Publications by authors named "Ewout Steyerberg"

845 Publications

Prediction of hospital admission from the emergency department: Clinician involvement, intended use, and interpretability.

Int J Med Inform 2021 Nov 20;155:104585. Epub 2021 Sep 20.

Department of Emergency Medicine, Leiden University Medical Centre, Albinusdreef 2, 2300 RC Leiden, the Netherlands.

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http://dx.doi.org/10.1016/j.ijmedinf.2021.104585DOI Listing
November 2021

A screening tool to identify risk for bronchiectasis progression in children with cystic fibrosis.

Pediatr Pulmonol 2021 Oct 1. Epub 2021 Oct 1.

Telethon Kids Institute, The University of Western Australia, Perth, Australia.

Background: The marked heterogeneity in cystic fibrosis (CF) disease complicates the selection of those most likely to benefit from existing or emergent treatments.

Objective: We aimed to predict the progression of bronchiectasis in preschool children with CF.

Methods: Using data collected up to 3 years of age, in the Australian Respiratory Early Surveillance Team for CF cohort study, clinical information, chest computed tomography (CT) scores, and biomarkers from bronchoalveolar lavage were assessed in a multivariable linear regression model as predictors for CT bronchiectasis at age 5-6.

Results: Follow-up at 5-6 years was available in 171 children. Bronchiectasis prevalence at 5-6 was 134/171 (78%) and median bronchiectasis score was 3 (range 0-12). The internally validated multivariate model retained eight independent predictors accounting for 37% (adjusted R ) of the variance in bronchiectasis score. The strongest predictors of future bronchiectasis were: pancreatic insufficiency, repeated intravenous treatment courses, recurrent lower respiratory infections in the first 3 years of life, and lower airway inflammation. Dichotomizing the resulting prediction score at a bronchiectasis score of above the median resulted in a diagnostic odds ratio of 13 (95% confidence interval [CI], 6.3-27) with positive and negative predictive values of 80% (95% CI, 72%-86%) and 77% (95% CI, 69%-83%), respectively.

Conclusion: Early assessment of bronchiectasis risk in children with CF is feasible with reasonable precision at a group level, which can assist in high-risk patient selection for interventional trials. The unexplained variability in disease progression at individual patient levels remains high, limiting the use of this model as a clinical prediction tool.
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http://dx.doi.org/10.1002/ppul.25712DOI Listing
October 2021

Development and Validation of a Nonremission Risk Prediction Model in First-Episode Psychosis: An Analysis of 2 Longitudinal Studies.

Schizophr Bull Open 2021 Jan 31;2(1):sgab041. Epub 2021 Aug 31.

Institute for Mental Health, University of Birmingham, Birmingham, UK.

Psychosis is a major mental illness with first onset in young adults. The prognosis is poor in around half of the people affected, and difficult to predict. The few tools available to predict prognosis have major weaknesses which limit their use in clinical practice. We aimed to develop and validate a risk prediction model of symptom nonremission in first-episode psychosis. Our development cohort consisted of 1027 patients with first-episode psychosis recruited between 2005 and 2010 from 14 early intervention services across the National Health Service in England. Our validation cohort consisted of 399 patients with first-episode psychosis recruited between 2006 and 2009 from a further 11 English early intervention services. The one-year nonremission rate was 52% and 54% in the development and validation cohorts, respectively. Multivariable logistic regression was used to develop a risk prediction model for nonremission, which was externally validated. The prediction model showed good discrimination C-statistic of 0.73 (0.71, 0.75) and adequate calibration with intercept alpha of 0.12 (0.02, 0.22) and slope beta of 0.98 (0.85, 1.11). Our model improved the net-benefit by 15% at a risk threshold of 50% compared to the strategy of treating all, equivalent to 15 more detected nonremitted first-episode psychosis individuals per 100 without incorrectly classifying remitted cases. Once prospectively validated, our first episode psychosis prediction model could help identify patients at increased risk of nonremission at initial clinical contact.
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http://dx.doi.org/10.1093/schizbullopen/sgab041DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8458108PMC
January 2021

External Validation of the SYNTAX Score II 2020.

J Am Coll Cardiol 2021 Sep;78(12):1227-1238

Department of Cardiology, National University of Ireland, Galway (NUIG), Galway, Ireland; NHLI, Imperial College London, London, United Kingdom. Electronic address:

Background: The SYNTAX score II 2020 (SSII-2020) was derived from cross correlation and externally validated in randomized trials to predict death and major adverse cardiac and cerebrovascular events (MACE) following percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG) in patients with 3-vessel disease (3VD) and/or left main coronary artery disease (LMCAD).

Objectives: The authors aimed to investigate the SSII-2020's value in identifying the safest modality of revascularization in a non-randomized setting.

Methods: Five-year mortality and MACE were assessed in 7,362 patients with 3VD and/or LMCAD enrolled in a Japanese PCI/CABG registry. The discriminative abilities of the SSII-2020 were assessed using Harrell's C statistic. Agreement between observed and predicted event rates following PCI or CABG and treatment benefit (absolute risk difference [ARD]) for these outcomes were assessed by calibration plots.

Results: The SSII-2020 for 5-year mortality well predicted the prognosis after PCI and CABG (C-index = 0.72, intercept = -0.11, slope = 0.92). When patients were grouped according to the predicted 5-year mortality ARD, <4.5% (equipoise of PCI and CABG) and ≥4.5% (CABG better), the observed mortality rates after PCI and CABG were not significantly different in patients with lower predicted ARD (observed ARD: 2.1% [95% CI: -0.4% to 4.4%]), and the significant difference in survival in favor of CABG was observed in patients with higher predicted ARD (observed ARD: 9.7% [95% CI: 6.1%-13.3%]). For MACE, the SSII-2020 could not recommend a specific treatment with sufficient accuracy.

Conclusions: The SSII-2020 for predicting 5-year death has the potential to support decision making on revascularization in patients with 3VD and/or LMCAD.
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http://dx.doi.org/10.1016/j.jacc.2021.07.027DOI Listing
September 2021

The burden of traumatic brain injury from low-energy falls among patients from 18 countries in the CENTER-TBI Registry: A comparative cohort study.

PLoS Med 2021 Sep 14;18(9):e1003761. Epub 2021 Sep 14.

Department of Neurosurgery, Antwerp University Hospital, Edegem, Belgium.

Background: Traumatic brain injury (TBI) is an important global public health burden, where those injured by high-energy transfer (e.g., road traffic collisions) are assumed to have more severe injury and are prioritised by emergency medical service trauma triage tools. However recent studies suggest an increasing TBI disease burden in older people injured through low-energy falls. We aimed to assess the prevalence of low-energy falls among patients presenting to hospital with TBI, and to compare their characteristics, care pathways, and outcomes to TBI caused by high-energy trauma.

