Diagnostic and prognostic research最新文献

{"title":"Exploring the general practitioners' point of view about clinical scores: a qualitative study.","authors":"Maxime Pautrat,&nbsp;Remy Palluau,&nbsp;Loic Druilhe,&nbsp;Jean Pierre Lebeau","doi":"10.1186/s41512-023-00149-x","DOIUrl":"https://doi.org/10.1186/s41512-023-00149-x","url":null,"abstract":"<p><strong>Background: </strong>Clinical scores help physicians to make clinical decisions, and some are recommended by health authorities for primary care use. As an increasing number of scores are becoming available, there is a need to understand general practitioner expectations for their use in primary care. The aim of this study was to explore general practitioner opinions about using scores in general practice.</p><p><strong>Method: </strong>This qualitative study, with a grounded theory approach, used focus groups with general practitioners recruited from their own surgeries to obtain verbatim. Two investigators performed verbatim analysis to ensure data triangulation. The verbatim was double-blind labeled for inductive categorization to conceptualize score use in general practice.</p><p><strong>Results: </strong>Five focus groups were planned, 21 general practitioners from central France participated. Participants appreciated scores for their clinical efficacy but felt that they were difficult to use in primary care. Their opinions revolved around validity, acceptability, and feasibility. Participants have little regard for score validity, they felt many scores are difficult to accept and do not capture contextual and human elements. Participants also felt that scores are unfeasible for primary care use. There are too many, they are hard to find, and either too short or too long. They also felt that scores were complex to administer and took up time for both patient and physician. Many participants felt learned societies should choose appropriate scores.</p><p><strong>Discussion: </strong>This study conceptualizes general practitioner opinions about score use in primary care. The participants weighed up score effectiveness with efficiency. For some participants, scores helped make decisions faster, others expressed being disappointed with the lack of patient-centeredness and limited bio-psycho-social approach.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"7 1","pages":"12"},"PeriodicalIF":0.0,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10262349/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10011074","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":"Decision curve analysis: confidence intervals and hypothesis testing for net benefit.","authors":"Andrew J Vickers, Ben Van Claster, Laure Wynants, Ewout W Steyerberg","doi":"10.1186/s41512-023-00148-y","DOIUrl":"10.1186/s41512-023-00148-y","url":null,"abstract":"<p><strong>Background: </strong>A number of recent papers have proposed methods to calculate confidence intervals and p values for net benefit used in decision curve analysis. These papers are sparse on the rationale for doing so. We aim to assess the relation between sampling variability, inference, and decision-analytic concepts.</p><p><strong>Methods and results: </strong>We review the underlying theory of decision analysis. When we are forced into a decision, we should choose the option with the highest expected utility, irrespective of p values or uncertainty. This is in some distinction to traditional hypothesis testing, where a decision such as whether to reject a given hypothesis can be postponed. Application of inference for net benefit would generally be harmful. In particular, insisting that differences in net benefit be statistically significant would dramatically change the criteria by which we consider a prediction model to be of value. We argue instead that uncertainty related to sampling variation for net benefit should be thought of in terms of the value of further research. Decision analysis tells us which decision to make for now, but we may also want to know how much confidence we should have in that decision. If we are insufficiently confident that we are right, further research is warranted.</p><p><strong>Conclusion: </strong>Null hypothesis testing or simple consideration of confidence intervals are of questionable value for decision curve analysis, and methods such as value of information analysis or approaches to assess the probability of benefit should be considered instead.