Recall bias in pain scores evaluating abdominal wall and groin pain surgery.

PURPOSE: To determine whether levels of pre-operative pain as recalled by a patient in the post-operative phase are possibly overestimated or underestimated compared to prospectively scored pain levels. If so, a subsequent misclassification may induce recall bias that may lead to an erroneous effect outcome. METHODS: Data of seven retrospective cohort studies on surgery for chronic abdominal wall and groin pain using three different pain scores were systematically analyzed. First, it was assessed whether retrospectively acquired pre-operative pain levels, as scored by the patient in the post-operative phase, differed from prospectively obtained pre-operative pain scores. Second, it was determined if errors associated with retrospectively obtained pain scores potentially lead to a misclassification of treatment outcome. Third, a meta-analysis established whether recall misclassifications, if present, affected overall study conclusions. RESULTS: Pain data of 313 patients undergoing remedial surgery were evaluated. The overall prevalence of misclassification due to a recall error was 13.7%. Patients not benefitting from surgery ('failures') judged their pre-operative pain level as more severe than it actually was. In contrast, patients who were pain free after remedial surgery ('successes') underestimated pre-operative pain scores. Recall misclassifications were significantly more present in failures than in successful patients (odds ratio 2.4 [95% CI 1.2-4.8]). CONCLUSION: One in seven patients undergoing remedial groin surgery is misclassified on the basis of retrospectively obtained pre-operative pain scores (success instead of failure, or vice versa). Misclassifications are relatively more present in failures after surgery. Therefore, the effect size of a therapy erroneously depends on its success rate.

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.

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.

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.

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

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

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.

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.

Risk factors for postoperative delirium after elective major abdominal surgery in elderly patients: A cohort study.

BACKGROUND: Prehabilitation programs have recently been suggested as potentially able to lower the incidence of delirium in elderly patients undergoing major abdominal surgery. For these prehabilitation programs to become successful, it is essential to identify those patients who are most likely to develop a delirium. MATERIAL AND METHODS: A single-centre cohort study was conducted. Inclusion criteria were: age ≥70 years and scheduled for abdominal surgery for colorectal cancer or an abdominal aortic aneurysm between January 2013 and June 2018. Baseline patient, surgical, anaesthesiologic and haematological characteristics were collected. A risk factor analysis was conducted, with postoperative delirium as primary outcome, by performing a multivariable logistic regression analysis. RESULTS: In this study, 627 patients were included, of whom 64 (10%) developed a delirium. Variables that differed significantly between delirious and non-delirious patients were age, burden of comorbidity, renal impairment, hypertension, cognitive impairment, history of delirium, physical and nutritional impairment, open surgery, preoperative anaemia and erythrocyte transfusion. After multivariable logistic regression analysis, risk factors for postoperative delirium after major abdominal surgery were renal impairment (OR 2.2; 95%CI 1.2-4.3), cognitive impairment (OR 4.1; 95%CI 1.8-9.2), an ASA score ≥ 3 (OR 2.0; 95% CI 1.0-3.9), being an active smoker (OR 2.7; 95%CI 1.3-5.8), ICU admission (OR 7.1; 95%CI 3.5-14.3), erythrocyte transfusion (OR 2.4; 95%CI 1.2-4.9) and a diagnosis of colorectal cancer (CRC); (OR 4.0; 95% CI 1.7-9.6). Prehabilitation had a protective effect (OR 0.5; 95% CI 0.3-0.9). CONCLUSION: Postoperative delirium is a frequent complication after major abdominal surgery in the elderly, especially in octogenarians and after open procedures. Renal impairment, cognitive impairment, being an active smoker, ICU admission, erythrocyte transfusion and a diagnosis of CRC are important risk factors for the development of delirium. Prehabilitation lowers the risk of developing a delirium.

Multimodal prehabilitation to reduce the incidence of delirium and other adverse events in elderly patients undergoing elective major abdominal surgery: An uncontrolled before-and-after study.

BACKGROUND: Delirium is a common and serious complication in elderly patients undergoing major abdominal surgery, with significant adverse outcomes. Successful strategies or therapies to reduce the incidence of delirium are scarce. The objective of this study was to assess the role of prehabilitation in reducing the incidence of delirium in elderly patients. METHODS: A single-center uncontrolled before-and-after study was conducted, including patients aged 70 years or older who underwent elective abdominal surgery for colorectal carcinoma or an abdominal aortic aneurysm between January 2013 and October 2015 (control group) and between November 2015 and June 2018 (prehabilitation group). The prehabilitation group received interventions to improve patients' physical health, nutritional status, factors of frailty and preoperative anaemia prior to surgery. The primary outcome was incidence of delirium, diagnosed with the DSM-V criteria or the confusion assessment method. Secondary outcomes were additional complications, length of stay, unplanned ICU admission, length of ICU stay, readmission rate, institutionalization, and in-hospital or 30-day mortality. RESULT: A total of 360 control patients and 267 prehabilitation patients were included in the final analysis. The mean number of prehabilitation days was 39 days. The prehabilitation group had a higher burden of comorbidities and was more physically and visually impaired at baseline. At adjusted logistic regression analysis, delirium incidence was reduced significantly from 11.7 to 8.2% (OR 0.56; 95% CI 0.32-0.98; P = 0.043). No statistically significant effects were seen on secondary outcomes. CONCLUSION: Current prehabilitation program is feasible and safe, and can reduce delirium incidence in elderly patients undergoing elective major abdominal surgery. This program merits further evaluation. TRIAL REGISTRATION: Dutch Trial Registration, NTR5932.

