Development and validation of a novel model to predict recurrence-free survival and melanoma-specific survival after sentinel lymph node biopsy in patients with melanoma: an international, retrospective, multicentre analysis.

BACKGROUND: The introduction of adjuvant systemic treatment for patients with high-risk melanomas necessitates accurate staging of disease. However, inconsistencies in outcomes exist between disease stages as defined by the American Joint Committee on Cancer (8th edition). We aimed to develop a tool to predict patient-specific outcomes in people with melanoma rather than grouping patients according to disease stage. METHODS: Patients older than 13 years with confirmed primary melanoma who underwent sentinel lymph node biopsy (SLNB) between Oct 29, 1997, and Nov 11, 2013, at four European melanoma centres (based in Berlin, Germany; Amsterdam and Rotterdam, the Netherlands; and Warsaw, Poland) were included in the development cohort. Potential predictors of recurrence-free and melanoma-specific survival assessed were sex, age, presence of ulceration, primary tumour location, histological subtype, Breslow thickness, sentinel node status, number of sentinel nodes removed, maximum diameter of the largest sentinel node metastasis, and Dewar classification. A prognostic model and nomogram were developed to predict 5-year recurrence-free survival on a continuous scale in patients with stage pT1b or higher melanomas. This model was also calibrated to predict melanoma-specific survival. Model performance was assessed by discrimination (area under the time-dependent receiver operating characteristics curve [AUC]) and calibration. External validation was done in a cohort of patients with primary melanomas who underwent SLNB between Jan 30, 1997, and Dec 12, 2013, at the Melanoma Institute Australia (Sydney, NSW, Australia). FINDINGS: The development cohort consisted of 4071 patients, of whom 2075 (51%) were female and 1996 (49%) were male. 889 (22%) had sentinel node-positive disease and 3182 (78%) had sentinel node-negative disease. The validation cohort comprised 4822 patients, of whom 1965 (41%) were female and 2857 (59%) were male. 891 (18%) had sentinel node-positive disease and 3931 (82%) had sentinel node-negative disease. Median follow-up was 4·8 years (IQR 2·3-7·8) in the development cohort and 5·0 years (2·2-8·9) in the validation cohort. In the development cohort, 5-year recurrence-free survival was 73·5% (95% CI 72·0-75·1) and 5-year melanoma-specific survival was 86·5% (85·3-87·8). In the validation cohort, the corresponding estimates were 66·1% (64·6-67·7) and 83·3% (82·0-84·6), respectively. The final model contained six prognostic factors: sentinel node status, Breslow thickness, presence of ulceration, age at SLNB, primary tumour location, and maximum diameter of the largest sentinel node metastasis. In the development cohort, for the model's prediction of recurrence-free survival, the AUC was 0·80 (95% CI 0·78-0·81); for prediction of melanoma-specific survival, the AUC was 0·81 (0·79-0·84). External validation showed good calibration for both outcomes, with AUCs of 0·73 (0·71-0·75) and 0·76 (0·74-0·78), respectively. INTERPRETATION: Our prediction model and nomogram accurately predicted patient-specific risk probabilities for 5-year recurrence-free and melanoma-specific survival. These tools could have important implications for clinical decision making when considering adjuvant treatments in patients with high-risk melanomas. FUNDING: Erasmus Medical Centre Cancer Institute.

Development and External Validation of a Prediction Model for Patients with Varicose Veins Suitable for Isolated Ambulatory Phlebectomy.

