Effects of pre-existing type 1 diabetes mellitus on survival outcome following out-of-hospital cardiac arrest: a registry-based observational study in Sweden.

BACKGROUND: It has been estimated that 80% of cases of out-of-hospital cardiac arrest (OHCA) are due to cardiac causes. It is well-documented that diabetes is a risk factor for conditions associated with sudden cardiac arrest. Type 1 diabetes (T1D) displays a threefold to fivefold increased risk of cardiovascular disease and death compared with the general population. OBJECTIVE: This study aims to assess the characteristics and survival outcomes of individuals with and without T1D who experienced an OHCA. Design: A registry-based nationwide observational study with two cohorts, patients with T1D and patients without T1D. Setting: All emergency medical services and hospitals in Sweden were included in the study. PARTICIPANTS: Using the Swedish Cardiopulmonary Resuscitation Registry, we enrolled 54 568 cases of OHCA where cardiopulmonary resuscitation was attempted between 2010 and 2020. Among them, 448 patients with T1D were identified using International Classification of Diseases-code: E10. METHODS: Survival analysis was performed using Kaplan-Meier and logistic regression. Multiple regression was adjusted for age, sex, cause of arrest, prevalence of T1D and time to cardiopulmonary resuscitation. MAIN OUTCOME MEASURES: The outcomes were discharge status (alive vs dead), 30 days survival and neurological outcome at discharge. RESULTS: There were no significant differences in patients discharged alive with T1D 37.3% versus, 46% among cases without T1D. There was also no difference in neurological outcome. Kaplan-Meier curves yielded no significant difference in long-term survival. Multiple regression showed no significant association with survival after accounting for covariates, OR 0.99 (95% CI 0.96 to 1.02), p value=0.7. Baseline characteristics indicate that patients with T1D were 5 years younger at OHCA occurrence and had proportionally fewer cases of heart disease as the cause of arrest (57.6% vs 62.7%). CONCLUSION: We conclude, with the current sample size, that there is no statistically significant difference in long-term or short-term survival between patients with and without T1D following OHCA.

Prediction of survival in out-of-hospital cardiac arrest: the updated Swedish cardiac arrest risk score (SCARS) model.

Aims: Out-of-hospital cardiac arrest (OHCA) is a major health concern worldwide. Although one-third of all patients achieve a return of spontaneous circulation and may undergo a difficult period in the intensive care unit, only 1 in 10 survive. This study aims to improve our previously developed machine learning model for early prognostication of survival in OHCA. Methods and results: We studied all cases registered in the Swedish Cardiopulmonary Resuscitation Registry during 2010 and 2020 (n = 55 615). We compared the predictive performance of extreme gradient boosting (XGB), light gradient boosting machine (LightGBM), logistic regression, CatBoost, random forest, and TabNet. For each framework, we developed models that optimized (i) a weighted F1 score to penalize models that yielded more false negatives and (ii) a precision-recall area under the curve (PR AUC). LightGBM assigned higher importance values to a larger set of variables, while XGB made predictions using fewer predictors. The area under the curve receiver operating characteristic (AUC ROC) scores for LightGBM were 0.958 (optimized for weighted F1) and 0.961 (optimized for a PR AUC), while for XGB, the scores were 0.958 and 0.960, respectively. The calibration plots showed a subtle underestimation of survival for LightGBM, contrasting with a mild overestimation for XGB models. In the crucial range of 0-10% likelihood of survival, the XGB model, optimized with the PR AUC, emerged as a clinically safe model. Conclusion: We improved our previous prediction model by creating a parsimonious model with an AUC ROC at 0.96, with excellent calibration and no apparent risk of underestimating survival in the critical probability range (0-10%). The model is available at www.gocares.se.

[Q fever endocarditis - a rare condition with high mortality]. / Q-feberendokardit ­ en ovanlig åkomma med hög mortalitet.

Coxiella burnetii is the causative agent of Q fever. It can manifest in both acute and chronic forms. Culture-negative endocarditis is the most common and serious presenting form of chronic Q fever. This occurs almost exclusively in patients with a pre-existing valvulopathy including valve prosthesis or immunocompromised patients as well as in pregnant women. Diagnosis is often delayed or missed due to the nonspecific symptoms of the condition. Without the proper antimicrobial therapy, the mortality is high. Q fever endocarditis should be suspected especially in people who recently had acute Q fever, people who come from endemic areas as well as people with occupational contact with sheep, goats and cattle and endocarditis symptoms. In this article we present a case with a patient who died of unknown cause and where PCR performed on autopsy of the valve revealed Q fever endocarditis.