After mild traumatic brain injury (mTBI), a substantial proportion of individuals do not fully recover on the Glasgow Outcome Scale Extended (GOSE) or experience persistent post-concussion symptoms... Show moreAfter mild traumatic brain injury (mTBI), a substantial proportion of individuals do not fully recover on the Glasgow Outcome Scale Extended (GOSE) or experience persistent post-concussion symptoms (PPCS). We aimed to develop prognostic models for the GOSE and PPCS at 6 months after mTBI and to assess the prognostic value of different categories of predictors (clinical variables; questionnaires; computed tomography [CT]; blood biomarkers). From the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study, we included participants aged 16 or older with Glasgow Coma Score (GCS) 13-15. We used ordinal logistic regression to model the relationship between predictors and the GOSE, and linear regression to model the relationship between predictors and the Rivermead Post-concussion Symptoms Questionnaire (RPQ) total score. First, we studied a pre-specified Core model. Next, we extended the Core model with other clinical and sociodemographic variables available at presentation (Clinical model). The Clinical model was then extended with variables assessed before discharge from hospital: early post-concussion symptoms, CT variables, biomarkers, or all three categories (extended models). In a subset of patients mostly discharged home from the emergency department, the Clinical model was extended with 2-3-week post-concussion and mental health symptoms. Predictors were selected based on Akaike's Information Criterion. Performance of ordinal models was expressed as a concordance index (C) and performance of linear models as proportion of variance explained (R2). Bootstrap validation was used to correct for optimism. We included 2376 mTBI patients with 6-month GOSE and 1605 patients with 6-month RPQ. The Core and Clinical models for GOSE showed moderate discrimination (C = 0.68 95% confidence interval 0.68 to 0.70 and C = 0.70[0.69 to 0.71], respectively) and injury severity was the strongest predictor. The extended models had better discriminative ability (C = 0.71[0.69 to 0.72] with early symptoms; 0.71[0.70 to 0.72] with CT variables or with blood biomarkers; 0.72[0.71 to 0.73] with all three categories). The performance of models for RPQ was modest (R2 = 4% Core; R2 = 9% Clinical), and extensions with early symptoms increased the R2 to 12%. The 2-3-week models had better performance for both outcomes in the subset of participants with these symptoms measured (C = 0.74 [0.71 to 0.78] vs. C = 0.63[0.61 to 0.67] for GOSE; R2 = 37% vs. 6% for RPQ). In conclusion, the models based on variables available before discharge have moderate performance for the prediction of GOSE and poor performance for the prediction of PPCS. Symptoms assessed at 2-3 weeks are required for better predictive ability of both outcomes. The performance of the proposed models should be examined in independent cohorts. Show less
Treatment effects are often anticipated to vary across groups of patients with different baseline risk. The Predictive Approaches to Treatment Effect Heterogeneity (PATH) statement focused on... Show moreTreatment effects are often anticipated to vary across groups of patients with different baseline risk. The Predictive Approaches to Treatment Effect Heterogeneity (PATH) statement focused on baseline risk as a robust predictor of treatment effect and provided guidance on risk-based assessment of treatment effect heterogeneity in a randomized controlled trial. The aim of this study is to extend this approach to the observational setting using a standardized scalable framework. The proposed framework consists of five steps: (1) definition of the research aim, i.e., the population, the treatment, the comparator and the outcome(s) of interest; (2) identification of relevant databases; (3) development of a prediction model for the outcome(s) of interest; (4) estimation of relative and absolute treatment effect within strata of predicted risk, after adjusting for observed confounding; (5) presentation of the results. We demonstrate our framework by evaluating heterogeneity of the effect of thiazide or thiazide-like diuretics versus angiotensin-converting enzyme inhibitors on three efficacy and nine safety outcomes across three observational databases. We provide a publicly available R software package for applying this framework to any database mapped to the Observational Medical Outcomes Partnership Common Data Model. In our demonstration, patients at low risk of acute myocardial infarction receive negligible absolute benefits for all three efficacy outcomes, though they are more pronounced in the highest risk group, especially for acute myocardial infarction. Our framework allows for the evaluation of differential treatment effects across risk strata, which offers the opportunity to consider the benefit-harm trade-off between alternative treatments. Show less
