Impact of annual volume of cases and intensive cardiac care unit availability on mortality of patients with acute myocardial infarction-related cardiogenic shock treated at revascularization capable centres.

AIMS: Cardiogenic shock (CS) is associated with high mortality. The purpose of this study was to assess the impact of hospital structure-related variables on mortality in patients with CS treated at percutaneous and surgical revascularization capable centres (psRCC) from a large nationwide registry. METHODS AND RESULTS: Retrospective observational study including consecutive patients with main or secondary diagnosis of CS and ST elevation myocardial infarction (STEMI). Patients discharged from Spanish National Healthcare System psRCC were included (2016-20). The association between the volume of CS cases attended by each centre, availability of intensive cardiac care unit (ICCU) and heart transplantation (HT) programmes, and in-hospital mortality was assessed by multilevel logistic regression models. The study population consisted of 3074 CS-STEMI episodes, of whom 1759 (57.2%) occurred in 26 centres with ICCU. A total of 17/44 hospitals (38.6%) were high-volume centres, and 19/44 (43%) centres had HT programmes availability. Treatment at HT centres was not associated with a lower mortality (P = 0.121). Both high volume of cases and ICCU showed a trend to an association with lower mortality in the adjusted model [odds ratio (OR): 0.87 and 0.88, respectively]. The interaction between both variables was significantly protective (OR 0.72; P = 0.024). After propensity score matching, mortality was lower in high-volume hospitals with ICCU (OR 0.79; P = 0.007). CONCLUSION: Most CS-STEMI patients were attended at psRCC with high volume of cases and ICCU available. The combination of high volume and ICCU availability showed the lowest mortality. These data should be taken into account when designing regional networks for CS management.

Long-Term Outcome of a Series of Patients With Narcolepsy Type 1 and Comorbidity With Immunopathological and Autoimmune Diseases.

Background: The aim of this study was to evaluate the long-term outcome of our series of narcolepsy type 1 (NT1) patients with comorbid autoimmune diseases (ADs) and other immunopathological diseases (IDs), focusing on the incidence of new ADs and IDs in this sample. Methods: A longitudinal observational study was conducted over 6 years (2014 - 2020) in a series of 158 Caucasians NT1 patients (96 males; mean age: 50.1 ± 19.0 years) from the previous study. All but one case (familial case) were HLA-DQB1*06:02-positive. The diagnosis of narcolepsy was made according to the International Classification of Sleep Disorders (ICSD-3). Results: Twenty-one patients have been diagnosed with a new ID, 10 of them with an AD (autoimmune thyroid disease, psoriasis, rheumatoid arthritis, transverse myelitis, granuloma annulare, primary biliary cirrhosis, alopecia areata and antiphospholipid syndrome), and 11 with other IDs (allergic rhinitis, allergic asthma, atopic dermatitis, food allergy, contact dermatitis and drug allergy). One patient was diagnosed with two new ADs. We found IDs in 46 patients (24 females and 22 males) and the overall prevalence in this series is actually 29.11%; 22 of them (13.92%) had an AD, with a percentage higher than estimated in the general population. Conclusions: The prevalence of AD/ID is high in our series, suggesting that NT1 might arise on a background of generalized susceptibility to immune-mediated processes. The occurrence of an ID can in turn influence the development of others in genetically predisposed individuals, which explains the increased associations observed in this long-term study.

Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning.

Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of poorly defined diseases with excessive daytime sleepiness as a core feature. Due to the considerable overlap of symptoms and the rarity of the diseases, it is difficult to identify distinct phenotypes of CH. Machine learning (ML) can help to identify phenotypes as it learns to recognize clinical features invisible for humans. Here we apply ML to data from the huge European Narcolepsy Network (EU-NN) that contains hundreds of mixed features of narcolepsy making it difficult to analyze with classical statistics. Stochastic gradient boosting, a supervised learning model with built-in feature selection, results in high performances in testing set. While cataplexy features are recognized as the most influential predictors, machine find additional features, e.g. mean rapid-eye-movement sleep latency of multiple sleep latency test contributes to classify NT1 and NT2 as confirmed by classical statistical analysis. Our results suggest ML can identify features of CH on machine scale from complex databases, thus providing 'ideas' and promising candidates for future diagnostic classifications.