Traumatic brain injury (TBI) remains one of the leading causes of death and disability worldwide. To better understand its impact on various outcome domains, this study pursues the following: (1)... Show moreTraumatic brain injury (TBI) remains one of the leading causes of death and disability worldwide. To better understand its impact on various outcome domains, this study pursues the following: (1) longitudinal outcome assessments at three, six, and twelve months post-injury; (2) an evaluation of sociodemographic, premorbid, and injury-related factors, and functional recovery contributing to worsening or improving outcomes after TBI. Using patient-reported outcome measures, recuperation trends after TBI were identified by applying Multivariate Latent Class Mixed Models (MLCMM). Instruments were grouped into TBI-specific and generic health-related quality of life (HRQoL; QOLIBRI-OS, SF-12v2), and psychological and post-concussion symptoms (GAD-7, PHQ-9, PCL-5, RPQ). Multinomial logistic regressions were carried out to identify contributing factors. For both outcome sets, the four-class solution provided the best match between goodness of fit indices and meaningful clinical interpretability. Both models revealed similar trajectory classes: stable good health status (HRQoL: n = 1944; symptoms: n = 1963), persistent health impairments (HRQoL: n = 442; symptoms: n = 179), improving health status (HRQoL: n = 83; symptoms: n = 243), and deteriorating health status (HRQoL: n = 86; symptoms: n = 170). Compared to individuals with stable good health status, the other groups were more likely to have a lower functional recovery status at three months after TBI (i.e., the GOSE), psychological problems, and a lower educational attainment. Outcome trajectories after TBI show clearly distinguishable patterns which are reproducible across different measures. Individuals characterized by persistent health impairments and deterioration require special attention and long-term clinical monitoring and therapy. Show less
Calster, B. van; Steyerberg, E.W.; Wynants, L.; Smeden, M. van 2023
Background Clinical prediction models should be validated before implementation in clinical practice. But is favorable performance at internal validation or one external validation sufficient to... Show moreBackground Clinical prediction models should be validated before implementation in clinical practice. But is favorable performance at internal validation or one external validation sufficient to claim that a prediction model works well in the intended clinical context? Main body We argue to the contrary because (1) patient populations vary, (2) measurement procedures vary, and (3) populations and measurements change over time. Hence, we have to expect heterogeneity in model performance between locations and settings, and across time. It follows that prediction models are never truly validated. This does not imply that validation is not important. Rather, the current focus on developing new models should shift to a focus on more extensive, well-conducted, and well-reported validation studies of promising models. Conclusion Principled validation strategies are needed to understand and quantify heterogeneity, monitor performance over time, and update prediction models when appropriate. Such strategies will help to ensure that prediction models stay up-to-date and safe to support clinical decision-making. Show less
OBJECTIVES: Many machine learning (ML) models have been developed for application in the ICU, but few models have been subjected to external validation. The performance of these models in new... Show moreOBJECTIVES: Many machine learning (ML) models have been developed for application in the ICU, but few models have been subjected to external validation. The performance of these models in new settings therefore remains unknown. The objective of this study was to assess the performance of an existing decision support tool based on a ML model predicting readmission or death within 7 days after ICU discharge before, during, and after retraining and recalibration.DESIGN: A gradient boosted ML model was developed and validated on electronic health record data from 2004 to 2021. We performed an independent validation of this model on electronic health record data from 2011 to 2019 from a different tertiary care center.SETTING: Two ICUs in tertiary care centers in The Netherlands.PATIENTS: Adult patients who were admitted to the ICU and stayed for longer than 12 hours.INTERVENTIONS: None.MEASUREMENTS AND MAIN RESULTS: We assessed discrimination by area under the receiver operating characteristic curve (AUC) and calibration (slope and intercept). We retrained and recalibrated the original model and assessed performance via a temporal validation design. The final retrained model was cross-validated on all data from the new site. Readmission or death within 7 days after ICU discharge occurred in 577 of 10,052 ICU admissions (5.7%) at the new site. External validation revealed moderate discrimination with an AUC of 0.72 (95% CI 0.67–0.76). Retrained models showed improved discrimination with AUC 0.79 (95% CI 0.75–0.82) for the final validation model. Calibration was poor initially and good after recalibration via isotonic regression.CONCLUSIONS: In this era of expanding availability of ML models, external validation and retraining are key steps to consider before applying ML models to new settings. Clinicians and decision-makers should take this into account when considering applying new ML models to their local settings. Show less