Rekkas, A.; Rijnbeek, P.R.; Kent, D.M.; Steyerberg, E.W.; Klaveren, D. van 2023
Background Baseline outcome risk can be an important determinant of absolute treatment benefit and has been used in guidelines for "personalizing" medical decisions. We compared easily applicable... Show moreBackground Baseline outcome risk can be an important determinant of absolute treatment benefit and has been used in guidelines for "personalizing" medical decisions. We compared easily applicable risk-based methods for optimal prediction of individualized treatment effects.Methods We simulated RCT data using diverse assumptions for the average treatment effect, a baseline prognostic index of risk, the shape of its interaction with treatment (none, linear, quadratic or non-monotonic), and the magnitude of treatment-related harms (none or constant independent of the prognostic index). We predicted absolute benefit using: models with a constant relative treatment effect; stratification in quarters of the prognostic index; models including a linear interaction of treatment with the prognostic index; models including an interaction of treatment with a restricted cubic spline transformation of the prognostic index; an adaptive approach using Akaike's Information Criterion. We evaluated predictive performance using root mean squared error and measures of discrimination and calibration for benefit.Results The linear-interaction model displayed optimal or close-to-optimal performance across many simulation scenarios with moderate sample size (N = 4,250; similar to 785 events). The restricted cubic splines model was optimal for strong non-linear deviations from a constant treatment effect, particularly when sample size was larger (N = 17,000). The adaptive approach also required larger sample sizes. These findings were illustrated in the GUSTO-I trial.Conclusions An interaction between baseline risk and treatment assignment should be considered to improve treatment effect predictions. Show less
Background: Following traumatic brain injury (TBI), the clinical focus is often on disability. However, patients' perceptions of well-being can be discordant with their disability level, referred... Show moreBackground: Following traumatic brain injury (TBI), the clinical focus is often on disability. However, patients' perceptions of well-being can be discordant with their disability level, referred to as the 'disability paradox'. We aimed to examine the relationship between disability and health-related quality of life (HRQoL) following TBI, while taking variation in personal, injury-related and environment factors into account. Methods: We used data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury study. Disability was assessed 6 months post-injury by the Glasgow Outcome Scale-Extended (GOSE). HRQoL was assessed by the SF-12v2 physical and mental component summary scores and the Quality of Life after Traumatic Brain Injury overall scale. We examined mean total and domain HRQoL scores by GOSE. We quantified variance in HRQoL explained by GOSE, personal, injury-related and environment factors with multivariable regression. Results: Six-month outcome assessments were completed in 2075 patients, of whom 78% had mild TBI (Glasgow Coma Scale 13-15). Patients with severe disability had higher HRQoL than expected on the basis of GOSE alone, particularly after mild TBI. Up to 50% of patients with severe disability reported HRQoL scores within the normative range. GOSE, personal, injury-related and environment factors explained a limited amount of variance in HRQoL (up to 29%). Conclusion: Contrary to the idea that discrepancies are unusual, many patients with poor functional outcomes reported well-being that was at or above the boundary considered satisfactory for the normative sample. These findings challenge the idea that satisfactory HRQoL in patients with disability should be described as 'paradoxical' and question common views of what constitutes 'unfavourable' outcome. Show less
Holl, D.C.; Mikolic, A.; Blaauw, J.; Lodewijkx, R.; Foppen, M.; Jellema, K.; ... ; Klaveren, D. van 2022
