Prediction of transforaminal epidural injection success in sciatica (POTEISS): a protocol for the development of a multivariable prediction model for outcome after transforaminal epidural steroid injection in patients with lumbar radicular pain due to disc herniation or stenosis.

BACKGROUND: Transforaminal epidural injections (TEI) can alleviate symptoms and help to maintain physical functioning and quality of life in patients with lumbar radicular pain. We aim to develop a prediction model for patient outcome after TEI in patients suffering from unilateral lumbar radicular pain due to lumbar disc herniation (LDH) or single-level spinal stenosis (LSS). The secondary aim is to estimate short-term patient outcome differences between LDH and LSS patients, the association between psychological variables and patient outcome, the rate of additional injections, surgery and complications, and to explore the short-term cost-effectiveness of TEI. METHODS: This study is designed as a multi-centre, observational, prospective cohort study in two large regional hospitals in the Netherlands. Patients diagnosed with unilateral lumbar radicular pain secondary to LDH or LSS and congruent with MRI findings, who are referred for TEI along usual care pathways, are eligible for study participation. A total of 388 patients with LDH or LSS will be included. A pre-defined set of demographic, clinical and radiological variables will be used as the predictors in the model. The primary outcome measure is the Numerical Rating Scale (NRS) for leg pain. Secondary outcome measures include back pain, physical functioning, perceived recovery, pain coping strategies, anxiety and depression and use of analgesics and physical therapy. Patients will be evaluated at baseline, 2 weeks and 6 weeks after treatment. NRS leg pain and Likert perceived recovery data will be used as the dependent variables in a generalized linear mixed model for prediction of TEI outcome, with internal validation of performance (explained variation) by bootstrap resampling. Cost-effectiveness for a period of 6 weeks prior to and after treatment will be performed with decision-analytic modelling. DISCUSSION: Patients with severe lumbar radicular pain often request additional treatment when conservative care is insufficient. TEI can offer relief of symptoms. Currently, it is not possible to predict responsiveness to this treatment for individual patients. This study is designed to explore predictors that can differentiate between patients that will and will not have a positive outcome after TEI. This information may support treatment strategies for this patient group. TRIAL REGISTRATION: This study is registered at ClinicalTrials.gov database under registry number NCT04540068 on September 1, 2020.

Instrumental variable analysis to estimate treatment effects: a simulation study showing potential benefits of conditioning on hospital.

BACKGROUND: Instrumental variable (IV) analysis holds the potential to estimate treatment effects from observational data. IV analysis potentially circumvents unmeasured confounding but makes a number of assumptions, such as that the IV shares no common cause with the outcome. When using treatment preference as an instrument, a common cause, such as a preference regarding related treatments, may exist. We aimed to explore the validity and precision of a variant of IV analysis where we additionally adjust for the provider: adjusted IV analysis. METHODS: A treatment effect on an ordinal outcome was simulated (beta - 0.5 in logistic regression) for 15.000 patients, based on a large data set (the IMPACT data, n = 8799) using different scenarios including measured and unmeasured confounders, and a common cause of IV and outcome. We compared estimated treatment effects with patient-level adjustment for confounders, IV with treatment preference as the instrument, and adjusted IV, with hospital added as a fixed effect in the regression models. RESULTS: The use of patient-level adjustment resulted in biased estimates for all the analyses that included unmeasured confounders, IV analysis was less confounded, but also less reliable. With correlation between treatment preference and hospital characteristics (a common cause) estimates were skewed for regular IV analysis, but not for adjusted IV analysis. CONCLUSION: When using IV analysis for comparing hospitals, some limitations of regular IV analysis can be overcome by adjusting for a common cause. TRIAL REGISTRATION: We do not report the results of a health care intervention.

Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review.

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 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.

A tailored intervention does not reduce low value MRI's and arthroscopies in degenerative knee disease when the secular time trend is taken into account: a difference-in-difference analysis.