Methods And Findings: We conducted a comparative cohort study utilising the CENTER-TBI (Collaborative European NeuroTrauma Effectiveness Research in TBI) Registry, which recorded patient demographics, injury, care pathway, and acute care outcome data in 56 acute trauma receiving hospitals across 18 countries (17 countries in Europe and Israel). Patients presenting with TBI and indications for computed tomography (CT) brain scan between 2014 to 2018 were purposively sampled. The main study outcomes were (i) the prevalence of low-energy falls causing TBI within the overall cohort and (ii) comparisons of TBI patients injured by low-energy falls to TBI patients injured by high-energy transfer-in terms of demographic and injury characteristics, care pathways, and hospital mortality. In total, 22,782 eligible patients were enrolled, and study outcomes were analysed for 21,681 TBI patients with known injury mechanism; 40% (95% CI 39% to 41%) (8,622/21,681) of patients with TBI were injured by low-energy falls. Compared to 13,059 patients injured by high-energy transfer (HE cohort), the those injured through low-energy falls (LE cohort) were older (LE cohort, median 74 [IQR 56 to 84] years, versus HE cohort, median 42 [IQR 25 to 60] years; p < 0.001), more often female (LE cohort, 50% [95% CI 48% to 51%], versus HE cohort, 32% [95% CI 31% to 34%]; p < 0.001), more frequently taking pre-injury anticoagulants or/and platelet aggregation inhibitors (LE cohort, 44% [95% CI 42% to 45%], versus HE cohort, 13% [95% CI 11% to 14%]; p < 0.001), and less often presenting with moderately or severely impaired conscious level (LE cohort, 7.8% [95% CI 5.6% to 9.8%], versus HE cohort, 10% [95% CI 8.7% to 12%]; p < 0.001), but had similar in-hospital mortality (LE cohort, 6.3% [95% CI 4.2% to 8.3%], versus HE cohort, 7.0% [95% CI 5.3% to 8.6%]; p = 0.83). The CT brain scan traumatic abnormality rate was 3% lower in the LE cohort (LE cohort, 29% [95% CI 27% to 31%], versus HE cohort, 32% [95% CI 31% to 34%]; p < 0.001); individuals in the LE cohort were 50% less likely to receive critical care (LE cohort, 12% [95% CI 9.5% to 13%], versus HE cohort, 24% [95% CI 23% to 26%]; p < 0.001) or emergency interventions (LE cohort, 7.5% [95% CI 5.4% to 9.5%], versus HE cohort, 13% [95% CI 12% to 15%]; p < 0.001) than patients injured by high-energy transfer. The purposive sampling strategy and censorship of patient outcomes beyond hospital discharge are the main study limitations.

Conclusions: We observed that patients sustaining TBI from low-energy falls are an important component of the TBI disease burden and a distinct demographic cohort; further, our findings suggest that energy transfer may not predict intracranial injury or acute care mortality in patients with TBI presenting to hospital. This suggests that factors beyond energy transfer level may be more relevant to prehospital and emergency department TBI triage in older people. A specific focus to improve prevention and care for patients sustaining TBI from low-energy falls is required.
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http://dx.doi.org/10.1371/journal.pmed.1003761DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8509890PMC
September 2021

Predictive accuracy of enhanced versions of the on-admission National Early Warning Score in estimating the risk of COVID-19 for unplanned admission to hospital: a retrospective development and validation study.

BMC Health Serv Res 2021 Sep 13;21(1):957. Epub 2021 Sep 13.

York Teaching Hospitals NHS Foundation Trust, York, England, UK.

Background: The novel coronavirus SARS-19 produces 'COVID-19' in patients with symptoms. COVID-19 patients admitted to the hospital require early assessment and care including isolation. The National Early Warning Score (NEWS) and its updated version NEWS2 is a simple physiological scoring system used in hospitals, which may be useful in the early identification of COVID-19 patients. We investigate the performance of multiple enhanced NEWS2 models in predicting the risk of COVID-19.

Methods: Our cohort included unplanned adult medical admissions discharged over 3 months (11 March 2020 to 13 June 2020 ) from two hospitals (YH for model development; SH for external model validation). We used logistic regression to build multiple prediction models for the risk of COVID-19 using the first electronically recorded NEWS2 within ± 24 hours of admission. Model M0' included NEWS2; model M1' included NEWS2 + age + sex, and model M2' extends model M1' with subcomponents of NEWS2 (including diastolic blood pressure + oxygen flow rate + oxygen scale). Model performance was evaluated according to discrimination (c statistic), calibration (graphically), and clinical usefulness at NEWS2 ≥ 5.

Results: The prevalence of COVID-19 was higher in SH (11.0 %=277/2520) than YH (8.7 %=343/3924) with a higher first NEWS2 scores ( SH 3.2 vs YH 2.8) but similar in-hospital mortality (SH 8.4 % vs YH 8.2 %). The c-statistics for predicting the risk of COVID-19 for models M0',M1',M2' in the development dataset were: M0': 0.71 (95 %CI 0.68-0.74); M1': 0.67 (95 %CI 0.64-0.70) and M2': 0.78 (95 %CI 0.75-0.80)). For the validation datasets the c-statistics were: M0' 0.65 (95 %CI 0.61-0.68); M1': 0.67 (95 %CI 0.64-0.70) and M2': 0.72 (95 %CI 0.69-0.75) ). The calibration slope was similar across all models but Model M2' had the highest sensitivity (M0' 44 % (95 %CI 38-50 %); M1' 53 % (95 %CI 47-59 %) and M2': 57 % (95 %CI 51-63 %)) and specificity (M0' 75 % (95 %CI 73-77 %); M1' 72 % (95 %CI 70-74 %) and M2': 76 % (95 %CI 74-78 %)) for the validation dataset at NEWS2 ≥ 5.

Conclusions: Model M2' appears to be reasonably accurate for predicting the risk of COVID-19. It may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned hospital admissions.
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http://dx.doi.org/10.1186/s12913-021-06951-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8435351PMC
September 2021

Cost-effectiveness of prophylactic hysterectomy in first-degree female relatives with Lynch syndrome of patients diagnosed with colorectal cancer in the United States: a microsimulation study.

Cancer Med 2021 Oct 12;10(19):6835-6844. Epub 2021 Sep 12.

Department of Public Health, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands.

Background: To evaluate the cost-effectiveness of prophylactic hysterectomy (PH) in women with Lynch syndrome (LS).

Methods: We developed a microsimulation model incorporating the natural history for the development of hyperplasia with and without atypia into endometrial cancer (EC) based on the MISCAN-framework. We simulated women identified as first-degree relatives (FDR) with LS of colorectal cancer patients after universal testing for LS. We estimated costs and benefits of offering this cohort PH, accounting for reduced quality of life after PH and for having EC. Three minimum ages (30/35/40) and three maximum ages (70/75/80) were compared to no PH.