Background: Several prognostic models for outcomes after chronic subdural hematoma (CSDH) treatment have been published in recent years. However, these models are not sufficiently validated for use... Show moreBackground: Several prognostic models for outcomes after chronic subdural hematoma (CSDH) treatment have been published in recent years. However, these models are not sufficiently validated for use in daily clinical practice. We aimed to assess the performance of existing prediction models for outcomes in patients diagnosed with CSDH. Methods: We systematically searched relevant literature databases up to February 2021 to identify prognostic models for outcome prediction in patients diagnosed with CSDH. For the external validation of prognostic models, we used a retrospective database, containing data of 2384 patients from three Dutch regions. Prognostic models were included if they predicted either mortality, hematoma recurrence, functional outcome, or quality of life. Models were excluded when predictors were absent in our database or available for < 150 patients in our database. We assessed calibration, and discrimination (quantified by the concordance index C) of the included prognostic models in our retrospective database. Results: We identified 1680 original publications of which 1656 were excluded based on title or abstract, mostly because they did not concern CSDH or did not define a prognostic model. Out of 18 identified models, three could be externally validated in our retrospective database: a model for 30-day mortality in 1656 patients, a model for 2 months, and another for 3-month hematoma recurrence both in 1733 patients. The models overestimated the proportion of patients with these outcomes by 11% (15% predicted vs. 4% observed), 1% (10% vs. 9%), and 2% (11% vs. 9%), respectively. Their discriminative ability was poor to modest (C of 0.70 [0.63-0.77]; 0.46 [0.35-0.56]; 0.59 [0.51-0.66], respectively). Conclusions: None of the examined models showed good predictive performance for outcomes after CSDH treatment in our dataset. This study confirms the difficulty in predicting outcomes after CSDH and emphasizes the heterogeneity of CSDH patients. The importance of developing high-quality models by using unified predictors and relevant outcome measures and appropriate modeling strategies is warranted. Show less
Background: While clinical prediction models (CPMs) are used increasingly commonly to guide patient care, the performance and clinical utility of these CPMs in new patient cohorts is poorly... Show moreBackground: While clinical prediction models (CPMs) are used increasingly commonly to guide patient care, the performance and clinical utility of these CPMs in new patient cohorts is poorly understood. Methods: We performed 158 external validations of 104 unique CPMs across 3 domains of cardiovascular disease (primary prevention, acute coronary syndrome, and heart failure). Validations were performed in publicly available clinical trial cohorts and model performance was assessed using measures of discrimination, calibration, and net benefit. To explore potential reasons for poor model performance, CPM-clinical trial cohort pairs were stratified based on relatedness, a domain-specific set of characteristics to qualitatively grade the similarity of derivation and validation patient populations. We also examined the model-based C-statistic to assess whether changes in discrimination were because of differences in case-mix between the derivation and validation samples. The impact of model updating on model performance was also assessed. Results: Discrimination decreased significantly between model derivation (0.76 [interquartile range 0.73-0.78]) and validation (0.64 [interquartile range 0.60-0.67], P<0.001), but approximately half of this decrease was because of narrower case-mix in the validation samples. CPMs had better discrimination when tested in related compared with distantly related trial cohorts. Calibration slope was also significantly higher in related trial cohorts (0.77 [interquartile range, 0.59-0.90]) than distantly related cohorts (0.59 [interquartile range 0.43-0.73], P=0.001). When considering the full range of possible decision thresholds between half and twice the outcome incidence, 91% of models had a risk of harm (net benefit below default strategy) at some threshold; this risk could be reduced substantially via updating model intercept, calibration slope, or complete re-estimation. Conclusions: There are significant decreases in model performance when applying cardiovascular disease CPMs to new patient populations, resulting in substantial risk of harm. Model updating can mitigate these risks. Care should be taken when using CPMs to guide clinical decision-making. Show less