PURPOSE: To evaluate the effectiveness of a tailored intervention to reduce low value MRIs and arthroscopies among patients ≥ 50 years with degenerative knee disease in 13 Dutch orthopaedic centers (intervention group) compared with all other Dutch orthopaedic centers (control group). METHODS: All patients with degenerative knee disease ≥ 50 years admitted to Dutch orthopaedic centers from January 2016 to December 2018 were included. The tailored intervention included participation of clinical champions, education on the Dutch Choosing Wisely recommendation for MRI's and arthroscopies in degenerative knee disease, training of orthopaedic surgeons to manage patient expectations, performance feedback, and provision of a patient brochure. A difference-in-difference analysis was used to compare the time trend before (admitted January 2016-June 2017) and after introduction of the intervention (July 2017-December 2018) between intervention and control hospitals. Primary outcome was the monthly percentage of patients receiving a MRI or knee arthroscopy, weighted by type of hospital. RESULTS: 136,446 patients were included, of whom 32,163 were treated in the intervention hospitals. The weighted percentage of patients receiving a MRI on average declined by 0.15% per month (ß = - 0.15, P < 0.001) and by 0.19% per month for arthroscopy (ß = - 0.19, P < 0.001). However, these changes over time did not differ between intervention and control hospitals, neither for MRI (ß = - 0.74, P = 0.228) nor arthroscopy (ß = 0.13, P = 0.688). CONCLUSIONS: The extent to which patients ≥ 50 years with degenerative knee disease received a MRI or arthroscopy declined significantly over time, but could not be attributed to the tailored intervention. This secular downward time trend may reflect anoverall focus of reducing low value care in The Netherlands. LEVEL OF EVIDENCE: III.

Performing a knee arthroscopy among patients with degenerative knee disease: one-third is potentially low value care.

PURPOSE: The purpose of this study was to assess in which proportion of patients with degenerative knee disease aged 50+ in whom a knee arthroscopy is performed, no valid surgical indication is reported in medical records, and to explore possible explanatory factors. METHODS: A retrospective study was conducted using administrative data from January to December 2016 in 13 orthopedic centers in the Netherlands. Medical records were selected from a random sample of 538 patients aged 50+ with degenerative knee disease in whom arthroscopy was performed, and reviewed on reported indications for the performed knee arthroscopy. Valid surgical indications were predefined based on clinical national guidelines and expert opinion (e.g., truly locked knee). A knee arthroscopy without a reported valid indication was considered potentially low value care. Multivariate logistic regression analysis was performed to assess whether age, diagnosis ("Arthrosis" versus "Meniscal lesion"), and type of care trajectory (initial or follow-up) were associated with performing a potentially low value knee arthroscopy. RESULTS: Of 26,991 patients with degenerative knee disease, 2556 (9.5%) underwent an arthroscopy in one of the participating orthopedic centers. Of 538 patients in whom an arthroscopy was performed, 65.1% had a valid indication reported in the medical record and 34.9% without a reported valid indication. From the patients without a valid indication, a joint patient-provider decision or patient request was reported as the main reason. Neither age [OR 1.013 (95% CI 0.984-1.043)], diagnosis [OR 0.998 (95% CI 0.886-1.124)] or type of care trajectory [OR 0.989 (95% CI 0.948-1.032)] were significantly associated with performing a potentially low value knee arthroscopy. CONCLUSIONS: In a random sample of knee arthroscopies performed in 13 orthopedic centers in 2016, 65% had valid indications reported in the medical records but 35% were performed without a reported valid indication and, therefore, potentially low value care. Patient and/or surgeons preference may play a large role in the decision to perform an arthroscopy without a valid indication. Therefore, interventions should be developed to increase adherence to clinical guidelines by surgeons that target invalid indications for a knee arthroscopy to improve care. LEVEL OF EVIDENCE: IV.

Prediction of pre-eclampsia-related complications in women with suspected or confirmed pre-eclampsia: development and internal validation of clinical prediction model.

OBJECTIVE: A model that can predict reliably the risk of pre-eclampsia (PE)-related pregnancy complications does not exist. The aim of this study was to develop and validate internally a clinical prediction model to predict the risk of a composite outcome of PE-related maternal and fetal complications within 7, 14 and 30 days of testing in women with suspected or confirmed PE. METHODS: The data for this study were derived from a prospective, multicenter, observational cohort study on women with a singleton pregnancy and suspected or confirmed PE at 20 to < 37 weeks' gestation. For the development of the prediction model, the possible contribution of clinical and standard laboratory variables, as well as the biomarkers soluble fms-like tyrosine kinase-1 (sFlt-1), placental growth factor (PlGF) and their ratio, in the prediction of a composite outcome of PE-related complications, consisting of maternal and fetal adverse events within 7, 14 and 30 days, was explored using multivariable competing-risks regression analysis. The discriminative ability of the model was assessed using the concordance (c-) statistic. A bootstrap validation procedure with 500 replications was used to correct the estimate of the prediction model performance for optimism and to compute a shrinkage factor for the regression coefficients to correct for overfitting. RESULTS: Among 384 women with suspected or confirmed PE, 96 (25%) had an adverse PE-related outcome at any time after hospital admission. Important predictors of adverse PE-related outcome included sFlt-1/PlGF ratio, gestational age at the time of biomarker measurement and protein-to-creatinine ratio as continuous variables. The c-statistics (corrected for optimism) for developing a PE-related complication within 7, 14 and 30 days were 0.89, 0.88 and 0.87, respectively. There was limited overfitting, as indicated by a shrinkage factor of 0.91. CONCLUSIONS: We propose a simple clinical prediction model with good discriminative performance to predict PE-related complications. Determination of its usefulness in clinical practice awaits further investigation and external validation. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Trauma models to identify major trauma and mortality in the prehospital setting.