Results: In the absence of PH, the estimated number of EC cases was 300 per 1,000 women with LS. Total associated costs for treatment of EC were $5.9 million. Offering PH to FDRs aged 40-80 years was considered optimal. This strategy reduced the number of endometrial cancer cases to 5.4 (-98%), resulting in 516 quality-adjusted life years (QALY) gained and increasing the costs (treatment of endometrial cancer and PH) to $15.0 million (+154%) per 1,000 women. PH from earlier ages was more costly and resulted in fewer QALYs, although this finding was sensitive to disutility for PH.

Conclusions: Offering PH to 40- to 80-year-old women with LS is expected to add 0.5 QALY per person at acceptable costs. Women may decide to have PH at a younger age, depending on their individual disutility for PH and premature menopause.
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http://dx.doi.org/10.1002/cam4.4080DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8495276PMC
October 2021

Patient-Related Prognostic Factors for Anastomotic Leakage, Major Complications, and Short-Term Mortality Following Esophagectomy for Cancer: A Systematic Review and Meta-Analyses.

Ann Surg Oncol 2021 Sep 5. Epub 2021 Sep 5.

Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands.

Objective: The aim of this study is to identify preoperative patient-related prognostic factors for anastomotic leakage, mortality, and major complications in patients undergoing oncological esophagectomy.

Background: Esophagectomy is a high-risk procedure with an incidence of major complications around 25% and short-term mortality around 4%.

Methods: We systematically searched the Medline and Embase databases for studies investigating the associations between patient-related prognostic factors and anastomotic leakage, major postoperative complications (Clavien-Dindo ≥ IIIa), and/or 30-day/in-hospital mortality after esophagectomy for cancer.

Results: Thirty-nine eligible studies identifying 37 prognostic factors were included. Cardiac comorbidity was associated with anastomotic leakage, major complications, and mortality. Male sex and diabetes were prognostic factors for anastomotic leakage and major complications. Additionally, American Society of Anesthesiologists (ASA) score > III and renal disease were associated with anastomotic leakage and mortality. Pulmonary comorbidity, vascular comorbidity, hypertension, and adenocarcinoma tumor histology were identified as prognostic factors for anastomotic leakage. Age > 70 years, habitual alcohol usage, and body mass index (BMI) 18.5-25 kg/m were associated with increased risk for mortality.

Conclusions: Various patient-related prognostic factors are associated with anastomotic leakage, major postoperative complications, and postoperative mortality following oncological esophagectomy. This knowledge may define case-mix adjustment models used in benchmarking or auditing and may assist in selection of patients eligible for surgery or tailored perioperative care.
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http://dx.doi.org/10.1245/s10434-021-10734-3DOI Listing
September 2021

Prediction or causality? A scoping review of their conflation within current observational research.

Eur J Epidemiol 2021 Sep 15;36(9):889-898. Epub 2021 Aug 15.

Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.

Etiological research aims to uncover causal effects, whilst prediction research aims to forecast an outcome with the best accuracy. Causal and prediction research usually require different methods, and yet their findings may get conflated when reported and interpreted. The aim of the current study is to quantify the frequency of conflation between etiological and prediction research, to discuss common underlying mistakes and provide recommendations on how to avoid these. Observational cohort studies published in January 2018 in the top-ranked journals of six distinct medical fields (Cardiology, Clinical Epidemiology, Clinical Neurology, General and Internal Medicine, Nephrology and Surgery) were included for the current scoping review. Data on conflation was extracted through signaling questions. In total, 180 studies were included. Overall, 26% (n = 46) contained conflation between etiology and prediction. The frequency of conflation varied across medical field and journal impact factor. From the causal studies 22% was conflated, mainly due to the selection of covariates based on their ability to predict without taking the causal structure into account. Within prediction studies 38% was conflated, the most frequent reason was a causal interpretation of covariates included in a prediction model. Conflation of etiology and prediction is a common methodological error in observational medical research and more frequent in prediction studies. As this may lead to biased estimations and erroneous conclusions, researchers must be careful when designing, interpreting and disseminating their research to ensure this conflation is avoided.
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http://dx.doi.org/10.1007/s10654-021-00794-wDOI Listing
September 2021

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

BMC Emerg Med 2021 08 6;21(1):93. Epub 2021 Aug 6.

Department of Public Health, Erasmus MC University Medical Center, Na-building, room Na-2318, Wytemaweg 80, 3015, Rotterdam, CN, The Netherlands.

Background: Prehospital triage protocols typically try to select patients with Injury Severity Score (ISS) above 15 for direct transportation to a Level-1 trauma center. However, ISS does not necessarily discriminate between patients who benefit from immediate care at Level-1 trauma centers. The aim of this study was to assess which patients benefit from direct transportation to Level-1 trauma centers.

Methods: We used the American National Trauma Data Bank (NTDB), a retrospective observational cohort. All adult patients (ISS > 3) between 2015 and 2016 were included. Patients who were self-presenting or had isolated limb injury were excluded. We used logistic regression to assess the association of direct transportation to Level-1 trauma centers with in-hospital mortality adjusted for clinically relevant confounders. We used this model to define benefit as predicted probability of mortality associated with transportation to a non-Level-1 trauma center minus predicted probability associated with transportation to a Level-1 trauma center. We used a threshold of 1% as absolute benefit. Potential interaction terms with transportation to Level-1 trauma centers were included in a penalized logistic regression model to study which patients benefit.

Results: We included 388,845 trauma patients from 232 Level-1 centers and 429 Level-2/3 centers. A small beneficial effect was found for direct transportation to Level-1 trauma centers (adjusted Odds Ratio: 0.96, 95% Confidence Interval: 0.92-0.99) which disappeared when comparing Level-1 and 2 versus Level-3 trauma centers. In the risk approach, predicted benefit ranged between 0 and 1%. When allowing for interactions, 7% of the patients (n = 27,753) had more than 1% absolute benefit from direct transportation to Level-1 trauma centers. These patients had higher AIS Head and Thorax scores, lower GCS and lower SBP. A quarter of the patients with ISS > 15 were predicted to benefit from transportation to Level-1 centers (n = 26,522, 22%).

Conclusions: Benefit of transportation to a Level-1 trauma centers is quite heterogeneous across patients and the difference between Level-1 and Level-2 trauma centers is small. In particular, patients with head injury and signs of shock may benefit from care in a Level-1 trauma center. Future prehospital triage models should incorporate more complete risk profiles.
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http://dx.doi.org/10.1186/s12873-021-00487-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8344140PMC
August 2021

Imputation strategies for missing baseline neurological assessment covariates after traumatic brain injury: A CENTER-TBI study.

