The Experts below are selected from a list of 43383 Experts worldwide ranked by ideXlab platform
Ewout W. Steyerberg - One of the best experts on this subject based on the ideXlab platform.
-
Prediction of pre-eclampsia-related complications in women with suspected/confirmed pre-eclampsia: development and internal validation of a Clinical Prediction Model.
Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology, 2020Co-Authors: Langeza Saleh, Daan Nieboer, Ewout W. Steyerberg, Yvonne Vergouwe, Maaike Alblas, Rugina I. Neuman, I Brussé, J.j. Duvekot, Hans J. Versendaal, Jan DanserAbstract:Background A Clinical Prediction Model that could reliably predict the risk of preeclampsia (PE)-related pregnancy complications does not exist. Methods We aimed to develop a Model to predict the composite outcome of PE-related pregnancy complications, consisting of maternal and fetal adverse within 7, 14 and 30 days in women with suspected or confirmed PE. Data of 384 women from a prospective, multicenter, observational cohort study (n=620) were used. For the development of the Prediction Model the possible contribution of Clinical and standard laboratory variables as well as the biomarkers soluble Fms like tyrosine kinase-1 (sFlt-1), placental growth factor (PlGF) and their ratio was explored using a multivariable competing risk regression analysis. We assessed the discriminative ability of the Model with the concordance (c-) statistic. A bootstrap validation procedure with 500 replications was used to correct the estimate of the Prediction Model performance for optimism and to compute a shrinkage factor for the regression coefficients to correct for overfitting. Results Among 384 women with suspected/confirmed PE, 96 had PE-related adverse outcomes at any time after hospital admission. Important predictors of PE-related outcomes included sFlt-1/PlGF ratio (continuous), gestational age at time of biomarker measurement (continuous) and protein-to-creatinine ratio (continuous). The c-statistics (corrected for optimism) for developing a PE-related complication within 7, 14 and 30 days were 0.89, 0.88 and 0.87 respectively. There was limited overfitting as indicated by a shrinkage factor of 0.91. Conclusions We propose a simple Clinical Prediction Model with good discriminative performance to predict short-term and longer term PE-related complications. Its usefulness in Clinical practice awaits further investigation and external validation. This article is protected by copyright. All rights reserved.
-
Prediction of pre eclampsia related complications in women with suspected confirmed pre eclampsia development and internal validation of a Clinical Prediction Model
Ultrasound in Obstetrics & Gynecology, 2020Co-Authors: Langeza Saleh, Daan Nieboer, Ewout W. Steyerberg, Yvonne Vergouwe, Maaike Alblas, Rugina I. Neuman, I Brussé, J.j. Duvekot, Hans J. Versendaal, Jan DanserAbstract:Background A Clinical Prediction Model that could reliably predict the risk of preeclampsia (PE)-related pregnancy complications does not exist. Methods We aimed to develop a Model to predict the composite outcome of PE-related pregnancy complications, consisting of maternal and fetal adverse within 7, 14 and 30 days in women with suspected or confirmed PE. Data of 384 women from a prospective, multicenter, observational cohort study (n=620) were used. For the development of the Prediction Model the possible contribution of Clinical and standard laboratory variables as well as the biomarkers soluble Fms like tyrosine kinase-1 (sFlt-1), placental growth factor (PlGF) and their ratio was explored using a multivariable competing risk regression analysis. We assessed the discriminative ability of the Model with the concordance (c-) statistic. A bootstrap validation procedure with 500 replications was used to correct the estimate of the Prediction Model performance for optimism and to compute a shrinkage factor for the regression coefficients to correct for overfitting. Results Among 384 women with suspected/confirmed PE, 96 had PE-related adverse outcomes at any time after hospital admission. Important predictors of PE-related outcomes included sFlt-1/PlGF ratio (continuous), gestational age at time of biomarker measurement (continuous) and protein-to-creatinine ratio (continuous). The c-statistics (corrected for optimism) for developing a PE-related complication within 7, 14 and 30 days were 0.89, 0.88 and 0.87 respectively. There was limited overfitting as indicated by a shrinkage factor of 0.91. Conclusions We propose a simple Clinical Prediction Model with good discriminative performance to predict short-term and longer term PE-related complications. Its usefulness in Clinical practice awaits further investigation and external validation. This article is protected by copyright. All rights reserved.