Objective: To assess whether the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and a shorter version of this tool can identify clinical prediction models (CPMs) that perform poorly at... Show moreObjective: To assess whether the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and a shorter version of this tool can identify clinical prediction models (CPMs) that perform poorly at external validation. Study Design and Setting: We evaluated risk of bias (ROB) on 102 CPMs from the Tufts CPM Registry, comparing PROBAST to a short form consisting of six PROBAST items anticipated to best identify high ROB. We then applied the short form to all CPMs in the Registry with at least 1 validation (n = 556) and assessed the change in discrimination (dAUC) in external validation cohorts (n = 1,147). Results: PROBAST classified 98/102 CPMS as high ROB. The short form identified 96 of these 98 as high ROB (98% sensitivity), with perfect specificity. In the full CPM registry, 527 of 556 CPMs (95%) were classified as high ROB, 20 (3.6%) low ROB, and 9 (1.6%) unclear ROB. Only one model with unclear ROB was reclassified to high ROB after full PROBAST assessment of all low and unclear ROB models. Median change in discrimination was significantly smaller in low ROB models (dAUC -0.9%, IQR -6.2-4.2%) compared to high ROB models (dAUC -11.7%, IQR -33.3-2.6%; P < 0.001). Conclusion: High ROB is pervasive among published CPMs. It is associated with poor discriminative performance at validation, supporting the application of PROBAST or a shorter version in CPM reviews. (c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http:// creativecommons.org/ licenses/ by- nc- nd/ 4.0/ ) Show less
BACKGROUND The SYNTAX score II 2020 (SSII-2020) was derived from cross correlation and externally validated in randomized trials to predict death and major adverse cardiac and cerebrovascular... Show moreBACKGROUND The SYNTAX score II 2020 (SSII-2020) was derived from cross correlation and externally validated in randomized trials to predict death and major adverse cardiac and cerebrovascular events (MACE) following percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG) in patients with 3-vessel disease (3VD) and/or left main coronary artery disease (LMCAD). OBJECTIVES The authors aimed to investigate the SSII-2020's value in identifying the safest modality of revascularization in a non-randomized setting. METHODS Five-year mortality and MACE were assessed in 7,362 patients with 3VD and/or LMCAD enrolled in a Japanese PCI/CABG registry. The discriminative abilities of the SSII-2020 were assessed using Harrell's C statistic. Agreement between observed and predicted event rates following PCI or CABG and treatment benefit (absolute risk difference [ARD]) for these outcomes were assessed by calibration plots. RESULTS The SSII-2020 for 5-year mortality well predicted the prognosis after PCI and CABG (C-index = 0.72, intercept =-0.11, slope = 0.92). When patients were grouped according to the predicted 5-year mortality ARD, <4.5% (equipoise of PCI and CABG) and $4.5% (CABG better), the observed mortality rates after PCI and CABG were not significantly different in patients with lower predicted ARD (observed ARD: 2.1% [95% CI:-0.4% to 4.4%]), and the significant difference in survival in favor of CABG was observed in patients with higher predicted ARD (observed ARD: 9.7% [95% CI: 6.1%-13.3%]). For MACE, the SSII-2020 could not recommend a specific treatment with sufficient accuracy. CONCLUSIONS The SSII-2020 for predicting 5-year death has the potential to support decision making on revascularization in patients with 3VD and/or LMCAD. (J Am Coll Cardiol 2021;78:1227-1238) (c) 2021 by the American College of Cardiology Foundation. Show less
Sewalt, C.A.; Gravesteijn, B.Y.; Nieboer, D.; Steyerberg, E.W.; Hartog, D. den; Klaveren, D. van 2021
Background Prehospital triage protocols typically try to select patients with Injury Severity Score (ISS) above 15 for direct transportation to a Level-1 trauma center. However, ISS does not... Show moreBackground Prehospital triage protocols typically try to select patients with Injury Severity Score (ISS) above 15 for direct transportation to a Level-1 trauma center. However, ISS does not necessarily discriminate between patients who benefit from immediate care at Level-1 trauma centers. The aim of this study was to assess which patients benefit from direct transportation to Level-1 trauma centers. Methods We used the American National Trauma Data Bank (NTDB), a retrospective observational cohort. All adult patients (ISS > 3) between 2015 and 2016 were included. Patients who were self-presenting or had isolated limb injury were excluded. We used logistic regression to assess the association of direct transportation to Level-1 trauma centers with in-hospital mortality adjusted for clinically relevant confounders. We used this model to define benefit as predicted probability of mortality associated with transportation to a non-Level-1 trauma center minus predicted probability associated with transportation to a Level-1 trauma center. We used a threshold of 1% as absolute benefit. Potential interaction terms with transportation to Level-1 trauma centers were included in a penalized logistic regression model to study which patients benefit. Results We included 388,845 trauma patients from 232 Level-1 centers and 429 Level-2/3 centers. A small beneficial effect was found