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Background: Considering the importance of cardiovascular disease (CVD) risk prediction for healthcare systems and the limited information available in the Middle East, we evaluated the SCORE and Globorisk models to predict CVD death in a country of this region. Methods: We included 24 427 participants (11 187 men) aged 40-80 years from four population-based cohorts in Iran. Updating approaches were used to recalibrate the baseline survival and the overall effect of the predictors of the models. We assessed the models’ discrimination using C-index and then compared the observed with the predicted risk of death using calibration plots. The sensitivity and specificity of the models were estimated at the risk thresholds of 3%, 5%, 7%, and 10%. An agreement between models was assessed using the intra-class correlation coefficient (ICC). We applied decision analysis to provide perception into the consequences of using the models in general practice; for this reason, the clinical usefulness of the models was assessed using the net benefit (NB) and decision curve analysis. The NB is a sensitivity penalized by a weighted false positive (FP) rate in population level. Results: After 154 522 person-years of follow-up, 437 cardiovascular deaths (280 men) occurred. The 10-year observed risks were 4.2% (95% CI: 3.7%-4.8%) in men and 2.1% (1.8-2%.5%) in women. The c-index for SCORE function was 0.784 (0.756-0.812) in men and 0.780 (0.744-0.815) in women. Corresponding values for Globorisk were 0.793 (0.766- 0.820) and 0.793 (0.757-0.829). The deviation of the calibration slopes from one reflected a need for recalibration; after which, the predicted-to-observed ratio for both models was 1.02 in men and 0.95 in women. Models showed good agreement (ICC 0.93 in men, and 0.89 in women). Decision curve showed that using both models results in the same clinical usefulness at the risk threshold of 5%, in both men and women; however, at the risk threshold of 10%, Globorisk had better clinical usefulness in women (Difference: 8%, 95% CI: 4%-13%). Conclusion: Original Globorisk and SCORE models overestimate the CVD risk in Iranian populations resulting in a high number of people who need intervention. Recalibration could adopt these models to precisely predict CVD mortality. Globorisk showed better performance clinically, only among high-risk women.

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 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.

Rationale and research objective: As a result of neoadjuvant chemotherapy (NAC), approximately 1/3 of clinically node positive (cN+) breast cancer patients convert to a pathologic complete response (pCR) of the axilla. Targeted Axillary Dissection appears to be most accurate to evaluate axillary treatment-response, but evidence to support this is limited. The RISAS trial was set up to determine the accuracy of sentinel lymph node biopsy (SLNB) combined with excision of the pretreatment marked positive lymph node for axillary staging after NAC in cN+ patients. Methods: This prospective, multicenter trial included cT1-4N1,2,3b patients treated with NAC. Prior to NAC, an iodine seed was placed in the pathologically confirmed positive lymph node. After NAC, all patients underwent SLNB together with excision of the marked lymph node (RISAS-procedure) followed by axillary lymph node dissection (ALND). The identification rate and the accuracy (i.e. false negative rate (FNR) and negative predictive value (NPV)) were calculated for the RISAS procedure. Assuming an FNR of 2%, a prevalence of a positive ALND of 64% and a 10% drop-out rate, a sample size of 248 patients was needed to determine non-inferiority. The NULL hypothesis of inferiority would be rejected at a significance level of 5% if the upper bound of the two-sided 90% Clopper-Pearson confidence interval of the observed FNR would be below the non-inferiority margin of 6.25%. Secondary objectives included the accuracy of the SLNB and excision of the marked lymph node separately. Results: A total of 252 pathologically proven cN+ patients provided informed consent of whom 227 underwent the RISAS procedure/Targeted Axillary Dissection (SLNB combined with excision of the marked positive lymph node). The identification rate of the RISAS procedure is 98% (223/227). Preliminary analysis shows a false negative rate of approximately 5% and a negative predictive value of approximately 91%. Furthermore, by combining SLNB with excision of the marked lymph node, both the identification rate and the accuracy are improved compared to either the SLNB alone or the excision of the marked lymph node alone. Conclusion: This prospective multicenter validation trial shows that the RISAS procedure/Targeted Axillary Dissection is most suitable in terms of identification rate and accuracy to replace ALND for axillary staging after NAC in cN+ patients. The trial was funded by the Dutch Cancer Society (KWF, grant number 2015-8023). Citation Format: Janine Simons, Thiemo JA v Nijnatten, Linetta B Koppert, Carmen C van der Pol, Paul J v Diest, Agnes Jager, David v Klaveren, Boen LR Kam, Marc BI Lobbes, Maaike de Boer, Cees Verhoef, Paul RA Sars, Harold J Heijmans, Els RM van Haaren, Wouter J Vles, Caroline ME Contant, Marian BE Menke, Leonie HM Smit, Wendy Kelder, Marike Boskamp, Ingeborg Mares-Engelberts, Ernest JT Luiten, Marjolein L Smidt. Radioactive Iodine Seed placement in the Axilla with Sentinel lymph node biopsy after neoadjuvant chemotherapy in breast cancer: Results of the prospective multicenter RISAS trial [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr GS1-10.

Background: The poorer prognosis of coronary artery disease in females compared with males is related mainly to differences in baseline characteristics. In the SYNTAX (Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery) trial, the effect of treatment with percutaneous coronary intervention (PCI) versus coronary artery bypass grafting surgery (CABG) on mortality at 5 years differed significantly between females and males; however, the optimal revascularization beyond 5 years according to sex has not been evaluated. Objectives: The aim of this study was to investigate the impact of sex on mortality and sex-treatment interaction at 10 years. Methods: The SYNTAXES (SYNTAX Extended Survival) study evaluated vital status up to 10 years in 1,800 patients with de novo 3-vessel and/or left main coronary artery disease randomized to treatment with PCI or CABG in the SYNTAX trial. All-cause death at 10 years was separately evaluated in female and male patients with complex coronary artery disease. Results: Of 1,800 patients, 402 (22.3%) were female and 1,398 (77.7%) were males. Females had a higher 10-year mortality rate compared with males (32.8% vs. 24.7%; log-rank p = 0.002), but female sex was not an independent predictor of mortality (adjusted hazard ratio: 1.02; 95% confidence interval: 0.76 to 1.36). Mortality at 10 years tended to be lower after CABG than after PCI, with a similar treatment effect for female and male patients (adjusted hazard ratio for females: 0.90 [95% confidence interval: 0.54 to 1.51]; adjusted hazard ratio for males: 0.76 [95% confidence interval: 0.56 to 1.02]; p for interaction = 0.952). Conclusions: Female sex was not an independent predictor of mortality at 10 years in patients with complex coronary artery disease. The interaction between sex and treatment with PCI or CABG that was observed at 5 years was no longer present at 10 years. (Synergy Between PCI With TAXUS and Cardiac Surgery: SYNTAX Extended Survival [SYNTAXES], NCT03417050; SYNTAX Study: TAXUS Drug-Eluting Stent Versus Coronary Artery Bypass Surgery for the Treatment of Narrowed Arteries [SYNTAX], NCT00114972)

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.

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.

Quality of life research 31(2), 451-471 (2022). doi:10.1007/s11136-021-02932-z, Published by Springer Science + Business Media B.V, Dordrecht [u.a.]

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 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/ )

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.