OBJECTIVE: Isolated ambulatory phlebectomy is a potential treatment option for patients with an incompetent great saphenous vein (GSV) or anterior accessory saphenous vein and one or more incompetent tributaries. Being able to determine which patients will most likely benefit from isolated phlebectomy is important. This study aimed to identify predictors for avoidance of secondary axial ablation after isolated phlebectomy and to develop and externally validate a multivariable model for predicting this outcome. METHODS: For model development, data from patients included in the SAPTAP trial were used. The investigated outcome was avoidance of ablation of the saphenous trunk one year after isolated ambulatory phlebectomy. Pre-defined candidate predictors were analysed with multivariable logistic regression. Predictors were selected using Akaike information criterion backward selection. Discriminative ability was assessed by the concordance index. Bootstrapping was used to correct regression coefficients, and the C index for overfitting. The model was externally validated using a population of 94 patients with an incompetent GSV and one or more incompetent tributaries who underwent isolated phlebectomy. RESULTS: For model development, 225 patients were used, of whom 167 (74.2%) did not undergo additional ablation of the saphenous trunk one year after isolated phlebectomy. The final model consisted of three predictors for avoidance of axial ablation: tributary length (< 15 cm vs. > 30 cm: odds ratio [OR] 0.09, 95% confidence interval [CI] 0.02 - 0.40; 15 - 30 cm vs. > 30 cm: OR 0.18, 95% CI 0.09 - 0.38); saphenofemoral junction (SFJ) reflux (absent vs. present: OR 2.53, 95% CI 0.81 - 7.87); and diameter of the saphenous trunk (per millimetre change: OR 0.63, 95% CI 0.41 - 0.96). The discriminative ability of the model was moderate (0.72 at internal validation; 0.73 at external validation). CONCLUSION: A model was developed for predicting avoidance of secondary ablation of the saphenous trunk one year after isolated ambulatory phlebectomy, which can be helpful in daily practice to determine the suitable treatment strategy in patients with an incompetent saphenous trunk and one or more incompetent tributaries. Patients having a longer tributary, smaller diameter saphenous trunk, and absence of terminal valve reflux in the SFJ are more likely to benefit from isolated phlebectomy.

Weighted metrics are required when evaluating the performance of prediction models in nested case-control studies.

BACKGROUND: Nested case-control (NCC) designs are efficient for developing and validating prediction models that use expensive or difficult-to-obtain predictors, especially when the outcome is rare. Previous research has focused on how to develop prediction models in this sampling design, but little attention has been given to model validation in this context. We therefore aimed to systematically characterize the key elements for the correct evaluation of the performance of prediction models in NCC data. METHODS: We proposed how to correctly evaluate prediction models in NCC data, by adjusting performance metrics with sampling weights to account for the NCC sampling. We included in this study the C-index, threshold-based metrics, Observed-to-expected events ratio (O/E ratio), calibration slope, and decision curve analysis. We illustrated the proposed metrics with a validation of the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA version 5) in data from the population-based Rotterdam study. We compared the metrics obtained in the full cohort with those obtained in NCC datasets sampled from the Rotterdam study, with and without a matched design. RESULTS: Performance metrics without weight adjustment were biased: the unweighted C-index in NCC datasets was 0.61 (0.58-0.63) for the unmatched design, while the C-index in the full cohort and the weighted C-index in the NCC datasets were similar: 0.65 (0.62-0.69) and 0.65 (0.61-0.69), respectively. The unweighted O/E ratio was 18.38 (17.67-19.06) in the NCC datasets, while it was 1.69 (1.42-1.93) in the full cohort and its weighted version in the NCC datasets was 1.68 (1.53-1.84). Similarly, weighted adjustments of threshold-based metrics and net benefit for decision curves were unbiased estimates of the corresponding metrics in the full cohort, while the corresponding unweighted metrics were biased. In the matched design, the bias of the unweighted metrics was larger, but it could also be compensated by the weight adjustment. CONCLUSIONS: Nested case-control studies are an efficient solution for evaluating the performance of prediction models that use expensive or difficult-to-obtain biomarkers, especially when the outcome is rare, but the performance metrics need to be adjusted to the sampling procedure.

Personalized decision-making for aneurysm treatment of aneurysmal subarachnoid hemorrhage: development and validation of a clinical prediction tool.