for direct transportation to Level-1 trauma centers (adjusted Odds Ratio: 0.96, 95% Confidence Interval: 0.92-0.99) which disappeared when comparing Level-1 and 2 versus Level-3 trauma centers. In the risk approach, predicted benefit ranged between 0 and 1%. When allowing for interactions, 7% of the patients (n = 27,753) had more than 1% absolute benefit from direct transportation to Level-1 trauma centers. These patients had higher AIS Head and Thorax scores, lower GCS and lower SBP. A quarter of the patients with ISS > 15 were predicted to benefit from transportation to Level-1 centers (n = 26,522, 22%). Conclusions Benefit of transportation to a Level-1 trauma centers is quite heterogeneous across patients and the difference between Level-1 and Level-2 trauma centers is small. In particular, patients with head injury and signs of shock may benefit from care in a Level-1 trauma center. Future prehospital triage models should incorporate more complete risk profiles. Show less
Background: There are many clinical prediction models (CPMs) available to inform treatment decisions for patients with cardiovascular disease. However, the extent to which they have been externally... Show moreBackground: There are many clinical prediction models (CPMs) available to inform treatment decisions for patients with cardiovascular disease. However, the extent to which they have been externally tested, and how well they generally perform has not been broadly evaluated. Methods: A SCOPUS citation search was run on March 22, 2017 to identify external validations of cardiovascular CPMs in the Tufts Predictive Analytics and Comparative Effectiveness CPM Registry. We assessed the extent of external validation, performance heterogeneity across databases, and explored factors associated with model performance, including a global assessment of the clinical relatedness between the derivation and validation data. Results: We identified 2030 external validations of 1382 CPMs. Eight hundred seven (58%) of the CPMs in the Registry have never been externally validated. On average, there were 1.5 validations per CPM (range, 0-94). The median external validation area under the receiver operating characteristic curve was 0.73 (25th-75th percentile [interquartile range (IQR)], 0.66-0.79), representing a median percent decrease in discrimination of -11.1% (IQR, -32.4% to +2.7%) compared with performance on derivation data. 81% (n=1333) of validations reporting area under the receiver operating characteristic curve showed discrimination below that reported in the derivation dataset. 53% (n=983) of the validations report some measure of CPM calibration. For CPMs evaluated more than once, there was typically a large range of performance. Of 1702 validations classified by relatedness, the percent change in discrimination was -3.7% (IQR, -13.2 to 3.1) for closely related validations (n=123), -9.0 (IQR, -27.6 to 3.9) for related validations (n=862), and -17.2% (IQR, -42.3 to 0) for distantly related validations (n=717; P<0.001). Conclusions: Many published cardiovascular CPMs have never been externally validated, and for those that have, apparent performance during development is often overly optimistic. A single external validation appears insufficient to broadly understand the performance heterogeneity across different settings. Show less
Helmrich, I.R.A.R.; Klaveren, D. van; Dijkland, S.A.; Lingsma, H.F.; Polinder, S.; Wilson, L.; ... ; CENTER TBI Collaborators 2021
Background Traumatic brain injury (TBI) is a leading cause of impairments affecting Health-Related Quality of Life (HRQoL). We aimed to identify predictors of and develop prognostic models for... Show moreBackground Traumatic brain injury (TBI) is a leading cause of impairments affecting Health-Related Quality of Life (HRQoL). We aimed to identify predictors of and develop prognostic models for HRQoL following TBI. Methods We used data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Core study, including patients with a clinical diagnosis of TBI and an indication for computed tomography presenting within 24 h of injury. The primary outcome measures were the SF-36v2 physical (PCS) and mental (MCS) health component summary scores and the Quality of Life after Traumatic Brain Injury (QOLIBRI) total score 6 months post injury. We considered 16 patient and injury characteristics in linear regression analyses. Model performance was expressed as proportion of variance explained (R-2) and corrected for optimism with bootstrap procedures. Results 2666 Adult patients completed the HRQoL questionnaires. Most were mild TBI patients (74%). The strongest predictors for PCS were Glasgow Coma Scale, major extracranial injury, and pre-injury health status, while MCS and QOLIBRI were mainly related to pre-injury mental health problems, level of education, and type of employment. R-2 of the full models was 19% for PCS, 9% for MCS, and 13% for the QOLIBRI. In a subset of patients following predominantly mild TBI (N = 436), including 2 week HRQoL assessment improved model performance substantially (R-2 PCS 15% to 37%, MCS 12% to 36%, and QOLIBRI 10% to 48%). Conclusion Medical and injury-related characteristics are of greatest importance for the prediction of PCS, whereas patient-related characteristics are more important for the prediction of MCS and the QOLIBRI following TBI. Show less
