The relation between prostate-specific antigen (PSA) and other relevant prebiopsy information is often combined in a risk calculator (RC). If the setting for RC use differs from that in which it... Show moreThe relation between prostate-specific antigen (PSA) and other relevant prebiopsy information is often combined in a risk calculator (RC). If the setting for RC use differs from that in which it was developed, there is a risk of making clinical decisions based on incorrect estimates of the absolute risk. The ERSPC-MRI RC predicts clinically significant prostate cancer (csPC; Gleason >= 3 + 4) on targeted and systematic biopsy using information on PSA, digital rectal examination, prostate volume, age, previous negative biopsy, and Prostate Imaging-Recording and Data System score. This calculator was developed on a clinical cohort of 961 men (2012-2017) with a csPC prevalence of 36%. Discrimination was good (area under the receiver operating characteristic curve 0.84). With the increasing use of multiparametric magnetic resonance imaging, we foresee that this RC will also be used for men with a lower a priori likelihood of PC. We investigated the effect of such a scenario on individual risk predictions. A small update of the intercept for the calculator can restore the accuracy to support decision-making with locally valid risk estimates.Patient summary: Decisions on who to refer for a prostate biopsy with its risk of sepsis and overdiagnosis require more than a prostate-specific antigen test. A prediction tool may take other relevant prebiopsy information into account, but may need to be updated to contemporary center-specific settings to provide accurate estimates of the risk of having prostate cancer. (C) 2019 Published by Elsevier B.V. on behalf of European Association of Urology. Show less
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... Show moreOBJECTIVES: 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 Pao(2) to Fio(2) 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 Pao(2) to Fio(2) 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. Show less
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... Show moreObjective: To assess whether the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and a shorter version of this tool can identify clinical prediction models (CPMs) that perform poorly at external validation. Study Design and Setting: We evaluated risk of bias (ROB) on 102 CPMs from the Tufts CPM Registry, comparing PROBAST to a short form consisting of six PROBAST items anticipated to best identify high ROB. We then applied the short form to all CPMs in the Registry with at least 1 validation (n = 556) and assessed the change in discrimination (dAUC) in external validation cohorts (n = 1,147). Results: PROBAST classified 98/102 CPMS as high ROB. The short form identified 96 of these 98 as high ROB (98% sensitivity), with perfect specificity. In the full CPM registry, 527 of 556 CPMs (95%) were classified as high ROB, 20 (3.6%) low ROB, and 9 (1.6%) unclear ROB. Only one model with unclear ROB was reclassified to high ROB after full PROBAST assessment of all low and unclear ROB models. Median change in discrimination was significantly smaller in low ROB models (dAUC -0.9%, IQR -6.2-4.2%) compared to high ROB models (dAUC -11.7%, IQR -33.3-2.6%; P < 0.001). Conclusion: High ROB is pervasive among published CPMs. It is associated with poor discriminative performance at validation, supporting the application of PROBAST or a shorter version in CPM reviews. (c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http:// creativecommons.org/ licenses/ by- nc- nd/ 4.0/ ) Show less
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... Show moreBACKGROUND 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. (J Am Coll Cardiol 2021;78:1227-1238) (c) 2021 by the American College of Cardiology Foundation. Show less
Faisal, M.; Mohammed, M.A.; Richardson, D.; Steyerberg, E.W.; Fiori, M.; Beatson, K. 2021
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... Show moreBackground 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. Show less
Alblas, M.; Peterse, E.F.P.; M.M. du; Zauber, A.G.; Steyerberg, E.W.; Leeuwen, N. van; Lansdorp-Vogelaar, I. 2021
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... Show moreBackground 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. Show less
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... Show moreObjective 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(2) 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. Show less
