Plummer, Daniel Chapman, Rima Jeske, Julia Butt, Michael Hill, Tim Waterboer, Iona Y Millwood, Ling Yang, Christiana Kartsonaki","doi":"10.1186/s41512-025-00202-x","DOIUrl":"10.1186/s41512-025-00202-x","url":null,"abstract":"<p><strong>Background: </strong>Helicobacter pylori (H. pylori) is a bacterium that colonizes the stomach and is a major risk factor for gastric cancer, with an estimated 89% of non-cardia gastric cancer cases worldwide attributable to H. pylori. Prospective studies provide reliable evidence for quantifying the association between gastric cancer and H. pylori, as they circumvent the risk of a false negative due to possible reduction in antibody levels before cancer development.</p><p><strong>Methods: </strong>In a large-scale prospective study within the China Kadoorie Biobank, H. pylori infection is being analysed as a risk factor for gastric cancer. The presence of infection is typically determined by serological tests. The immunoblot test, although well established, is more labour intensive and uses a larger amount of plasma than the alternative high-throughput multiplex serology test. Immunoblot outputs a binary positive/negative serostatus classification, while multiplex outputs a vector of continuous antigen measurements. When mapping such multidimensional continuous measurements onto a binary classification, statistical challenges arise in defining classification cut-offs and accounting for the differences in infection evidence provided by different antigens. We discuss these challenges and propose a novel solution to optimize the translation of the continuous measurements from multiplex serology into probabilities of H. pylori infection, using classification algorithms (Bayesian additive regressive trees (BART), multidimensional monotone BART, logistic regression, random forest and elastic net). We (i) calibrate and apply classification models to predict probabilities of H. pylori infection given multiplex measurements, (ii) compare the predictive performance of the models using immunoblot as reference, (iii) discuss reasons for the differences in predictive performance and (iv) apply the calibrated models to gain insights on the relative strengths of infection evidence provided by the various antigens.</p><p><strong>Results: </strong>All models showed high discriminative ability with at least 95% area under the curve (AUC) estimates on the training and test data. There was no substantial difference between the performance of models on the training and test data.</p><p><strong>Conclusions: </strong>Classification algorithms can be used to calibrate the H. pylori multiplex serology test to the immunoblot test in the China Kadoorie Biobank. This study furthers our understanding of the applicability of classification algorithms to the context of serologic tests.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"17"},"PeriodicalIF":2.6,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12337413/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144818449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting patient outcomes and risk for revision surgery after hip and knee replacement surgery: study protocol for a comparison of modelling approaches using the Swiss National Joint Registry (SIRIS).","authors":"Léonie Hofstetter, Nathalie Schweyckart, Christof Seiler, Christian Brand, Laura C Rosella, Mazda Farshad, Milo A Puhan, Cesar A Hincapié","doi":"10.1186/s41512-025-00200-z","DOIUrl":"10.1186/s41512-025-00200-z","url":null,"abstract":"<p><strong>Background: </strong>Prediction of postoperative patient-reported outcomes and risk for revision surgery after total hip arthroplasty (THA) or total knee arthroplasty (TKA) can inform clinical decision-making, health resource allocation, and care planning. Machine learning (ML) algorithms are increasingly used as an alternative to traditional logistic regression (LR) prediction, but there is uncertainty about their superiority in overall model performance. The aim of this study is to compare the predictive performance of LR with different ML approaches for predicting patient outcomes and risk for revision surgery after THA and TKA.