Sturms, L.M.; Driessen, M.L.S.; Klaveren, D. van; Duis, H.J. ten; Kommer, G.J.; Bloemers, F.W.; ... ; Jongh, M. de 2021
Background: The goal of trauma systems is to match patient care needs to the capabilities of the receiving centre. Severely injured patients have shown better outcomes if treated in a major trauma... Show moreBackground: The goal of trauma systems is to match patient care needs to the capabilities of the receiving centre. Severely injured patients have shown better outcomes if treated in a major trauma centre (MTC). We aimed to evaluate patient distribution in the Dutch trauma system. Furthermore, we sought to identify factors associated with the undertriage and transport of severely injured patients (Injury Severity Score (ISS) >15) to the MTC by emergency medical services (EMS).Methods: Data on all acute trauma admissions in the Netherlands (2015-2016) were extracted from the Dutch national trauma registry. An ambulance driving time model was applied to calculate MTC transport times and transport times of ISS >15 patients to the closest MTC and non-MTC. A multivariable logistic regression analysis was performed to identify factors associated with ISS >15 patients' EMS undertriage to an MTC.Results: Of the annual average of 78,123 acute trauma admissions, 4.9% had an ISS >15. The nonseverely injured patients were predominantly treated at non-MTCs (79.2%), and 65.4% of patients with an ISS >15 received primary MTC care. This rate varied across the eleven Dutch trauma networks (36.8%-88.4%) and was correlated with the transport times to an MTC (Pearson correlation -0.753, p=0.007). The trauma networks also differed in the rates of secondary transfers of ISS >15 patients to MTC hospitals (7.8% 59.3%) and definitive MTC care (43.6% - 93.2%). Factors associated with EMS undertriage of ISS >15 patients to the MTC were female sex, older age, severe thoracic and abdominal injury, and longer additional EMS transport times.Conclusions: Approximately one-third of all severely injured patients in the Netherlands are not initially treated at an MTC. Special attention needs to be directed to identifying patient groups with a high risk of undertriage. Furthermore, resources to overcome longer transport times to an MTC, including the availability of ambulance and helicopter services, may improve direct MTC care and result in a decrease in the variation of the undertriage of severely injured patients to MTCs among the Dutch trauma networks. Furthermore, attention needs to be directed to improving primary triage guidelines and instituting uniform interfacility transfer agreements. (C) 2021 Elsevier Ltd. All rights reserved. Show less
BackgroundGenomic tests may improve upon clinical risk estimation with traditional prognostic factors. We aimed to explore how evidence on the prognostic strength of a genomic signature (clinical... Show moreBackgroundGenomic tests may improve upon clinical risk estimation with traditional prognostic factors. We aimed to explore how evidence on the prognostic strength of a genomic signature (clinical validity) can contribute to individualized decision making on starting chemotherapy for women with breast cancer (clinical utility).MethodsThe MINDACT trial was a randomized trial that enrolled 6693 women with early-stage breast cancer. A 70-gene signature (Mammaprint) was used to estimate genomic risk, and clinical risk was estimated by a dichotomized version of the Adjuvant!Online risk calculator. Women with discordant risk results were randomized to the use of chemotherapy. We simulated the full risk distribution of these women and estimated individual benefit, assuming a constant relative effect of chemotherapy.ResultsThe trial showed a prognostic effect of the genomic signature (adjusted hazard ratio 2.4). A decision-analytic modeling approach identified far fewer women as candidates for genetic testing (4% rather than 50%) and fewer benefiting from chemotherapy (3% rather than 27%) as compared with the MINDACT trial report. The selection of women benefitting from genetic testing and chemotherapy depended strongly on the required benefit from treatment and the assumed therapeutic effect of chemotherapy.ConclusionsA high-quality pragmatic trial was insufficient to directly inform clinical practice on the utility of a genomic test for individual women. The indication for genomic testing may be far more limited than suggested by the MINDACT trial. Show less