BACKGROUND: Patients with major trauma might benefit from treatment in a trauma centre, but early identification of major trauma (Injury Severity Score (ISS) over 15) remains difficult. The aim of this study was to undertake an external validation of existing prognostic models for injured patients to assess their ability to predict mortality and major trauma in the prehospital setting. METHODS: Prognostic models were identified through a systematic literature search up to October 2017. Injured patients transported by Emergency Medical Services to an English hospital from the Trauma Audit and Research Network between 2013 and 2016 were included. Outcome measures were major trauma (ISS over 15) and in-hospital mortality. The performance of the models was assessed in terms of discrimination (concordance index, C-statistic) and net benefit to assess the clinical usefulness. RESULTS: A total of 154 476 patients were included to validate six previously proposed prediction models. Discriminative ability ranged from a C-statistic value of 0·602 (95 per cent c.i. 0·596 to 0·608) for the Mechanism, Glasgow Coma Scale, Age and Arterial Pressure model to 0·793 (0·789 to 0·797) for the modified Rapid Emergency Medicine Score (mREMS) in predicting in-hospital mortality (11 882 patients). Major trauma was identified in 52 818 patients, with discrimination from a C-statistic value of 0·589 (0·586 to 0·592) for mREMS to 0·735 (0·733 to 0·737) for the Kampala Trauma Score in predicting major trauma. None of the prediction models met acceptable undertriage and overtriage rates. CONCLUSION: Currently available prehospital trauma models perform reasonably in predicting in-hospital mortality, but are inadequate in identifying patients with major trauma. Future research should focus on which patients would benefit from treatment in a major trauma centre.

ANTECEDENTES: Los pacientes con traumatismo mayor pueden beneficiarse del tratamiento en un centro de trauma, pero la identificación precoz del traumatismo mayor (Injury Severity Score, ISS > 15) sigue siendo difícil. El objetivo de este estudio fue validar externamente los modelos pronósticos existentes para los pacientes con traumatismos con el fin de evaluar su capacidad para predecir el traumatismo mayor y la mortalidad en el entorno pre-hospitalario. MÉTODOS: Los modelos pronóstico se identificaron mediante una búsqueda sistemática de la literatura hasta octubre de 2017. Los pacientes incluidos fueron pacientes con traumatismos que fueron trasladados mediante los servicios de emergencia médica (emergency medical services, EMS) a un hospital inglés perteneciente a Trauma Audit and Research Network (TARN) entre 2013 y 2016. Las variables evaluadas fueron los traumatismos graves (ISS > 15) y la mortalidad hospitalaria. El rendimiento de los modelos se analizó en términos de discriminación (índice de concordancia, c) y de beneficio neto para evaluar la utilidad clínica. RESULTADOS: Se incluyeron un total de 154.476 pacientes para validar los seis modelos de predicción propuestos previamente. La capacidad discriminatoria osciló entre c = 0,602 (i.c. del 95%: 0,596-0,608) para el modelo que incluye mecanismo, escala de coma de Glasgow, edad y presión arterial (MGAP) hasta c = 0,793 (0,789-0,797) para la puntuación de medicina de emergencia rápida modificada (mREMS) en la predicción de la mortalidad hospitalaria (n = 11.882). Se identificó un traumatismo mayor en 52.818 pacientes, con una discriminación de c = 0,589 (0,586-0,592) para mREMS a c = 0,735 (0,733-0,737) para la puntuación de trauma de Kampala en la predicción de traumatismo mayor. Ninguno de los modelos de predicción cumplió con las tasas aceptables de subtriaje (undertriage) y sobretriaje (overtriage). CONCLUSIÓN: Los modelos de trauma pre-hospitalarios actualmente disponibles tienen un rendimiento razonable para predecir la mortalidad hospitalaria, pero son inadecuados para identificar a los pacientes con traumatismo mayor. En el futuro, las investigaciones deberían centrarse en identificar a los pacientes que se podrían beneficiar del tratamiento en un centro de trauma especializado.