-
risk factors and a Clinical Prediction Model for low maternal thyroid function during early pregnancy two population based prospective cohort studies
Clinical Endocrinology, 2016Co-Authors: Tim I M Korevaar, Daan Nieboer, Peter H Bisschop, Mariette Goddijn, Marco Medici, Layal Chaker, Yolanda B De Rijke, Vincent W V Jaddoe, Theo J Visser, Ewout W. SteyerbergAbstract:SummaryBackground Low maternal thyroid function during early pregnancy is associated with various adverse outcomes including impaired neurocognitive development of the offspring, premature delivery and abnormal birthweight. Aim To aid doctors in the risk assessment of thyroid dysfunction during pregnancy, we set out to investigate Clinical risk factors and derive a Prediction Model based on easily obtainable Clinical variables. Methods In total, 9767 women during early pregnancy (≤18 week) were selected from two population-based prospective cohorts: the Generation R Study (N = 5985) and the ABCD study (N = 3782). We aimed to investigate the association of easily obtainable Clinical subject characteristics such as maternal age, BMI, smoking status, ethnicity, parity and gestational age at blood sampling with the risk of low free thyroxine (FT4) and elevated thyroid stimulating hormone (TSH), determined according to the 2·5th–97·5th reference range in TPOAb negative women. Results BMI, nonsmoking and ethnicity were risk factors for elevated TSH levels; however, the discriminative ability was poor (range c-statistic of 0·57–0·60). Sensitivity analysis showed that addition of TPOAbs to the Model yielded a c-statistic of 0·73–0·75. Maternal age, BMI, smoking, parity and gestational age at blood sampling were risk factors for low FT4, which taken together provided adequate discrimination (range c-statistic of 0·72–0·76). Conclusions Elevated TSH levels depend predominantly on TPOAb levels, and Prediction of elevated TSH levels is not possible with Clinical characteristics only. In contrast, the validated Clinical Prediction Model for FT4 had high discriminative value to assess the likelihood of low FT4 levels.
-
Clinical Prediction Model to identify vulnerable patients in ambulatory surgery: towards optimal medical decision-making
Canadian Journal of Anesthesia Journal canadien d'anesthésie, 2016Co-Authors: Herjan Mijderwijk, Robert Jan Stolker, Hugo J. Duivenvoorden, Markus Klimek, Ewout W. SteyerbergAbstract:Contexte Les patients de chirurgie ambulatoire courent un risque de complications psychologiques telles que l’anxiété, l’agressivité, la fatigue et la dépression. Nous avons mis au point et validé un modèle de prédiction clinique permettant d’identifier les patients vulnérables à ces paramètres de complications psychologiques. Méthode Nous avons évalué la vulnérabilité psychologique de 383 patients de chirurgie ambulatoire des deux sexes de façon prospective. La vulnérabilité psychologique a été définie comme la présence d’anxiété (état/trait), d’agressivité (état/trait), de fatigue et de dépression sept jours après la chirurgie. Trois catégories de vulnérabilité psychologique ont été prises en compte, c’est-à-dire aucun, un ou plusieurs scores bas. Un score bas était défini en tant que score excédant un écart type au-dessus de la moyenne pour chacune des variables spécifiques selon les données normatives. Les déterminants suivants ont été évalués en période préopératoire : les variables sociodémographiques (âge, sexe, degré d’instruction, situation d’emploi, état matrimonial, présence d’enfants, religion, nationalité), les variables médicales (fréquence cardiaque et indice de masse corporel) et les variables psychologiques (estime de soi et connaissance de ses propres capacités), ainsi que l’anxiété, l’agressivité, la fatigue et la dépression. Un modèle de prédiction a été élaboré en se servant d’une analyse de régression logistique polytomique ordinale, et une méthode de ré-échantillonnage de type Bootstrap a été appliquée pour la validation interne. L’indice c