PLoS One 2021 6;16(8):e0253425. Epub 2021 Aug 6.

Department of Public Health, Erasmus University Medical Center, Rotterdam, Netherlands.

Statistical models for outcome prediction are central to traumatic brain injury research and critical to baseline risk adjustment. Glasgow coma score (GCS) and pupil reactivity are crucial covariates in all such models but may be measured at multiple time points between the time of injury and hospital and are subject to a variable degree of unreliability and/or missingness. Imputation of missing data may be undertaken using full multiple imputation or by simple substitution of measurements from other time points. However, it is unknown which strategy is best or which time points are more predictive. We evaluated the pseudo-R2 of logistic regression models (dichotomous survival) and proportional odds models (Glasgow Outcome Score-extended) using different imputation strategies on the The Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study dataset. Substitution strategies were easy to implement, achieved low levels of missingness (<< 10%) and could outperform multiple imputation without the need for computationally costly calculations and pooling multiple final models. While model performance was sensitive to imputation strategy, this effect was small in absolute terms and clinical relevance. A strategy of using the emergency department discharge assessments and working back in time when these were missing generally performed well. Full multiple imputation had the advantage of preserving time-dependence in the models: the pre-hospital assessments were found to be relatively unreliable predictors of survival or outcome. The predictive performance of later assessments was model-dependent. In conclusion, simple substitution strategies for imputing baseline GCS and pupil response can perform well and may be a simple alternative to full multiple imputation in many cases.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0253425PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8345855PMC
August 2021

Prediction models: stepwise development and simultaneous validation is a step back.

J Clin Epidemiol 2021 Aug 1. Epub 2021 Aug 1.

Department of Development and Regenaration, KU Leuven, Leuven, Belgium; EPI-center, KU Leuven, Belgium; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands.

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http://dx.doi.org/10.1016/j.jclinepi.2021.07.019DOI Listing
August 2021

Cost effectiveness of breast cancer screening and prevention: a systematic review with a focus on risk-adapted strategies.

Eur J Health Econ 2021 Aug 3. Epub 2021 Aug 3.

Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT-University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnoefer-Zentrum I, 6060, Hall i.T, Austria.

Objectives: Benefit and cost effectiveness of breast cancer screening are still matters of controversy. Risk-adapted strategies are proposed to improve its benefit-harm and cost-benefit relations. Our objective was to perform a systematic review on economic breast cancer models evaluating primary and secondary prevention strategies in the European health care setting, with specific focus on model results, model characteristics, and risk-adapted strategies.

Methods: Literature databases were systematically searched for economic breast cancer models evaluating the cost effectiveness of breast cancer screening and prevention strategies in the European health care context. Characteristics, methodological details and results of the identified studies are reported in evidence tables. Economic model outputs are standardized to achieve comparable cost-effectiveness ratios.

Results: Thirty-two economic evaluations of breast cancer screening and seven evaluations of primary breast cancer prevention were included. Five screening studies and none of the prevention studies considered risk-adapted strategies. Studies differed in methodologic features. Only about half of the screening studies modeled overdiagnosis-related harms, most often indirectly and without reporting their magnitude. All models predict gains in life expectancy and/or quality-adjusted life expectancy at acceptable costs. However, risk-adapted screening was shown to be more effective and efficient than conventional screening.

Conclusions: Economic models suggest that breast cancer screening and prevention are cost effective in the European setting. All screening models predict gains in life expectancy, which has not yet been confirmed by trials. European models evaluating risk-adapted screening strategies are rare, but suggest that risk-adapted screening is more effective and efficient than conventional screening.
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http://dx.doi.org/10.1007/s10198-021-01338-5DOI Listing
August 2021

Preoperative risk factors for major postoperative complications after complex gastrointestinal cancer surgery: A systematic review.

Eur J Surg Oncol 2021 Jul 28. Epub 2021 Jul 28.

Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands.

Patients undergoing complex gastrointestinal surgery are at high risk of major postoperative complications (e.g., anastomotic leakage, sepsis), classified as Clavien-Dindo (CD) ≥ IIIa. Identification of preoperative risk factors can lead to the identification of high-risk patients. These risk factors can also be used to design personalized perioperative care. This systematic review focuses on the identification of these factors. The Medline and Embase databases were searched for prospective, retrospective cohort studies and randomized controlled trials investigating the effect of risk factors on the occurrence of major postoperative complications and/or mortality after complex gastrointestinal cancer surgery. Risk of bias was assessed using the Quality in Prognostic Studies tool. The level of evidence was graded based on the number of studies reporting a significant association between risk factors and major complications. A total of 207 eligible studies were retrieved, identifying 33 risk factors for major postoperative complications and 13 preoperative laboratory results associated with postoperative complications. The present systematic review provides a comprehensive overview of preoperative risk factors associated with major postoperative complications. A wide range of risk factors are amenable to actions in perioperative care and prehabilitation programs, which may lead to improved outcomes for high-risk patients. Additionally, the knowledge of this study is important for benchmarking surgical outcomes.
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http://dx.doi.org/10.1016/j.ejso.2021.07.021DOI Listing
July 2021

External Validations of Cardiovascular Clinical Prediction Models: A Large-Scale Review of the Literature.

Circ Cardiovasc Qual Outcomes 2021 Aug 3;14(8):e007858. Epub 2021 Aug 3.

Predictive Analytics and Comparative Effectiveness (PACE) (B.S.W., J.N., J.G.P., H.G., G.G., R.B., D.v.K., J.K.P., D.M.K.), Tufts Medical Center, Boston, MA.

Background: There are many clinical prediction models (CPMs) available to inform treatment decisions for patients with cardiovascular disease. However, the extent to which they have been externally tested, and how well they generally perform has not been broadly evaluated.

Methods: A SCOPUS citation search was run on March 22, 2017 to identify external validations of cardiovascular CPMs in the Tufts Predictive Analytics and Comparative Effectiveness CPM Registry. We assessed the extent of external validation, performance heterogeneity across databases, and explored factors associated with model performance, including a global assessment of the clinical relatedness between the derivation and validation data.

Results: We identified 2030 external validations of 1382 CPMs. Eight hundred seven (58%) of the CPMs in the Registry have never been externally validated. On average, there were 1.5 validations per CPM (range, 0-94). The median external validation area under the receiver operating characteristic curve was 0.73 (25th-75th percentile [interquartile range (IQR)], 0.66-0.79), representing a median percent decrease in discrimination of -11.1% (IQR, -32.4% to +2.7%) compared with performance on derivation data. 81% (n=1333) of validations reporting area under the receiver operating characteristic curve showed discrimination below that reported in the derivation dataset. 53% (n=983) of the validations report some measure of CPM calibration. For CPMs evaluated more than once, there was typically a large range of performance. Of 1702 validations classified by relatedness, the percent change in discrimination was -3.7% (IQR, -13.2 to 3.1) for closely related validations (n=123), -9.0 (IQR, -27.6 to 3.9) for related validations (n=862), and -17.2% (IQR, -42.3 to 0) for distantly related validations (n=717; <0.001).