Mortality is a frequently reported outcome in clinical studies of acute respiratory distress syndrome (ARDS). However, timing of mortality assessment has not been well characterized. We aimed to... Show moreMortality is a frequently reported outcome in clinical studies of acute respiratory distress syndrome (ARDS). However, timing of mortality assessment has not been well characterized. We aimed to identify a crossing-point between cumulative survival and death in the intensive care unit (ICU) of patients with moderate-to-severe ARDS, beyond which the number of survivors would exceed the number of deaths. We hypothesized that this intersection would occur earlier in a successful clinical trial vs. observational studies of moderate/severe ARDS and predict treatment response. We conducted an ancillary study of 1580 patients with moderate-to-severe ARDS managed with lung-protective ventilation to assess the relevance and timing of measuring ICU mortality rates at different time-points during ICU stay. First, we analyzed 1303 patients from four multicenter, observational cohorts enrolling consecutive patients with moderate/severe ARDS. We assessed cumulative ICU survival from the time of moderate/severe ARDS diagnosis to ventilatory support discontinuation within 7-days, 28-days, 60-days, and at ICU discharge. Then, we compared these findings to those of a successful randomized trial of 277 moderate/severe ARDS patients. In the observational cohorts, ICU mortality (487/1303, 37.4%) and 28-day mortality (425/1102, 38.6%) were similar (p = 0.549). Cumulative proportion of ICU survivors and non-survivors crossed at day-7; after day-7, the number of ICU survivors was progressively higher compared to non-survivors. Measures of oxygenation, lung mechanics, and severity scores were different between survivors and non-survivors at each point-in-time (p < 0.001). In the trial cohort, the cumulative proportion of survivors and non-survivors in the treatment group crossed before day-3 after diagnosis of moderate/severe ARDS. In clinical ARDS studies, 28-day mortality closely approximates and may be used as a surrogate for ICU mortality. For patients with moderate-to-severe ARDS, ICU mortality assessment within the first week of a trial might be an early predictor of treatment response. Show less
Aims Indications for surgery in patients with degenerative mitral regurgitation (DMR) are increasingly liberal in all clinical guidelines but the role of secondary outcome determinants (left atrial... Show moreAims Indications for surgery in patients with degenerative mitral regurgitation (DMR) are increasingly liberal in all clinical guidelines but the role of secondary outcome determinants (left atrial volume index >= 60 mL/m(2), atrial fibrillation, pulmonary artery systolic pressure >= 50 mmHg and moderate to severe tricuspid regurgitation) and their impact on post-operative outcome remain disputed. Whether these secondary outcome markers are just reflective of the DMR severity or intrinsically affect survival after DMR surgery is uncertain and may have critical importance in the management of patients with DMR. To address these gaps of knowledge the present study gathered a large cohort of patients with quantified DMR, accounted for the number of secondary outcome markers and examined their independent impact on survival after surgical correction of the DMR. Methods and results The Mitral Regurgitation International DAtabase-Quantitative registry includes patients with isolated DMR from centres across North America, Europe, and the Middle East. Patient enrolment extended from January 2003 to January 2020. All patients undergoing mitral valve surgery within 1 year of registry enrolment were selected. A total of 2276 patients [65 (55-73) years, 32% male] across five centres met study eligibility criteria. Over a median follow-up of 5.6 (3.6 to 8.7) years, 278 patients (12.2%) died. In a comprehensive multivariable Cox regression model adjusted for age, EuroSCORE II, symptoms, left ventricular ejection fraction (LVEF), left ventricular end-systolic diameter (LV ESD) and DMR severity, the number of secondary outcome determinants was independently associated with post-operative all-cause mortality, with adjusted hazard ratios of 1.56 [95% confidence interval (CI): 1.11-2.20, P = 0.011], 1.78 (95% CI: 1.23-2.58, P = 0.002) and 2.58 (95% CI: 1.73-3.83, P < 0.0001) for patients with one, two, and three or four secondary outcome determinants, respectively. A model incorporating the number of secondary outcome determinants demonstrated