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"7 1","pages":"11"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10243069/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9962890","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":"Methodological concerns about \"concordance-statistic for benefit\" as a measure of discrimination in predicting treatment benefit.","authors":"Yuan Xia, Paul Gustafson, Mohsen Sadatsafavi","doi":"10.1186/s41512-023-00147-z","DOIUrl":"10.1186/s41512-023-00147-z","url":null,"abstract":"<p><p>Prediction algorithms that quantify the expected benefit of a given treatment conditional on patient characteristics can critically inform medical decisions. Quantifying the performance of treatment benefit prediction algorithms is an active area of research. A recently proposed metric, the concordance statistic for benefit (cfb), evaluates the discriminative ability of a treatment benefit predictor by directly extending the concept of the concordance statistic from a risk model with a binary outcome to a model for treatment benefit. In this work, we scrutinize cfb on multiple fronts. Through numerical examples and theoretical developments, we show that cfb is not a proper scoring rule. We also show that it is sensitive to the unestimable correlation between counterfactual outcomes and to the definition of matched pairs. We argue that measures of statistical dispersion applied to predicted benefits do not suffer from these issues and can be an alternative metric for the discriminatory performance of treatment benefit predictors.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"7 1","pages":"10"},"PeriodicalIF":0.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10186693/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9478062","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":"Leave-one-out cross-validation, penalization, and differential bias of some prediction model performance measures-a simulation study.","authors":"Angelika Geroldinger,&nbsp;Lara Lusa,&nbsp;Mariana Nold,&nbsp;Georg Heinze","doi":"10.1186/s41512-023-00146-0","DOIUrl":"https://doi.org/10.1186/s41512-023-00146-0","url":null,"abstract":"<p><strong>Background: </strong>The performance of models for binary outcomes can be described by measures such as the concordance statistic (c-statistic, area under the curve), the discrimination slope, or the Brier score. At internal validation, data resampling techniques, e.g., cross-validation, are frequently employed to correct for optimism in these model performance criteria. Especially with small samples or rare events, leave-one-out cross-validation is a popular choice.</p><p><strong>Methods: </strong>Using simulations and a real data example, we compared the effect of different resampling techniques on the estimation of c-statistics, discrimination slopes, and Brier scores for three estimators of logistic regression models, including the maximum likelihood and two maximum penalized likelihood estimators.</p><p><strong>Results: </strong>Our simulation study confirms earlier studies reporting that leave-one-out cross-validated c-statistics can be strongly biased towards zero. In addition, our study reveals that this bias is even more pronounced for model estimators shrinking estimated probabilities towards the observed event fraction, such as ridge regression. Leave-one-out cross-validation also provided pessimistic estimates of the discrimination slope but nearly unbiased estimates of the Brier score.</p><p><strong>Conclusions: </strong>We recommend to use leave-pair-out cross-validation, fivefold cross-validation with repetitions, the enhanced or the .632+ bootstrap to estimate c-statistics, and leave-pair-out or fivefold cross-validation to estimate discrimination slopes.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"7 1","pages":"9"},"PeriodicalIF":0.0,"publicationDate":"2023-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10152625/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9460319","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":"Effects of influential points and sample size on the selection and replicability of multivariable fractional polynomial models.","authors":"Willi Sauerbrei, Edwin Kipruto, James Balmford","doi":"10.1186/s41512-023-00145-1","DOIUrl":"10.1186/s41512-023-00145-1","url":null,"abstract":"<p><strong>Background: </strong>The multivariable fractional polynomial (MFP) approach combines variable selection using backward elimination with a function selection procedure (FSP) for fractional polynomial (FP) functions. It is a relatively simple approach which can be easily understood without advanced training in statistical modeling. For continuous variables, a closed test procedure is used to decide between no effect, linear, FP1, or FP2 functions. Influential points (IPs) and small sample sizes can both have a strong impact on a selected function and MFP model.</p><p><strong>Methods: </strong>We used simulated data with six continuous and four categorical predictors to illustrate approaches which can help to identify IPs with an influence on function selection and the MFP model. Approaches use leave-one or two-out and two related techniques for a multivariable assessment. In eight subsamples, we also investigated the effects of sample size and model replicability, the latter by using three non-overlapping subsamples with the same sample size. For better illustration, a structured profile was used to provide an overview of all analyses conducted.