Variation in the practice of tracheal intubation in Europe after traumatic brain injury: a prospective cohort study.

Traumatic brain injury patients frequently undergo tracheal intubation. We aimed to assess current intubation practice in Europe and identify variation in practice. We analysed data from patients with traumatic brain injury included in the prospective cohort study collaborative European neurotrauma effectiveness research in traumatic brain injury (CENTER-TBI) in 45 centres in 16 European countries. We included patients who were transported to hospital by emergency medical services. We used mixed-effects multinomial regression to quantify the effects on pre-hospital or in-hospital tracheal intubation of the following: patient characteristics; injury characteristics; centre; and trauma system characteristics. A total of 3843 patients were included. Of these, 1322 (34%) had their tracheas intubated; 839 (22%) pre-hospital and 483 (13%) in-hospital. The fit of the model with only patient characteristics predicting intubation was good (Nagelkerke R2 64%). The probability of tracheal intubation increased with the following: younger age; lower pre-hospital or emergency department GCS; higher abbreviated injury scale scores (head and neck, thorax and chest, face or abdomen abbreviated injury score); and one or more unreactive pupils. The adjusted median odds ratio for intubation between two randomly chosen centres was 3.1 (95%CI 2.1-4.3) for pre-hospital intubation, and 2.7 (95%CI 1.9-3.5) for in-hospital intubation. Furthermore, the presence of an anaesthetist was independently associated with more pre-hospital intubation (OR 2.9, 95%CI 1.3-6.6), in contrast to the presence of ambulance personnel who are allowed to intubate (OR 0.5, 95%CI 0.3-0.8). In conclusion, patient and injury characteristics are key drivers of tracheal intubation. Between-centre differences were also substantial. Further studies are needed to improve the evidence base supporting recommendations for tracheal intubation.

Predicting the chances of having a baby with or without treatment at different time points in couples with unexplained subfertility.

STUDY QUESTION: Can we develop a prediction model that can estimate the chances of conception leading to live birth with and without treatment at different points in time in couples with unexplained subfertility? SUMMARY ANSWER: Yes, a dynamic model was developed that predicted the probability of conceiving under expectant management and following active treatments (in vitro fertilisation (IVF), intrauterine insemination with ovarian stimulation (IUI + SO), clomiphene) at different points in time since diagnosis. WHAT IS KNOWN ALREADY: Couples with no identified cause for their subfertility continue to have a realistic chance of conceiving naturally, which makes it difficult for clinicians to decide when to intervene. Previous fertility prediction models have attempted to address this by separately estimating either the chances of natural conception or the chances of conception following certain treatments. These models only make predictions at a single point in time and are therefore inadequate for informing continued decision-making at subsequent consultations. STUDY DESIGN, SIZE, DURATION: A population-based study of 1316 couples with unexplained subfertility attending a regional clinic between 1998 and 2011. PARTICIPANTS/MATERIALS, SETTING, METHODS: A dynamic prediction model was developed that estimates the chances of conception within 6 months from the point when a diagnosis of unexplained subfertility was made. These predictions were recomputed each month to provide a dynamic assessment of the individualised chances of conception while taking account of treatment status in each month. Conception must have led to live birth and treatments included clomiphene, IUI + SO, and IVF. Predictions for natural conception were externally validated using a prospective cohort from The Netherlands. MAIN RESULTS AND THE ROLE OF CHANCE: A total of 554 (42%) couples started fertility treatment within 2 years of their first fertility consultation. The natural conception leading to live birth rate was 0.24 natural conceptions per couple per year. Active treatment had a higher chance of conception compared to those who remained under expectant management. This association ranged from weak with clomiphene to strong with IVF [clomiphene, hazard ratio (HR) = 1.42 (95% confidence interval, 1.05 to 1.91); IUI + SO, HR = 2.90 (2.06 to 4.08); IVF, HR = 5.09 (4.04 to 6.40)]. Female age and duration of subfertility were significant predictors, without clear interaction with the relative effect of treatment. LIMITATIONS, REASONS FOR CAUTION: We were unable to adjust for other potentially important predictors, e.g. measures of ovarian reserve, which were not available in the linked Grampian dataset that may have made predictions more specific. This study was conducted using single centre data meaning that it may not be generalizable to other centres. However, the model performed as well as previous models in reproductive medicine when externally validated using the Dutch cohort. WIDER IMPLICATIONS OF THE FINDINGS: For the first time, it is possible to estimate the chances of conception following expectant management and different fertility treatments over time in couples with unexplained subfertility. This information will help inform couples and their clinicians of their likely chances of success, which may help manage expectations, not only at diagnostic workup completion but also throughout their fertility journey. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by a Chief Scientist Office postdoctoral training fellowship in health services research and health of the public research (ref PDF/12/06). B.W.M. is supported by an NHMRC Practitioner Fellowship (GNT1082548). B.W.M. reports consultancy for ObsEva, Merck, and Guerbet. None of the other authors declare any conflicts of interest.

