Predictive Models of Long-Term Outcome in Patients with Moderate to Severe Traumatic Brain Injury are Biased Toward Mortality Prediction.

BACKGROUND: The prognostication of long-term functional outcomes remains challenging in patients with traumatic brain injury (TBI). Our aim was to demonstrate that intensive care unit (ICU) variables are not efficient to predict 6-month functional outcome in survivors with moderate to severe TBI (msTBI) but are mostly associated with mortality, which leads to a mortality bias for models predicting a composite outcome of mortality and severe disability. METHODS: We analyzed the data from the multicenter randomized controlled Continuous Hyperosmolar Therapy in Traumatic Brain-Injured Patients trial and developed predictive models using machine learning methods and baseline characteristics and predictors collected during ICU stay. We compared our models' predictions of 6-month binary Glasgow Outcome Scale extended (GOS-E) score in all patients with msTBI (unfavorable GOS-E 1-4 vs. favorable GOS-E 5-8) with mortality (GOS-E 1 vs. GOS-E 2-8) and binary functional outcome in survivors with msTBI (severe disability GOS-E 2-4 vs. moderate to no disability GOS-E 5-8). We investigated the link between ICU variables and long-term functional outcomes in survivors with msTBI using predictive modeling and factor analysis of mixed data and validated our hypotheses on the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) model. RESULTS: Based on data from 370 patients with msTBI and classically used ICU variables, the prediction of the 6-month outcome in survivors was inefficient (mean area under the receiver operating characteristic 0.52). Using factor analysis of mixed data graph, we demonstrated that high-variance ICU variables were not associated with outcome in survivors with msTBI (p = 0.15 for dimension 1, p = 0.53 for dimension 2) but mostly with mortality (p < 0.001 for dimension 1), leading to a mortality bias for models predicting a composite outcome of mortality and severe disability. We finally identified this mortality bias in the IMPACT model. CONCLUSIONS: We demonstrated using machine learning-based predictive models that classically used ICU variables are strongly associated with mortality but not with 6-month outcome in survivors with msTBI, leading to a mortality bias when predicting a composite outcome of mortality and severe disability.

An automated histological classification system for precision diagnostics of kidney allografts.

For three decades, the international Banff classification has been the gold standard for kidney allograft rejection diagnosis, but this system has become complex over time with the integration of multimodal data and rules, leading to misclassifications that can have deleterious therapeutic consequences for patients. To improve diagnosis, we developed a decision-support system, based on an algorithm covering all classification rules and diagnostic scenarios, that automatically assigns kidney allograft diagnoses. We then tested its ability to reclassify rejection diagnoses for adult and pediatric kidney transplant recipients in three international multicentric cohorts and two large prospective clinical trials, including 4,409 biopsies from 3,054 patients (62.05% male and 37.95% female) followed in 20 transplant referral centers in Europe and North America. In the adult kidney transplant population, the Banff Automation System reclassified 83 out of 279 (29.75%) antibody-mediated rejection cases and 57 out of 105 (54.29%) T cell-mediated rejection cases, whereas 237 out of 3,239 (7.32%) biopsies diagnosed as non-rejection by pathologists were reclassified as rejection. In the pediatric population, the reclassification rates were 8 out of 26 (30.77%) for antibody-mediated rejection and 12 out of 39 (30.77%) for T cell-mediated rejection. Finally, we found that reclassification of the initial diagnoses by the Banff Automation System was associated with an improved risk stratification of long-term allograft outcomes. This study demonstrates the potential of an automated histological classification to improve transplant patient care by correcting diagnostic errors and standardizing allograft rejection diagnoses.ClinicalTrials.gov registration: NCT05306795 .

Folliculitis induced by EGFR inhibitors, preventive and curative efficacy of tetracyclines in the management and incidence rates according to the type of EGFR inhibitor administered: a systematic literature review.

INTRODUCTION: Folliculitis is the most common side effect of epidermal growth factor receptor (EGFR) inhibitors (EGFRIs). It is often apparent, altering patients' quality of life and possibly impacting compliance. Variations in terms of the treatment-related incidence and intensity have not been fully elucidated. Tetracyclines have been recommended for the prophylaxis and treatment of folliculitis but their efficacy is yet to be established. MATERIALS AND METHODS: We carried out two systematic literature reviews. The first assessed the preventive and curative efficacy of tetracyclines. The second assessed the incidence of grade 3-4 folliculitis in the main clinical studies published. RESULTS: In four randomized studies, preventive tetracycline treatment was associated with a significantly lower incidence of grade 2-3 folliculitis and a better quality of life in three of the four studies. In curative terms, tetracycline efficacy was not evaluated in any randomized study, but an improvement in grade ≥2 folliculitis was reported in case series. The frequency and severity of folliculitis seem to be greater with the antibodies than with the tyrosine kinase inhibitors. Analysis restricted to lung cancer studies showed a statistically greater incidence in terms of grade 3-4 folliculitis with cetuximab (9%) and erlotinib (8%) than with gefitinib (2%) (p < .0001). CONCLUSION: Unless contraindicated, a tetracycline should be routinely prescribed prophylactically for patients treated with an EGFRI (level of evidence, B2). In curative therapy, the level of evidence for tetracycline efficacy is low (level of evidence, D). The incidence of grade 3-4 folliculitis induced by EGFRIs appears to be lower with gefitinib.