</p><p><strong>Results: </strong>The results showed that one or more IPs can drive the functions and models selected. In addition, with a small sample size, MFP was not able to detect some non-linear functions and the selected model differed substantially from the true underlying model. However, when the sample size was relatively large and regression diagnostics were carefully conducted, MFP selected functions or models that were similar to the underlying true model.</p><p><strong>Conclusions: </strong>For smaller sample size, IPs and low power are important reasons that the MFP approach may not be able to identify underlying functional relationships for continuous variables and selected models might differ substantially from the true model. However, for larger sample sizes, a carefully conducted MFP analysis is often a suitable way to select a multivariable regression model which includes continuous variables. In such a case, MFP can be the preferred approach to derive a multivariable descriptive model.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"7 1","pages":"7"},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10111698/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9336562","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 study protocol of external validation of eight COVID-19 prognostic models for predicting mortality risk in older populations in a hospital, primary care, and nursing home setting.","authors":"Anum Zahra,&nbsp;Kim Luijken,&nbsp;Evertine J Abbink,&nbsp;Jesse M van den Berg,&nbsp;Marieke T Blom,&nbsp;Petra Elders,&nbsp;Jan Festen,&nbsp;Jacobijn Gussekloo,&nbsp;Karlijn J Joling,&nbsp;René Melis,&nbsp;Simon Mooijaart,&nbsp;Jeannette B Peters,&nbsp;Harmke A Polinder-Bos,&nbsp;Bas F M van Raaij,&nbsp;Annemieke Smorenberg,&nbsp;Hannah M la Roi-Teeuw,&nbsp;Karel G M Moons,&nbsp;Maarten van Smeden","doi":"10.1186/s41512-023-00144-2","DOIUrl":"https://doi.org/10.1186/s41512-023-00144-2","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic has a large impact worldwide and is known to particularly affect the older population. This paper outlines the protocol for external validation of prognostic models predicting mortality risk after presentation with COVID-19 in the older population. These prognostic models were originally developed in an adult population and will be validated in an older population (≥ 70 years of age) in three healthcare settings: the hospital setting, the primary care setting, and the nursing home setting.</p><p><strong>Methods: </strong>Based on a living systematic review of COVID-19 prediction models, we identified eight prognostic models predicting the risk of mortality in adults with a COVID-19 infection (five COVID-19 specific models: GAL-COVID-19 mortality, 4C Mortality Score, NEWS2 + model, Xie model, and Wang clinical model and three pre-existing prognostic scores: APACHE-II, CURB65, SOFA). These eight models will be validated in six different cohorts of the Dutch older population (three hospital cohorts, two primary care cohorts, and a nursing home cohort). All prognostic models will be validated in a hospital setting while the GAL-COVID-19 mortality model will be validated in hospital, primary care, and nursing home settings. The study will include individuals ≥ 70 years of age with a highly suspected or PCR-confirmed COVID-19 infection from March 2020 to December 2020 (and up to December 2021 in a sensitivity analysis). The predictive performance will be evaluated in terms of discrimination, calibration, and decision curves for each of the prognostic models in each cohort individually. For prognostic models with indications of miscalibration, an intercept update will be performed after which predictive performance will be re-evaluated.</p><p><strong>Discussion: </strong>Insight into the performance of existing prognostic models in one of the most vulnerable populations clarifies the extent to which tailoring of COVID-19 prognostic models is needed when models are applied to the older population. Such insight will be important for possible future waves of the COVID-19 pandemic or future pandemics.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"7 1","pages":"8"},"PeriodicalIF":0.0,"publicationDate":"2023-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10069944/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9270992","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 validation of a physical frailty phenotype index-based model to estimate the frailty index.","authors":"Yong-Hao Pua, Laura Tay, Ross Allan Clark, Julian Thumboo, Ee-Ling Tay, Shi-Min Mah, Pei-Yueng Lee, Yee-Sien Ng","doi":"10.1186/s41512-023-00143-3","DOIUrl":"10.1186/s41512-023-00143-3","url":null,"abstract":"<p><strong>Background: </strong>The conventional count-based physical frailty phenotype (PFP) dichotomizes its criterion predictors-an approach that creates information loss and depends on the availability of population-derived cut-points. This study proposes an alternative approach to computing the PFP by developing and validating a model that uses PFP components to predict the frailty index (FI) in community-dwelling older adults, without the need for predictor dichotomization.