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.

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.

Added value of frailty and social support in predicting risk of 30-day unplanned re-admission or death for patients with heart failure: An analysis from OPERA-HF.

BACKGROUND: Models for predicting the outcome of patients hospitalized for heart failure (HF) rarely take a holistic view. We assessed the ability of measures of frailty and social support in addition to demographic, clinical, imaging and laboratory variables to predict short-term outcome for patients discharged after a hospitalization for HF. METHODS: OPERA-HF is a prospective observational cohort, enrolling patients hospitalized for HF in a single center in Hull, UK. Variables were combined in a logistic regression model after multiple imputation of missing data to predict the composite outcome of death or readmission at 30 days. Comparisons were made to a model using clinical variables alone. The discriminative performance of each model was internally validated with bootstrap re-sampling. RESULTS: 1094 patients were included (mean age 77 [interquartile range 68-83] years; 40% women; 56% with moderate to severe left ventricular systolic dysfunction) of whom 213 (19%) had an unplanned re-admission and 60 (5%) died within 30 days. For the composite outcome, a model containing clinical variables alone had an area under the receiver-operating characteristic curve (AUC) of 0.68 [95% CI 0.64-0.72]. Adding marital status, support from family and measures of physical frailty increased the AUC (p < 0.05) to 0.70 [95% CI 0.66-0.74]. CONCLUSIONS: Measures of physical frailty and social support improve prediction of 30-day outcome after an admission for HF but predicting near-term events remains imperfect. Further external validation and improvement of the model is required.

Optimization of the APOP screener to predict functional decline or mortality in older emergency department patients: Cross-validation in four prospective cohorts.

INTRODUCTION: Many screening instruments to predict adverse health outcomes in older patients visiting the emergency department (ED) have been developed, but successful implementation has been hampered because they are insufficiently validated or not tailored for the intended use of everyday clinical practice. The present study aims to refine and validate an existing screening instrument (the APOP screener) to predict 90-day functional decline or mortality in older ED patients. METHODS: Consecutive older patients (≥70 years) visiting the EDs of four hospitals were included and prospectively followed. First, an expert panel used predefined criteria to decide which independent predictors (including demographics, illness severity and geriatric parameters) were suitable for refinement of the model predicting functional decline or mortality after 90 days. Second, the model was cross-validated in all four hospitals and predictive performance was assessed. Additionally, a pilot study among triage nurses experiences and clinical usability of the APOP screener was conducted. RESULTS: In total 2629 older patients were included, with a median age of 79 years (IQR 74-84). After 90 days 805 patients (30.6%) experienced functional decline or mortality. The refined prediction model included age, gender, way of arrival, need of regular help, need help in bathing/showering, hospitalization the prior six months and impaired cognition. Calibration was good and cross-validation was successful with a pooled area under the curve of 0.71 (0.69-0.73). In the top 20% patients predicted to be at highest risk in total 58% (95%CI 54%-62%) experienced functional decline or mortality. Triage nurses found the screener well suited for clinical use, with room for improvement. CONCLUSION: In conclusion, optimization of the APOP screener resulted in a short and more simplified screener, which adequately identifies older ED patients at highest risk for functional decline or mortality. The findings of the pilot study were promising for clinical use.

Evaluation of current prediction models for Lynch syndrome: updating the PREMM5 model to identify PMS2 mutation carriers.

Until recently, no prediction models for Lynch syndrome (LS) had been validated for PMS2 mutation carriers. We aimed to evaluate MMRpredict and PREMM5 in a clinical cohort and for PMS2 mutation carriers specifically. In a retrospective, clinic-based cohort we calculated predictions for LS according to MMRpredict and PREMM5. The area under the operator receiving characteristic curve (AUC) was compared between MMRpredict and PREMM5 for LS patients in general and for different LS genes specifically. Of 734 index patients, 83 (11%) were diagnosed with LS; 23 MLH1, 17 MSH2, 31 MSH6 and 12 PMS2 mutation carriers. Both prediction models performed well for MLH1 and MSH2 (AUC 0.80 and 0.83 for PREMM5 and 0.79 for MMRpredict) and fair for MSH6 mutation carriers (0.69 for PREMM5 and 0.66 for MMRpredict). MMRpredict performed fair for PMS2 mutation carriers (AUC 0.72), while PREMM5 failed to discriminate PMS2 mutation carriers from non-mutation carriers (AUC 0.51). The only statistically significant difference between PMS2 mutation carriers and non-mutation carriers was proximal location of colorectal cancer (77 vs. 28%, p < 0.001). Adding location of colorectal cancer to PREMM5 considerably improved the models performance for PMS2 mutation carriers (AUC 0.77) and overall (AUC 0.81 vs. 0.72). We validated these results in an external cohort of 376 colorectal cancer patients, including 158 LS patients. MMRpredict and PREMM5 cannot adequately identify PMS2 mutation carriers. Adding location of colorectal cancer to PREMM5 may improve the performance of this model, which should be validated in larger cohorts.

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.