a higher C-index and was significantly more concordant with post-operative mortality than models incorporating traditional Class I indications alone [the presence of symptoms (P = 0.0003), or LVEF <= 60% (P = 0.006), or LV ESD >= 40 mm (P = 0.014)], while there was no significant difference in concordance observed compared with a model that incorporated the number of Class I indications for surgery combined (P = 0.71). Conclusion In this large cohort of patients treated surgically for DMR, the presence and number of secondary outcome determinants was independently associated with post-surgical survival and demonstrated better outcome discrimination than traditional Class I indications for surgery. Randomised controlled trials are needed to determine if patients with severe DMR who demonstrate a cardiac phenotype with an increasing number of secondary outcome determinants would benefit from earlier surgery. Show less
Meijden, S.L. van der; Hond, A.A.H. de; Thoral, P.J.; Steyerberg, E.W.; Kant, I.M.J.; Cinà, G.; Arbous, M.S. 2023
Background: Artificial intelligence–based clinical decision support (AI-CDS) tools have great potential to benefit intensive care unit (ICU) patients and physicians. There is a gap between the... Show moreBackground: Artificial intelligence–based clinical decision support (AI-CDS) tools have great potential to benefit intensive care unit (ICU) patients and physicians. There is a gap between the development and implementation of these tools.Objective: We aimed to investigate physicians’ perspectives and their current decision-making behavior before implementing a discharge AI-CDS tool for predicting readmission and mortality risk after ICU discharge.Methods: We conducted a survey of physicians involved in decision-making on discharge of patients at two Dutch academic ICUs between July and November 2021. Questions were divided into four domains: (1) physicians’ current decision-making behavior with respect to discharging ICU patients, (2) perspectives on the use of AI-CDS tools in general, (3) willingness to incorporate a discharge AI-CDS tool into daily clinical practice, and (4) preferences for using a discharge AI-CDS tool in daily workflows.Results: Most of the 64 respondents (of 93 contacted, 69%) were familiar with AI (62/64, 97%) and had positive expectations of AI, with 55 of 64 (86%) believing that AI could support them in their work as a physician. The respondents disagreed on whether the decision to discharge a patient was complex (23/64, 36% agreed and 22/64, 34% disagreed); nonetheless, most (59/64, 92%) agreed that a discharge AI-CDS tool could be of value. Significant differences were observed between physicians from the 2 academic sites, which may be related to different levels of involvement in the development of the discharge AI-CDS tool.Conclusions: ICU physicians showed a favorable attitude toward the integration of AI-CDS tools into the ICU setting in general, and in particular toward a tool to predict a patient’s risk of readmission and mortality within 7 days after discharge. The findings of this questionnaire will be used to improve the implementation process and training of end users. Show less
Roumans, C.A.M.; Bogt, R.D. van der; Nieboer, D.; Steyerberg, E.W.; Rizopoulos, D.; Lansdorp-Vogelaar, I.; ... ; ProBar Study Grp 2022
Half of Barrett's esophagus (BE) surveillance endoscopies do not adhere to guideline recommendations. In this multicenter prospective cohort study, we assessed the clinical consequences of... Show moreHalf of Barrett's esophagus (BE) surveillance endoscopies do not adhere to guideline recommendations. In this multicenter prospective cohort study, we assessed the clinical consequences of nonadherence to recommended surveillance intervals and biopsy protocol. Data from BE surveillance patients were collected from endoscopy and pathology reports; questionnaires were distributed among endoscopists. We estimated the association between (non)adherence and (i) endoscopic curability of esophageal adenocarcinoma (EAC), (ii) mortality, and (iii) misclassification of histological diagnosis according to a multistate hidden Markov model. Potential explanatory parameters (patient, facility, endoscopist variables) for nonadherence, related to clinical impact, were analyzed. In 726 BE patients, 3802 endoscopies were performed by 167 endoscopists. Adherence to surveillance interval was 16% for non-dysplastic (ND)BE, 55% for low-grade dysplasia (LGD), and 54% of endoscopies followed the Seattle protocol. There was no evidence to support the following statements: longer surveillance intervals or fewer biopsies than recommended affect endoscopic curability of EAC or cause-specific mortality (P > 0.20); insufficient biopsies affect the probability of NDBE (OR 1.0) or LGD (OR 2.3) being misclassified as high-grade dysplasia/EAC (P > 0.05). Better adherence was associated with older patients (OR 1.1), BE segments <= 2 cm (OR 8.3), visible abnormalities (OR 1.8, all P <= 0.05), endoscopists with a subspecialty (OR 3.2), and endoscopists who deemed histological diagnosis an adequate marker (OR 2.0). Clinical consequences of nonadherence to guidelines appeared to be limited with respect to endoscopic curability of EAC and mortality. This indicates that BE surveillance recommendations should be optimized to minimize the burden of endoscopies. Show less