Predicting health status in the first year after trauma.

BACKGROUND: Although mortality rates following major trauma are continuing to decline, a growing number of patients are experiencing long-term disability. The aim of this study was to identify factors associated with health status in the first year following trauma and develop prediction models based on a defined trauma population. METHODS: The Brabant Injury Outcome Surveillance (BIOS) study was a multicentre prospective observational cohort study. Adult patients with traumatic injury were included from August 2015 to November 2016 if admitted to one of the hospitals of the Noord-Brabant region in the Netherlands. Outcome measures were EuroQol Five Dimensions 5D-3L (EQ-5D™ utility and visual analogue scale (VAS)) and Health Utilities Index (HUI) 2 and 3 scores 1 week and 1, 3, 6 and 12 months after injury. Prediction models were developed using linear mixed models, with patient characteristics, preinjury health status, injury severity and frailty as possible predictors. Predictors that were significant (P < 0·050) for one of the outcome measures were included in all models. Performance was assessed using explained variance (R2 ). RESULTS: In total, 4883 patients participated in the BIOS study (50·0 per cent of the total), of whom 3366 completed the preinjury questionnaires. Preinjury health status and frailty were the strongest predictors of health status during follow-up. Age, sex, educational level, severe head or face injury, severe torso injury, injury severity, Functional Capacity Index score, co-morbidity and duration of hospital stay were also relevant in the multivariable models predicting health status. R2 ranged from 35 per cent for EQ-VAS to 48 per cent for HUI 3. CONCLUSION: The most important predictors of health status in the first year after trauma in this population appeared to be preinjury health status and frailty.

Systematic review of clinical prediction models for survival after surgery for resectable pancreatic cancer.

BACKGROUND: As more therapeutic options for pancreatic cancer are becoming available, there is a need to improve outcome prediction to support shared decision-making. A systematic evaluation of prediction models in resectable pancreatic cancer is lacking. METHODS: This systematic review followed the CHARMS and PRISMA guidelines. PubMed, Embase and Cochrane Library databases were searched up to 11 October 2017. Studies reporting development or validation of models predicting survival in resectable pancreatic cancer were included. Models without performance measures, reviews, abstracts or more than 10 per cent of patients not undergoing resection in postoperative models were excluded. Studies were appraised critically. RESULTS: After screening 4403 studies, 22 (44 319 patients) were included. There were 19 model development/update studies and three validation studies, altogether concerning 21 individual models. Two studies were deemed at low risk of bias. Eight models were developed for the preoperative setting and 13 for the postoperative setting. Most frequently included parameters were differentiation grade (11 of 21 models), nodal status (8 of 21) and serum albumin (7 of 21). Treatment-related variables were included in three models. The C-statistic/area under the curve values ranged from 0·57 to 0·90. Based on study design, validation methods and the availability of web-based calculators, two models were identified as the most promising. CONCLUSION: Although a large number of prediction models for resectable pancreatic cancer have been reported, most are at high risk of bias and have not been validated externally. This overview of prognostic factors provided practical recommendations that could help in designing easily applicable prediction models to support shared decision-making.

Impact of predictor measurement heterogeneity across settings on the performance of prediction models: A measurement error perspective.

It is widely acknowledged that the predictive performance of clinical prediction models should be studied in patients that were not part of the data in which the model was derived. Out-of-sample performance can be hampered when predictors are measured differently at derivation and external validation. This may occur, for instance, when predictors are measured using different measurement protocols or when tests are produced by different manufacturers. Although such heterogeneity in predictor measurement between derivation and validation data is common, the impact on the out-of-sample performance is not well studied. Using analytical and simulation approaches, we examined out-of-sample performance of prediction models under various scenarios of heterogeneous predictor measurement. These scenarios were defined and clarified using an established taxonomy of measurement error models. The results of our simulations indicate that predictor measurement heterogeneity can induce miscalibration of prediction and affects discrimination and overall predictive accuracy, to extents that the prediction model may no longer be considered clinically useful. The measurement error taxonomy was found to be helpful in identifying and predicting effects of heterogeneous predictor measurements between settings of prediction model derivation and validation. Our work indicates that homogeneity of measurement strategies across settings is of paramount importance in prediction research.

