Search

Additional file 1: formulas used. Figure S1: correlation plots. Stepwise description of discarded variables due to correlation. Missing data. Figure S2a: density plots of imputed variables. Figure S2b: strip plots of imputed variables. Table S1: main outcomes and transitions between complete case and imputation models. Figure S3a: profile plot of all recruitable subphenotypes. Figure S3b: profile plot of all non-recruitable subphenotypes. Figure S4: alluvial plot of patient flow among models. Table S2: changes per lung region. Figure S5a: changes in in end-expiratory lung volumes before and after recruitment. Figure S5b: changes in lung weight before and after recruitment. Figure S6a: volumes in different aeration regions before and after recruitment. Figure S6b: weight in different aeration regions before and after recruitment. Table S3: LASSO regression results. Table S4: GLM results of nested variable models. Table S5: AUROCs for variable subsets. Figure S7: ROC curves for variable subsets. Table S6a: Fine and Gray regression results of subphenotype membership and duration of MV. Table S6b: Cox regression results of subphenotype membership and survival. Figure S8: Kaplan-Meier plot of survival. Table S7. Goodness-of-fit tests. Figure S9: Shoenfeld plots for covariates used in survival analysis. Figure S10: Cumulative incidence plot using only complete case analyses. Figure S11: Kaplan-Meier using only complete cases. Table S8a: Fine and Gray regression results of subphenotype membership and duration of MV using complete cases only. Table S8b: Cox regression results of subphenotype membership and survival using complete cases only. References of supplementary materials.

Objectives Initial trauma care could potentially be improved when conventional imaging and selective CT scanning is omitted and replaced by immediate total-body CT (iTBCT) scanning. Because of the potentially increased radiation exposure by this diagnostic approach, proper selection of the severely injured patients is mandatory. Methods In the REACT-2 trial, severe trauma patients were randomized to iTBCT or conventional imaging and selective CT based on predefined criteria regarding compromised vital parameters, clinical suspicion of severe injuries, or high-risk trauma mechanisms in five trauma centers. By logistic regression analysis with backward selection on the 15 study inclusion criteria, a revised set of criteria was derived and subsequently tested for prediction of severe injury and shifts in radiation exposure. Results In total, 1083 patients were enrolled with median ISS of 20 (IQR 9–29) and median GCS of 13 (IQR 3–15). Backward logistic regression resulted in a revised set consisting of nine original and one adjusted criteria. Positive predictive value improved from 76% (95% CI 74–79%) to 82% (95% CI 80–85%). Sensitivity decreased by 9% (95% CI 7–11%). The area under the receiver operating characteristics curve remained equal and was 0.80 (95% CI 0.77–0.83), original set 0.80 (95% CI 0.77–0.83). The revised set retains 8.78 mSv (95% CI 6.01–11.56) for 36% of the non-severely injured patients. Conclusions Selection criteria for iTBCT can be reduced from 15 to 10 clinically criteria. This improves the positive predictive value for severe injury and reduces radiation exposure for less severely injured patients. Key Points • Selection criteria for iTBCT can be reduced to 10 clinically useful criteria. • This reduces radiation exposure in 36% of less severely injured patients. • Overall discriminative capacity for selection of severely injured patients remained equal.

Background A challenge in imaging research is a diagnostic classification of study participants. We hypothesised that a structured approach would be efficient and that classification by medical students, residents, and an expert panel whenever necessary would be as valid as classification of all patients by experts. Methods OPTIMACT is a randomised trial designed to evaluate the effectiveness of replacing chest x-ray for ultra-low-dose chest computed tomography (CT) at the emergency department. We developed a handbook with diagnostic guidelines and randomly selected 240 cases from 2,418 participants enrolled in OPTIMACT. Each case was independently classified by two medical students and, if they disagreed, by the students and a resident in a consensus meeting. Cases without consensus and cases classified as complex were assessed by a panel of medical specialists. To evaluate the validity, 60 randomly selected cases not referred to the panel by the students and the residents were reassessed by the specialists. Results Overall, the students and, if necessary, residents were able to assign a diagnosis in 183 of the 240 cases (76% concordance; 95% confidence interval [CI] 71–82%). We observed agreement between students and residents versus medical specialists in 50/60 cases (83% concordance; 95% CI 74–93%). Conclusions A structured approach in which study participants are assigned diagnostic labels by assessors with increasing levels of medical experience was an efficient and valid classification method, limiting the workload for medical specialists. We presented a viable option for classifying study participants in large-scale imaging trials (Netherlands National Trial Register number NTR6163).

Supplemental Digital Content is available in the text., Background and Purpose— The modified Rankin Scale (mRS) at 3 months is the most commonly used primary outcome measure in stroke treatment trials, but it lacks specificity and requires long-term follow-up interviews, which consume time and resources. An alternative may be the National Institutes of Health Stroke Scale (NIHSS), early after stroke. Our aim was to evaluate whether the NIHSS assessed within 1 week after treatment could serve as a primary outcome measure for trials of acute treatment for ischemic stroke. Methods— We used data from 2 randomized controlled trials of endovascular treatment for ischemic stroke: the positive MR CLEAN (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands; N=500) and the neutral IMS (Interventional Management of Stroke) III trial (N=656). We used a causal mediation model, with linear and ordinal logistic regression adjusted for confounders, to evaluate the NIHSS 24 hours and 5 to 7 days after endovascular treatment as primary outcome measures (instead of the mRS at 3 months) in both trials. Patients who had died before the NIHSS was assessed received the maximum score of 42. NIHSS+1 was then log10-transformed. Results— In both trials, there was a significant correlation between the NIHSS at 24 hours and 5 to 7 days and the mRS. In MR CLEAN, we found a significant effect of endovascular treatment on the mRS and on the NIHSS at 24 hours and 5 to 7 days. After adjustment for NIHSS at 24 hours and 5 to 7 days, the effect of endovascular treatment on the mRS decreased from common odds ratio 1.68 (95% CI, 1.22–2.32) to respectively 1.36 (95% CI, 0.97–1.91) and 1.24 (95% CI, 0.87–1.79), indicating that treatment effect on the mRS is in large part mediated by the NIHSS. In the IMS III trial there was no treatment effect on the NIHSS at 24 hours and 5 to 7 days, corresponding with the absence of a treatment effect on the mRS. Conclusions— The NIHSS within 1 week satisfies the requirements for a surrogate end point and may be used as a primary outcome measure in trials of acute treatment for ischemic stroke, particularly in phase II(b) trials. This could reduce stroke-outcome assessment to its essentials (ie, neurological deficit), and reduce trial duration and costs. Whether and under which conditions it could be used in phase III trials requires a debate in the field with all parties. Clinical Trial Registration— URL: http://www.isrctn.com. Unique identifier: ISRCTN10888758; https://www.clinicaltrials.gov. Unique identifier: NCT00359424.

In a high-prevalence emergency department setting, chest CT showed a high probability of COVID-19 in 30% of patients with a negative or indeterminate initial RT-PCR result https://bit.ly/38hLDzR