Roumans, C.A.M.; Bogt, R.D. van der; Nieboer, D.; Steyerberg, E.W.; Rizopoulos, D.; Lansdorp-Vogelaar, I.; ... ; ProBar Study Grp 2022
Half of Barrett's esophagus (BE) surveillance endoscopies do not adhere to guideline recommendations. In this multicenter prospective cohort study, we assessed the clinical consequences of... Show moreHalf of Barrett's esophagus (BE) surveillance endoscopies do not adhere to guideline recommendations. In this multicenter prospective cohort study, we assessed the clinical consequences of nonadherence to recommended surveillance intervals and biopsy protocol. Data from BE surveillance patients were collected from endoscopy and pathology reports; questionnaires were distributed among endoscopists. We estimated the association between (non)adherence and (i) endoscopic curability of esophageal adenocarcinoma (EAC), (ii) mortality, and (iii) misclassification of histological diagnosis according to a multistate hidden Markov model. Potential explanatory parameters (patient, facility, endoscopist variables) for nonadherence, related to clinical impact, were analyzed. In 726 BE patients, 3802 endoscopies were performed by 167 endoscopists. Adherence to surveillance interval was 16% for non-dysplastic (ND)BE, 55% for low-grade dysplasia (LGD), and 54% of endoscopies followed the Seattle protocol. There was no evidence to support the following statements: longer surveillance intervals or fewer biopsies than recommended affect endoscopic curability of EAC or cause-specific mortality (P > 0.20); insufficient biopsies affect the probability of NDBE (OR 1.0) or LGD (OR 2.3) being misclassified as high-grade dysplasia/EAC (P > 0.05). Better adherence was associated with older patients (OR 1.1), BE segments <= 2 cm (OR 8.3), visible abnormalities (OR 1.8, all P <= 0.05), endoscopists with a subspecialty (OR 3.2), and endoscopists who deemed histological diagnosis an adequate marker (OR 2.0). Clinical consequences of nonadherence to guidelines appeared to be limited with respect to endoscopic curability of EAC and mortality. This indicates that BE surveillance recommendations should be optimized to minimize the burden of endoscopies. Show less
McLernon, D.J.; Giardiello, D.; Calster, B. van; Wynants, L.; Geloven, N. van; Smeden, M. van; ... ; STRATOS Initiative 2022
Risk prediction models need thorough validation to assess their performance. Validation of models for survival outcomes poses challenges due to the censoring of observations and the varying time... Show moreRisk prediction models need thorough validation to assess their performance. Validation of models for survival outcomes poses challenges due to the censoring of observations and the varying time horizon at which predictions can be made. This article describes measures to evaluate predictions and the potential improvement in decision making from survival models based on Cox proportional hazards regression.As a motivating case study, the authors consider the prediction of the composite outcome of recurrence or death (the "event ") in patients with breast cancer after surgery. They developed a simple Cox regression model with 3 predictors, as in the Nottingham Prognostic Index, in 2982 women (1275 events over 5 years of follow-up) and externally validated this model in 686 women (285 events over 5 years). Improvement in performance was assessed after the addition of progesterone receptor as a prognostic biomarker.The model predictions can be evaluated across the full range of observed follow-up times or for the event occurring by the end of a fixed time horizon of interest. The authors first discuss recommended statistical measures that evaluate model performance in terms of discrimination, calibration, or overall performance. Further, they evaluate the potential clinical utility of the model to support clinical decision making according to a net benefit measure. They provide SAS and R code to illustrate internal and external validation.The authors recommend the proposed set of performance measures for transparent reporting of the validity of predictions from survival models. Show less