BACKGROUND: In patients with aneurysmal subarachnoid hemorrhage suitable for endovascular coiling and neurosurgical clip-reconstruction, the aneurysm treatment decision-making process could be improved by considering heterogeneity of treatment effect and durability of treatment. We aimed to develop and validate a tool to predict individualized treatment benefit of endovascular coiling compared to neurosurgical clip-reconstruction. METHODS: We used randomized data (International Subarachnoid Aneurysm Trial, n = 2143) to develop models to predict 2-month functional outcome and to predict time-to-rebleed-or-retreatment. We modeled for heterogeneity of treatment effect by adding interaction terms of treatment with prespecified predictors and with baseline risk of the outcome. We predicted outcome with both treatments and calculated absolute treatment benefit. We described the patient characteristics of patients with ≥ 5% point difference in the predicted probability of favorable functional outcome (modified Rankin Score 0-2) and of no rebleed or retreatment within 10 years. Model performance was expressed with the c-statistic and calibration plots. We performed bootstrapping and leave-one-cluster-out cross-validation and pooled cluster-specific c-statistics with random effects meta-analysis. RESULTS: The pooled c-statistics were 0.72 (95% CI: 0.69-0.75) for the prediction of 2-month favorable functional outcome and 0.67 (95% CI: 0.63-0.71) for prediction of no rebleed or retreatment within 10 years. We found no significant interaction between predictors and treatment. The average predicted benefit in favorable functional outcome was 6% (95% CI: 3-10%) in favor of coiling, but 11% (95% CI: 9-13%) for no rebleed or retreatment in favor of clip-reconstruction. 134 patients (6%), young and in favorable clinical condition, had negligible functional outcome benefit of coiling but had a ≥ 5% point benefit of clip-reconstruction in terms of durability of treatment. CONCLUSIONS: We show that young patients in favorable clinical condition and without extensive vasospasm have a negligible benefit in functional outcome of endovascular coiling - compared to neurosurgical clip-reconstruction - while at the same time having a substantially lower probability of retreatment or rebleeding from neurosurgical clip-reconstruction - compared to endovascular coiling. The SHARP prediction tool ( https://sharpmodels.shinyapps.io/sharpmodels/ ) could support and incentivize a multidisciplinary discussion about aneurysm treatment decision-making by providing individualized treatment benefit estimates.

Estimating individualized treatment effects from randomized controlled trials: a simulation study to compare risk-based approaches.

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 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; ~ 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.

The evolution of the loss of life expectancy in patients with chronic myeloid leukaemia: a population-based study in the Netherlands, 1989-2018.

Studies on the conditional life expectancy of patients with chronic myeloid leukaemia (CML) are lacking. Using data from the Netherlands Cancer Registry, we examined the life expectancy of patients with CML in the Netherlands diagnosed during 1989-2018. As of the early 2010s, the life expectancy of patients with CML who survived several years after diagnosis came narrowly close to the general population's life expectancy, regardless of age. This finding can essentially be ascribed to the introduction and broader application of tyrosine kinase inhibitors (TKIs) and provide optimism to patients with CML who can look forward to a near-normal life expectancy in a modern TKI era.

Prognostic models for COVID-19 needed updating to warrant transportability over time and space.

BACKGROUND: Supporting decisions for patients who present to the emergency department (ED) with COVID-19 requires accurate prognostication. We aimed to evaluate prognostic models for predicting outcomes in hospitalized patients with COVID-19, in different locations and across time. METHODS: We included patients who presented to the ED with suspected COVID-19 and were admitted to 12 hospitals in the New York City (NYC) area and 4 large Dutch hospitals. We used second-wave patients who presented between September and December 2020 (2137 and 3252 in NYC and the Netherlands, respectively) to evaluate models that were developed on first-wave patients who presented between March and August 2020 (12,163 and 5831). We evaluated two prognostic models for in-hospital death: The Northwell COVID-19 Survival (NOCOS) model was developed on NYC data and the COVID Outcome Prediction in the Emergency Department (COPE) model was developed on Dutch data. These models were validated on subsequent second-wave data at the same site (temporal validation) and at the other site (geographic validation). We assessed model performance by the Area Under the receiver operating characteristic Curve (AUC), by the E-statistic, and by net benefit. RESULTS: Twenty-eight-day mortality was considerably higher in the NYC first-wave data (21.0%), compared to the second-wave (10.1%) and the Dutch data (first wave 10.8%; second wave 10.0%). COPE discriminated well at temporal validation (AUC 0.82), with excellent calibration (E-statistic 0.8%). At geographic validation, discrimination was satisfactory (AUC 0.78), but with moderate over-prediction of mortality risk, particularly in higher-risk patients (E-statistic 2.9%). While discrimination was adequate when NOCOS was tested on second-wave NYC data (AUC 0.77), NOCOS systematically overestimated the mortality risk (E-statistic 5.1%). Discrimination in the Dutch data was good (AUC 0.81), but with over-prediction of risk, particularly in lower-risk patients (E-statistic 4.0%). Recalibration of COPE and NOCOS led to limited net benefit improvement in Dutch data, but to substantial net benefit improvement in NYC data. CONCLUSIONS: NOCOS performed moderately worse than COPE, probably reflecting unique aspects of the early pandemic in NYC. Frequent updating of prognostic models is likely to be required for transportability over time and space during a dynamic pandemic.