Aims We aimed to update the logistic clinical SYNTAX score to predict 2 year all-cause mortality after contemporary percutaneous coronary intervention (PCI).Methods and results We analyzed 15,883... Show moreAims We aimed to update the logistic clinical SYNTAX score to predict 2 year all-cause mortality after contemporary percutaneous coronary intervention (PCI).Methods and results We analyzed 15,883 patients in the GLOBAL LEADERS study who underwent PCI. The logistic clinical SYNTAX model was updated after imputing missing values by refitting the original model (refitted original model) and fitting an extended new model (new model, with, selection based on the Akaike Information Criterion). External validation was performed in 10,100 patients having PCI at Fu Wai hospital.Chronic obstructive pulmonary disease, prior stroke, current smoker, hemoglobin level, and white blood cell count were identified as additional independent predictors of 2 year all-cause mortality and included into the new model.The c-indexes of the original, refitted original and the new model in the derivation cohort were 0.74 (95% CI 0.72-0.76), 0.75 (95% CI 0.73-0.77), and 0.78 (95% CI 0.76-0.80), respectively. The c-index of the new model was lower in the validation cohort than in the derivation cohort, but still showed improved discriminative ability of the newly developed model (0.72; 95% CI 0.67-0.77) compared to the refitted original model (0.69; 95% CI 0.64-0.74). The models overestimated the observed 2 year all-cause mortality of 1.11% in the Chinese external validation cohort by 0.54 percentage points, indicating the need for calibration of the model to the Chinese patient population.Conclusions The new model of the logistic clinical SYNTAX score better predicts 2 year all-cause mortality after PCI than the original model. The new model could guide clinical decision making by risk stratifying patients undergoing PCI. Show less
Background Randomised controlled trials are considered the gold standard for testing the efficacy of novel therapeutic interventions, and typically report the average treatment effect as a summary... Show moreBackground Randomised controlled trials are considered the gold standard for testing the efficacy of novel therapeutic interventions, and typically report the average treatment effect as a summary result. As the result of treatment can vary between patients, basing treatment decisions for individual patients on the overall average treatment effect could be suboptimal. We aimed to develop an individualised decision making tool to select an optimal revascularisation strategy in patients with complex coronary artery disease.Methods The SYNTAX Extended Survival (SYNTAXES) study is an investigator-driven extension follow-up of a multicentre, randomised controlled trial done in 85 hospitals across 18 North American and European countries between March, 2005, and April, 2007. Patients with de-novo three-vessel and left main coronary artery disease were randomly assigned (1:1) to either the percutaneous coronary intervention (PCI) group or coronary artery bypass grafting (CABG) group. The SYNTAXES study ascertained 10-year all-cause deaths. We used Cox regression to develop a clinical prognostic index for predicting death over a 10-year period, which was combined, in a second stage, with assigned treatment (PCI or CABG) and two prespecified effect-modifiers, which were selected on the basis of previous evidence: disease type (three-vessel disease or left main coronary artery disease) and anatomical SYNTAX score. We used similar techniques to develop a model to predict the 5-year risk of major adverse cardiovascular events (defined as a composite of all-cause death, non-fatal stroke, or non-fatal myocardial infarction) in patients receiving PCI or CABG. We then assessed the ability of these models to predict the risk of death or a major adverse cardiovascular event, and their differences (ie, the estimated benefit of CABG versus PCI by calculating the absolute risk difference between the two strategies) by cross-validation with the SYNTAX trial (n=1800 participants) and external validation in the pooled population (n=3380 participants) of the FREEDOM, BEST, and PRECOMBAT trials. The concordance (C)-index was used to measure discriminative ability, and calibration plots were used to assess the degree of agreement between predictions and observations.Findings At cross-validation, the newly developed SYNTAX score II, termed SYNTAX score II 2020, showed a helpful discriminative ability in both treatment groups for predicting 10-year all-cause deaths (C-index=0.73 [95% CI 0.69-0.76] for PCI and 0.73 [0.69-0.76] for CABG) and 5-year major