</p><p><strong>Methods: </strong>A sample of 998 community-dwelling older adults (mean [SD], 68 [7] years) participated in this prospective cohort study. Participants completed a multi-domain geriatric screen and a physical fitness assessment from which the count-based PFP and the 36-item FI were computed. One-year prospective falls and hospitalization rates were also measured. Bayesian beta regression analysis, allowing for nonlinear effects of the non-dichotomized PFP criterion predictors, was used to develop a model for FI (\"model-based PFP\"). Approximate leave-one-out (LOO) cross-validation was used to examine model overfitting.</p><p><strong>Results: </strong>The model-based PFP showed good calibration with the FI, and it had better out-of-sample predictive performance than the count-based PFP (LOO-R², 0.35 vs 0.22). In clinical terms, the improvement in prediction (i) translated to improved classification agreement with the FI (Cohen's k_w, 0.47 vs 0.36) and (ii) resulted primarily in a 23% (95%CI, 18-28%) net increase in FI-defined \"prefrail/frail\" participants correctly classified. The model-based PFP showed stronger prognostic performance for predicting falls and hospitalization than did the count-based PFP.</p><p><strong>Conclusion: </strong>The developed model-based PFP predicted FI and clinical outcomes more strongly than did the count-based PFP in community-dwelling older adults. By not requiring predictor cut-points, the model-based PFP potentially facilitates usage and feasibility. Future validation studies should aim to obtain clear evidence on the benefits of this approach.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"7 1","pages":"5"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10029224/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9161565","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":"Clinical prediction model for prognosis in kidney transplant recipients (KIDMO): study protocol.","authors":"Simon Schwab, Daniel Sidler, Fadi Haidar, Christian Kuhn, Stefan Schaub, Michael Koller, Katell Mellac, Ueli Stürzinger, Bruno Tischhauser, Isabelle Binet, Déla Golshayan, Thomas Müller, Andreas Elmer, Nicola Franscini, Nathalie Krügel, Thomas Fehr, Franz Immer","doi":"10.1186/s41512-022-00139-5","DOIUrl":"10.1186/s41512-022-00139-5","url":null,"abstract":"<p><strong>Background: </strong>Many potential prognostic factors for predicting kidney transplantation outcomes have been identified. However, in Switzerland, no widely accepted prognostic model or risk score for transplantation outcomes is being routinely used in clinical practice yet. We aim to develop three prediction models for the prognosis of graft survival, quality of life, and graft function following transplantation in Switzerland.</p><p><strong>Methods: </strong>The clinical kidney prediction models (KIDMO) are developed with data from a national multi-center cohort study (Swiss Transplant Cohort Study; STCS) and the Swiss Organ Allocation System (SOAS). The primary outcome is the kidney graft survival (with death of recipient as competing risk); the secondary outcomes are the quality of life (patient-reported health status) at 12 months and estimated glomerular filtration rate (eGFR) slope. Organ donor, transplantation, and recipient-related clinical information will be used as predictors at the time of organ allocation. We will use a Fine & Gray subdistribution model and linear mixed-effects models for the primary and the two secondary outcomes, respectively. Model optimism, calibration, discrimination, and heterogeneity between transplant centres will be assessed using bootstrapping, internal-external cross-validation, and methods from meta-analysis.</p><p><strong>Discussion: </strong>Thorough evaluation of the existing risk scores for the kidney graft survival or patient-reported outcomes has been lacking in the Swiss transplant setting. In order to be useful in clinical practice, a prognostic score needs to be valid, reliable, clinically relevant, and preferably integrated into the decision-making process to improve long-term patient outcomes and support informed decisions for clinicians and their patients. The state-of-the-art methodology by taking into account competing risks and variable selection using expert knowledge is applied to data from a nationwide prospective multi-center cohort study. Ideally, healthcare providers together with patients can predetermine the risk they are willing to accept from a deceased-donor kidney, with graft survival, quality of life, and graft function estimates available for their consideration.</p><p><strong>Study registration: </strong>Open Science Framework ID: z6mvj.