Predicting neurosurgical referral outcomes in patients with chronic subdural hematomas using machine learning algorithms - A multi-center feasibility study.

Background: Chronic subdural hematoma (CSDH) incidence and referral rates to neurosurgery are increasing. Accurate and automated evidence-based referral decision-support tools that can triage referrals are required. Our objective was to explore the feasibility of machine learning (ML) algorithms in predicting the outcome of a CSDH referral made to neurosurgery and to examine their reliability on external validation. Methods: Multicenter retrospective case series conducted from 2015 to 2020, analyzing all CSDH patient referrals at two neurosurgical centers in the United Kingdom. 10 independent predictor variables were analyzed to predict the binary outcome of either accepting (for surgical treatment) or rejecting the CSDH referral with the aim of conservative management. 5 ML algorithms were developed and externally tested to determine the most reliable model for deployment. Results: 1500 referrals in the internal cohort were analyzed, with 70% being rejected referrals. On a holdout set of 450 patients, the artificial neural network demonstrated an accuracy of 96.222% (94.444-97.778), an area under the receiver operating curve (AUC) of 0.951 (0.927-0.973) and a brier score loss of 0.037 (0.022-0.056). On a 1713 external validation patient cohort, the model demonstrated an AUC of 0.896 (0.878-0.912) and an accuracy of 92.294% (90.952-93.520). This model is publicly deployed: https://medmlanalytics.com/neural-analysis-model/. Conclusion: ML models can accurately predict referral outcomes and can potentially be used in clinical practice as CSDH referral decision making support tools. The growing demand in healthcare, combined with increasing digitization of health records raises the opportunity for ML algorithms to be used for decision making in complex clinical scenarios.

Development and Validation of the Chronic Subdural HematOma Referral oUtcome Prediction Using Statistics (CHORUS) Score: A Retrospective Study at a National Tertiary Center.

BACKGROUND: Chronic subdural hematoma (CSDH) is a common neurosurgical condition with an increasing rate of patient referrals. CSDH referral decision-making is a subjective clinical process, and our aim was to develop a simple scoring system capable of acting as a decision support tool aiding referral triage. METHODS: A single tertiary center retrospective case series analysis of all CSDH patient referrals from 2015 to 2020 was conducted. Ten independent variables used in the referral process were analyzed to predict the binary outcome of either accepting or rejecting the CSDH referral. Following feature selection analysis, a multivariable scoring system was developed and evaluated. RESULTS: 1500 patient referrals were included. Stepwise multivariable logistic and least absolute shrinkage and selection operator regression identified age <85 years, the presence of headaches, dementia, motor weakness, radiological midline shift, a reasonable premorbid quality of life, and a large sized hematoma to be statistically significant predictors of CSDH referral acceptance (P <0.04). These variables derived a scoring system ranging from -9 to 6 with an optimal cut-off for referral acceptance at any score >1 (P <0.0001). This scoring system demonstrated optimal calibration (brier score loss = 0.0552), with a score >1 predicting referral acceptance with an area under the curve of 0.899 (0.876-0.922), a sensitivity of 83.838% (76.587-91.089), and a specificity of 96.000% (94.080-97.920). CONCLUSIONS: Certain patient specific clinical and radiological characteristics can predict the acceptance or rejection of a CSDH referral. Considering the precision of this scoring system, it has the potential for effectively triaging CSDH referrals.

Practical Approach To Explore the Effects of Polyphenols on Aryl Hydrocarbon Receptor Regulated Immune Function.

The ligand-activated aryl hydrocarbon receptor (AhR) is an important molecular regulator of immune function, whose activity can be modulated by dietary glucosinolate- and tryptophan-derived metabolites. In contrast, the potential use of polyphenols as dietary regulators of AhR-dependent immunity remains unclear. In this perspective, we discuss how cellular metabolism may alter the net effect of polyphenols on AhR, thus potentially reconciling some of the conflicting observations reported in the literature. We further provide a methodological roadmap, across the fields of immunology, metabolomics, and gut microbial ecology, to explore the potential effects of polyphenol-rich diets on AhR-regulated immune function in humans.