Discrepancy between disability and reported well-being after traumatic brain injury.

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 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.

Development of prognostic models for Health-Related Quality of Life following traumatic brain injury.

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 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 (R2) 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. R2 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 (R2 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.

Preconception and early-pregnancy risk prediction for birth complications: development of prediction models within a population-based prospective cohort.

BACKGROUND: Suboptimal maternal health already from preconception onwards is strongly linked to an increased risk of birth complications. To enable identification of women at risk of birth complications, we aimed to develop a prediction model for birth complications using maternal preconception socio-demographic, lifestyle, medical history and early-pregnancy clinical characteristics in a general population. METHODS: In a population-based prospective cohort study among 8340 women, we obtained information on 33 maternal characteristics at study enrolment in early-pregnancy. These characteristics covered the preconception period and first half of pregnancy (< 21 weeks gestation). Preterm birth was < 37 weeks gestation. Small-for-gestational-age (SGA) and large-for-gestational-age (LGA) at birth were gestational-age-adjusted birthweight in the lowest or highest decile, respectively. Because of their co-occurrence, preterm birth and SGA were combined into a composite outcome. RESULTS: The basic preconception model included easy obtainable maternal characteristics in the preconception period including age, ethnicity, parity, body mass index and smoking. This basic preconception model had an area under the receiver operating characteristics curve (AUC) of 0.63 (95% confidence interval (CI) 0.61 to 0.65) and 0.64 (95% CI 0.62 to 0.66) for preterm birth/SGA and LGA, respectively. Further extension to more complex models by adding maternal socio-demographic, lifestyle, medical history and early-pregnancy clinical characteristics led to small, statistically significant improved models. The full model for prediction of preterm birth/SGA had an AUC 0.66 (95% CI 0.64 to 0.67) with a sensitivity of 22% at a 90% specificity. The full model for prediction of LGA had an AUC of 0.67 (95% CI 0.65 to 0.69) with sensitivity of 28% at a 90% specificity. The developed models had a reasonable level of calibration within highly different socio-economic subsets of our population and predictive performance for various secondary maternal, delivery and neonatal complications was better than for primary outcomes. CONCLUSIONS: Prediction of birth complications is limited when using maternal preconception and early-pregnancy characteristics, which can easily be obtained in clinical practice. Further improvement of the developed models and subsequent external validation is needed.

External validation of prognostic models predicting outcome after chronic subdural hematoma.

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 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.

Redevelopment and validation of the SYNTAX score II to individualise decision making between percutaneous and surgical revascularisation in patients with complex coronary artery disease: secondary analysis of the multicentre randomised controlled SYNTAXES trial with external cohort validation.

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 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. FUNDING: The German Heart Research Foundation and the Patient-Centered Outcomes Research Institute.

Preterm neonates benefit from low prophylactic platelet transfusion threshold despite varying risk of bleeding or death.