ordinal (ORC) quantifiait la capacité discriminatoire du modèle, outre les mesures de la performance globale du modèle (le R ^ 2 de Nagelkerke). Résultats Dans cette population, 137 (36 %) patients ont été identifiés comme étant psychologiquement vulnérables après la chirurgie en ce qui touchait à au moins un des critères psychologiques. Le modèle de prédiction le plus parcimonieux et optimal combinait des variables sociodémographiques (degré d’instruction, présence d’enfants et nationalité) à nos variables psychologiques (trait d’anxiété, état/trait d’agressivité, fatigue et dépression). La performance du modèle était prometteuse : R ^ 2 = 30 % et ORC = 0,76 après correction pour tenir compte de l’optimisme. Conclusion Cette étude a identifié un groupe considérable de patients vulnérables en chirurgie ambulatoire. Le modèle de prédiction clinique proposée pourrait donner aux professionnels de la santé l’occasion d’identifier les patients vulnérables en chirurgie ambulatoire, bien que des modifications et une validation supplémentaires soient nécessaires. (Numéro ClinicalTrials.gov, NCT01441843). Background Ambulatory surgery patients are at risk of adverse psychological outcomes such as anxiety, aggression, fatigue, and depression. We developed and validated a Clinical Prediction Model to identify patients who were vulnerable to these psychological outcome parameters. Methods We prospectively assessed 383 mixed ambulatory surgery patients for psychological vulnerability, defined as the presence of anxiety (state/trait), aggression (state/trait), fatigue, and depression seven days after surgery. Three psychological vulnerability categories were considered–i.e., none, one, or multiple poor scores, defined as a score exceeding one standard deviation above the mean for each single outcome according to normative data. The following determinants were assessed preoperatively: sociodemographic (age, sex, level of education, employment status, marital status, having children, religion, nationality), medical (heart rate and body mass index), and psychological variables (self-esteem and self-efficacy), in addition to anxiety, aggression, fatigue, and depression. A Prediction Model was constructed using ordinal polytomous logistic regression analysis, and bootstrapping was applied for internal validation. The ordinal c -index (ORC) quantified the discriminative ability of the Model, in addition to measures for overall Model performance (Nagelkerke’s R ^ 2 ). Results In this population, 137 (36%) patients were identified as being psychologically vulnerable after surgery for at least one of the psychological outcomes. The most parsimonious and optimal Prediction Model combined sociodemographic variables (level of education, having children, and nationality) with psychological variables (trait anxiety, state/trait aggression, fatigue, and depression). Model performance was promising: R ^ 2 = 30% and ORC = 0.76 after correction for optimism. Conclusion This study identified a substantial group of vulnerable patients in ambulatory surgery. The proposed Clinical Prediction Model could allow healthcare professionals the opportunity to identify vulnerable patients in ambulatory surgery, although additional modification and validation are needed. (ClinicalTrials.gov number, NCT01441843).
-
Clinical Prediction Model to identify vulnerable patients in ambulatory surgery: towards optimal medical decision-making.
Canadian journal of anaesthesia = Journal canadien d'anesthesie, 2016Co-Authors: Herjan Mijderwijk, Robert Jan Stolker, Hugo J. Duivenvoorden, Markus Klimek, Ewout W. SteyerbergAbstract:Background Ambulatory surgery patients are at risk of adverse psychological outcomes such as anxiety, aggression, fatigue, and depression. We developed and validated a Clinical Prediction Model to identify patients who were vulnerable to these psychological outcome parameters.
Yvonne Vergouwe - One of the best experts on this subject based on the ideXlab platform.