Background This study aimed to develop an easy to use prediction model to predict the risk of having a total of 1 to 2, >= 3, or >= 4 positive axillary lymph nodes (LNs), for patients with sentinel lymph node (SLN) positive breast cancer. Methods Data of 911 SLN positive breast cancer patients were used for model development. The model was validated externally in an independent population of 180 patients with SLN positive breast cancer. Results Final pathology after ALND showed additional positive LN for 259 (28%) of the patients. A total of 726 (81%) out of 911 patients had a total of 1 to 2 positive nodes, whereas 175 (19%) had >= 3 positive LNs. The model included three predictors: the tumor size (in mm), the presence of a negative SLN, and the size of the SLN metastases (in mm). At external validation, the model showed a good discriminative ability (area under the curve = 0.82; 95% confidence interval = 0.74-0.90) and good calibration over the full range of predicted probabilities. Conclusion This new and validated model predicts the extent of nodal involvement in node-positive breast cancer and will be useful for counseling patients regarding their personalized axillary treatment.

Aims To evaluate the impact of chronic obstructive pulmonary disease (COPD) on 10-year all-cause death and the treatment effect of CABG versus PCI on 10-year all-cause death in patients with three-vessel disease (3VD) and/or left main coronary artery disease (LMCAD) and COPD. Methods Patients were stratified according to COPD status and compared with regard to clinical outcomes. Ten-year all-cause death was examined according to the presence of COPD and the revascularization strategy. Results COPD status was available for all randomized 1800 patients, of whom, 154 had COPD (8.6%) at the time of randomization. Regardless of the revascularization strategy, patients with COPD had a higher risk of 10-year all-cause death, compared with those without COPD (43.1% vs. 24.9%; hazard ratio [HR]: 2.03; 95% confidence interval [CI]: 1.56–2.64; p p = 0.858), whereas among those without COPD, CABG had a significantly lower risk of 10-year all-cause death (22.7% vs. 27.1%; HR: 0.81; 95% CI: 0.67–0.99, p = 0.041). There was no significant differential treatment effect of CABG versus PCI on 10-year all-cause death between patients with and without COPD (pinteraction = 0.544). Conclusions COPD was associated with a higher risk of 10-year all-cause death after revascularization for complex coronary artery disease. The presence of COPD did not significantly modify the beneficial effect of CABG versus PCI on 10-year all-cause death. Trial registration: SYNTAX: ClinicalTrials.gov reference: NCT00114972. SYNTAX Extended Survival: ClinicalTrials.gov reference: NCT03417050 Graphic abstract

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.

OBJECTIVE: Approximately 84 million people in the US have pre-diabetes, but only a fraction of them receive proven effective therapies to prevent type 2 diabetes. We estimated the value of prioritizing individuals at highest risk of progression to diabetes for treatment, compared to non-targeted treatment of individuals meeting inclusion criteria for the Diabetes Prevention Program (DPP). METHODS: Using microsimulation to project outcomes in the DPP trial population, we compared two interventions to usual care: (1) lifestyle modification and (2) metformin administration. For each intervention, we compared targeted and non-targeted strategies, assuming either limited or unlimited program capacity. We modeled the individualized risk of developing diabetes and projected diabetic outcomes to yield lifetime costs and quality-adjusted life expectancy, from which we estimated net monetary benefits (NMB) for both lifestyle and metformin versus usual care. RESULTS: Compared to usual care, lifestyle modification conferred positive benefits and reduced lifetime costs for all eligible individuals. Metformin’s NMB was negative for the lowest population risk quintile. By avoiding use when costs outweighed benefits, targeted administration of metformin conferred a benefit of $500 per person. If only 20% of the population could receive treatment, when prioritizing individuals based on diabetes risk, rather than treating a 20% random sample, the difference in NMB ranged from $14,000 to $20,000 per person. CONCLUSIONS: Targeting active diabetes prevention to patients at highest risk could improve health outcomes and reduce costs compared to providing the same intervention to a similar number of patients with pre-diabetes without targeted selection.

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 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.

PURPOSE: Based on recent advances in the management of patients with sentinel node (SN)-positive melanoma, we aimed to develop prediction models for recurrence, distant metastasis (DM) and overall mortality (OM).METHODS: The derivation cohort consisted of 1080 patients with SN-positive melanoma from nine European Organization for Research and Treatment of Cancer (EORTC) centres. Prognostic factors for recurrence, DM and OM were studied with Cox regression analysis. Significant factors were incorporated in the models. Performance was assessed by discrimination (c-index) and calibration in cross-validation across centres. The models were externally validated using a prospective cohort consisting of 705 German patients with SN-positive: 473 trial participants of the German Dermatologic Cooperative Oncology Group study (DeCOG-SLT) and 232 screened patients. A nomogram was developed for graphical presentation.RESULTS: The final model for recurrence and the calibrated models for DM and OM included ulceration, age, SN tumour burden and Breslow thickness. The models showed reasonable calibration. The c-index for the recurrence, DM and OM model was 0.68, 0.70 and 0.70, respectively, and 0.70, 0.72 and 0.74, respectively, in external validation. The EORTC-DeCOG model identified a robust low-risk group, with all identified low-risk patients (approximately 4% of the entire population) having a 5-year recurrence probability of CONCLUSIONS: The EORTC-DeCOG nomogram provides an adequate prognostic tool for patients with SN-positive melanoma, without the need for CLND. It showed consistent results across validation. The nomogram could be used for patient counselling and might aid in adjuvant therapy decision-making.

OBJECTIVES The SYNTAX score II (SSII) was developed from the SYNTAX trial to predict the 4-year all-cause mortality after left main or multivessel disease revascularization and to facilitate the decision-making process. The SSII provides the following treatment recommendations: (i) coronary artery bypass grafting (CABG) or percutaneous coronary intervention (PCI) (equipoise risk), (ii) CABG preferred (excessive risk for PCI) or (iii) PCI preferred (excessive risk for CABG). We sought to externally validate SSII and to investigate the impact of not abiding by the SSII recommendations in the randomized EXCEL trial of PCI versus CABG for left main disease. METHODS The calibration plot of predicted versus observed 4-year mortality was constructed from individual values of SSII in EXCEL. To assess overestimation versus underestimation of predicted mortality risk, an optimal fit regression line with slope and intercept was determined. Prospective treatment recommendations based on SSII were compared with actual treatments and all-cause mortality at 4 years. RESULTS SSII variables were available from EXCEL trial in 1807/1905 (95%) patients. For the entire cohort, discrimination was possibly helpful (C statistic = 0.670). SSII-predicted all-cause mortality at 4 years overestimated the observed mortality, particularly in the highest-risk percentiles, as confirmed by the fit regression line [intercept 2.37 (1.51–3.24), P = 0.003; slope 0.67 (0.61–0.74), P CONCLUSIONS In the EXCEL trial of patients with left main disease, the SSII-predicted 4-year mortality overestimated the 4-year observed mortality with a possibly helpful discrimination. Non-compliance with SSII CABG treatment recommendations (i.e. randomized to PCI) was associated with higher 4-year all-cause mortality.

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.

The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed to promote the conduct of, and provide guidance for, predictive analyses of heterogeneous treatment effects (HTE) in clinical trials. 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, to support more personalized clinical decision making than can be made based only on an overall average treatment effect. We distinguished two categories of predictive HTE approaches (a “risk modeling” and an “effect modeling” approach) and developed four sets of guidance statements: 1) criteria to determine when risk modeling approaches are likely to identify clinically meaningful HTE; 2) methodological aspects of risk modeling methods; 3) considerations for translation to clinical practice; and 4) considerations and caveats in the use of effect modeling approaches. We also 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.