Conclusions: Many published cardiovascular CPMs have never been externally validated, and for those that have, apparent performance during development is often overly optimistic. A single external validation appears insufficient to broadly understand the performance heterogeneity across different settings.
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http://dx.doi.org/10.1161/CIRCOUTCOMES.121.007858DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8366535PMC
August 2021

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

Qual Life Res 2021 Jul 30. Epub 2021 Jul 30.

Department of Public Health, Center for Medical Decision Making, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands.

Background: Traumatic brain injury (TBI) is a leading cause of impairments affecting Health-Related Quality of Life (HRQoL). We aimed to identify predictors of and develop prognostic models for HRQoL following TBI.

Methods: We used data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Core study, including patients with a clinical diagnosis of TBI and an indication for computed tomography presenting within 24 h of injury. The primary outcome measures were the SF-36v2 physical (PCS) and mental (MCS) health component summary scores and the Quality of Life after Traumatic Brain Injury (QOLIBRI) total score 6 months post injury. We considered 16 patient and injury characteristics in linear regression analyses. Model performance was expressed as proportion of variance explained (R) and corrected for optimism with bootstrap procedures.

Results: 2666 Adult patients completed the HRQoL questionnaires. Most were mild TBI patients (74%). The strongest predictors for PCS were Glasgow Coma Scale, major extracranial injury, and pre-injury health status, while MCS and QOLIBRI were mainly related to pre-injury mental health problems, level of education, and type of employment. R of the full models was 19% for PCS, 9% for MCS, and 13% for the QOLIBRI. In a subset of patients following predominantly mild TBI (N = 436), including 2 week HRQoL assessment improved model performance substantially (R PCS 15% to 37%, MCS 12% to 36%, and QOLIBRI 10% to 48%).

Conclusion: Medical and injury-related characteristics are of greatest importance for the prediction of PCS, whereas patient-related characteristics are more important for the prediction of MCS and the QOLIBRI following TBI.
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http://dx.doi.org/10.1007/s11136-021-02932-zDOI Listing
July 2021

Performance of the Hull Salford Cambridge Decision Rule (HSC DR) for early discharge of patients with findings on CT scan of the brain: a CENTER-TBI validation study.

Emerg Med J 2021 Jul 27. Epub 2021 Jul 27.

Centre for Urgent and Emergency Care Research (CURE), School of Health and Related Research (ScHARR). Emergency Department, Salford Royal Hospital, University of Sheffield and Salford Royal Hospital, Sheffield, UK.

Background: There is international variation in hospital admission practices for patients with mild traumatic brain injury (TBI) and injuries on CT scan. Only a small proportion of patients require neurosurgical intervention, while many guidelines recommend routine admission of all patients. We aim to validate the Hull Salford Cambridge Decision Rule (HSC DR) and the Brain Injury Guidelines (BIG) criteria to select low-risk patients for discharge from the emergency department.

Method: A cohort from 18 countries of Glasgow Coma Scale 13-15 patients with injuries on CT imaging was identified from the multicentre Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) Study (conducted from 2014 to 2017) for secondary analysis. A composite outcome measure encompassing need for ongoing hospital admission was used, including seizure activity, death, intubation, neurosurgical intervention and neurological deterioration. We assessed the performance of our previously derived prognostic model, the HSC DR and the BIG criteria at predicting deterioration in this validation cohort.

Results: Among 1047 patients meeting the inclusion criteria, 267 (26%) deteriorated. Our prognostic model achieved a C-statistic of 0.81 (95% CI: 0.78 to 0.84). The HSC DR achieved a sensitivity of 100% (95% CI: 97% to 100%) and specificity of only 4.7% (95% CI: 3.3% to 6.5%) for deterioration. Using the BIG criteria for discharge from the ED achieved a higher specificity (13.3%, 95% CI: 10.9% to 16.1%) and lower sensitivity (94.6%, 95% CI: 90.5% to 97%), with 12/105 patients recommended for discharge subsequently deteriorating, compared with 0/34 with the HSC DR.

Conclusion: Our decision rule would have allowed 3.5% of patients to be discharged, none of whom would have deteriorated. Use of the BIG criteria may select patients for discharge who have too high a risk of subsequent deterioration to be used clinically. Further validation and implementation studies are required to support use in clinical practice.
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http://dx.doi.org/10.1136/emermed-2020-210975DOI Listing
July 2021

Fluid balance and outcome in critically ill patients with traumatic brain injury (CENTER-TBI and OzENTER-TBI): a prospective, multicentre, comparative effectiveness study.

Lancet Neurol 2021 08;20(8):627-638

Department of Intensive Care Adults, Erasmus MC - University Medical Center, Rotterdam, Netherlands. Electronic address:

Background: Fluid therapy-the administration of fluids to maintain adequate organ tissue perfusion and oxygenation-is essential in patients admitted to the intensive care unit (ICU) with traumatic brain injury. We aimed to quantify the variability in fluid management policies in patients with traumatic brain injury and to study the effect of this variability on patients' outcomes.

Methods: We did a prospective, multicentre, comparative effectiveness study of two observational cohorts: CENTER-TBI in Europe and OzENTER-TBI in Australia. Patients from 55 hospitals in 18 countries, aged 16 years or older with traumatic brain injury requiring a head CT, and admitted to the ICU were included in this analysis. We extracted data on demographics, injury, and clinical and treatment characteristics, and calculated the mean daily fluid balance (difference between fluid input and loss) and mean daily fluid input during ICU stay per patient. We analysed the association of fluid balance and input with ICU mortality and functional outcome at 6 months, measured by the Glasgow Outcome Scale Extended (GOSE). Patient-level analyses relied on adjustment for key characteristics per patient, whereas centre-level analyses used the centre as the instrumental variable.