Impact of neoadjuvant chemoradiotherapy on health-related quality of life in long-term survivors of esophageal or junctional cancer: results from the randomized CROSS trial.

Background: Neoadjuvant chemoradiotherapy (nCRT) plus surgery is a standard of care for patients with esophageal or junctional cancer, but the long-term impact of nCRT on health-related quality of life (HRQOL) is unknown. The purpose of this study is to compare very long-term HRQOL in long-term survivors of esophageal cancer who received nCRT plus surgery or surgery alone. Patients and methods: Patients were randomly assigned to receive nCRT (carboplatin/paclitaxel with 41.4-Gy radiotherapy) plus surgery or surgery alone. HRQOL was measured using EORTC-QLQ-C30, EORTC-QLQ-OES24 and K-BILD questionnaires after a minimum follow-up of 6 years. To allow for examination over time, EORTC-QLQ-C30 and QLQ-OES24 questionnaire scores were compared with pretreatment and 12 months postoperative questionnaire scores. Physical functioning (QLQ-C30), eating problems (QLQ-OES24) and respiratory problems (K-BILD) were predefined primary end points. Predefined secondary end points were global quality of life and fatigue (both QLQ-C30). Results: After a median follow-up of 105 months, 123/368 included patients (33%) were still alive (70 nCRT plus surgery, 53 surgery alone). No statistically significant or clinically relevant differential effects in HRQOL end points were found between both groups. Compared with 1-year postoperative levels, eating problems, physical functioning, global quality of life and fatigue remained at the same level in both groups. Compared with pretreatment levels, eating problems had improved (Cohen's d -0.37, P = 0.011) during long-term follow-up, whereas physical functioning and fatigue were not restored to pretreatment levels in both groups (Cohen's d -0.56 and 0.51, respectively, both P < 0.001). Conclusions: Although physical functioning and fatigue remain reduced after long-term follow-up, no adverse impact of nCRT is apparent on long-term HRQOL compared with patients who were treated with surgery alone. In addition to the earlier reported improvement in survival and the absence of impact on short-term HRQOL, these results support the view that nCRT according to CROSS can be considered as a standard of care. Trial registration number: Netherlands Trial Register NTR487.

Pretransplant Numbers of CD16+ Monocytes as a Novel Biomarker to Predict Acute Rejection After Kidney Transplantation: A Pilot Study.

Acute rejection is one of the major immunological determinants of kidney graft function and survival. Early biomarkers to predict rejection are lacking. Emerging evidence reveals a crucial role for the monocyte/macrophage lineage cells in the pathogenesis of rejection. We hypothesized that higher pretransplant numbers of proinflammatory CD16+ monocytes can predict rejection. The study cohort consisted of 104 kidney transplant recipients (58 with no rejection and 46 with biopsy-proven rejection) and 33 healthy persons. Posttransplant median follow-up time was 14.7 mo (interquartile range 0.3-34 mo). Pretransplantation blood samples were analyzed by flow cytometry for monocyte immunophenotypes. Groups were compared by Cox regression models for the occurrence of acute rejection. We documented a significantly increased absolute number of pretransplant CD16+ monocytes in patients who developed biopsy-proven rejection after transplantation compared with those with no rejection (hazard ratio [HR] 1.60, 95% CI 1.28-2.00, p < 0.001) and healthy persons (HR 1.47, 95% CI 1.18-1.82, p < 0.001). In parallel, significantly fewer absolute numbers of CD16- monocytes were observed at pretransplant time points in rejectors versus nonrejectors (HR 0.74, 95% CI 0.58-0.94, p < 0,014). A higher pretransplant number of CD16+ monocytes is significantly associated with a higher risk of acute rejection after kidney transplantation.

Multimodality treatment for esophageal adenocarcinoma: multi-center propensity-score matched study.