-
Prediction of pre eclampsia related complications in women with suspected confirmed pre eclampsia development and internal validation of a Clinical Prediction Model
Ultrasound in Obstetrics & Gynecology, 2020Co-Authors: Langeza Saleh, Daan Nieboer, Ewout W. Steyerberg, Yvonne Vergouwe, Maaike Alblas, Rugina I. Neuman, I Brussé, J.j. Duvekot, Hans J. Versendaal, Jan DanserAbstract:Background A Clinical Prediction Model that could reliably predict the risk of preeclampsia (PE)-related pregnancy complications does not exist. Methods We aimed to develop a Model to predict the composite outcome of PE-related pregnancy complications, consisting of maternal and fetal adverse within 7, 14 and 30 days in women with suspected or confirmed PE. Data of 384 women from a prospective, multicenter, observational cohort study (n=620) were used. For the development of the Prediction Model the possible contribution of Clinical and standard laboratory variables as well as the biomarkers soluble Fms like tyrosine kinase-1 (sFlt-1), placental growth factor (PlGF) and their ratio was explored using a multivariable competing risk regression analysis. We assessed the discriminative ability of the Model with the concordance (c-) statistic. A bootstrap validation procedure with 500 replications was used to correct the estimate of the Prediction Model performance for optimism and to compute a shrinkage factor for the regression coefficients to correct for overfitting. Results Among 384 women with suspected/confirmed PE, 96 had PE-related adverse outcomes at any time after hospital admission. Important predictors of PE-related outcomes included sFlt-1/PlGF ratio (continuous), gestational age at time of biomarker measurement (continuous) and protein-to-creatinine ratio (continuous). The c-statistics (corrected for optimism) for developing a PE-related complication within 7, 14 and 30 days were 0.89, 0.88 and 0.87 respectively. There was limited overfitting as indicated by a shrinkage factor of 0.91. Conclusions We propose a simple Clinical Prediction Model with good discriminative performance to predict short-term and longer term PE-related complications. Its usefulness in Clinical practice awaits further investigation and external validation. This article is protected by copyright. All rights reserved.
-
Prediction of pre-eclampsia-related complications in women with suspected/confirmed pre-eclampsia: development and internal validation of a Clinical Prediction Model.
Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology, 2020Co-Authors: Langeza Saleh, Daan Nieboer, Ewout W. Steyerberg, Yvonne Vergouwe, Maaike Alblas, Rugina I. Neuman, I Brussé, J.j. Duvekot, Hans J. Versendaal, Jan DanserAbstract:Background A Clinical Prediction Model that could reliably predict the risk of preeclampsia (PE)-related pregnancy complications does not exist. Methods We aimed to develop a Model to predict the composite outcome of PE-related pregnancy complications, consisting of maternal and fetal adverse within 7, 14 and 30 days in women with suspected or confirmed PE. Data of 384 women from a prospective, multicenter, observational cohort study (n=620) were used. For the development of the Prediction Model the possible contribution of Clinical and standard laboratory variables as well as the biomarkers soluble Fms like tyrosine kinase-1 (sFlt-1), placental growth factor (PlGF) and their ratio was explored using a multivariable competing risk regression analysis. We assessed the discriminative ability of the Model with the concordance (c-) statistic. A bootstrap validation procedure with 500 replications was used to correct the estimate of the Prediction Model performance for optimism and to compute a shrinkage factor for the regression coefficients to correct for overfitting. Results Among 384 women with suspected/confirmed PE, 96 had PE-related adverse outcomes at any time after hospital admission. Important predictors of PE-related outcomes included sFlt-1/PlGF ratio (continuous), gestational age at time of biomarker measurement (continuous) and protein-to-creatinine ratio (continuous). The c-statistics (corrected for optimism) for developing a PE-related complication within 7, 14 and 30 days were 0.89, 0.88 and 0.87 respectively. There was limited overfitting as indicated by a shrinkage factor of 0.91. Conclusions We propose a simple Clinical Prediction Model with good discriminative performance to predict short-term and longer term PE-related complications. Its usefulness in Clinical practice awaits further investigation and external validation. This article is protected by copyright. All rights reserved.