BACKGROUND AND PURPOSE: Benefit of early endovascular treatment (EVT) for ischemic stroke varies considerably among patients. The MR PREDICTS decision tool, derived from MR CLEAN (Multicenter... Show moreBACKGROUND AND PURPOSE: Benefit of early endovascular treatment (EVT) for ischemic stroke varies considerably among patients. The MR PREDICTS decision tool, derived from MR CLEAN (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands), predicts outcome and treatment benefit based on baseline characteristics. Our aim was to externally validate and update MR PREDICTS with data from international trials and daily clinical practice.METHODS: We used individual patient data from 6 randomized controlled trials within the HERMES (Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials) collaboration to validate the original model. Then, we updated the model and performed a second validation with data from the observational MR CLEAN Registry. Primary outcome was functional independence (defined as modified Rankin Scale score 0-2) 3 months after stroke. Treatment benefit was defined as the difference between the probability of functional independence with and without EVT. Discriminative performance was evaluated using a concordance (C) statistic.RESULTS: We included 1242 patients from HERMES (633 assigned to EVT, 609 assigned to control) and 3156 patients from the MR CLEAN Registry (all of whom underwent EVT within 6.5 hours). The C-statistic for functional independence was 0.74 (95% CI, 0.72-0.77) in HERMES and, after model updating, 0.80 (0.78-0.82) in the Registry. Median predicted treatment benefit of routinely treated patients (Registry) was 10.3% (interquartile range, 5.8%-14.4%). Patients with low (<1%) predicted treatment benefit (n=135/3156 [4.3%]) had low rates of functional independence, irrespective of reperfusion status, suggesting potential absence of treatment benefit. The updated model was made available online for clinicians and researchers at .CONCLUSIONS: Because of the substantial treatment effect and small potential harm of EVT, most patients arriving within 6 hours at an endovascular-capable center should be treated regardless of their clinical characteristics. MR PREDICTS can be used to support clinical judgement when there is uncertainty about the treatment indication, when resources are limited, or before a patient is to be transferred to an endovascular-capable center. Show less
Objective: The aim of this manuscript is to compare characteristics, management, and outcomes of patients with severe Traumatic Brain Injury (TBI) between Australia, the United Kingdom (UK) and... Show moreObjective: The aim of this manuscript is to compare characteristics, management, and outcomes of patients with severe Traumatic Brain Injury (TBI) between Australia, the United Kingdom (UK) and Europe. Methods: We enrolled patients with severe TBI in Victoria, Australia (OzENTER-TBI), in the UK and Europe (CENTER-TBI) from 2015 to 2017. Main outcome measures were mortality and unfavourable outcome (Glasgow Outcome Scale Extended < 5) 6 months after injury. Expected outcomes were compared according to the IMPACT-CT prognostic model, with observed to expected (O/E) ratios and 95% confidence intervals. Results: We included 107 patients from Australia, 171 from UK, and 596 from Europe. Compared to the UK and Europe, patients in Australia were younger (median 32 vs 44 vs 44 years), a larger proportion had secondary brain insults including hypotension (30% vs 17% vs 21%) and a larger proportion received ICP monitoring (75% vs 74% vs 58%). Hospital length of stay was shorter in Australia than in the UK (median: 17 vs 23 vs 16 days), and a higher proportion of patients were discharged to a rehabilitation unit in Australia than in the UK and Europe (64% vs 26% vs 28%). Mortality overall was lower than expected (27% vs 35%, O/E ratio 0.77 [95% CI: 0.64 - 0.87]. O/E ratios were comparable between regions for mortality in Australia 0.86 [95% CI: 0.49-1.23] vs UK 0.82 [0.51-1.15] vs Europe 0.76 [0.60-0.87]). Unfavourable outcome rates overall were in line with historic expectations (O/E ratio 1.32 [0.96-1.68] vs 1.13 [0.841.42] vs 0.96 [0.85-1.09]). Conclusions: There are major differences in case-mix between Australia, UK, and Europe; Australian patients are younger and have a higher rate of secondary brain insults. Despite some differences in management and discharge policies, mortality was less than expected overall, and did not differ between regions. Functional outcomes were similar between regions, but worse than expected, emphasizing the need to improve treatment for patients with severe TBI. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ) Show less
Lecky, F.E.; Otesile, O.; Marincowitz, C.; Majdan, M.; Nieboer, D.; Lingsma, H.F.; ... ; Moyer, C. 2021