Findings: 2125 patients enrolled in CENTER-TBI and OzENTER-TBI between Dec 19, 2014, and Dec 17, 2017, were eligible for inclusion in this analysis. The median age was 50 years (IQR 31 to 66) and 1566 (74%) of patients were male. The median of the mean daily fluid input ranged from 1·48 L (IQR 1·12 to 2·09) to 4·23 L (3·78 to 4·94) across centres. The median of the mean daily fluid balance ranged from -0·85 L (IQR -1·51 to -0·49) to 1·13 L (0·99 to 1·37) across centres. In patient-level analyses, a mean positive daily fluid balance was associated with higher ICU mortality (odds ratio [OR] 1·10 [95% CI 1·07 to 1·12] per 0·1 L increase) and worse functional outcome (1·04 [1·02 to 1·05] per 0·1 L increase); higher mean daily fluid input was also associated with higher ICU mortality (1·05 [1·03 to 1·06] per 0·1 L increase) and worse functional outcome (1·04 [1·03 to 1·04] per 1-point decrease of the GOSE per 0·1 L increase). Centre-level analyses showed similar associations of higher fluid balance with ICU mortality (OR 1·17 [95% CI 1·05 to 1·29]) and worse functional outcome (1·07 [1·02 to 1·13]), but higher fluid input was not associated with ICU mortality (OR 0·95 [0·90 to 1·00]) or worse functional outcome (1·01 [0·98 to 1·03]).

Interpretation: In critically ill patients with traumatic brain injury, there is significant variability in fluid management, with more positive fluid balances being associated with worse outcomes. These results, when added to previous evidence, suggest that aiming for neutral fluid balances, indicating a state of normovolaemia, contributes to improved outcome.

Funding: European Commission 7th Framework program and the Australian Health and Medical Research Council.
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http://dx.doi.org/10.1016/S1474-4422(21)00162-9DOI Listing
August 2021

Improving triage for children with comorbidity using the ED-PEWS: an observational study.

Arch Dis Child 2021 Jul 21. Epub 2021 Jul 21.

Department of General Paediatrics, Erasmus MC-Sophia Childrens Hospital, Rotterdam, Netherlands.

Objective: To assess the value of the Emergency Department-Pediatric Early Warning Score (ED-PEWS) for triage of children with comorbidity.

Design: Secondary analysis of a prospective cohort.

Setting And Patients: 53 829 consecutive ED visits of children <16 years in three European hospitals (Netherlands, UK and Austria) participating in the TrIAGE (Triage Improvements Across General Emergency departments) project in different periods (2012-2015).

Intervention: ED-PEWS, a score consisting of age and six physiological parameters.

Main Outcome Measure: A three-category reference standard as proxy for true patient urgency. We assessed discrimination and calibration of the ED-PEWS for children with comorbidity (complex and non-complex) and without comorbidity. In addition, we evaluated the value of adding the ED-PEWS to the routinely used Manchester Triage System (MTS).

Results: 5053 (9%) children had underlying non-complex morbidity and 5537 (10%) had complex comorbidity. The c-statistic for identification of high-urgency patients was 0.86 (95% prediction interval 0.84-0.88) for children without comorbidity, 0.87 (0.82-0.92) for non-complex and 0.86 (0.84-0.88) for complex comorbidity. For high and intermediate urgency, the c-statistic was 0.63 (0.62-0.63), 0.63 (0.61-0.65) and 0.63 (0.55-0.73) respectively. Sensitivity was slightly higher for children with comorbidity (0.73-0.75 vs 0.70) at the cost of a lower specificity (0.86-0.87 vs 0.92). Calibration was largely similar. Adding the ED-PEWS to the MTS for children with comorbidity improved performance, except in the setting with few high-urgency patients.

Conclusions: The ED-PEWS has a similar performance in children with and without comorbidity. Adding the ED-PEWS to the MTS for children with comorbidity improves triage, except in the setting with few high-urgency patients.
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http://dx.doi.org/10.1136/archdischild-2021-322068DOI Listing
July 2021

Diagnostic yield of bacteriological tests and predictors of severe outcome in adult patients with COVID-19 presenting to the emergency department.

Emerg Med J 2021 Sep 21;38(9):685-691. Epub 2021 Jul 21.

Internal Medicine, Haga Teaching Hospital, Den Haag, The Netherlands.

Background: Guidelines recommend maximal efforts to obtain blood and sputum cultures in patients with COVID-19, as bacterial coinfection is associated with worse outcomes. The aim of this study was to evaluate the yield of bacteriological tests, including blood and sputum cultures, and the association of multiple biomarkers and the Pneumonia Severity Index (PSI) with clinical and microbiological outcomes in patients with COVID-19 presenting to the emergency department (ED).

Methods: This is a substudy of a large observational cohort study (PredictED study). The PredictED included adult patients from whom a blood culture was drawn at the ED of Haga Teaching Hospital, The Netherlands. For this substudy, all patients who tested positive for SARS-CoV-2 by PCR in March and April 2020 were included. The primary outcome was the incidence of bacterial coinfection. We used logistic regression analysis for associations of procalcitonin, C reactive protein (CRP), ferritin, lymphocyte count and PSI score with a severe disease course, defined as intensive care unit admission and/or 30-day mortality. The area under the receiver operating characteristics curve (AUC) quantified the discriminatory performance.

Results: We included 142 SARS-CoV-2 positive patients. On presentation, the median duration of symptoms was 8 days. 41 (29%) patients had a severe disease course and 24 (17%) died within 30 days. The incidence of bacterial coinfection was 2/142 (1.4%). None of the blood cultures showed pathogen growth while 6.3% was contaminated. The AUCs for predicting severe disease were 0.76 (95% CI 0.68 to 0.84), 0.70 (0.61 to 0.79), 0.62 (0.51 to 0.74), 0.62 (0.51 to 0.72) and 0.72 (0.63 to 0.81) for procalcitonin, CRP, ferritin, lymphocyte count and PSI score, respectively.

Conclusion: Blood cultures appear to have limited value while procalcitonin and the PSI appear to be promising tools in helping physicians identify patients at risk for severe disease course in COVID-19 at presentation to the ED.
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http://dx.doi.org/10.1136/emermed-2020-211027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8300553PMC
September 2021

A clinical prediction model to identify children at risk for revisits with serious illness to the emergency department: A prospective multicentre observational study.

PLoS One 2021 15;16(7):e0254366. Epub 2021 Jul 15.

Department of General Paediatrics, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands.

Background: To develop a clinical prediction model to identify children at risk for revisits with serious illness to the emergency department.