-
Clinical Prediction Model to aid emergency doctors managing febrile children at risk of serious bacterial infections diagnostic study
BMJ, 2013Co-Authors: Ruud G Nijman, Ewout W. Steyerberg, Yvonne Vergouwe, Matthew Thompson, Mirjam Van Veen, Alfred H J Van Meurs, Johan Van Der Lei, Henriette A Moll, Rianne OostenbrinkAbstract:Objective To derive, cross validate, and externally validate a Clinical Prediction Model that assesses the risks of different serious bacterial infections in children with fever at the emergency department. out the presence of other SBIs. Discriminative ability (C statistic) to predict pneumonia was 0.81 (95% confidence interval 0.73 to 0.88); for other SBIs this was even better: 0.86 (0.79 to 0.92). Risk thresholds of 10% or more were useful to identify children with serious bacterial infections; risk thresholds less than 2.5% were useful to rule out the presence of serious bacterial infections. External validation showed good discrimination for the Prediction of pneumonia (0.81, 0.69 to 0.93); discriminative ability for the Prediction of other SBIs was lower (0.69, 0.53 to 0.86).
-
A simple method to adjust Clinical Prediction Models to local circumstances.
Canadian journal of anaesthesia = Journal canadien d'anesthesie, 2009Co-Authors: Kristel J M Janssen, Yvonne Vergouwe, Cor J Kalkman, Diederick E. Grobbee, Karel G M MoonsAbstract:Introduction Clinical Prediction Models estimate the risk of having or developing a particular outcome or disease. Researchers often develop a new Model when a previously developed Model is validated and the performance is poor. However, the Model can be adjusted (updated) using the new data. The updated Model is then based on both the development and validation data. We show how a simple updating method may suffice to update a Clinical Prediction Model.
-
updating methods improved the performance of a Clinical Prediction Model in new patients
Journal of Clinical Epidemiology, 2008Co-Authors: Kristel J M Janssen, Karel G M Moons, Cor J Kalkman, D E Grobbee, Yvonne VergouweAbstract:Objective: Ideally, Clinical Prediction Models are generalizable to other patient groups. Unfortunately, they perform regularly worse when validated in new patients and are then often redeveloped. While the original Prediction Model usually has been developed on a large data set, redevelopment then often occurs on the smaller validation set. Recently, methods to update existing Prediction Models with the data of new patients have been proposed. We used an existing Model that preoperatively predicts the risk of severe postoperative pain (SPP) to compare five updating methods. Study Design and Setting: The Model was tested and updated with a set of 752 new patients (274 [36] with SPP). We studied the discrimination (ability to distinguish between patients with and without SPP) and calibration (agreement between the predicted risks and observed frequencies of SPP) of the five updated Models in 283 other patients (100 [35%] with SPP). Results: Simple recalibration methods improved the calibration to a similar extent as revision methods that made more extensive adjustments to the original Model. Discrimination could not be improved by any of the methods. Conclusion: When the performance is poor in new patients, updating methods can be applied to adjust the Model, rather than to develop
Karel G M Moons - One of the best experts on this subject based on the ideXlab platform.
-
calculating the sample size required for developing a Clinical Prediction Model
BMJ, 2020Co-Authors: Richard D Riley, Joie Ensor, Kym I E Snell, Frank E Harrell, Glen P Martin, Johannes B Reitsma, Karel G M Moons, Gary S Collins, Maarten Van SmedenAbstract:Clinical Prediction Models aim to predict outcomes in individuals, to inform diagnosis or prognosis in healthcare. Hundreds of Prediction Models are published in the medical literature each year, yet many are developed using a dataset that is too small for the total number of participants or outcome events. This leads to inaccurate Predictions and consequently incorrect healthcare decisions for some individuals. In this article, the authors provide guidance on how to calculate the sample size required to develop a Clinical Prediction Model.
-
new guideline for the reporting of studies developing validating or updating a multivariable Clinical Prediction Model the tripod statement
Advances in Anatomic Pathology, 2015Co-Authors: Karel G M Moons, Johannes B Reitsma, Douglas G Altman, Gary S CollinsAbstract:Prediction Models are developed to aid health care providers in estimating the probability that a specific outcome or disease is present (diagnostic Prediction Models) or will occur in the future (prognostic Prediction Models), to inform their decision making. Prognostic Models here also include Models to predict treatment outcomes or responses; in the cancer literature often referred to as predictive Models. Clinical Prediction Models have become abundant. Pathology measurement or results are frequently included as predictors in such Prediction Models, certainly in the cancer domain. Only when full information on all aspects of a Prediction Modeling study are clearly reported, risk of bias and potential usefulness of the Prediction Model can be adequately assessed. Many reviews have illustrated that the quality of reports on the development, validation, and/or adjusting (updating) of Prediction Models, is very poor. Hence, the Transparent Reporting of a multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD) initiative has developed a comprehensive and user-friendly checklist for the reporting of studies on, both diagnostic and prognostic, Prediction Models. The TRIPOD Statement intends to improve the transparency and completeness of reporting of studies that report solely on development, both development and validation, and solely on the validation (with or without updating) of diagnostic or prognostic, including predictive, Models.
