Socioeconomic disparities in prehospital factors and survival after out-of-hospital cardiac arrest.

OBJECTIVE: It remains unknown whether patient socioeconomic factors affect interventions and survival after out-of-hospital cardiac arrest (OHCA), and whether a socioeconomic effect on bystander interventions affects survival. Therefore, this study examined patient socioeconomic disparities in prehospital factors and survival. METHODS: From the Danish Cardiac Arrest Registry, patients with OHCA ≥30 years were identified, 2001-2014, and divided into quartiles of household income (highest, high, low, lowest). Associations between income and bystander cardiopulmonary resuscitation (CPR) and 30-day survival with bystander CPR as mediator were analysed by logistic regression and mediation analysis in private witnessed, public witnessed, private unwitnessed and public unwitnessed arrests, adjusted for confounders. RESULTS: We included 21 480 patients. Highest income patients were younger, had higher education and were less comorbid relative to lowest income patients. They had higher odds for bystander CPR with the biggest difference in private unwitnessed arrests (OR 1.74, 95% CI 1.47 to 2.05). For 30-day survival, the biggest differences were in public witnessed arrests with 26.0% (95% CI 22.4% to 29.7%) higher survival in highest income compared with lowest income patients. Had bystander CPR been the same for lowest income as for highest income patients, then survival would be 25.3% (95% CI 21.5% to 29.0%) higher in highest income compared with lowest income patients, resulting in elimination of 0.79% (95% CI 0.08% to 1.50%) of the income disparity in survival. Similar trends but smaller were observed in low and high-income patients, the other three subgroups and with education instead of income. From 2002 to 2014, increases were observed in both CPR and survival in all income groups. CONCLUSION: Overall, lower socioeconomic status was associated with poorer prehospital factors and survival after OHCA that was not explained by patient or cardiac arrest-related factors.

A note on the evaluation of novel biomarkers: do not rely on integrated discrimination improvement and net reclassification index.

The 'integrated discrimination improvement' (IDI) and the 'net reclassification index' (NRI) are statistics proposed as measures of the incremental prognostic impact that a new biomarker will have when added to an existing prediction model for a binary outcome. By design, both measures were meant to be intuitively appropriate, and the IDI and NRI formulae do look intuitively plausible. Both have become increasingly popular. We shall argue, however, that their use is not always safe. If IDI and NRI are used to measure gain in prediction performance, then poorly calibrated models may appear advantageous, and in a simulation study, even the model that actually generates the data (and hence is the best possible model) can be improved on without adding measured information. We illustrate these shortcomings in actual cancer data as well as by Monte Carlo simulations. In these examples, we contrast IDI and NRI with the area under ROC and the Brier score. Unlike IDI and NRI, these traditional measures have the characteristic that prognostic performance cannot be accidentally or deliberately inflated.

The validation and assessment of machine learning: a game of prediction from high-dimensional data.

In applied statistics, tools from machine learning are popular for analyzing complex and high-dimensional data. However, few theoretical results are available that could guide to the appropriate machine learning tool in a new application. Initial development of an overall strategy thus often implies that multiple methods are tested and compared on the same set of data. This is particularly difficult in situations that are prone to over-fitting where the number of subjects is low compared to the number of potential predictors. The article presents a game which provides some grounds for conducting a fair model comparison. Each player selects a modeling strategy for predicting individual response from potential predictors. A strictly proper scoring rule, bootstrap cross-validation, and a set of rules are used to make the results obtained with different strategies comparable. To illustrate the ideas, the game is applied to data from the Nugenob Study where the aim is to predict the fat oxidation capacity based on conventional factors and high-dimensional metabolomics data. Three players have chosen to use support vector machines, LASSO, and random forests, respectively.