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      Using 30-day modified rankin scale score to predict 90-day score in patients with intracranial hemorrhage: Derivation and validation of prediction model

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          Abstract

          Whether 30-day modified Rankin Scale (mRS) scores can predict 90-day scores is unclear. This study derived and validated a model to predict ordinal 90-day mRS score in an intracerebral hemorrhage (ICH) population using 30-day mRS values and routinely available baseline variables. Adults enrolled in the Antihypertensive Treatment of Acute Cerebral Hemorrhage-2 (ATACH-2) trial between May 2011 and September 2015 with acute ICH, who were alive at 30 days and had mRS scores reported at both 30 and 90 days were included in this post-hoc analysis. A proportional odds regression model for predicting ordinal 90-day mRS scores was developed and internally validated using bootstrapping. Variables in the model included: mRS score at 30 days, age (years), hematoma volume (cm 3), hematoma location (deep [basal ganglia, thalamus], lobar, or infratentorial), presence of intraventricular hemorrhage (IVH), baseline Glasgow Coma Scale (GCS) score, and National Institutes of Health Stroke Scale (NIHSS) score at randomization. We assessed model fit, calibration, discrimination, and agreement (ordinal, dichotomized functional independence), and EuroQol-5D ([EQ-5D] utility weighted) between predicted and observed 90-day mRS. A total of 898/1000 participants were included. Following bootstrap internal validation, our model (calibration slope = 0.967) had an optimism-corrected c-index of 0.884 (95% CI = 0.873–0.896) and R 2 = 0.712 for 90-day mRS score. The weighted ĸ for agreement between observed and predicted ordinal 90-day mRS score was 0.811 (95% CI = 0.787–0.834). Agreement between observed and predicted functional independence (mRS score of 0–2) at 90 days was 74.3% (95% CI = 69.9–78.7%). The mean ± SD absolute difference between predicted and observed EQ-5D–weighted mRS score was negligible (0.005 ± 0.145). This tool allows practitioners and researchers to utilize clinically available information along with the mRS score 30 days after ICH to reliably predict the mRS score at 90 days.

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          Assessing the performance of prediction models: a framework for traditional and novel measures.

          The performance of prediction models can be assessed using a variety of methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic [ROC] curve), and goodness-of-fit statistics for calibration.Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision-analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration, we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n = 544 for model development, n = 273 for external validation).We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.
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            Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.

            Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
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              Calculating the sample size required for developing a clinical prediction model

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: ValidationRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                21 May 2024
                2024
                : 19
                : 5
                : e0303757
                Affiliations
                [1 ] University of Connecticut School of Pharmacy, Storrs, CT, United States of America
                [2 ] Evidence-Based Practice Center, Hartford Hospital, Hartford, CT, United States of America
                [3 ] Division of Neurology, Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada
                [4 ] Guy’s and St. Thomas’ Hospitals, King’s College London, London, United Kingdom
                [5 ] Medical and Payer Evidence, BioPharmaceuticals Medical, AstraZeneca, Cambridge, United Kingdom
                [6 ] AstraZeneca Pharmaceuticals, Wilmington, DE, United States of America
                [7 ] Statistical Consulting Services, Center for Open Research Resources & Equipment, University of Connecticut, Storrs, CT, United States of America
                [8 ] Department of Statistics, University of Connecticut, Storrs, CT, United States of America
                Bach Mai Hospital, VIET NAM
                Author notes

                Competing Interests: WLB and GF have no conflicts of interest to report. MS reports receiving research grants from Bristol Myers Squibb, Bayer, and Javelin Medical; and consulting fees from Janssen, HLS Therapeutics, AstraZeneca, and Bayer. AC has received fees for serving on an adjudication committee from Boehringer Ingelheim and AbbVie; grant support and fees for serving on committees from AstraZeneca, Bristol Myers Squibb, Daiichi Sankyo, and Pfizer; consulting fees from Janssen, Portola Pharmaceuticals, and Ono Pharmaceuticals; and fees for serving on a steering committee and consulting fees from Bayer. MJC is a previous employee of AstraZeneca. MO and BC are employees of AstraZeneca. TEM has nothing to disclose. CIC has received research funding and/or consulting honoraria from Janssen Pharmaceuticals, Bayer AG, and AstraZeneca.

                Author information
                https://orcid.org/0000-0003-4868-7158
                Article
                PONE-D-23-30083
                10.1371/journal.pone.0303757
                11108121
                38771834
                d7caf81f-9195-4ff2-9eb2-5229b5db4d44
                © 2024 Baker et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 21 September 2023
                : 30 April 2024
                Page count
                Figures: 2, Tables: 3, Pages: 11
                Funding
                Funded by: Alexion, AstraZeneca Rare Disease
                This study was sponsored by Alexion, AstraZeneca Rare Disease. The funders were involved with the study design, data collection and analysis, and critical revision of the manuscript. Editorial support by Lumanity Communications, Inc was also sponsored by Alexion, AstraZeneca Rare Disease.
                Categories
                Research Article
                Medicine and Health Sciences
                Clinical Medicine
                Signs and Symptoms
                Hemorrhage
                Medicine and Health Sciences
                Vascular Medicine
                Hemorrhage
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Forecasting
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Forecasting
                Medicine and Health Sciences
                Medical Conditions
                Cerebrovascular Diseases
                Stroke
                Hemorrhagic Stroke
                Medicine and Health Sciences
                Neurology
                Cerebrovascular Diseases
                Stroke
                Hemorrhagic Stroke
                Medicine and Health Sciences
                Vascular Medicine
                Stroke
                Hemorrhagic Stroke
                Medicine and Health Sciences
                Medical Conditions
                Cerebrovascular Diseases
                Stroke
                Ischemic Stroke
                Medicine and Health Sciences
                Neurology
                Cerebrovascular Diseases
                Stroke
                Ischemic Stroke
                Medicine and Health Sciences
                Vascular Medicine
                Stroke
                Ischemic Stroke
                Biology and Life Sciences
                Anatomy
                Brain
                Thalamus
                Medicine and Health Sciences
                Anatomy
                Brain
                Thalamus
                Medicine and Health Sciences
                Neurology
                Coma
                Biology and Life Sciences
                Anatomy
                Brain
                Basal Ganglia
                Medicine and Health Sciences
                Anatomy
                Brain
                Basal Ganglia
                Medicine and Health Sciences
                Medical Conditions
                Cerebrovascular Diseases
                Stroke
                Medicine and Health Sciences
                Neurology
                Cerebrovascular Diseases
                Stroke
                Medicine and Health Sciences
                Vascular Medicine
                Stroke
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                All relevant data are within the paper and its Supporting Information files.

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