Background: Clinical prediction models are often not evaluated properly in specific settings or updated, for instance, with information from new markers. These key steps are needed such that models... Show moreBackground: Clinical prediction models are often not evaluated properly in specific settings or updated, for instance, with information from new markers. These key steps are needed such that models are fit for purpose and remain relevant in the long-term. We aimed to present an overview of methodological guidance for the evaluation (i.e., validation and impact assessment) and updating of clinical prediction models. Methods: We systematically searched nine databases from January 2000 to January 2022 for articles in English with methodological recommendations for the post-derivation stages of interest. Qualitative analysis was used to summarize the 70 selected guidance papers. Results: Key aspects for validation are the assessment of statistical performance using measures for discrimination (e.g., C-statistic) and calibration (e.g., calibration-in-the-large and calibration slope). For assessing impact or usefulness in clinical decision-making, recent papers advise using decision-analytic measures (e.g., the Net Benefit) over simplistic classification measures that ignore clinical consequences (e.g., accuracy, overall Net Reclassification Index). Commonly recommended methods for model updating are recalibration (i.e., adjustment of intercept or baseline hazard and/or slope), revision (i.e., re-estimation of individual predictor effects), and extension (i.e., addition of new markers). Additional methodological guidance is needed for newer types of updating (e.g., meta-model and dynamic updating) and machine learning-based models. Conclusion: Substantial guidance was found for model evaluation and more conventional updating of regression-based models. An important development in model evaluation is the introduction of a decision-analytic framework for assessing clinical usefulness. Consensus is emerging on methods for model updating. Show less
Early detection of severe asthma exacerbations through home monitoring data in patients with stable mild-to-moderate chronic asthma could help to timely adjust medication. We evaluated the... Show moreEarly detection of severe asthma exacerbations through home monitoring data in patients with stable mild-to-moderate chronic asthma could help to timely adjust medication. We evaluated the potential of machine learning methods compared to a clinical rule and logistic regression to predict severe exacerbations. We used daily home monitoring data from two studies in asthma patients (development: n = 165 and validation: n = 101 patients). Two ML models (XGBoost, one class SVM) and a logistic regression model provided predictions based on peak expiratory flow and asthma symptoms. These models were compared with an asthma action plan rule. Severe exacerbations occurred in 0.2% of all daily measurements in the development (154/92,787 days) and validation cohorts (94/40,185 days). The AUC of the best performing XGBoost was 0.85 (0.82-0.87) and 0.88 (0.86-0.90) for logistic regression in the validation cohort. The XGBoost model provided overly extreme risk estimates, whereas the logistic regression underestimated predicted risks. Sensitivity and specificity were better overall for XGBoost and logistic regression compared to one class SVM and the clinical rule. We conclude that ML models did not beat logistic regression in predicting short-term severe asthma exacerbations based on home monitoring data. Clinical application remains challenging in settings with low event incidence and high false alarm rates with high sensitivity. Show less
Background: Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model ... Show moreBackground: Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors.Methods: We included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models.Results: The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI) 0.56-0.74) versus 0.63 (95%Pl 0.54-0.71). PredictCBC-2.0 was well calibrated with an observed/ expected ratio at 10 years of 0.92 (95%Pl 0.34-2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed the potential clinical utility of PredictCBC-2.0 between thresholds of 4 and 12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers.Conclusions: Additional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision-making toward CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging. Show less
Introduction: In patients with acute respiratory distress syndrome (ARDS), the PaO2/FiO(2) ratio at the time of ARDS diagnosis is weakly associated with mortality. We hypothesized that setting a... Show moreIntroduction: In patients with acute respiratory distress syndrome (ARDS), the PaO2/FiO(2) ratio at the time of ARDS diagnosis is weakly associated with mortality. We hypothesized that setting a PaO2/FiO(2) threshold in 150 mm Hg at 24 h from moderate/severe ARDS diagnosis would improve predictions of death in the intensive care unit (ICU). Methods: We conducted an ancillary study in 1303 patients with moderate to severe ARDS managed with lung-protective ventilation enrolled consecutively in four prospective multicenter cohorts in a network of ICUs. The first three cohorts were pooled (n = 1000) as a testing cohort; the fourth cohort (n = 303) served as a confirmatory cohort. Based on the thresholds for PaO2/FiO(2) (150 mm Hg) and positive end-expiratory pressure (PEEP) (10 cm H2O), the patients were classified into four possible subsets at baseline and at 24 h using a standardized PEEP-FiO(2) approach: (I) PaO2/FiO(2) >= 150 at PEEP < 10, (II) PaO2/FiO(2) > 150 at PEEP >= 10, (III) PaO2/FiO(2) < 