</p><p><strong>Methods: </strong>A population-based historical cohort study will be developed using routinely collected data from all primary and revision THA and TKA procedures performed in Switzerland and registered in the Swiss National Joint Registry (SIRIS). Patients of age ≥ 18 years with surgery for primary osteoarthritis from 01 January 2015 up to 31 December 2023 will be included. Outcomes of interest will be (1) 12-month postoperative poor pain outcome (defined as < 50% improvement of pain or < 3 absolute reduction in pain on a 11-point (0 to 10) numeric rating scale) and poor satisfaction outcome, and (2) early revision within 5 years after primary surgery. Prespecified predictor variables will include demographic characteristics, comorbidity score, patient-reported health status measures, and surgical variables. Measures of overall predictive accuracy, discrimination, and calibration will be used to compare predictive performance, and decision curve analysis performed to evaluate the clinical usefulness of models. The models will be internally validated using cross-validation and externally validated using geographical validation. Development of the models will be informed by the updated Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD + AI) statement.</p><p><strong>Discussion: </strong>This study will develop, validate, and compare prediction models for postoperative patient-reported outcomes and risk for revision surgery after THA and TKA using SIRIS data.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"16"},"PeriodicalIF":2.6,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12320300/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144777053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparison of modeling approaches for static and dynamic prediction of central line-associated bloodstream infections using electronic health records (part 2): random forest models.","authors":"Elena Albu, Shan Gao, Pieter Stijnen, Frank E Rademakers, Christel Janssens, Veerle Cossey, Yves Debaveye, Laure Wynants, Ben Van Calster","doi":"10.1186/s41512-025-00194-8","DOIUrl":"10.1186/s41512-025-00194-8","url":null,"abstract":"<p><strong>Objective: </strong>Prognostic outcomes related to hospital admissions typically do not suffer from censoring, and can be modeled either categorically or as time-to-event. Competing events are common but often ignored. We compared the performance of static and dynamic random forest (RF) models to predict the risk of central line-associated bloodstream infections (CLABSI) using different outcome operationalizations.</p><p><strong>Methods: </strong>We included data from 27,478 admissions to the University Hospitals Leuven, covering 30,862 catheter episodes (970 CLABSI, 1466 deaths and 28,426 discharges) to build static and dynamic RF models for binary (CLABSI vs no CLABSI), multinomial (CLABSI, discharge, death or no event), survival (time to CLABSI) and competing risks (time to CLABSI, discharge or death) outcomes to predict the 7-day CLABSI risk. Static models used information at the onset of the catheter episode, while dynamic models updated predictions daily for 30 days (landmark 0-30). We evaluated model performance across 100 train/test splits.</p><p><strong>Results: </strong>Performance of binary, multinomial and competing risks models was similar: AUROC was 0.74 for predictions at catheter onset, rose to 0.77 for predictions at landmark 5, and decreased thereafter. Survival models overestimated the risk of CLABSI (E:O ratios between 1.2 and 1.6), and had AUROCs about 0.01 lower than other models. Binary and multinomial models had lowest computation times. Models including multiple outcome events (multinomial and competing risks) display a different internal structure compared to binary and survival models, choosing different variables for early splits in trees.</p><p><strong>Discussion and conclusion: </strong>In the absence of censoring, complex modelling choices do not considerably improve the predictive performance compared to a binary model for CLABSI prediction in our studied settings. Survival models censoring the competing events at their time of occurrence should be avoided.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"21"},"PeriodicalIF":0.0,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12278561/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144683722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparison of modeling approaches for static and dynamic prediction of central-line bloodstream infections using electronic health records (part 1): regression models.","authors":"Shan Gao, Elena Albu, Hein Putter, Pieter Stijnen, Frank E Rademakers, Veerle Cossey, Yves Debaveye, Christel Janssens, Ben Van Calster, Laure Wynants","doi":"10.1186/s41512-025-00199-3","DOIUrl":"10.1186/s41512-025-00199-3","url":null,"abstract":"<p><strong>Background: </strong>Hospitals register information in the electronic health records (EHRs) continuously until discharge or death. As such, there is no censoring for in-hospital outcomes. We aimed to compare different static and dynamic regression modeling approaches to predict central line-associated bloodstream infections (CLABSIs) in EHR while accounting for competing events precluding CLABSI.