Coronavirus disease 2019 (COVID-19), with an increasing number of deaths worldwide, has created a tragic global health and economic emergency. The disease, caused by severe acute respiratory syndrome coronavirus 2019 (SARS-CoV-19), is a multi-system inflammatory disease with many of COVID-19-positive patients requiring intensive medical care due to multi-organ failures. Biomarkers to reliably predict the patient’s clinical cause of the virus infection, ideally, to be applied in point of care testing or through routine diagnostic approaches, are highly needed. We aimed to probe if routinely assessed clinical lab values can predict the severity of the COVID-19 course. Therefore, we have retrospectively analyzed on admission laboratory findings in 224 consecutive patients from four hospitals and show that systemic immune inflammation index (SII) is a potent marker for predicting the requirement for invasive ventilator support and for worse clinical outcome of the infected patient. Patients’ survival and severity of SARS-CoV-2 infection could reliably be predicted at admission by calculating the systemic inflammatory index of individual blood values. We advocate this approach to be a feasible and easy-to-implement assay that may be particularly useful to improve patient management during high influx crisis. We believe with this work to contribute to improving infrastructure availability and case management associated with COVID-19 pandemic hurdles.

Clinical prediction models in neurosurgery are increasingly reported. These models aim to provide an evidence-based approach to the estimation of the probability of a neurosurgical outcome by combining 2 or more prognostic variables. Model development and model reporting are often suboptimal. A basic understanding of the methodology of clinical prediction modeling is needed when interpreting these models. We address basic statistical background, 7 modeling steps, and requirements of these models such that they may fulfill their potential for major impact for our daily clinical practice and for future scientific work.

The identification of musculoskeletal impairments from gait analysis in children with cerebral palsy is a complex task, as is formulating (surgical) recommendations. In this paper, we present how we built a decision support system based on gait kinematics, anthropometrics, and physical examination data. The decision support system was trained to learn the association between these data and the list of impairments and recommendations formulated historically by experienced clinicians. Our aim was 2-fold, train a computational model that would be representative of data-based clinical reasoning in our center, and support new or junior clinicians by providing pre-processed impairments and recommendations with the associated supportive evidence. We present some of the challenges we faced, such as the issues of dimensionality reduction for kinematic data, missing data imputations, class imbalance and choosing an appropriate model evaluation metric. Most models, i.e., one model for each impairments and recommendations, achieved a weighted Brier score lower than 0.20, and sensitivity and specificity greater than 0.70 and 0.80, respectively. The results of the models are accessible through a web-based application which displays the probability predictions as well as the (up to) 5 best predictors.

Background: Despite intensive research on preventing and treating vasospasm and delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage (aSAH), mortality and morbidity rates remain high. Early brain injury (EBI) has emerged as possibly the major significant factor in aSAH pathophysiology, emphasizing the need to investigate EBI-associated clinical events for improved patient management and decision-making. This study aimed to identify early clinical and radiological events within 72 h after aSAH to develop a conclusive predictive EBI score for clinical practice. Methods: This retrospective analysis included 561 consecutive patients with aSAH admitted to our neurovascular center between 01/2014 and 09/2022. Fourteen potential predictors occurring within the initial 72 h after hemorrhage were analyzed. The modified Rankin Scale (mRS) score at 6 months, discretized to three levels (0–2, favorable; 3–5, poor; 6, dead), was used as the outcome variable. Univariate ordinal regression ranked predictors by significance, and forward selection with McFadden’s pseudo-R² determined the optimal set of predictors for multivariate proportional odds logistic regression. Collinear parameters were excluded, and fivefold cross-validation was used to avoid overfitting. Results: The analysis resulted in the Subarachnoid Hemorrhage Associated Early Brain Injury Outcome Prediction score (SHELTER-score), comprising seven clinical and radiological events: age (0–4 points), World Federation of Neurosurgical Societies (0–2.5 points), cardiopulmonary resuscitation (CPR) (2 points), mydriasis (1–2 points), midline shift (0.5–1 points), early deterioration (1 point), and early ischemic lesion (2 points). McFadden’s pseudo-R² = 0.339, area under the curve for death or disability 0.899 and 0.877 for death. A SHELTER-score below 5 indicated a favorable outcome (mRS 0–2), 5–6.5 predicted a poor outcome (mRS 3–5), and ≥ 7 correlated with death (mRS 6) at 6 months. Conclusions: The novel SHELTER-score, incorporating seven clinical and radiological features of EBI, demonstrated strong predictive performance in determining clinical outcomes. This scoring system serves as a valuable tool for neurointensivists to identify patients with poor outcomes and guide treatment decisions, reflecting the great impact of EBI on the overall outcome of patients with aSAH.

BACKGROUND AND PURPOSE: Impairment of tissue oxygenation caused by inhomogeneous microscopic blood flow distribution, the so-called capillary transit time heterogeneity, is thought to contribute to delayed cerebral ischemia after aneurysmal SAH but has so far not been systematically evaluated in patients. We hypothesized that heterogeneity of the MTT, derived from CTP parameters, would give insight into the clinical course of patients with aneurysmal SAH and may identify patients at risk of poor outcome. MATERIALS AND METHODS: We retrospectively analyzed the heterogeneity of the MTT using the coefficient of variation in CTP scans from 132 patients. A multivariable logistic regression model was used to model the dichotomized mRS outcome. Linear regression was used to eliminate variables with high linear dependence. T tests were used to compare the means of 2 groups. Furthermore, the time of the maximum coefficient of variation for MTT after bleeding was evaluated for correlation with the mRS after 6 months. RESULTS: On average, each patient underwent 5.3 CTP scans during his or her stay. Patients with high coefficient of variation for MTT presented more often with higher modified Fisher (P = .011) and World Federation of Neurosurgical Societies grades (P = .014). A high coefficient of variation for MTT at days 3–21 after aneurysmal SAH correlated significantly with a worse mRS score after 6 months (P = .016). We found no correlation between the time of the maximum coefficient of variation for MTT after bleeding and the patients’ outcomes after 6 months (P = .203). CONCLUSIONS: Heterogeneity of MTT in CTP after aneurysmal SAH correlates with the patients’ outcomes. Because the findings are in line with the pathophysiologic concept of the capillary transit time heterogeneity, future studies should seek to verify the coefficient of variation for MTT as a potential imaging biomarker for outcome.