Methods And Findings: A secondary analysis of a prospective multicentre observational study in five European EDs (the TRIAGE study), including consecutive children aged <16 years who were discharged following their initial ED visit ('index' visit), in 2012-2015. Standardised data on patient characteristics, Manchester Triage System urgency classification, vital signs, clinical interventions and procedures were collected. The outcome measure was serious illness defined as hospital admission or PICU admission or death in ED after an unplanned revisit within 7 days of the index visit. Prediction models were developed using multivariable logistic regression using characteristics of the index visit to predict the likelihood of a revisit with a serious illness. The clinical model included day and time of presentation, season, age, gender, presenting problem, triage urgency, and vital signs. An extended model added laboratory investigations, imaging, and intravenous medications. Cross validation between the five sites was performed, and discrimination and calibration were assessed using random effects models. A digital calculator was constructed for clinical implementation. 7,891 children out of 98,561 children had a revisit to the ED (8.0%), of whom 1,026 children (1.0%) returned to the ED with a serious illness. Rates of revisits with serious illness varied between the hospitals (range 0.7-2.2%). The clinical model had a summary Area under the operating curve (AUC) of 0.70 (95% CI 0.65-0.74) and summary calibration slope of 0.83 (95% CI 0.67-0.99). 4,433 children (5%) had a risk of > = 3%, which was useful for ruling in a revisit with serious illness, with positive likelihood ratio 4.41 (95% CI 3.87-5.01) and specificity 0.96 (95% CI 0.95-0.96). 37,546 (39%) had a risk <0.5%, which was useful for ruling out a revisit with serious illness (negative likelihood ratio 0.30 (95% CI 0.25-0.35), sensitivity 0.88 (95% CI 0.86-0.90)). The extended model had an improved summary AUC of 0.71 (95% CI 0.68-0.75) and summary calibration slope of 0.84 (95% CI 0.71-0.97). As study limitations, variables on ethnicity and social deprivation could not be included, and only return visits to the original hospital and not to those of surrounding hospitals were recorded.

Conclusion: We developed a prediction model and a digital calculator which can aid physicians identifying those children at highest and lowest risks for developing a serious illness after initial discharge from the ED, allowing for more targeted safety netting advice and follow-up.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0254366PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8281990PMC
July 2021

Stratification for Identification of Prognostic Categories In the Acute RESpiratory Distress Syndrome (SPIRES) Score.

Crit Care Med 2021 10;49(10):e920-e930

Department of Respiratory Care, Massachusetts General Hospital, Boston, MA.

Objectives: To develop a scoring model for stratifying patients with acute respiratory distress syndrome into risk categories (Stratification for identification of Prognostic categories In the acute RESpiratory distress syndrome score) for early prediction of death in the ICU, independent of the underlying disease and cause of death.

Design: A development and validation study using clinical data from four prospective, multicenter, observational cohorts.

Setting: A network of multidisciplinary ICUs.

Patients: One-thousand three-hundred one patients with moderate-to-severe acute respiratory distress syndrome managed with lung-protective ventilation.

Interventions: None.

Measurements And Main Results: The study followed Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines for prediction models. We performed logistic regression analysis, bootstrapping, and internal-external validation of prediction models with variables collected within 24 hours of acute respiratory distress syndrome diagnosis in 1,000 patients for model development. Primary outcome was ICU death. The Stratification for identification of Prognostic categories In the acute RESpiratory distress syndrome score was based on patient's age, number of extrapulmonary organ failures, values of end-inspiratory plateau pressure, and ratio of Pao2 to Fio2 assessed at 24 hours of acute respiratory distress syndrome diagnosis. The pooled area under the receiver operating characteristic curve across internal-external validations was 0.860 (95% CI, 0.831-0.890). External validation in a new cohort of 301 acute respiratory distress syndrome patients confirmed the accuracy and robustness of the scoring model (area under the receiver operating characteristic curve = 0.870; 95% CI, 0.829-0.911). The Stratification for identification of Prognostic categories In the acute RESpiratory distress syndrome score stratified patients in three distinct prognostic classes and achieved better prediction of ICU death than ratio of Pao2 to Fio2 at acute respiratory distress syndrome onset or at 24 hours, Acute Physiology and Chronic Health Evaluation II score, or Sequential Organ Failure Assessment scale.

Conclusions: The Stratification for identification of Prognostic categories In the acute RESpiratory distress syndrome score represents a novel strategy for early stratification of acute respiratory distress syndrome patients into prognostic categories and for selecting patients for therapeutic trials.
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http://dx.doi.org/10.1097/CCM.0000000000005142DOI Listing
October 2021

Prediction of Early Recurrence After Surgery for Liver Tumor (ERASL): An International Validation of the ERASL Risk Models.

Ann Surg Oncol 2021 Jul 7. Epub 2021 Jul 7.

Erasmus MC Transplant Institute Department of Surgery, Division of HPB & Transplant Surgery, University Medical Centre, Rotterdam, The Netherlands.

Background: This study aimed to assess the performance of the pre- and postoperative early recurrence after surgery for liver tumor (ERASL) models at external validation. Prediction of early hepatocellular carcinoma (HCC) recurrence after resection is important for individualized surgical management. Recently, the preoperative (ERASL-pre) and postoperative (ERASL-post) risk models were proposed based on patients from Hong Kong. These models showed good performance although they have not been validated to date by an independent research group.

Methods: This international cohort study included 279 patients from the Netherlands and 392 patients from Japan. The patients underwent first-time resection and showed a diagnosis of HCC on pathology. Performance was assessed according to discrimination (concordance [C] statistic) and calibration (correspondence between observed and predicted risk) with recalibration in a Weibull model.

Results: The discriminatory power of both models was lower in the Netherlands than in Japan (C statistic, 0.57 [95% confidence interval {CI} 0.52-0.62] vs 0.69 [95% CI 0.65-0.73] for the ERASL-pre model and 0.62 [95% CI 0.57-0.67] vs 0.70 [95% CI 0.66-0.74] for the ERASL-post model), whereas their prognostic profiles were similar. The predictions of the ERASL models were systematically too optimistic for both cohorts. Recalibrated ERASL models improved local applicability for both cohorts.

Conclusions: The discrimination of ERASL models was poorer for the Western patients than for the Japanese patients, who showed good performance. Recalibration of the models was performed, which improved the accuracy of predictions. However, in general, a model that explains the East-West difference or one tailored to Western patients still needs to be developed.
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http://dx.doi.org/10.1245/s10434-021-10235-3DOI Listing
July 2021

A Prediction Model for Severe Complications after Elective Colorectal Cancer Surgery in Patients of 70 Years and Older.

Cancers (Basel) 2021 Jun 22;13(13). Epub 2021 Jun 22.

Department of Medical Oncology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands.

Introduction Older patients have an increased risk of morbidity and mortality after colorectal cancer (CRC) surgery. Existing CRC surgical prediction models have not incorporated geriatric predictors, limiting applicability for preoperative decision-making. The objective was to develop and internally validate a predictive model based on preoperative predictors, including geriatric characteristics, for severe postoperative complications after elective surgery for stage I-III CRC in patients ≥70 years.

Patients And Methods: A prospectively collected database contained 1088 consecutive patients from five Dutch hospitals (2014-2017) with 171 severe complications (16%). The least absolute shrinkage and selection operator (LASSO) method was used for predictor selection and prediction model building. Internal validation was done using bootstrapping.