Author summary Why was this study done? Traumatic brain injury (TBI) poses a huge global disease burden, considered to mainly result from high-energy transfer mechanisms such as road traffic... Show moreAuthor summary Why was this study done? Traumatic brain injury (TBI) poses a huge global disease burden, considered to mainly result from high-energy transfer mechanisms such as road traffic collisions, sports, falls from a height, and interpersonal violence.People injured through low-energy transfer (ground- or low-level falls) are considered less likely to sustain significant TBI, so can be given lower priority for acute specialist care within emergency medical services (triage decisions).Recent multinational studies challenge these assumptions by identifying falls as an important TBI causal mechanism-but these studies seldom describe fall height.The lack of clarity concerning the low-energy TBI disease burden hampers effective prevention and clinical management. What did the researchers do and find? We studied 21,681 patients with TBI presenting to 56 hospital emergency departments across Europe and Israel using an efficient registry methodology enabling a real-world approach.We found that the 40% of patients with TBI who were injured through low-energy falls were significantly older, more likely to be female, and more likely to be taking pre-injury drugs that prevent blood clotting than patients with TBI sustained through high-energy transfer.Despite similar rates of significant injury on the CT brain scan and of dying in hospital, patients injured through low-energy falls were half as likely to receive critical care or emergency intervention compared to those injured by high-energy transfer. What do these findings mean? Low-energy falls contribute to a significant portion of the TBI disease burden, which will increase as the global population ages.In older people, the assumption that energy transfer predicts brain injury severity and threat to life appears to lack validity.Factors beyond energy transfer level may be more relevant to prehospital and emergency department TBI triage in older people. The appropriateness of providing less intensive acute hospital care after low-energy TBI requires further study.Reduction of TBI disease burden requires specific prevention and therapy initiatives targeted at low-energy TBI.BackgroundTraumatic 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 findingsWe 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. ConclusionsWe 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. Show less
Ramspek, C.L.; Steyerberg, E.W.; Riley, R.D.; Rosendaal, F.R.; Dekkers, O.M.; Dekker, F.W.; Diepen, M. van 2021
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,... Show moreEtiological 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. Show less
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... Show moreStatistical 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-R-2 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. Show less
Sewalt, C.A.; Gravesteijn, B.Y.; Nieboer, D.; Steyerberg, E.W.; Hartog, D. den; Klaveren, D. van 2021
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... Show moreBackground 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. Show less
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... Show moreBackground 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 1middot48 L (IQR 1middot12 to 2middot09) to 4middot23 L (3middot78 to 4middot94) across centres. The median of the mean daily fluid balance ranged from -0middot85 L (IQR -1middot51 to -0middot49) to 1middot13 L (0middot99 to 1middot37) across centres. In patient-level analyses, a mean positive daily fluid balance was associated with higher ICU mortality (odds ratio [OR] 1middot10 [95% CI 1middot07 to 1middot12] per 0middot1 L increase) and worse functional outcome (1middot04 [1middot02 to 1middot05] per 0middot1 L increase); higher mean daily fluid input was also associated with higher ICU mortality (1middot05 [1middot03 to 1middot06] per 0middot1 L increase) and worse functional outcome (1middot04 [1middot03 to 1middot04] per 1-point decrease of the GOSE per 0middot1 L increase). Centre-level analyses showed similar associations of higher fluid balance with ICU mortality (OR 1middot17 [95% CI 1middot05 to 1middot29]) and worse functional outcome (1middot07 [1middot02 to 1middot13]), but higher fluid input was not associated with ICU mortality (OR 0middot95 [0middot90 to 1middot00]) or worse functional outcome (1middot01 [0middot98 to 1middot03]).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. Copyright (C) 2021 Elsevier Ltd. All rights reserved. Show less
Helmrich, I.R.A.R.; Klaveren, D. van; Dijkland, S.A.; Lingsma, H.F.; Polinder, S.; Wilson, L.; ... ; CENTER TBI Collaborators 2021
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... Show moreBackground 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-2) 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-2 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-2 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. Show less
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... Show moreObjective 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. Show less
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... Show moreBackground 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. Show less
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... Show moreBackground 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. Show less
O'Malley, T.J.; Steyerberg, E.W.; Tchantchaleishvili, V. 2021