-
A simple method to adjust Clinical Prediction Models to local circumstances.
Canadian journal of anaesthesia = Journal canadien d'anesthesie, 2009Co-Authors: Kristel J M Janssen, Yvonne Vergouwe, Cor J Kalkman, Diederick E. Grobbee, Karel G M MoonsAbstract:Introduction Clinical Prediction Models estimate the risk of having or developing a particular outcome or disease. Researchers often develop a new Model when a previously developed Model is validated and the performance is poor. However, the Model can be adjusted (updated) using the new data. The updated Model is then based on both the development and validation data. We show how a simple updating method may suffice to update a Clinical Prediction Model.
-
updating methods improved the performance of a Clinical Prediction Model in new patients
Journal of Clinical Epidemiology, 2008Co-Authors: Kristel J M Janssen, Karel G M Moons, Cor J Kalkman, D E Grobbee, Yvonne VergouweAbstract:Objective: Ideally, Clinical Prediction Models are generalizable to other patient groups. Unfortunately, they perform regularly worse when validated in new patients and are then often redeveloped. While the original Prediction Model usually has been developed on a large data set, redevelopment then often occurs on the smaller validation set. Recently, methods to update existing Prediction Models with the data of new patients have been proposed. We used an existing Model that preoperatively predicts the risk of severe postoperative pain (SPP) to compare five updating methods. Study Design and Setting: The Model was tested and updated with a set of 752 new patients (274 [36] with SPP). We studied the discrimination (ability to distinguish between patients with and without SPP) and calibration (agreement between the predicted risks and observed frequencies of SPP) of the five updated Models in 283 other patients (100 [35%] with SPP). Results: Simple recalibration methods improved the calibration to a similar extent as revision methods that made more extensive adjustments to the original Model. Discrimination could not be improved by any of the methods. Conclusion: When the performance is poor in new patients, updating methods can be applied to adjust the Model, rather than to develop
Guohai Zhou - One of the best experts on this subject based on the ideXlab platform.
-
A transformation of oxygen saturation (the saturation virtual shunt) to improve Clinical Prediction Model calibration and interpretation
Pediatric Research, 2019Co-Authors: Guohai Zhou, Walter Karlen, Rollin Brant, Niranjan Kissoon, Matthew Wiens, J. Mark AnserminoAbstract:Background The relationship between peripheral oxygen saturation (SpO_2) and the inspired oxygen concentration is non-linear. SpO_2 is frequently used as a dichotomized predictor, to manage this non-linearity. We propose the saturation virtual shunt (VS) as a transformation of SpO_2 to a continuous linear variable to improve interpretation of disease severity within Clinical Prediction Models. Method We calculate the saturation VS based on an empirically derived approximation formula between physiological VS and SpO_2. We evaluated the utility of the saturation VS in a Clinical study predicting the need for facility admission in children in a low resource health-care setting. Results The transformation was saturation VS = 68.864 × log_10(103.711 − SpO_2) − 52.110. The ability to predict hospital admission based on a dichotomized SpO_2 produced an area under the receiver operating characteristic curve of 0.57, compared to 0.71 based on the untransformed SpO_2 and saturation VS. However, the untransformed SpO_2 demonstrated a lack of fit compared to the saturation VS (goodness-of-fit test p value
-
a transformation of oxygen saturation the saturation virtual shunt to improve Clinical Prediction Model calibration and interpretation