Background Pneumonectomy in lung cancer treatment is associated with considerable morbidity and mortality. Its use is reserved only for patients in whom a complete oncological resection by (sleeve) lobectomy is not possible. It is unclear whether a patients’ risk of receiving a pneumonectomy is equally distributed. This study examined between-hospital variation of pneumonectomy use for primary lung cancer in the Netherlands. Methods Data from the Dutch Lung Cancer Audit for Surgery from 2012 to 2016 were used to study the use of pneumonectomy for primary lung cancer in the Netherlands. Using multivariable logistic regression, factors associated with pneumonectomy use were identified and the expected number of pneumonectomies per hospital was determined. Subsequently, the observed/expected ratio (O/E ratio) per hospital was calculated to study between-hospital differences. Results Of the 8446 included patients, 659 (7.8%) underwent a pneumonectomy with a mean postoperative mortality of 7.1% (n = 47). Factors associated with receiving a pneumonectomy were age, gender, cardiac and pulmonary comorbidities, tumor side, size and histopathology. The pneumonectomy use in the Netherlands varied considerably between hospitals (IQR 5.5–10.1%). Three hospitals out of 51 performed significantly less pneumonectomies than expected (O/E ratio O/E ratio > 1.7). In the latter group, severe complications were more frequent, taking other influencing factors into account (OR 1.51, 95% CI 1.05–2.19). Conclusions There is a considerable between-hospital variation in pneumonectomy use in lung cancer treatment. To further optimize surgical lung cancer care, we suggest center-specific feedback on pneumonectomy use and the development of a risk-adjusted pneumonectomy indicator.

Objective: To compare overall survival in patients with esophageal adenocarcinoma who underwent transhiatal esophagectomy (THE) with limited lymphadenectomy or transthoracic esophagectomy (TTE) with extended lymphadenectomy with or without neoadjuvant chemoradiotherapy (nCRT). Background: The application of neoadjuvant therapy might change the association between the extent of lymphadenectomy and survival in patients with esophageal adenocarcinoma. This may influence the choice of surgical approach in patients treated with nCRT. Methods: Patients with potentially curable subcarinal esophageal adenocarcinoma treated with surgery alone or nCRT followed by surgery in 7 centers were included. The effect of surgical approach on overall survival, differentiated by the addition or omission of nCRT, was analyzed using a multivariable Cox regression model that included well-known prognostic factors and factors that might have influenced the choice of surgical approach. Results: In total, 701 patients were included, of whom 318 had TTE with extended lymphadenectomy and 383 had THE with limited lymphadenectomy. TTE had differential effects on survival (P for interaction = 0.02), with a more favorable prognostic effect in patients who were treated with surgery alone [hazard ratio (HR) = 0.77, 95% confidence interval (CI) 0.58-1.03]. This association was statistically significant in a subgroup of patients with 1 to 8 positive lymph nodes in the resection specimen (HR = 0.62, 95% CI 0.43-0.90). The favorable prognostic effect of TTE over THE was absent in the nCRT and surgery group (HR = 1.16, 95% CI 0.80-1.66) and in the subgroup of nCRT patients with 1 to 8 positive lymph nodes in the resection specimen (HR = 1.00, 95% CI 0.61-1.68). Conclusions: Compared to surgery alone, the addition of nCRT may reduce the need for TTE with extended lymphadenectomy to improve long-term survival in patients with esophageal adenocarcinoma.

Background Dual antiplatelet therapy (DAPT) with aspirin plus a P2Y12 inhibitor prevents ischaemic events after coronary stenting, but increases bleeding. Guidelines support weighting bleeding risk before the selection of treatment duration, but no standardised tool exists for this purpose. Methods A total of 14 963 patients treated with DAPT after coronary stenting—largely consisting of aspirin and clopidogrel and without indication to oral anticoagulation—were pooled at a single-patient level from eight multicentre randomised clinical trials with independent adjudication of events. Using Cox proportional hazards regression, we identified predictors of out-of-hospital Thrombosis in Myocardial Infarction (TIMI) major or minor bleeding stratified by trial, and developed a numerical bleeding risk score. The predictive performance of the novel score was assessed in the derivation cohort and validated in patients treated with percutaneous coronary intervention from the PLATelet inhibition and patient Outcomes (PLATO) trial (n=8595) and BernPCI registry (n=6172). The novel score was assessed within patients randomised to different DAPT durations (n=10 081) to identify the effect on bleeding and ischaemia of a long (12–24 months) or short (3–6 months) treatment in relation to baseline bleeding risk. Findings The PRECISE-DAPT score (age, creatinine clearance, haemoglobin, white-blood-cell count, and previous spontaneous bleeding) showed a c-index for out-of-hospital TIMI major or minor bleeding of 0·73 (95% CI 0·61–0·85) in the derivation cohort, and 0·70 (0·65–0·74) in the PLATO trial validation cohort and 0·66 (0·61–0·71) in the BernPCI registry validation cohort. A longer DAPT duration significantly increased bleeding in patients at high risk (score ≥25), but not in those with lower risk profiles (pinteraction=0·007), and exerted a significant ischaemic benefit only in this latter group. Interpretation The PRECISE-DAPT score is a simple five-item risk score, which provides a standardised tool for the prediction of out-of-hospital bleeding during DAPT. In the context of a comprehensive clinical evaluation process, this tool can support clinical decision making for treatment duration. Funding None.

Background: In 1 of 3 patients with initial lymph node-positive (cN(+)) breast cancer, neoadjuvant chemotherapy (NAC) results in an axillary pathologic complete response (ax-pCR). This urges the need for a less-invasive axillary staging method. Recently introduced less-invasive procedures have been insufficient in accurately identifying ax-pCR. Therefore, we propose a novel less-invasive axillary staging procedure: the Radioactive Iodine Seed localization in the Axilla with the Sentinel node procedure (RISAS), a combination of the procedure of marking axillary lymph nodes with radioactive iodine seeds (MARI) and sentinel lymph node biopsy (SLNB). Patients and Methods: In the present open single-arm multicenter validation study, 225 cN(+) (biopsy-proven) patients will undergo the RISAS procedure, in which a positive lymph node is marked by an iodine-125 seed before NAC. After NAC completion, this iodine-125 seed-marked lymph node is removed, together with any additional sentinel lymph nodes. The RISAS procedure is subsequently followed by completion axillary lymph node dissection (ALND). The RISAS lymph nodes will be compared with the lymph nodes from the completion ALND specimen. The primary endpoint is accuracy of the RISAS procedure. The identification rate, false-negative rate, negative predictive value, and possible concordance between the MARI and SLNB will be reported. Conclusion: The present prospective multicenter RISAS trial will enable us to validate the combination of MARI and SLNB for assessing the axillary response to NAC in cN(+) patients. If RISAS proves to be an accurate axillary staging procedure, ALND could safely be abandoned in the case of ax-pCR confirmed using the RISAS procedure.

OBJECTIVES: The study aimed to (i) determine the influence of poor health on competing exit routes from paid employment among older workers in Europe, (ii) assess whether these risks are different among welfare state regimes in Europe, and (iii) evaluate differences in estimates between two different competing risk approaches. METHODS: The study population consisted of 5273 respondents (6-years follow-up) from the Survey of Health, Ageing, and Retirement in Europe (SHARE). The effect of poor health on exit routes from paid employment was assessed with a cause-specific Cox model and a Fine & Gray (F&G) model. These two competing risk analyses were used to calculate absolute risks of labor force exit among welfare state regimes in Europe. RESULTS: In both models, poor health was a risk factor for disability benefit [hazard ratio (HR) 3.36; subdistribution hazard ratio (SHR) 3.22], and unemployment (HR 1.43, SHR 1.32). Both models produced similar absolute risks. In countries with a Bismarckian welfare state regime, low-educated older workers living alone and in poor health had an 11% risk of disability benefit, 7% of unemployment, 46% of early retirement, and 7% of becoming economically inactive. In countries with a Scandinavian welfare state regime, the risks were 10%, 7%, 29%, and 3%, respectively, and in Southern European welfare state regimes 4%, 5%, 35%, and 7%. CONCLUSIONS: Workers with poor health are more likely to leave the labor force than workers with good health. The absolute risks of early retirement and becoming economically inactive were lowest in countries with a Scandinavian welfare state regime. For disability benefit and unemployment, absolute risks were lowest in Southern European welfare state regimes. The direct estimation of absolute risks of leaving the labor force in the presence of competing exit routes is an appealing feature of the F&G model.