</p><p><strong>Methods: </strong>We analyzed data from 30,862 catheter episodes at University Hospitals Leuven from 2012 and 2013 to predict 7-day risk of CLABSI. Competing events are discharge and death. Static models using information at catheter onset included logistic, multinomial logistic, Cox, cause-specific hazard, and Fine-Gray regression. Dynamic models updated predictions daily up to 30 days after catheter onset (i.e., landmarks 0 to 30 days) and included landmark supermodel extensions of the static models, separate Fine-Gray models per landmark time, and regularized multi-task learning (RMTL). Model performance was assessed using 100 random 2:1 train-test splits.</p><p><strong>Results: </strong>The Cox model performed worst of all static models in terms of area under the receiver operating characteristic curve (AUROC) and calibration. Dynamic landmark supermodels reached peak AUROCs between 0.741 and 0.747 at landmark 5. The Cox landmark supermodel had the worst AUROCs (≤ 0.731) and calibration up to landmark 7. Separate Fine-Gray models per landmark performed worst for later landmarks, when the number of patients at risk was low.</p><p><strong>Conclusions: </strong>Categorical and time-to-event approaches had similar performance in the static and dynamic settings, except Cox models. Ignoring competing risks caused problems for risk prediction in the time-to-event framework (Cox), but not in the categorical framework (logistic regression).</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"20"},"PeriodicalIF":0.0,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12278581/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144683723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A decomposition of Fisher's information to inform sample size for developing or updating fair and precise clinical prediction models for individual risk-part 1: binary outcomes.","authors":"Richard D Riley, Gary S Collins, Rebecca Whittle, Lucinda Archer, Kym I E Snell, Paula Dhiman, Laura Kirton, Amardeep Legha, Xiaoxuan Liu, Alastair K Denniston, Frank E Harrell, Laure Wynants, Glen P Martin, Joie Ensor","doi":"10.1186/s41512-025-00193-9","DOIUrl":"10.1186/s41512-025-00193-9","url":null,"abstract":"<p><strong>Background: </strong>When using a dataset to develop or update a clinical prediction model, small sample sizes increase concerns of overfitting, instability, poor predictive performance and a lack of fairness. For models estimating the risk of a binary outcome, previous research has outlined sample size calculations that target low overfitting and a precise overall risk estimate. However, more guidance is needed for targeting precise and fair individual-level risk estimates.</p><p><strong>Methods: </strong>We propose a decomposition of Fisher's information matrix to help examine sample sizes required for developing or updating a model, aiming for precise and fair individual-level risk estimates. We outline a five-step process for use before data collection or when an existing dataset or pilot study is available. It requires researchers to specify the overall risk in the target population, the (anticipated) distribution of key predictors in the model and an assumed 'core model' either specified directly (i.e. a logistic regression equation is provided) or based on a specified C-statistic and relative effects of (standardised) predictors.</p><p><strong>Results: </strong>We produce closed-form solutions that decompose the variance of an individual's risk estimate into the Fisher's unit information matrix, predictor values and the total sample size. This allows researchers to quickly calculate and examine the anticipated precision of individual-level predictions and classifications for specified sample sizes. The information can be presented to key stakeholders (e.g. health professionals, patients, grant funders) to inform target sample sizes for prospective data collection or whether an existing dataset is sufficient. Our proposal is implemented in our new software module pmstabilityss. We provide two real examples and emphasise the importance of clinical context, including any risk thresholds for decision making and fairness checks.</p><p><strong>Conclusions: </strong>Our approach helps researchers examine potential sample sizes required to target precise and fair individual-level predictions when developing or updating prediction models for binary outcomes.