150 at PEEP < 10, and (IV) PaO2/FiO(2) < 150 at PEEP >= 10. Primary outcome was death in the ICU. Results: ICU mortalities were similar in the testing and confirmatory cohorts (375/1000, 37.5% vs. 112/303, 37.0%, respectively). At baseline, most patients from the testing cohort (n = 792/1000, 79.2%) had a PaO2/FiO(2) < 150, with similar mortality among the four subsets (p = 0.23). When assessed at 24 h, ICU mortality increased with an advance in the subset: 17.9%, 22.8%, 40.0%, and 49.3% (p < 0.0001). The findings were replicated in the confirmatory cohort (p < 0.0001). However, independent of the PEEP levels, patients with PaO2/FiO(2) < 150 at 24 h followed a distinct 30-day ICU survival compared with patients with PaO2/FiO(2) >= 150 (hazard ratio 2.8, 95% CI 2.2-3.5, p < 0.0001). Conclusions: Subsets based on PaO2/FiO(2) thresholds of 150 mm Hg assessed after 24 h of moderate/severe ARDS diagnosis are clinically relevant for establishing prognosis, and are helpful for selecting adjunctive therapies for hypoxemia and for enrolling patients into therapeutic trials. Show less
Huinink, S.T.; Jong, D.C. de; Nieboer, D.; Thomassen, D.; Steyerberg, E.W.; Dijkgraaf, M.G.W.; ... ; Vries, A.C. de 2022
Background Anti-tumor necrosis factor (TNF) therapy is effective for the treatment of Crohn's disease. Cessation may be considered in patients with a low risk of relapse. We aimed to externally... Show moreBackground Anti-tumor necrosis factor (TNF) therapy is effective for the treatment of Crohn's disease. Cessation may be considered in patients with a low risk of relapse. We aimed to externally validate and update our previously developed prediction model to estimate the risk of relapse after cessation of anti-TNF therapy. Methods We performed a retrospective cohort study in 17 Dutch hospitals. Crohn's disease patients in clinical, biochemical or endoscopic remission were included after anti-TNF cessation. Primary outcome was a relapse necessitating treatment. Discrimination and calibration of the previously developed model were assessed. After external validation, the model was updated. The performance of the updated prediction model was assessed in internal-external validation and by using decision curve analysis. Results 486 patients were included with a median follow-up of 1.7 years. Relapse rates were 35 and 54% after 1 and 2 years. At external validation, the discriminative ability of the prediction model was equal to that found at the development of the model [c-statistic 0.58 (95% confidence interval (CI) 0.54-0.62)], though the model was not well-calibrated on our cohort [calibration slope: 0.52 (0.28-0.76)]. After an update, a c-statistic of 0.60 (0.58-0.63) and calibration slope of 0.89 (0.69-1.09) were reported in internal-external validation. Conclusion Our previously developed and updated prediction model for the risk of relapse after cessation of anti-TNF in Crohn's disease shows reasonable performance. The use of the model may support clinical decision-making to optimize patient selection in whom anti-TNF can be withdrawn. Clinical validation is ongoing in a prospective randomized trial. Show less
Lobular primary breast cancer (PBC) histology has been proposed as a risk factor for contralateral breast cancer (CBC), but results have been inconsistent. We investigated CBC risk and the impact... Show moreLobular primary breast cancer (PBC) histology has been proposed as a risk factor for contralateral breast cancer (CBC), but results have been inconsistent. We investigated CBC risk and the impact of systemic therapy in lobular versus ductal PBC. Further, CBC characteristics following these histologic subtypes were explored. We selected 74,373 women diagnosed between 2003 and 2010 with stage I-III invasive PBC from the nationwide Netherlands Cancer Registry. We assessed absolute risk of CBC taking into account competing risks among those with lobular (n = 8903), lobular mixed with other types (n = 3512), versus ductal (n = 62,230) histology. Hazard ratios (HR) for CBC were estimated in a cause-specific Cox model, adjusting for age at PBC diagnosis, radiotherapy, chemotherapy and/or endocrine therapy. Multivariable HRs for CBC were 1.18 (95% CI: 1.04-1.33) for lobular and 1.37 (95% CI: 1.16-1.63) for lobular mixed versus ductal PBC. Ten-year cumulative CBC incidences in patients with lobular, lobular mixed versus ductal PBC were 3.2%, 3.6% versus 2.8% when treated with systemic therapy and 6.6%, 7.7% versus 5.6% in patients without systemic therapy, respectively. Metachronous CBCs were diagnosed in a less favourable stage in 19%, 26% and 23% and less favourable differentiation grade in 22%, 33% and 27% than the PBCs of patients with lobular, lobular mixed and ductal PBC, respectively. In conclusion, lobular and lobular mixed PBC histology are associated with modestly increased CBC risk. Personalised CBC risk assessment needs to consider PBC histology, including systemic treatment administration. The impact on prognosis of CBCs with unfavourable characteristics warrants further evaluation. Show less