Background. The benefits and costs of a treatment are typically heterogeneous across individual patients. Randomized clinical trials permit the examination of individualized treatment benefits over the trial horizon but extrapolation to lifetime horizon usually involves combining trial-based individualized estimates of short-term risk reduction with less detailed (less granular) population life tables. However, the underlying assumption of equal post-trial life expectancy for low- and high-risk patients of the same sex and age is unrealistic. We aimed to study the influence of unequal granularity between models of short-term risk reduction and life expectancy on individualized estimates of cost-effectiveness of aggressive thrombolysis for patients with an acute myocardial infarction. Methods. To estimate life years gained, we multiplied individualized estimates of short-term risk reduction either with less granular and with equally granular post-trial life expectancy estimates. Estimates of short-term risk reduction were obtained from GUSTO trial data (30,510 patients) using logistic regression analysis with treatment, sex, and age as predictor variables. Life expectancy estimates were derived from sex- and age-specific US life tables. Results. Based on sex- and age-specific, short-term risk reductions but average population life expectancy (less granularity), we found that aggressive thrombolysis was cost-effective (incremental cost-effectiveness ratio below $50,000) for women above age 49 y and men above age 53 y (92% and 69% of the population, respectively). Considering sex- and age-specific short-term mortality risk reduction and correspondingly sex- and age-specific life expectancy (equal granularity), aggressive thrombolysis was cost-effective for men above age 45 y and women above age 50 y (95% and 76% of the population, respectively). Conclusions. Failure to model short-term risk reduction and life expectancy at an equal level of granularity may bias our estimates of individualized cost-effectiveness and misallocate resources.

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

Aims: The aim of this study was the external validation of the updated logistic clinical SYNTAX score for two-year all-cause mortality after PCI in the GLOBAL LEADERS trial. Methods and results: The GLOBAL LEADERS trial was an investigator-initiated, prospective randomised, multicentre, open-label trial comparing two strategies of antiplatelet therapy in 15,991 patients undergoing PCI. As a predefined analysis, we studied the first 4,006 consecutive patients enrolled between July 2013 and April 2014 for whom the anatomic SYNTAX scores were calculated by an independent core lab. The updated logistic clinical SYNTAX score was available in 3,271 patients. Patients were divided into quintiles according to the score. The C-statistic of the updated logistic clinical SYNTAX score for twoyear all-cause mortality was 0.71 (95% confidence interval [CI]: 0.64-0.77). The updated logistic clinical SYNTAX score identified patients at very high risk for two-year all-cause mortality after PCI. Although it systematically overestimated two-year all-cause mortality, predicted and observed two-year all-cause mortality in the majority of the patients (four out of five quintiles) were in agreement. Conclusions: Overall discrimination for two-year all-cause mortality of the updated logistic clinical SYNTAX score is either borderline acceptable or possibly helpful. Calibration in the majority of patients is appropriate. The score is potentially useful in selecting enriched high-risk populations.

Background ACEF score has been shown to have predictive ability in the patients undergoing percutaneous coronary intervention (PCI). The ACEF II score has recently been developed to predict short-term mortality after cardiac surgery. We compared the predictive ability of the ACEF and ACEF II scores to predict mortality after PCI in the all-comers population. Methods The ACEF and ACEF II scores were calculated in 15,968 patients enrolled in the GLOBAL LEADERS study. Discrimination and calibration were assessed for outcomes after PCI. Recalibration of the regression model by updating the intercept and slope were performed to adjust the original ACEF model to the PCI setting. In a stratified approach, patients were divided into quintiles according to the score. Outcomes were compared between quintiles. Results The ACEF and ACEF II score were available in 14,941 and 14,355 patients respectively. Discrimination for 30-day all-cause mortality was acceptable for both scores (C-statistic ACEF 0.75 and ACEF II 0.77). For 2-year all-cause mortality, the discrimination of ACEF score was acceptable (C-statistic 0.72) while the discrimination of ACEF II score was moderate (C-statistic 0.69). Both scores identified patients at high risk of mortality but overestimated all-cause mortality at 30 days in all quintiles. After recalibration, agreement between predicted and observed 30-day all-cause mortality in both scores are close to the identity line. Conclusions The ACEF II model did not improve the predictive ability of the ACEF score. Recalibrated ACEF model can be used to estimated all-cause mortality rate at 30 days after PCI.

Background Discriminative ability is an important aspect of prediction model performance, but challenging to assess in clustered (e.g., multicenter) data. Concordance (c)-indexes may be too extreme within small clusters. We aimed to define a new approach for the assessment of discriminative ability in clustered data. Methods We assessed discriminative ability of a prediction model for the binary outcome mortality after traumatic brain injury within centers of the CRASH trial. With multilevel logistic regression analysis, we estimated cluster-specific calibration slopes which we used to obtain the recently proposed calibrated model-based concordance (c-mbc) within each cluster. We compared the c-mbc with the naïve c-index in centers of the CRASH trial and in simulations of clusters with varying calibration slopes. Results The c-mbc was less extreme in distribution than the c-index in 19 European centers (internal validation; n = 1716) and 36 non-European centers (external validation; n = 3135) of the CRASH trial. In simulations, the c-mbc was biased but less variable than the naïve c-index, resulting in lower root mean squared errors. Conclusions The c-mbc, based on multilevel regression analysis of the calibration slope, is an attractive alternative to the c-index as a measure of discriminative ability in multicenter studies with patient clusters of limited sample size. Electronic supplementary material The online version of this article (10.1186/s41512-019-0055-8) contains supplementary material, which is available to authorized users.

Background Complex percutaneous coronary intervention (PCI) is associated with higher ischemic risk, which can be mitigated by long-term dual antiplatelet therapy (DAPT). However, concomitant high bleeding risk (HBR) may be present, making it unclear whether short- or long-term DAPT should be prioritized. Objectives This study investigated the effects of ischemic (by PCI complexity) and bleeding (by PRECISE-DAPT [PREdicting bleeding Complications in patients undergoing stent Implantation and SubsequEnt Dual AntiPlatelet Therapy] score) risks on clinical outcomes and on the impact of DAPT duration after coronary stenting. Methods Complex PCI was defined as ≥3 stents implanted and/or ≥3 lesions treated, bifurcation stenting and/or stent length >60 mm, and/or chronic total occlusion revascularization. Ischemic and bleeding outcomes in high (≥25) or non-high ( Results Among 14,963 patients from 8 randomized trials, 3,118 underwent complex PCI and experienced a higher rate of ischemic, but not bleeding, events. Long-term DAPT in non-HBR patients reduced ischemic events in both complex (absolute risk difference: −3.86%; 95% confidence interval: −7.71 to +0.06) and noncomplex PCI strata (absolute risk difference: −1.14%; 95% confidence interval: −2.26 to −0.02), but not among HBR patients, regardless of complex PCI features. The bleeding risk according to the Thrombolysis In Myocardial Infarction scale was increased by long-term DAPT only in HBR patients, regardless of PCI complexity. Conclusions Patients who underwent complex PCI had a higher risk of ischemic events, but benefitted from long-term DAPT only if HBR features were not present. These data suggested that when concordant, bleeding, more than ischemic risk, should inform decision-making on the duration of DAPT.