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"14"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12235806/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and temporal evaluation of sex-specific models to predict 4-year atherosclerotic cardiovascular disease risk based on age and neighbourhood characteristics in South Limburg, the Netherlands.","authors":"Anke Bruninx, Lianne Ippel, Rob Willems, Andre Dekker, Iñigo Bermejo","doi":"10.1186/s41512-025-00198-4","DOIUrl":"10.1186/s41512-025-00198-4","url":null,"abstract":"<p><strong>Background: </strong>To improve screening for atherosclerotic cardiovascular disease (ASCVD), we aimed to develop and temporally evaluate sex-specific models to predict 4-year ASCVD risk in South Limburg based on age and neighbourhood characteristics concerning home address.</p><p><strong>Methods: </strong>We included 40- to 70-year-olds living in South Limburg on 1 January 2015 for model development, and 40- to 70-year-olds living in South Limburg on 1 January 2016 for model evaluation. We randomly sampled people selected in 1 year and in both years to create development and evaluation data sets. Follow-up of ASCVD and competing events (overall mortality excluding ASCVD) lasted until 31 December 2020. Candidate predictors were the individual's age, the neighbourhood's socio-economic status, and the neighbourhood's particulate matter concentration. Using the evaluation data sets, we compared two model types, subdistribution and cause-specific hazard models, and eight model structures. Discrimination was assessed by the area under the receiver operating characteristic curve (AUROC). Calibration was assessed by calculating overall expected-observed ratios (E/O). For the final models, calibration plots were made additionally.</p><p><strong>Results: </strong>The development data sets consisted of 67,549 males (4-year cumulative ASCVD incidence: 3.08%) and 67,947 females (4-year cumulative ASCVD incidence: 1.50%). The evaluation data sets consisted of 66,068 males (4-year cumulative ASCVD incidence: 3.22%) and 66,231 females (4-year cumulative ASCVD incidence: 1.49%). For males, the AUROC of the final model equalled 0.6548. The E/O equalled 0.9466. For females, the AUROC equalled 0.6744. The E/O equalled 0.9838.</p><p><strong>Conclusions: </strong>The resulting model shows promise for further research. These models may be used for ASCVD screening in the future.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"15"},"PeriodicalIF":0.0,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12220320/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ischemic modified albumin and thiol levels in Coronavirus disease 19: a systematic review and meta-analysis.","authors":"Asma Mousavi, Shayan Shojaei, Peyvand Parhizkar, Razman Arabzadeh Bahri, Sanam Alilou, Hanieh Radkhah","doi":"10.1186/s41512-025-00196-6","DOIUrl":"10.1186/s41512-025-00196-6","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic has imposed a significant global health burden. Identifying prognostic markers for COVID-19 and its severity could contribute to improved patient outcomes by reducing morbidity and mortality. This systematic review and meta-analysis aimed to evaluate the relationship between ischemic-modified albumin (IMA) and thiol levels, both indicators of oxidative stress, in patients diagnosed with COVID-19.</p><p><strong>Method: </strong>We conducted a comprehensive search across PubMed, Scopus, Embase, and Web of Science for eligible original studies. The study assessed IMA and thiol levels in COVID-19 patients, examining their association with both disease severity and mortality. A random effect analysis was conducted to estimate the standardized mean difference (SMD) and confidence intervals (CI).</p><p><strong>Results: </strong>Sixteen studies comprising 2010 COVID-19 patients and 982 controls were included. A diagnosis of COVID-19 was associated with significantly elevated IMA levels (Hedges's g = 1.02, 95% CI: 0.45 to 1.60) and reduced total thiol levels (Hedges's g = -1.08, 95% CI: -2.10 to -0.07). However, native thiol levels did not reveal a significant difference between infected patients and healthy participants. Subgroup analysis showed significantly lower total thiol levels in patients with critical and severe COVID-19, as well as lower native thiol levels specifically in critical COVID-19 patients. IMA levels were significantly higher across the critical, severe, and moderate COVID-19 groups.</p><p><strong>Conclusion: </strong>Elevated IMA and reduced thiol levels may serve as novel markers for predicting COVID-19 severity and prognosis. Further research is needed to explore therapeutic interventions that target oxidative imbalance in COVID-19 patients.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"13"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12183906/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144478058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}