CRP (C-Reactive Protein) in Outcome Prediction After Subarachnoid Hemorrhage and the Role of Machine Learning.

Background and Purpose: Outcome prediction after aneurysmal subarachnoid hemorrhage (aSAH) is challenging. CRP (C-reactive protein) has been reported to be associated with outcome, but it is unclear if this is independent of other predictors and applies to aSAH of all grades. Therefore, the role of CRP in aSAH outcome prediction models is unknown. The purpose of this study is to assess if CRP is an independent predictor of outcome after aSAH, develop new prognostic models incorporating CRP, and test whether these can be improved by application of machine learning. Methods: This was an individual patient-level analysis of data from patients within 72 hours of aSAH from 2 prior studies. A panel of statistical learning methods including logistic regression, random forest, and support vector machines were used to assess the relationship between CRP and modified Rankin Scale. Models were compared with the full Subarachnoid Hemmorhage International Trialists' (SAHIT) prediction tool of outcome after aSAH and internally validated using cross-validation. Results: One thousand and seventeen patients were included for analysis. CRP on the first day after ictus was an independent predictor of outcome. The full SAHIT model achieved an area under the receiver operator characteristics curve (AUC) of 0.831. Addition of CRP to the predictors of the full SAHIT model improved model performance (AUC, 0.846, P=0.01). This improvement was not enhanced when learning was performed using a random forest (AUC, 0.807), but was with a support vector machine (AUC of 0.960, P <0.001). Conclusions: CRP is an independent predictor of outcome after aSAH. Its inclusion in prognostic models improves performance, although the magnitude of improvement is probably insufficient to be relevant clinically on an individual patient level, and of more relevance in research. Greater improvements in model performance are seen with support vector machines but these models have the highest classification error rate on internal validation and require external validation and calibration.

Saving lives with public access defibrillation: A deadly game of hide and seek.

BACKGROUND: Early defibrillation is a critical link in the chain of survival. Public access defibrillation (PAD) programmes utilising automated external defibrillators (AEDs) aim to decrease the time-to-first-shock, and improve survival from out-of-hospital cardiac arrest. Effective use of PADs requires rapid location of the device, facilitated by adequate signage. We aimed to therefore assess the quality of signage for PADs in the community. METHOD: From April 2017 to January 2018 we surveyed community PADs available for public use on the 'Save a Life' AED locator mobile application in and around Southampton, UK. Location and signage characteristics were collected, and the distance from the furthest sign to the AED was measured. RESULTS: Researchers evaluated 201 separate PADs. All devices visited were included in the final analysis. No signage at all was present for 135 (67.2%) devices. Only 15/201 (7.5%) AEDs had signage at a distance from AED itself. In only 5 of these cases (2.5%) was signage mounted more than 5.0 m from the AED. When signage was present, 46 used 2008 ILCOR signage and 15 used 2006 Resuscitation Council (UK) signage. Signage visibility was partially or severely obstructed at 27/66 (40.9%) sites. None of the 45 GP surgeries surveyed used exterior signage or an exterior 24/7 access box. CONCLUSIONS: Current signage of PADs is poor and limits the device effectiveness by impeding public awareness and location of AEDs. Recommendations should promote visible signage within the operational radius of each AED.