Results: A geriatric model that included gender, previous DVT or pulmonary embolism, COPD/asthma/emphysema, rectal cancer, the use of a mobility aid, ADL assistance, previous delirium and polypharmacy showed satisfactory discrimination with an AUC of 0.69 (95% CI 0.73-0.64); the AUC for the optimism corrected model was 0.65. Based on these predictors, the eight-item colorectal geriatric model (GerCRC) was developed.

Conclusion: The GerCRC is the first prediction model specifically developed for older patients expected to undergo CRC surgery. Combining tumour- and patient-specific predictors, including geriatric predictors, improves outcome prediction in the heterogeneous older population.
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http://dx.doi.org/10.3390/cancers13133110DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8268502PMC
June 2021

Translation and Linguistic Validation of Outcome Instruments for Traumatic Brain Injury Research and Clinical Practice: A Step-by-Step Approach within the Observational CENTER-TBI Study.

J Clin Med 2021 Jun 28;10(13). Epub 2021 Jun 28.

Institute of Medical Psychology and Medical Sociology, University Medical Center Göttingen, Waldweg 37A, 37073 Göttingen, Germany.

Assessing outcomes in multinational studies on traumatic brain injury (TBI) poses major challenges and requires relevant instruments in languages other than English. Of the 19 outcome instruments selected for use in the observational Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) study, 17 measures lacked translations in at least one target language. To fill this gap, we aimed to develop well-translated linguistically and psychometrically validated instruments. We performed translations and linguistic validations of patient-reported measures (PROMs), clinician-reported (ClinRO), and performance-based (PerfO) outcome instruments, using forward and backward translations, reconciliations, cognitive debriefings with up to 10 participants, iterative revisions, and international harmonization with input from over 150 international collaborators. In total, 237 translations and 211 linguistic validations were carried out in up to 20 languages. Translations were evaluated at the linguistic and cultural level by coding changes when the original versions are compared with subsequent translation steps, using the output of cognitive debriefings, and using comprehension rates. The average comprehension rate per instrument varied from 88% to 98%, indicating a good quality of the translations. These outcome instruments provide a solid basis for future TBI research and clinical practice and allow the aggregation and analysis of data across different countries and languages.
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http://dx.doi.org/10.3390/jcm10132863DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8269004PMC
June 2021

ASO Visual Abstract: Prediction of Early Hepatocellular Carcinoma Recurrence After Resection-An International Validation of the ERASL Risk Models.

Ann Surg Oncol 2021 Jun 30. Epub 2021 Jun 30.

Erasmus MC Transplant Institute Department of Surgery, Division of HPB & Transplant Surgery, University Medical Centre Rotterdam, Rotterdam, The Netherlands.

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http://dx.doi.org/10.1245/s10434-021-10132-9DOI Listing
June 2021

Large-scale validation of the prediction model risk of bias assessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination.

J Clin Epidemiol 2021 Jun 24;138:32-39. Epub 2021 Jun 24.

Predictive Analytics and Comparative Effectiveness Center, Tufts Medical Center, Boston, MA, USA. Electronic address:

Objective: To assess whether the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and a shorter version of this tool can identify clinical prediction models (CPMs) that perform poorly at external validation.

Study Design And Setting: We evaluated risk of bias (ROB) on 102 CPMs from the Tufts CPM Registry, comparing PROBAST to a short form consisting of six PROBAST items anticipated to best identify high ROB. We then applied the short form to all CPMs in the Registry with at least 1 validation (n=556) and assessed the change in discrimination (dAUC) in external validation cohorts (n=1,147).

Results: PROBAST classified 98/102 CPMS as high ROB. The short form identified 96 of these 98 as high ROB (98% sensitivity), with perfect specificity. In the full CPM registry, 527 of 556 CPMs (95%) were classified as high ROB, 20 (3.6%) low ROB, and 9 (1.6%) unclear ROB. Only one model with unclear ROB was reclassified to high ROB after full PROBAST assessment of all low and unclear ROB models. Median change in discrimination was significantly smaller in low ROB models (dAUC -0.9%, IQR -6.2-4.2%) compared to high ROB models (dAUC -11.7%, IQR -33.3-2.6%; P<0.001).

Conclusion: High ROB is pervasive among published CPMs. It is associated with poor discriminative performance at validation, supporting the application of PROBAST or a shorter version in CPM reviews.
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http://dx.doi.org/10.1016/j.jclinepi.2021.06.017DOI Listing
June 2021

Prediction of Acute Respiratory Failure Requiring Advanced Respiratory Support in Advance of Interventions and Treatment: A Multivariable Prediction Model From Electronic Medical Record Data.

Crit Care Explor 2021 May 12;3(5):e0402. Epub 2021 May 12.

Department of Biomedical Informatics, Emory University, Atlanta, GA.

Background: Acute respiratory failure occurs frequently in hospitalized patients and often begins outside the ICU, associated with increased length of stay, cost, and mortality. Delays in decompensation recognition are associated with worse outcomes.

Objectives: The objective of this study is to predict acute respiratory failure requiring any advanced respiratory support (including noninvasive ventilation). With the advent of the coronavirus disease pandemic, concern regarding acute respiratory failure has increased.

Derivation Cohort: All admission encounters from January 2014 to June 2017 from three hospitals in the Emory Healthcare network (82,699).

Validation Cohort: External validation cohort: all admission encounters from January 2014 to June 2017 from a fourth hospital in the Emory Healthcare network (40,143). Temporal validation cohort: all admission encounters from February to April 2020 from four hospitals in the Emory Healthcare network coronavirus disease tested (2,564) and coronavirus disease positive (389).

Prediction Model: All admission encounters had vital signs, laboratory, and demographic data extracted. Exclusion criteria included invasive mechanical ventilation started within the operating room or advanced respiratory support within the first 8 hours of admission. Encounters were discretized into hour intervals from 8 hours after admission to discharge or advanced respiratory support initiation and binary labeled for advanced respiratory support. Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment, our eXtreme Gradient Boosting-based algorithm, was compared against Modified Early Warning Score.

Results: Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment had significantly better discrimination than Modified Early Warning Score (area under the receiver operating characteristic curve 0.85 vs 0.57 [test], 0.84 vs 0.61 [external validation]). Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment maintained a positive predictive value (0.31-0.21) similar to that of Modified Early Warning Score greater than 4 (0.29-0.25) while identifying 6.62 (validation) to 9.58 (test) times more true positives. Furthermore, Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment performed more effectively in temporal validation (area under the receiver operating characteristic curve 0.86 [coronavirus disease tested], 0.93 [coronavirus disease positive]), while achieving identifying 4.25-4.51× more true positives.

Conclusions: Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment is more effective than Modified Early Warning Score in predicting respiratory failure requiring advanced respiratory support at external validation and in coronavirus disease 2019 patients. Silent prospective validation necessary before local deployment.
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http://dx.doi.org/10.1097/CCE.0000000000000402DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8162520PMC
May 2021
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