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"7 1","pages":"6"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9990297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9084527","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":"IMplementing Predictive Analytics towards efficient COPD Treatments (IMPACT): protocol for a stepped-wedge cluster randomized impact study.","authors":"Kristina D Michaux,&nbsp;Rebecca K Metcalfe,&nbsp;Paloma Burns,&nbsp;Annalijn I Conklin,&nbsp;Alison M Hoens,&nbsp;Daniel Smith,&nbsp;Laura Struik,&nbsp;Abdollah Safari,&nbsp;Don D Sin,&nbsp;Mohsen Sadatsafavi","doi":"10.1186/s41512-023-00140-6","DOIUrl":"https://doi.org/10.1186/s41512-023-00140-6","url":null,"abstract":"<p><strong>Introduction: </strong>Personalized disease management informed by quantitative risk prediction has the potential to improve patient care and outcomes. The integration of risk prediction into clinical workflow should be informed by the experiences and preferences of stakeholders, and the impact of such integration should be evaluated in prospective comparative studies. The objectives of the IMplementing Predictive Analytics towards efficient chronic obstructive pulmonary disease (COPD) treatments (IMPACT) study are to integrate an exacerbation risk prediction tool into routine care and to determine its impact on prescription appropriateness (primary outcome), medication adherence, quality of life, exacerbation rates, and sex and gender disparities in COPD care (secondary outcomes).</p><p><strong>Methods: </strong>IMPACT will be conducted in two phases. Phase 1 will include the systematic and user-centered development of two decision support tools: (1) a decision tool for pulmonologists called the ACCEPT decision intervention (ADI), which combines risk prediction from the previously developed Acute COPD Exacerbation Prediction Tool with treatment algorithms recommended by the Canadian Thoracic Society's COPD pharmacotherapy guidelines, and (2) an information pamphlet for COPD patients (patient tool), tailored to their prescribed medication, clinical needs, and lung function. In phase 2, we will conduct a stepped-wedge cluster randomized controlled trial in two outpatient respiratory clinics to evaluate the impact of the decision support tools on quality of care and patient outcomes. Clusters will be practicing pulmonologists (n ≥ 24), who will progressively switch to the intervention over 18 months. At the end of the study, a qualitative process evaluation will be carried out to determine the barriers and enablers of uptake of the tools.</p><p><strong>Discussion: </strong>The IMPACT study coincides with a planned harmonization of electronic health record systems across tertiary care centers in British Columbia, Canada. The harmonization of these systems combined with IMPACT's implementation-oriented design and partnership with stakeholders will facilitate integration of the tools into routine care, if the results of the proposed study reveal positive association with improvement in the process and outcomes of clinical care. The process evaluation at the end of the trial will inform subsequent design iterations before largescale implementation.</p><p><strong>Trial registration: </strong>NCT05309356.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"7 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9926816/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10793486","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 diagnostic prediction model for psoriasis severity.","authors":"Mie Sylow Liljendahl,&nbsp;Nikolai Loft,&nbsp;Alexander Egeberg,&nbsp;Lone Skov,&nbsp;Tri-Long Nguyen","doi":"10.1186/s41512-023-00141-5","DOIUrl":"https://doi.org/10.1186/s41512-023-00141-5","url":null,"abstract":"<p><strong>Background: </strong>While administrative health records such as national registries may be useful data sources to study the epidemiology of psoriasis, they do not generally contain information on disease severity.</p><p><strong>Objectives: </strong>To develop a diagnostic model to distinguish psoriasis severity based on administrative register data.</p><p><strong>Method: </strong>We conducted a retrospective registry-based cohort study using the Danish Skin Cohort linked with the Danish national registries. We developed a diagnostic model using a gradient boosting machine learning technique to predict moderate-to-severe psoriasis. We performed an internal validation of the model by bootstrapping to account for any optimism.</p><p><strong>Results: </strong>Among 4016 adult psoriasis patients (55.8% women, mean age 59 years) included in this study, 1212 (30.2%) patients were identified as having moderate-to-severe psoriasis. The diagnostic prediction model yielded a bootstrap-corrected discrimination performance: c-statistic equal to 0.73 [95% CI: 0.71-0.74]. The internal validation by bootstrap correction showed no substantial optimism in the results with a c-statistic of 0.72 [95% CI: 0.70-0.74]. A bootstrap-corrected slope of 1.10 [95% CI: 1.07-1.13] indicated a slight under-fitting.</p><p><strong>Conclusion: </strong>Based on register data, we developed a gradient boosting diagnostic model returning acceptable prediction of patients with moderate-to-severe psoriasis.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"7 1","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9903561/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10674399","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}