Objective Validation of clinical prediction models traditionally refers to the assessment of model performance in new patients. We studied different approaches to geographic and temporal validation in the setting of multicenter data from two time periods. Study Design and Setting We illustrated different analytic methods for validation using a sample of 14,857 patients hospitalized with heart failure at 90 hospitals in two distinct time periods. Bootstrap resampling was used to assess internal validity. Meta-analytic methods were used to assess geographic transportability. Each hospital was used once as a validation sample, with the remaining hospitals used for model derivation. Hospital-specific estimates of discrimination (c-statistic) and calibration (calibration intercepts and slopes) were pooled using random-effects meta-analysis methods. I 2 statistics and prediction interval width quantified geographic transportability. Temporal transportability was assessed using patients from the earlier period for model derivation and patients from the later period for model validation. Results Estimates of reproducibility, pooled hospital-specific performance, and temporal transportability were on average very similar, with c-statistics of 0.75. Between-hospital variation was moderate according to I 2 statistics and prediction intervals for c-statistics. Conclusion This study illustrates how performance of prediction models can be assessed in settings with multicenter data at different time periods.

Objectives The study sought to validate the SYNTAX (Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery) score II mortality prediction model after percutaneous coronary intervention (PCI) or coronary artery bypass grafting in a large pooled population of patients with multivessel coronary disease (MVD) and/or unprotected left main disease (UPLMD) enrolled in the PRECOMBAT (Bypass Surgery Versus Angioplasty Using Sirolimus-Eluting Stent in Patients With Left Main Coronary Artery Disease) and BEST (Artery Bypass Surgery and Everolimus-Eluting Stent Implantation in the Treatment of Patients with Multivessel Coronary Artery Disease) randomized controlled trials. Background For patients with MVD and/or UPLMD, the choice of the best revascularization strategy remains challenging. Methods Pooled individual patient-level data from PRECOMBAT and BEST were used to assess calibration and discrimination of the SYNTAX score II prediction model for all-cause mortality after PCI and coronary artery bypass grafting at 4-year follow-up. The study population comprised 1,480 patients (600 with UPLMD, 880 with MVD). Results The overall incidence of all-cause mortality was 6.1% after a median follow-up period of 4.9 years. Validation plots showed good model calibration overall and across treatment groups but tended to overestimate all-cause mortality in the highest risk quintiles of patients in the whole population and the PCI arm. The SYNTAX score II showed moderate discrimination ability for the whole population (C index = 0.685) but better for patients receiving PCI than CABG (C index = 0.718 vs. 0.662 in patients with UPLMD, C index = 0.700 vs. 0.661 in those with MVD). Observed all-cause mortality was higher when the treatment received was at variance with that recommended by the model and similar when it was concordant. Conclusions The SYNTAX score II has good calibration but only moderate discrimination ability for long-term mortality prediction in this randomized population. This score provides an important tool to help guide the heart team’s decision-making process regarding the selection of the best revascularization strategy for patients with MVD and/or UPLMD. (Bypass Surgery Versus Angioplasty Using Sirolimus-Eluting Stent in Patients With Left Main Coronary Artery Disease, NCT00422968; Bypass Surgery Versus Everolimus-Eluting Stent Implantation for Multivessel Coronary Artery Disease, NCT00997828)

Concordance measures are frequently used for assessing the discriminative ability of risk prediction models. The interpretation of estimated concordance at external validation is difficult if the case-mix differs from the model development setting. We aimed to develop a concordance measure that provides insight into the influence of case-mix heterogeneity and is robust to censoring of time-to-event data. We first derived a model-based concordance (mbc) measure that allows for quantification of the influence of case-mix heterogeneity on discriminative ability of proportional hazards and logistic regression models. This mbc can also be calculated including a regression slope that calibrates the predictions at external validation (c-mbc), hence assessing the influence of overall regression coefficient validity on discriminative ability. We derived variance formulas for both mbc and c-mbc. We compared the mbc and the c-mbc with commonly used concordance measures in a simulation study and in two external validation settings. The mbc was asymptotically equivalent to a previously proposed resampling-based case-mix corrected c-index. The c-mbc remained stable at the true value with increasing proportions of censoring, while Harrell's c-index and to a lesser extent Uno's concordance measure increased unfavorably. Variance estimates of mbc and c-mbc were well in agreement with the simulated empirical variances. We conclude that the mbc is an attractive closed-form measure that allows for a straightforward quantification of the expected change in a model's discriminative ability due to case-mix heterogeneity. The c-mbc also reflects regression coefficient validity and is a censoring-robust alternative for the c-index when the proportional hazards assumption holds. Copyright © 2016 John Wiley & Sons, Ltd.

The performance of previously developed models to predict non sentinel lymph node status for patients with sentinel lymph node-positive breast cancer was poor for our Dutch patient population. Therefore, a new model was developed that includes only 2 predictors, is easy to use, and can help clinicians and their patients in the clinical decision-making regarding axillary treatment. Background: Multiple predictive systems have previously been developed to identify the sentinel lymph node (SLN)-positive patients at low risk of additional axillary non-SLN involvement and for whom completion axillary lymph node dissection (ALND) could be avoided. However, previous studies showed that these tools had poor performance in Dutch patients with breast cancer, probably owing to variations in pathology settings and differences in population characteristics. The aim of the present study was to develop a predictive tool for the risk of non-SLN involvement in a Dutch population with SLN-positive breast cancer. Materials and Methods: The data from 513 patients with SLN-positive breast cancer at 10 participating hospitals, who had undergone ALND from January 2007 to December 2008 were studied. The uni- and multivariable associations of predictors for non-SLN metastases were analyzed, and a predictive model was developed. The discriminatory ability of the model was measured by the area under the receiver operating characteristic curve (AUC) and the agreement between predicted probabilities and observed frequencies was visualized by a calibration plot. Results: A predictive model was developed that included the 2 strongest predictors: the size of the SLN metastases in millimeters and the presence of a negative sentinel lymph node. The model showed good discriminative ability (AUC, 0.75) and good calibration over the complete range of predicted probabilities. Conclusion: We have developed a tool to predict additional non-SLN metastases in Dutch patients with SLN-positive breast cancer that is easy to use in daily clinical breast cancer practice. (C) 2016 Elsevier Inc. All rights reserved.

Objectives To develop prediction models for Chlamydia trachomatis (Ct) infection with different levels of detail in information, that is, from readily available data in registries and from additional questionnaires. Methods All inhabitants of Rotterdam and Amsterdam aged 16–29 were invited yearly from 2008 until 2011 for home-based testing. Their registry data included gender, age, ethnicity and neighbourhood-level socioeconomic status (SES). Participants were asked to fill in a questionnaire on education, sexually transmitted infection history, symptoms, partner information and sexual behaviour. We developed prediction models for Ct infection using first-time participant data—including registry variables only and with additional questionnaire variables—by multilevel logistic regression analysis to account for clustering within neighbourhoods. We assessed the discriminative ability by the area under the receiver operating characteristic curve (AUC). Results Four per cent (3540/80 385) of the participants was infected. The strongest registry predictors for Ct infection were young age (especially for women) and Surinamese, Antillean or sub-Saharan African ethnicity. Neighbourhood-level SES was of minor importance. Strong questionnaire predictors were low to intermediate education level, ethnicity of the partner (non-Dutch) and having sex with casual partners. When using a prediction model including questionnaire risk factors (AUC 0.74, 95% CI 0.736 to 0.752) for selective screening, 48% of the participating population needed to be screened to find 80% (95% CI 78.4% to 81.0%) of Ct infections. The model with registry risk factors only (AUC 0.67, 95% CI 0.656 to 0.675) required 60% to be screened to find 78% (95% CI 76.6% to 79.4%) of Ct infections. Conclusions A registry-based prediction model can facilitate selective Ct screening at population level, with further refinement at the individual level by including questionnaire risk factors.