The Platelets for Neonatal Thrombocytopenia (PlaNeT-2) trial reported an unexpected overall benefit of a prophylactic platelet transfusion threshold of 25 × 109/L compared with 50 × 109/L for major bleeding and/or mortality in preterm neonates (7% absolute-risk reduction). However, some neonates in the trial may have experienced little benefit or even harm from the 25 × 109/L threshold. We wanted to assess this heterogeneity of treatment effect in the PlaNet-2 trial, to investigate whether all preterm neonates benefit from the low threshold. We developed a multivariate logistic regression model in the PlaNet-2 data to predict baseline risk of major bleeding and/or mortality for all 653 neonates. We then ranked the neonates based on their predicted baseline risk and categorized them into 4 risk quartiles. Within these quartiles, we assessed absolute-risk difference between the 50 × 109/L- and 25 × 109/L-threshold groups. A total of 146 neonates died or developed major bleeding. The internally validated C-statistic of the model was 0.63 (95% confidence interval, 0.58-0.68). The 25 × 109/L threshold was associated with absolute-risk reduction in all risk groups, varying from 4.9% in the lowest risk group to 12.3% in the highest risk group. These results suggest that a 25 × 109/L prophylactic platelet count threshold can be adopted in all preterm neonates, irrespective of predicted baseline outcome risk. Future studies are needed to improve the predictive accuracy of the baseline risk model. This trial was registered at www.isrctn.com as #ISRCTN87736839.

Predicting 2-year all-cause mortality after contemporary PCI: Updating the logistic clinical SYNTAX score.

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 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.

The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration.

The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed to promote the conduct of, and provide guidance for, predictive analyses of heterogeneity of treatment effects (HTE) in clinical trials. The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risk with versus without the intervention, taking into account all relevant patient attributes simultaneously, to support more personalized clinical decision making than can be made on the basis of only an overall average treatment effect. The authors distinguished 2 categories of predictive HTE approaches (a "risk-modeling" and an "effect-modeling" approach) and developed 4 sets of guidance statements: criteria to determine when risk-modeling approaches are likely to identify clinically meaningful HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. They discuss limitations of these methods and enumerate research priorities for advancing methods designed to generate more personalized evidence. This explanation and elaboration document describes the intent and rationale of each recommendation and discusses related analytic considerations, caveats, and reservations.

The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement.

Heterogeneity of treatment effect (HTE) refers to the nonrandom variation in the magnitude or direction of a treatment effect across levels of a covariate, as measured on a selected scale, against a clinical outcome. In randomized controlled trials (RCTs), HTE is typically examined through a subgroup analysis that contrasts effects in groups of patients defined "1 variable at a time" (for example, male vs. female or old vs. young). The authors of this statement present guidance on an alternative approach to HTE analysis, "predictive HTE analysis." The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risks with versus without the intervention, taking into account all relevant patient attributes simultaneously. The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed using a multidisciplinary technical expert panel, targeted literature reviews, simulations to characterize potential problems with predictive approaches, and a deliberative process engaging the expert panel. The authors distinguish 2 categories of predictive HTE approaches: a "risk-modeling" approach, wherein a multivariable model predicts the risk for an outcome and is applied to disaggregate patients within RCTs to define risk-based variation in benefit, and an "effect-modeling" approach, wherein a model is developed on RCT data by incorporating a term for treatment assignment and interactions between treatment and baseline covariates. Both approaches can be used to predict differential absolute treatment effects, the most relevant scale for clinical decision making. The authors developed 4 sets of guidance: criteria to determine when risk-modeling approaches are likely to identify clinically important HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. The PATH Statement, together with its explanation and elaboration document, may guide future analyses and reporting of RCTs.

Identifying trauma patients with benefit from direct transportation to Level-1 trauma centers.

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 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.

A 4-item PRECISE-DAPT score for dual antiplatelet therapy duration decision-making.