adverse cardiovascular events (C-index=0.65 [0.61-0.69] for PCI and C-index=0.71 [0.67-0.75] for CABG). At external validation, the SYNTAX score II 2020 showed helpful discrimination (C-index=0.67 [0.63-0.70] for PCI and C-index=0.62 [0.58-0.66] for CABG) and good calibration for predicting 5-year major adverse cardiovascular events. The estimated treatment benefit of CABG over PCI varied substantially among patients in the trial population, and the benefit predictions were well calibrated.Interpretation The SYNTAX score II 2020 for predicting 10-year deaths and 5-year major adverse cardiovascular events can help to identify individuals who will benefit from either CABG or PCI, thereby supporting heart teams, patients, and their families to select optimal revascularisation strategies. Copyright (C) 2020 Elsevier Ltd. All rights reserved. Show less
Rekkas, A.; Paulus, J.K.; Raman, G.; Wong, J.B.; Steyerberg, E.W.; Rijnbeek, P.R.; ... ; Klaveren, D. van 2020
Background: Recent evidence suggests that there is often substantial variation in the benefits and harms across a trial population. We aimed to identify regression modeling approaches that assess... Show moreBackground: Recent evidence suggests that there is often substantial variation in the benefits and harms across a trial population. We aimed to identify regression modeling approaches that assess heterogeneity of treatment effect within a randomized clinical trial.Methods: We performed a literature review using a broad search strategy, complemented by suggestions of a technical expert panel.Results: The approaches are classified into 3 categories: 1) Risk-based methods (11 papers) use only prognostic factors to define patient subgroups, relying on the mathematical dependency of the absolute risk difference on baseline risk; 2) Treatment effect modeling methods (9 papers) use both prognostic factors and treatment effect modifiers to explore characteristics that interact with the effects of therapy on a relative scale. These methods couple data-driven subgroup identification with approaches to prevent overfitting, such as penalization or use of separate data sets for subgroup identification and effect estimation. 3) Optimal treatment regime methods (12 papers) focus primarily on treatment effect modifiers to classify the trial population into those who benefit from treatment and those who do not. Finally, we also identified papers which describe model evaluation methods (4 papers).Conclusions: Three classes of approaches were identified to assess heterogeneity of treatment effect. Methodological research, including both simulations and empirical evaluations, is required to compare the available methods in different settings and to derive well-informed guidance for their application in RCT analysis. Show less
Mikolic, A.; Polinder, S.; Steyerberg, E.W.; Helmrich, I.R.A.R.; Giacino, J.T.; Maas, A.I.R.; ... ; CENTER TBI Study Pa 2020
The majority of traumatic brain injuries (TBIs) are categorized as mild, according to a baseline Glasgow Coma Scale (GCS) score of 13-15. Prognostic models that were developed to predict functional... Show moreThe majority of traumatic brain injuries (TBIs) are categorized as mild, according to a baseline Glasgow Coma Scale (GCS) score of 13-15. Prognostic models that were developed to predict functional outcome and persistent post-concussive symptoms (PPCS) after mild TBI have rarely been externally validated. We aimed to externally validate models predicting 3-12-month Glasgow Outcome Scale Extended (GOSE) or PPCS in adults with mild TBI. We analyzed data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) project, which included 2862 adults with mild TBI, with 6-month GOSE available for 2374 and Rivermead Post-Concussion Symptoms Questionnaire (RPQ) results available for 1605 participants. Model performance was evaluated based on calibration (graphically and characterized by slope and intercept) and discrimination (C-index). We validated five published models for 6-month GOSE and three for 6-month PPCS scores. The models used different cutoffs for outcome and some included symptoms measured 2 weeks post-injury. Discriminative ability varied substantially (C-index between 0.58 and 0.79). The models developed in the Corticosteroid Randomisation After Significant Head Injury (CRASH) trial for prediction of GOSE <5 discriminated best (C-index 0.78 and 0.79), but were poorly calibrated. The best performing models for PPCS included 2-week symptoms (C-index 0.75 and 0.76). In conclusion, none of the prognostic models for early prediction of GOSE and PPCS has both good calibration and discrimination in persons with mild TBI. In future studies, prognostic models should be tailored to the population with mild TBI, predicting relevant end-points based on readily available predictors. Show less