The use of evidence from clinical trials to support decisions for individual patients is a form of “reference class forecasting”: implicit predictions for an individual are made on the basis of outcomes in a reference class of “similar” patients treated with alternative therapies. Evidence based medicine has generally emphasized the broad reference class of patients qualifying for a trial. Yet patients in a trial (and in clinical practice) differ from one another in many ways that can affect the outcome of interest and the potential for benefit. The central goal of personalized medicine, in its various forms, is to narrow the reference class to yield more patient specific effect estimates to support more individualized clinical decision making. This article will review fundamental conceptual problems with the prediction of outcome risk and heterogeneity of treatment effect (HTE), as well as the limitations of conventional (one-variable-at-a-time) subgroup analysis. It will also discuss several regression based approaches to “predictive” heterogeneity of treatment effect analysis, including analyses based on “risk modeling” (such as stratifying trial populations by their risk of the primary outcome or their risk of serious treatment-related harms) and analysis based on “effect modeling” (which incorporates modifiers of relative effect). It will illustrate these approaches with clinical examples and discuss their respective strengths and vulnerabilities.

Objectives: We aimed to compare the performance of different regression modeling approaches for the prediction of heterogeneous treatment effects. Study Design and Setting: We simulated trial samples (n = 3,600; 80% power for a treatment odds ratio of 0.8) from a superpopulation (N = 1,000,000) with 12 binary risk predictors, both without and with six true treatment interactions. We assessed predictions of treatment benefit for four regression models: a “risk model” (with a constant effect of treatment assignment) and three “effect models” (including interactions of risk predictors with treatment assignment). Three novel performance measures were evaluated: calibration for benefit (i.e., observed vs. predicted risk difference in treated vs. untreated), discrimination for benefit, and prediction error for benefit. Results: The risk modeling approach was well-calibrated for benefit, whereas effect models were consistently overfit, even with doubled sample sizes. Penalized regression reduced miscalibration of the effect models considerably. In terms of discrimination and prediction error, the risk modeling approach was superior in the absence of true treatment effect interactions, whereas penalized regression was optimal in the presence of true treatment interactions. Conclusion: A risk modeling approach yields models consistently well calibrated for benefit. Effect modeling may improve discrimination for benefit in the presence of true interactions but is prone to overfitting. Hence, effect models—including only plausible interactions—should be fitted using penalized regression.

Aims: The aim of the study was to evaluate the effect of strut protrusion (SP) on wall shear stress (WSS) and neointimal growth (NG) one and five years after implantation of an Absorb bioresorbable vascular scaffold. Methods and results: Eight patients were selected from a first-in-man study. Following three-dimensional (3D) reconstruction of coronaries, WSS was quantified using Newtonian steady-flow simulation in each cross-section at 5° subunits (sectors) of the circumferential luminal surface. At one year, neointimal thickness (NT) was measured by optical coherence tomography (OCT) and correlated to WSS and SP post procedure. Median SP was 112.9 (90.8, 133.1) µm post implantation. Post procedure, a logarithmic inverse relationship between SP and post-implantation WSS (r=–0.425, p

Background: In light of the evolving landscape of adjuvant therapy in melanoma and the recently confirmed absent survival benefit of completion lymph node dissection (CLND), it becomes important to explore possible consequences of omitting CLND, and whether it is possible to adequately stratify positive sentinel node (SN) patients solely based on information retrieved from the melanoma up to the sentinel lymph node biopsy (SLNB).Methods: A retrospective cohort from nine European Organization for Research and Treatment of Cancer Melanoma Group centres was used. Patients were staged based on SLNB and CLND result according to the American Joint Committee on Cancer (AJCC) criteria and stratified by ulceration and SN tumour burden. These were incorporated in Cox regression models. Predictive ability was assessed using Harrell's concordance index (c-index) and the Akaike information criterion (AIC).Results: In total, 1015 patients were eligible. CLND led to upstaging in N-category in 19% and in AJCC stage in 5-6%. The model incorporating only ulceration and SN tumour burden performed equally well as the model incorporating substages after CLND. The model incorporating substages based on SLNB had the lowest predictive ability. Stratifying by ulceration and SN tumour burden resulted in four positive SN groups from which low-, intermediate- and high-risk prognostic classes could be derived.Conclusions: Adequate stratification of positive SN patients was possible based on ulceration and SN tumour burden category. The identification of low-, intermediate- and high-risk patients could guide adjuvant therapy in clinical practice. Omitting CLND seems to have little consequences. (C) 2018 Elsevier Ltd. All rights reserved.

Objectives Clinical prediction models that support treatment decisions are usually evaluated for their ability to predict the risk of an outcome rather than treatment benefit–the difference between outcome risk with vs. without therapy. We aimed to define performance metrics for a model's ability to predict treatment benefit. Study Design and Setting We analyzed data of the Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) trial and of three recombinant tissue plasminogen activator trials. We assessed alternative prediction models with a conventional risk concordance-statistic (c-statistic) and a novel c-statistic for benefit. We defined observed treatment benefit by the outcomes in pairs of patients matched on predicted benefit but discordant for treatment assignment. The ‘c-for-benefit’ represents the probability that from two randomly chosen matched patient pairs with unequal observed benefit, the pair with greater observed benefit also has a higher predicted benefit. Results Compared to a model without treatment interactions, the SYNTAX score II had improved ability to discriminate treatment benefit (c-for-benefit 0.590 vs. 0.552), despite having similar risk discrimination (c-statistic 0.725 vs. 0.719). However, for the simplified stroke–thrombolytic predictive instrument (TPI) vs. the original stroke-TPI, the c-for-benefit (0.584 vs. 0.578) was similar. Conclusion The proposed methodology has the potential to measure a model's ability to predict treatment benefit not captured with conventional performance metrics.

BACKGROUND: Many patients with resectable perihilar cholangiocarcinoma (PHC) on imaging are intra-operatively diagnosed with occult metastatic or locally advanced disease precluding a curative-intent resection. This study aimed to develop and validate a preoperative risk score. STUDY DESIGN: Patients with resectable PHC on imaging who underwent surgery in two high-volume centers (USA and Europe) between 2000 and 2015 were included. Multivariable logistic regression analysis was used to develop the risk score. Cross-validation was used to validate the score, alternating the two centers as ‘training’ and ‘testing’ dataset. RESULTS: Of 566 patients who underwent surgery 309 patients (55%) underwent a resection, while in 257 patients (45%) a curative-intent resection was precluded due to distant metastasis (n=151; 27%) or locally advanced disease (n=106; 19%). Preoperative predictors included bilirubin above 2 mg/dL, bile duct involvement on imaging, portal vein involvement on imaging (≥ 180 degrees), hepatic artery involvement on imaging (≥ 180 degrees), and suspicious lymph nodes on imaging. The new risk score (c-index 0.75 after cross-validation) provided significantly more accurate predictions than the Bismuth classification (c-index 0.62), Blumgart T-staging (c-index 0.67), and cTNM staging (c-index 0.68). The new risk score identified four risk groups for occult metastatic or locally advanced disease: low (14.7%), intermediate (29.5%), high (47.3%), and very high risk (81.3%). The preoperative score groups also predicted survival after surgery, irrespective of intra-operative findings (P

