Publications by authors named "E Steyerberg"

Objective: During the COVID-19 pandemic, dynamic factors such as governmental policies, improved treatment and prevention options and viral mutations changed the incidence of outcomes and possibly changed the relation between predictors and outcomes. The aim of the present study was to assess whether the dynamic context of the pandemic influenced the predictive performance of mortality predictions over time in older patients hospitalised for COVID-19.

Study Design And Setting: The COVID-OLD study, a multicentre cohort study in the Netherlands, included COVID-19 patients aged 70 years and older hospitalised during the first (early 2020), second (late 2020), third (late 2021) or fourth wave (early 2022).

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Background: Even patients with normal computed tomography (CT) head imaging may experience persistent symptoms for months to years after mild traumatic brain injury (mTBI). There is currently no good way to predict recovery and triage patients who may benefit from early follow-up and targeted intervention. We aimed to assess if existing prognostic models can be improved by serum biomarkers or diffusion tensor imaging metrics (DTI) from MRI, and if serum biomarkers can identify patients for DTI.

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Background: Post-traumatic stress disorder (PTSD) and depression are common after mild traumatic brain injury (mTBI), but their biological drivers are uncertain. We therefore explored whether polygenic risk scores (PRS) derived for PTSD and major depressive disorder (MDD) are associated with the development of cognate TBI-related phenotypes.

Methods: Meta-analyses were conducted using data from two multicenter, prospective observational cohort studies of patients with mTBI: the CENTER-TBI study (ClinicalTrials.

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Background: Postoperative infections significantly impact patient outcomes and costs, exacerbated by late diagnoses, yet early reliable predictors are scarce. Existing artificial intelligence (AI) models for postoperative infection prediction often lack external validation or perform poorly in local settings when validated. We aimed to develop locally valid models as part of the PERISCOPE AI system to enable early detection, safer discharge, and more timely treatment of patients.

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To compare the incremental prognostic value of pupillary reactivity captured as part of the Glasgow Coma Scale-Pupils (GCS-P) score or added as separate variable to the GCS+P, in traumatic brain injury (TBI). We analyzed patients enrolled between 2014 and 2018 in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI, = 3521) and the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI, = 1439) cohorts. Logistic regression was utilized to quantify the prognostic performances of GCS-P (GCS minus number of unreactive pupils) and GCS+P versus GCS alone according to Nagelkerke's .

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