Background: The primary aim of this study was to compare survival from neoadjuvant chemoradiotherapy plus surgery (NCRS) versus neoadjuvant chemotherapy plus surgery (NCS) for the treatment of esophageal or junctional adenocarcinoma. The secondary aims were to compare pathological effects, short-term mortality and morbidity, and to evaluate the effect of lymph node harvest upon survival in both treatment groups. Methods: Data were collected from 10 European centers from 2001 to 2012. Six hundred and eight patients with stage II or III oesophageal or oesophago-gastric junctional adenocarcinoma were included; 301 in the NCRS group and 307 in the NCS group. Propensity score matching and Cox regression analyses were used to compensate for differences in baseline characteristics. Results: NCRS resulted in significant pathological benefits with more ypT0 (26.7% versus 5%; P < 0.001), more ypN0 (63.3% versus 32.1%; P < 0.001), and reduced R1/2 resection margins (7.7% versus 21.8%; P < 0.001). Analysis of short-term outcomes showed no statistically significant differences in 30-day or 90-day mortality, but increased incidence of anastomotic leak (23.1% versus 6.8%; P < 0.001) in NCRS patients. There were no statistically significant differences between the groups in 3-year overall survival (57.9% versus 53.4%; Hazard Ratio (HR)= 0.89, 95%C.I. 0.67-1.17, P = 0.391) nor disease-free survival (52.9% versus 48.9%; HR = 0.90, 95%C.I. 0.69-1.18, P = 0.443). The pattern of recurrence was also similar (P = 0.660). There was a higher lymph node harvest in the NCS group (27 versus 14; P < 0.001), which was significantly associated with a lower recurrence rate and improved disease free survival within the NCS group. Conclusion: The survival differences between NCRS and NCS maybe modest, if present at all, for the treatment of locally advanced esophageal or junctional adenocarcinoma. Future large-scale randomized trials must control and monitor indicators of the quality of surgery, as the extent of lymphadenectomy appears to influence prognosis in patients treated with NCS, from this large multi-center European study.

sIgE Ana o 1, 2 and 3 accurately distinguish tolerant from allergic children sensitized to cashew nuts.

BACKGROUND: The double-blind, placebo-controlled food challenge test (DBPCFC) is the gold standard in cashew nut allergy. This test is costly, time consuming and not without side effects. Analysis of IgE reactivity to cashew nut components may reduce the need for food challenge tests. METHODS: In a prospective and multicentre study, children with suspected cashew nut allergy underwent a DBPCFC with cashew nut. Specific IgE to cashew nut and to the components Ana o 1, 2 and 3 were determined. A skin prick test (SPT) with cashew nut extract was performed. The association between the outcome of the food challenge test and specific IgE to Ana o 1, 2 and 3 was assessed with logistic regression analyses, unadjusted and adjusted for other diagnostic variables. Discriminative ability was quantified with a concordance index (c). RESULTS: A total of 173 children (103 boys, 60%) with a median age of 9 years were included. About 79% had a positive challenge test outcome. A steep rise in the risk of a positive challenge was observed for specific IgE to each individual component Ana o 1, 2 and 3 with estimated risks up to approximately 100%. Median values of Ana o 1, 2, 3 were 1.29 kU/l (range 0-100 kU/l), 4.77 kU/l (range 0-100 kU/l) and 8.33 kU/l (range 0-100 kU/l) respectively and varied significantly (p < 0.001). Specific IgE to Ana o 1, 2 and 3 was better distinguished between cashew-allergic and tolerant children (c = 0.87, 0.85 and 0.89, respectively) than specific IgE to cashew nut or SPT (c = 0.76 and 0.83, respectively). CONCLUSION: The major cashew nut allergens Ana o 1, 2 and 3 are each individually predictive for the outcome of food challenge tests in cashew-allergic children.

Constructing the crystal ball: how to get reliable prognostic information for the management of subfertile couples.

Couples in whom the results of an initial fertility workup fail to identify the presence of any obvious barriers to conception are diagnosed with unexplained subfertility. Couples who have tried to conceive for a relatively short time have a good chance of natural conception and thus may not benefit from immediate access to ART. As fertility decreases over time, the main dilemma that clinicians and couples face is when to abandon an expectant approach in favour of active treatment. Several prognostic or predictive models have been used to try to discriminate between couples with high and low chances of conception but have been unable to compare individualized chances of conception associated with ART relative to chances of natural conception at various time points. These models are also unable to recalculate the chances of pregnancy at subsequent time points in those who return after a period of unsuccessful expectant management. In this paper, we discuss currently available models. We conclude that in order to provide accurate, individualized and dynamic fertility prognoses associated with and without treatment at different points in time, we need to develop, validate and update clinical prediction models which are fit for purpose. We suggest several steps to move the field forwards.

Occurrence of metachronous basal cell carcinomas: a prognostic model.