We welcome the comments by Dr. Montalto and colleagues regarding our recent paper in the Journal [(1)][1], suggesting that PRECISE-DAPT (Predicting Bleeding Complications in Patients Undergoing Stent Implantation and Subsequent Dual Antiplatelet Therapy) should be validated and recalibrated in

Knowledge about lower leg chronic exertional compartment syndrome (CECS) is largely obtained from highly selected populations. Patient characteristics may therefore not be appropriate for the general population. Our purpose was to describe a heterogeneous population of individuals suspected of lower leg CECS and to identify predictors of CECS. Charts of individuals who were analyzed for exercise-induced lower leg pain in a referral center between 2001 and 2013 were retrospectively studied. Patients were included if history and physical examination were suggestive of CECS and if they had undergone a dynamic intracompartmental pressure measurement. Six hundred ninety-eight of 1411 individuals were diagnosed with CECS in one or more of three lower leg muscle compartments (anterior tibial, deep flexor, lateral). Prevalence of CECS peaked around the age of 20–25 years and decreased thereafter, although a plateau around 50 years was found. Age, gender, bilateral symptoms, previous lower leg pathology, sports (running and skating) and tender muscle compartments were identified as independent predictors of lower leg CECS. The proposed predictive model has moderate discriminative ability (AUC 0.66) and good calibration over the complete range of predicted probabilities. The predictive model, displayed as a nomogram, may aid in selecting individuals requiring an invasive dynamic intracompartmental muscle pressure measurement.

Recurrence of bladder cancer can occur repeatedly in the same patient after treatment of the primary tumor. Models predicting the risk of a next recurrence may inform individualized decision-making on surveillance frequency. We aimed to assess the usefulness of extensions of the Cox proportional hazards model for repeated events in this context. We analyzed 531 Dutch patients with bladder cancer (1990-2012) with information on 7 prespecified predictors at the time of diagnosis of the primary and recurrent tumors. We considered 3 aspects of model variants: how to model time to the repeated events (calendar time, gap time, elapsed time); the number of preceding events (predictor, stratum variable); and the within-subject correlation (ignored in a simple Cox model, robust standard errors in a variance-correction model, random effect in a frailty model). First to fourth recurrences of bladder cancer occurred in 313, 174, 103, and 66 patients, respectively, with median calendar follow-up times of 1.1, 2.5, 3.8, and 4.5 years, respectively. We focused on gap time in the detailed analyses, allowing for clinically meaningful predictions. Variance-correction models may be useful if predictor selection is part of the model development. Frailty models may be useful when within-subject correlation is strong.

Summary Background Incisional hernia is a frequent long-term complication after abdominal surgery, with a prevalence greater than 30% in high-risk groups. The aim of the PRIMA trial was to evaluate the effectiveness of mesh reinforcement in high-risk patients, to prevent incisional hernia. Methods We did a multicentre, double-blind, randomised controlled trial at 11 hospitals in Austria, Germany, and the Netherlands. We included patients aged 18 years or older who were undergoing elective midline laparotomy and had either an abdominal aortic aneurysm or a body-mass index (BMI) of 27 kg/m 2 or higher. We randomly assigned participants using a computer-generated randomisation sequence to one of three treatment groups: primary suture; onlay mesh reinforcement; or sublay mesh reinforcement. The primary endpoint was incidence of incisional hernia during 2 years of follow-up, analysed by intention to treat. Adjusted odds ratios (ORs) were estimated by logistic regression. This trial is registered at ClinicalTrials.gov, number NCT00761475. Findings Between March, 2009, and December, 2012, 498 patients were enrolled to the study, of whom 18 were excluded before randomisation. Therefore, we included 480 patients in the primary analysis: 107 were assigned primary suture only, 188 were allocated onlay mesh reinforcement, and 185 were assigned sublay mesh reinforcement. 92 patients were identified with an incisional hernia, 33 (30%) who were allocated primary suture only, 25 (13%) who were assigned onlay mesh reinforcement, and 34 (18%) who were assigned sublay mesh reinforcement (onlay mesh reinforcement vs primary suture, OR 0·37, 95% CI 0·20–0·69; p=0·0016; sublay mesh reinforcement vs primary suture, 0·55, 0·30–1·00; p=0·05). Seromas were more frequent in patients allocated onlay mesh reinforcement (34 of 188) than in those assigned primary suture (five of 107; p=0·002) or sublay mesh reinforcement (13 of 185; p=0·002). The incidence of wound infection did not differ between treatment groups (14 of 107 primary suture; 25 of 188 onlay mesh reinforcement; and 19 of 185 sublay mesh reinforcement). Interpretation A significant reduction in incidence of incisional hernia was achieved with onlay mesh reinforcement compared with sublay mesh reinforcement and primary suture only. Onlay mesh reinforcement has the potential to become the standard treatment for high-risk patients undergoing midline laparotomy. Funding Baxter; B Braun Surgical SA.

Background— The impact of sex on clinical outcomes of percutaneous coronary intervention and coronary artery bypass graft for patients with multivessel coronary disease and unprotected left main disease could be dissimilar between Western and Asian populations. Methods and Results— To assess clinical outcomes after percutaneous coronary intervention or coronary artery bypass graft in women and men with multivessel coronary disease and unprotected left main disease, a pooled analysis (n=3280) was performed using the patient-level data from 3 large randomized trials: SYNTAX (Synergy between PCI with Taxus and Cardiac Surgery), PRECOMBAT (Bypass Surgery Versus Angioplasty Using Sirolimus-Eluting Stent in Patients With Left Main Coronary Artery Disease), and BEST (Bypass Surgery and Everolimus-Eluting Stent Implantation in the Treatment of Patients with Multivessel Coronary Artery Disease) trials. The primary end point was all-cause death. Of 3280 patients, 794 patients (24.2%) were women. The median follow-up period was 1806 days (1611–1837 days). In women, a high heterogeneity of the treatment effect among the 3 trials was found for all-cause death ( I 2 >50%), whereas in men, it was consistent across the 3 trials. In the Western trial (SYNTAX), female sex favored coronary artery bypass graft compared with percutaneous coronary intervention (hazard ratio (percutaneous coronary intervention) 2.213; 95% confidence interval, 1.242–3.943; P =0.007), whereas in the Asian women (PRECOMBAT and BEST), the treatment effect was neutral between both strategies. Sex interaction with treatment strategy was evident in the Western trial ( P interaction =0.019) but not in the Asian trials (PRECOMBAT P interaction =0.469 and BEST P interaction =0.472; I 2 =58%). Conclusions— The present meta-analysis suggested the presence of the heterogeneous sex–treatment interaction across Asian and Western trials. Considering the ongoing globalization of our medical practice, the heterogeneity of the sex–treatment interaction needs to be well recognized and taken into account during the decision making of the treatment strategy. Clinical Trial Registrations— URL: https://www.clinicaltrials.gov . Unique identifiers: NCT00114972, NCT00997828, NCT00422968.