Pediatric Research, 2019Co-Authors: Guohai Zhou, Walter Karlen, Rollin Brant, Matthew O Wiens, Niranjan Kissoon, Mark J AnserminoAbstract:BACKGROUND The relationship between peripheral oxygen saturation (SpO2) and the inspired oxygen concentration is non-linear. SpO2 is frequently used as a dichotomized predictor, to manage this non-linearity. We propose the saturation virtual shunt (VS) as a transformation of SpO2 to a continuous linear variable to improve interpretation of disease severity within Clinical Prediction Models. METHOD We calculate the saturation VS based on an empirically derived approximation formula between physiological VS and SpO2. We evaluated the utility of the saturation VS in a Clinical study predicting the need for facility admission in children in a low resource health-care setting. RESULTS The transformation was saturation VS = 68.864 × log10(103.711 - SpO2) - 52.110. The ability to predict hospital admission based on a dichotomized SpO2 produced an area under the receiver operating characteristic curve of 0.57, compared to 0.71 based on the untransformed SpO2 and saturation VS. However, the untransformed SpO2 demonstrated a lack of fit compared to the saturation VS (goodness-of-fit test p value < 0.0001 vs 0.098). The observed admission rates varied non-linearly with the untransformed SpO2 but varied linearly with the saturation VS. CONCLUSION The saturation VS estimates a continuous linearly interpretable disease severity based on SpO2 and improves Clinical Prediction.
-
a transformation of oxygen saturation the saturation virtual shunt to improve Clinical Prediction Model calibration and interpretation
bioRxiv, 2019Co-Authors: Guohai Zhou, Walter Karlen, Rollin Brant, Matthew O Wiens, Niranjan Kissoon, Mark J AnserminoAbstract:ABSTRACT Background The relationship between peripheral oxygen saturation (SpO2) and the inspired oxygen concentration is non-linear. SpO2 is frequently used as a dichotomized predictor, to manage this non-linearity. We propose the saturation virtual shunt (VS) as a transformation of SpO2 to a continuous linear variable to improve interpretation of disease severity within Clinical Prediction Models. Method We calculate the saturation VS based on an empirically derived approximation formula between physiological VS and SpO2. We evaluated the utility of the saturation VS in a Clinical study predicting the need for facility admission in children in a low resource health-care setting. Results The transformation was saturation VS = 68.864*log10(103.711 − SpO2) −52.110. The ability to predict hospital admission based on a dichotomized SpO2 produced an area under the receiver operating characteristic curve of 0.57, compared to 0.71 based on the untransformed SpO2 and saturation VS. However, the untransformed SpO2 demonstrated a lack of fit compared to the saturation VS (goodness-of-fit test p-value Conclusion The saturation VS estimates a continuous linearly interpretable disease severity based on SpO2 and improves Clinical Prediction.
Gary S Collins - One of the best experts on this subject based on the ideXlab platform.
-
Minimum sample size for external validation of a Clinical Prediction Model with a continuous outcome
Statistics in medicine, 2020Co-Authors: Lucinda Archer, Joie Ensor, Kym I E Snell, Gary S Collins, Mohammed T Hudda, Richard D RileyAbstract:Clinical Prediction Models provide individualized outcome Predictions to inform patient counseling and Clinical decision making. External validation is the process of examining a Prediction Model's performance in data independent to that used for Model development. Current external validation studies often suffer from small sample sizes, and subsequently imprecise estimates of a Model's predictive performance. To address this, we propose how to determine the minimum sample size needed for external validation of a Clinical Prediction Model with a continuous outcome. Four criteria are proposed, that target precise estimates of (i) R2 (the proportion of variance explained), (ii) calibration-in-the-large (agreement between predicted and observed outcome values on average), (iii) calibration slope (agreement between predicted and observed values across the range of predicted values), and (iv) the variance of observed outcome values. Closed-form sample size solutions are derived for each criterion, which require the user to specify anticipated values of the Model's performance (in particular R2 ) and the outcome variance in the external validation dataset. A sensible starting point is to base values on those for the Model development study, as obtained from the publication or study authors. The largest sample size required to meet all four criteria is the recommended minimum sample size needed in the external validation dataset. The calculations can also be applied to estimate expected precision when an existing dataset with a fixed sample size is available, to help gauge if it is adequate. We illustrate the proposed methods on a case-study predicting fat-free mass in children.