The originally-proposed PRECISE-DAPT score is a 5-item risk score supporting decision-making for dual antiplatelet therapy1 duration after PCI. It is unknown if a simplified version of the score based on 4 factors (age, hemoglobin, creatinine clearance, prior bleeding), and lacking white-blood cell count, retains potential to guide DAPT duration. The 4-item PRECISE-DAPT was used to categorize 10,081 patients who were randomized to short (3-6 months) or long (12-24 months) DAPT regimen according to high (HBR defined by PRECISE-DAPT ≥25 points) or non-high bleeding risk (PRECISE-DAPT<25) status. Long treatment duration was associated with higher bleeding rates in HBR (ARD +2.22% [95% CI +0.53 to +3.90]) but not in non-HBR patients (ARD +0.25% [-0.14 to +0.64]; pint = 0.026), and associated with lower ischemic risks in non-HBR (ARD -1.44% [95% CI -2.56 to -0.31]), but not in HBR patients (ARD +1.16% [-1.91 to +4.22]; pint = 0.11). Only non-HBR patients experienced lower net clinical adverse events (NACE) with longer DAPT (pint = 0.043). A 4-item simplified version of the PRECISE-DAPT score retains the potential to categorize patients who benefit from prolonged DAPT without concomitant bleeding liability from those who do not.

External Validation of Pretreatment Pathological Tumor Extent in Patients with Neoadjuvant Chemoradiotherapy Plus Surgery for Esophageal Cancer.

BACKGROUND: This study was conducted to validate a pretreatment (i.e. prior to neoadjuvant chemoradiotherapy) pathological staging system in the resection specimen after neoadjuvant chemoradiotherapy for esophageal cancer. The study investigated the prognostic value of pretreatment pathological T and N categories (prepT and prepN categories) in both an independent and a combined patient cohort. METHODS: Patients with esophageal cancer treated with neoadjuvant chemotherapy and esophagectomy between 2012 and 2015 were included. PrepT and prepN categories were estimated based on the extent of tumor regression and regressional changes of lymph nodes in the resection specimen. The difference in Akaike's information criterion (ΔAIC) was used to assess prognostic performance. PrepN and ypN categories were combined to determine the effect of nodal sterilization on prognosis. A multivariable Cox regression model was used to identify combined prepN and ypN categories as independent prognostic factors. RESULTS: The prognostic strength of the prepT category was better than the cT and ypT categories (ΔAIC 7.7 vs. 3.0 and 2.9, respectively), and the prognostic strength of the prepN category was better than the cN category and similar to the ypN category (ΔAIC 29.2 vs. - 1.0 and 27.9, respectively). PrepN + patients who became ypN0 had significantly worse survival than prepN0 patients (2-year overall survival 69% vs. 86% in 137 patients; p = 0.044). Similar results were found in a combined cohort of 317 patients (2-year overall survival 62% vs. 85%; p = 0.002). Combined prepN/ypN stage was independently associated with overall survival. CONCLUSIONS: These results independently confirm the prognostic value of prepTNM staging. PrepTNM staging is of additional prognostic value to cTNM and ypTNM. PrepN0/ypN0 patients have a better survival than prepN +/ypN0 patients.

Graphical calibration curves and the integrated calibration index (ICI) for survival models.

In the context of survival analysis, calibration refers to the agreement between predicted probabilities and observed event rates or frequencies of the outcome within a given duration of time. We aimed to describe and evaluate methods for graphically assessing the calibration of survival models. We focus on hazard regression models and restricted cubic splines in conjunction with a Cox proportional hazards model. We also describe modifications of the Integrated Calibration Index, of E50 and of E90. In this context, this is the average (respectively, median or 90th percentile) absolute difference between predicted survival probabilities and smoothed survival frequencies. We conducted a series of Monte Carlo simulations to evaluate the performance of these calibration measures when the underlying model has been correctly specified and under different types of model mis-specification. We illustrate the utility of calibration curves and the three calibration metrics by using them to compare the calibration of a Cox proportional hazards regression model with that of a random survival forest for predicting mortality in patients hospitalized with heart failure. Under a correctly specified regression model, differences between the two methods for constructing calibration curves were minimal, although the performance of the method based on restricted cubic splines tended to be slightly better. In contrast, under a mis-specified model, the smoothed calibration curved constructed using hazard regression tended to be closer to the true calibration curve. The use of calibration curves and of these numeric calibration metrics permits for a comprehensive comparison of the calibration of competing survival models.