BACKGROUND: A third of patients with a first basal cell carcinoma (BCC) will develop subsequent (metachronous) BCCs. OBJECTIVES: To study the prognostic effect of the number of previous BCC diagnosis dates a patient has experienced to derive a prediction model to assess the risk of metachronous BCCs that may inform individualized decision making on surveillance. METHODS: We considered participants of north-western European ancestry from a prospective population-based cohort study (Rotterdam Study). After linkage with the Dutch Pathology Registry, 1077 patients with a first BCC were included. Candidate predictors for metachronous BCCs included patient, lifestyle and tumour characteristics. The prognostic model was developed with Fine and Gray regression analysis to account for competing risk of death. We used bootstrapping to correct for within-patient correlation and statistical optimism in predictive performance. RESULTS: Second to fifth BCCs occurred in 293, 122, 58 and 36 patients, with median follow-up times of 3·0, 2·1, 1·7 and 1·8 years after the previous BCC, respectively. The risk of a new BCC was higher for patients with more metachronous BCCs. Having more than one BCC at diagnosis was another strong predictor of metachronous BCCs. Discriminative ability of the model was reasonable with an optimism-corrected c-index of 0·70 at 3 years. CONCLUSIONS: The number of previous BCC diagnosis dates was a strong prognostic factor and should be considered when predicting the risk of metachronous BCCs. When the number of previous BCC diagnosis dates is combined with other readily available characteristics into a prognostic model, patients at high risk of a new BCC can be identified.

Patients with oesophageal cancer report elevated distress and problems yet do not have an explicit wish for referral prior to receiving their medical treatment plan.

OBJECTIVE: This study aims to identify patients with oesophageal cancer's level of distress, type of problems, and wish for referral prior to treatment. To identify the clinical relevance of patients with oesophageal cancer's level of distress and type of problems, we build models to predict elevated distress, wish for referral, and overall survival. METHODS: We implemented the Distress Thermometer and Problem List in daily clinical practice. A score of ≥5 on the Distress Thermometer reflected elevated distress. We first created an initial model including predictors based on the literature. We then added predictors to the initial model to create an extended model based on the sample data. We used the 'least absolute shrinkage and selection operator' to define our final model. RESULTS: We obtained data from 187 patients (47.9%, of 390 eligible patients with oesophageal cancer) which were similar to non-respondents in their demographic and clinical characteristics. One-hundred thirteen (60%) patients reported elevated distress. The five most frequently reported problems were as follows: eating, tension, weight change, fatigue, and pain. Most patients did not have a wish for referral. Predictors for elevated distress were as follows: being female, total number of practical, emotional, and physical problems, pain, and fatigue. For referral, we identified age, the total number of emotional problems, the level of distress, and fear. The level of distress added prognostic information in a model to predict overall survival. CONCLUSIONS: Patients with oesophageal cancer report elevated distress and a myriad of problems yet do not have an explicit wish for referral prior to receiving their medical treatment plan. Copyright © 2016 John Wiley & Sons, Ltd.

Decision support systems for incurable non-small cell lung cancer: a systematic review.

BACKGROUND: Individually tailored cancer treatment is essential to ensure optimal treatment and resource use. Treatments for incurable metastatic non-small cell lung cancer (NSCLC) are evolving rapidly, and decision support systems (DSS) for this patient population have been developed to balance benefits and harms for decision-making. The aim of this systematic review was to inventory DSS for stage IIIB/IV NSCLC patients. METHODS: A systematic literature search was performed in Pubmed, Embase and the Cochrane Library. DSS were described extensively, including their predictors, model performances (i.e., discriminative ability and calibration), levels of validation and user friendliness. RESULTS: The systematic search yielded 3531 articles. In total, 67 articles were included after additional reference tracking. The 39 identified DSS aim to predict overall survival and/or progression-free survival, but give no information about toxicity or cost-effectiveness. Various predictors were incorporated, such as performance status, serum and inflammatory markers, and patient and tumor characteristics. Some DSS were developed for the entire incurable NSCLC population, whereas others were specifically for patients with brain or spinal metastases. Few DSS had been validated externally using recent clinical data, and the discrimination and calibration were often poor. CONCLUSIONS: Many DSS have been developed for incurable NSCLC patients, but DSS are still lacking that are up-to-date with a good model performance, while covering the entire treatment spectrum. Future DSS should incorporate genetic and biological markers based on state-of-the-art evidence, and compare multiple treatment options to estimate survival, toxicity and cost-effectiveness.