BACKGROUND: Stability in baseline risk and estimated predictor effects both geographically and temporally is a desirable property of clinical prediction models. However, this issue has received little attention in the methodological literature. Our objective was to examine methods for assessing temporal and geographic heterogeneity in baseline risk and predictor effects in prediction models.METHODS: We studied 14,857 patients hospitalized with heart failure at 90 hospitals in Ontario, Canada, in two time periods. We focussed on geographic and temporal variation in baseline risk (intercept) and predictor effects (regression coefficients) of the EFFECT-HF mortality model for predicting 1-year mortality in patients hospitalized for heart failure. We used random effects logistic regression models for the 14,857 patients.RESULTS: The baseline risk of mortality displayed moderate geographic variation, with the hospital-specific probability of 1-year mortality for a reference patient lying between 0.168 and 0.290 for 95% of hospitals. Furthermore, the odds of death were 11% lower in the second period than in the first period. However, we found minimal geographic or temporal variation in predictor effects. Among 11 tests of differences in time for predictor variables, only one had a modestly significant P value (0.03).CONCLUSIONS: This study illustrates how temporal and geographic heterogeneity of prediction models can be assessed in settings with a large sample of patients from a large number of centers at different time periods.

Objective: We aimed to determine pretreatment pathological tumor extent in the resection specimen after neoadjuvant chemoradiotherapy (nCRT) and to assess its prognostic value in patients with esophageal cancer. Methods: Patients with esophageal cancer, treated with nCRT plus surgery were included (2003-2011). Pretreatment pathological T-stage (prepT-stage) and N-stage (prepN-stage) were estimated based on the extent of regressional changes and residual tumor cells in the resection specimen. Interobserver agreement was determined between 3 pathologists. The prognostic performance of prepT-stage and prepN-stage was scored using the difference in Akaike information criterion (AAIC). PrepN-stage and posttreatment pathological N-stage (ypN-stage) were combined to determine the effect of nodal sterilization on prognosis. Results: Overall concordance for prepT-stage and prepN-stage was 0.69 and 0.84, respectively. Prognostic strength of prepT-stage was similar to clinical T-stage and worse compared with ypT-stage (AAIC 1.3 versus 2.0 and 8.9, respectively). In contrast, prognostic strength of prepN-stage was better than cN-stage and similar to ypN-stage (AAIC 17.9 versus 6.2 and 17.2, respectively). PrepN+ patients who become ypN0 after nCRT have a worse survival compared with prepN0 patients, with a five year overall survival of 51% versus 68%, P = 0.019, respectively. Conclusions: PrepT-stage and prepN-stage can be estimated reproducibly. Prognostic strength of prepT-stage is comparable with clinical T-stage, whereas prepN-stage is better than cN-stage. PrepN+ patients who become ypN0 after nCRT have a worse survival compared with prepN0 patients. Pretreatment pathological staging should be considered useful as a new staging parameter for esophageal cancer and could also be of interest for other tumor types.

Background— Outpatients with heart failure (HF) who are at high risk for HF hospitalization and death may benefit from early identification. We sought to develop and externally validate a model to predict both HF hospitalization and mortality that accounts for the semicompeting nature of the 2 outcomes and captures the risk associated with the transition from the stable outpatient state to the post-HF hospitalization state. Methods and Results— A multistate model to predict HF hospitalization and all-cause mortality was derived using data (n=3834) from the HEAAL study (Heart Failure Endpoint evaluation of Angiotensin II Antagonist Losartan), a multinational randomized trial in symptomatic patients with reduced left ventricular ejection fraction. Twelve easily and reliably obtainable demographic and clinical predictors were prespecified for model inclusion. Model performance was assessed in the SCD-HeFT cohort (Sudden Cardiac Death in Heart Failure Trial; n=2521). At 1 year, the probability of being alive without HF hospitalization was 94% for a typical patient in the lowest risk quintile and 77% for a typical patient in the highest risk quintile and this variability in risk continued through 7 years of follow-up. The model c-index was 0.72 in the derivation cohort, 0.66 in the validation cohort, and 0.69 in the implantable cardiac defibrillator arm of the validation cohort. There was excellent calibration across quintiles of predicted risk. Conclusions— Our findings illustrate the advantages of a multistate modeling approach, providing estimates of HF hospitalization and death in the same model, comparison of predictors for the different outcomes and demonstrating the different trajectories of patients based on baseline characteristics and intermediary events. Clinical Trial Registration— URL: http://www.clinicaltrials.gov . Unique identifiers: NCT00000609 and NCT00090259.

Background Digoxin has been shown to reduce heart failure hospitalizations with a neutral effect on mortality. It is unknown whether there is heterogeneity of treatment effect for digitalis therapy according to predicted risk of heart failure hospitalization. Methods and Results We conducted a post hoc analysis of the Digitalis Investigator Group (DIG) studies, randomized controlled trials of digoxin vs placebo in participants with heart failure and left ventricular ejection fraction ≤45% (main DIG study, n = 6800) or >45% (ancillary DIG study, n = 988). Using a previously derived multistate model to risk-stratify DIG study participants, we determined the differential treatment effect on hospitalization and mortality outcomes. There was a 13% absolute reduction in the risk of any heart failure hospitalizations (39% vs 52%; odds ratio 0.58; 95% confidence interval 0.47-0.71) in the digoxin vs placebo arms in the highest-risk quartile, compared with a 3% absolute risk reduction for any heart failure hospitalization (17% vs 20%; odds ratio 0.84; 95% confidence interval, 0.66-1.08) in the lowest-risk quartile. There were 12 fewer total all-cause hospitalizations per 100 person-years in the highest-risk quartile compared with an increase of 8 hospitalizations per 100 person-years in the lowest-risk quartile. There was neutral effect of digoxin on mortality in all risk quartiles and no interaction between baseline risk and the effect of digoxin on mortality ( P = .94). Conclusions Participants in the DIG study at higher risk of hospitalization as identified by a multistate model were considerably more likely to benefit from digoxin therapy to reduce heart failure hospitalization.

Background Targeting low-dose computed tomography (LDCT) for lung cancer screening to persons at highest risk for lung cancer mortality has been suggested to improve screening efficiency. Objective To quantify the value of risk-targeted selection for lung cancer screening compared with National Lung Screening Trial (NLST) eligibility criteria. Design Cost-effectiveness analysis using a multistate prediction model. Data sources NLST. Target population Current and former smokers eligible for lung cancer screening. Time horizon Lifetime. Perspective Health care sector. Intervention Risk-targeted versus NLST-based screening. Outcome measures Incremental 7-year mortality, life expectancy, quality-adjusted life-years (QALYs), costs, and cost-effectiveness of screening with LDCT versus chest radiography at each decile of lung cancer mortality risk. Results of base-case analysis Participants at greater risk for lung cancer mortality were older and had more comorbid conditions and higher screening-related costs. The incremental lung cancer mortality benefits during the first 7 years ranged from 1.2 to 9.5 lung cancer deaths prevented per 10 000 person-years for the lowest to highest risk deciles, respectively (extreme decile ratio, 7.9). The gradient of benefits across risk groups, however, was attenuated in terms of life-years (extreme decile ratio, 3.6) and QALYs (extreme decile ratio, 2.4). The incremental cost-effectiveness ratios (ICERs) were similar across risk deciles ($75 000 per QALY in the lowest risk decile to $53 000 per QALY in the highest risk decile). Payers willing to pay $100 000 per QALY would pay for LDCT screening for all decile groups. Results of sensitivity analysis Alternative assumptions did not substantially alter our findings. Limitation Our model did not account for all correlated differences between lung cancer mortality risk and quality of life. Conclusions Although risk targeting may improve screening efficiency in terms of early lung cancer mortality per person screened, the gains in efficiency are attenuated and modest in terms of life-years, QALYs, and cost-effectiveness. Primary funding source National Institutes of Health (U01NS086294).