-
calculating the sample size required for developing a Clinical Prediction Model
BMJ, 2020Co-Authors: Richard D Riley, Joie Ensor, Kym I E Snell, Frank E Harrell, Glen P Martin, Johannes B Reitsma, Karel G M Moons, Gary S Collins, Maarten Van SmedenAbstract:Clinical Prediction Models aim to predict outcomes in individuals, to inform diagnosis or prognosis in healthcare. Hundreds of Prediction Models are published in the medical literature each year, yet many are developed using a dataset that is too small for the total number of participants or outcome events. This leads to inaccurate Predictions and consequently incorrect healthcare decisions for some individuals. In this article, the authors provide guidance on how to calculate the sample size required to develop a Clinical Prediction Model.
-
reporting of Clinical Prediction Model studies in journal and conference abstracts tripod for abstracts
2016Co-Authors: Pauline Heus, Johannes B Reitsma, Gary S Collins, Lotty Hooft, R J P M Scholten, Douglas G Altman, Kgm MoonsAbstract:Background: Informative titles and abstracts are important for the identification of potentially relevant studies and communication of research results. Many readers and reviewers base their decision to read the full text of a publication on clarity and detail presented in the title and abstract. Clear and informative reporting in title and abstract is therefore essential. The TRIPOD Statement, published in 2015, is a guideline for Transparent Reporting of a multivariable Prediction Model for Individual Prognosis Or Diagnosis. TRIPOD provides general recommendations for the reporting of title and abstracts, however, more detailed guidance is desirable. Objectives: To develop specific guidance for informative reporting of diagnostic or prognostic Prediction Model studies in both journal and conference abstracts. Methods: We conducted a literature review on the reporting of Prediction Model studies and established a list of potentially relevant items to report in abstracts. This list served as the basis for a modified Delphi procedure. In the first round a panel of 110 experts in the field of Prediction Modeling studies were asked to rate to what extent each candidate item is essential. A maximum of two Delphi rounds will be carried out to reach consensus whether to include an item and to provide insight into potential wording. Results: Preliminary analyses from our literature review showed that objectives, setting, participants, sample size, outcome and conclusions were reported in over 75% of 134 abstracts. Candidate predictors, internal validation technique and results for calibration were addressed in fewer than 25% of abstracts. The modified Delphi procedure is currently being carried out. We will present the results of this procedure and the guidance resulting from it. Conclusions: We present the development of a specific checklist and corresponding guidance for the reporting of diagnostic or prognostic Prediction Model studies in both journal and conference abstracts: TRIPOD for Abstracts. The guidance will be applicable to abstracts of publications that describe development or external validation of a Prediction Model.
-
new guideline for the reporting of studies developing validating or updating a multivariable Clinical Prediction Model the tripod statement
Advances in Anatomic Pathology, 2015Co-Authors: Karel G M Moons, Johannes B Reitsma, Douglas G Altman, Gary S CollinsAbstract:Prediction Models are developed to aid health care providers in estimating the probability that a specific outcome or disease is present (diagnostic Prediction Models) or will occur in the future (prognostic Prediction Models), to inform their decision making. Prognostic Models here also include Models to predict treatment outcomes or responses; in the cancer literature often referred to as predictive Models. Clinical Prediction Models have become abundant. Pathology measurement or results are frequently included as predictors in such Prediction Models, certainly in the cancer domain. Only when full information on all aspects of a Prediction Modeling study are clearly reported, risk of bias and potential usefulness of the Prediction Model can be adequately assessed. Many reviews have illustrated that the quality of reports on the development, validation, and/or adjusting (updating) of Prediction Models, is very poor. Hence, the Transparent Reporting of a multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD) initiative has developed a comprehensive and user-friendly checklist for the reporting of studies on, both diagnostic and prognostic, Prediction Models. The TRIPOD Statement intends to improve the transparency and completeness of reporting of studies that report solely on development, both development and validation, and solely on the validation (with or without updating) of diagnostic or prognostic, including predictive, Models.