Association between common cardiovascular risk factors and clinical phenotype in patients with hypertrophic cardiomyopathy from the European Society of Cardiology (ESC) EurObservational Research Programme (EORP) Cardiomyopathy/Myocarditis registry

AIMS

The interaction between common cardiovascular risk factors (CVRF) and hypertrophic cardiomyopathy (HCM) is poorly studied. We sought to explore the relation between CVRF and the clinical characteristics of patients with HCM enrolled in the EURObservational Research Programme (EORP) Cardiomyopathy registry.

METHODS AND RESULTS

1739 patients with HCM were studied. The relation between hypertension (HT), diabetes (DM), body mass index (BMI), and clinical traits was analysed. Analyses were stratified according to the presence or absence of a pathogenic variant in a sarcomere gene. The prevalence of HT, DM, and obesity (Ob) was 37, 10, and 21%, respectively. HT, DM, and Ob were associated with older age (P<0.001), less family history of HCM (HT and DM P<0.001), higher New York Heart Association (NYHA) class (P<0.001), atrial fibrillation (HT and DM P<0.001; Ob p = 0.03) and LV (left ventricular) diastolic dysfunction (HT and Ob P<0.001; DM P = 0.003). Stroke was more frequent in HT (P<0.001) and mutation-positive patients with DM (P = 0.02). HT and Ob were associated with higher provocable LV outflow tract gradients (HT P<0.001, Ob P = 0.036). LV hypertrophy was more severe in Ob (P = 0.018). HT and Ob were independently associated with NYHA class (OR 1.419, P = 0.017 and OR 1.584, P = 0.004, respectively). Other associations, including a higher proportion of females in HT and of systolic dysfunction in HT and Ob, were observed only in mutation-positive patients.

CONCLUSION

Common CVRF are associated with a more severe HCM phenotype, suggesting a proactive management of CVRF should be promoted. An interaction between genotype and CVRF was observed for some traits.

Development and validation of a semi-automated surveillance system-lowering the fruit for non-ventilator-associated hospital-acquired pneumonia (nvHAP) prevention

Objectives

Conducting manual surveillance of non-ventilator-associated hospital-acquired pneumonia (nvHAP) using ECDC (European Centre for Disease Prevention and Control) surveillance criteria is very resource intensive. We developed and validated a semi-automated surveillance system for nvHAP, and describe nvHAP incidence and aetiology at our hospital.

Methods

We applied an automated classification algorithm mirroring ECDC definition criteria to distinguish patients ‘not at risk’ from patients ‘at risk’ for suffering from nvHAP. ‘At risk’-patients were manually screened for nvHAP. For validation, we applied the reference standard of full manual evaluation to three validation samples comprising 2091 patients.

Results

Among the 39 519 University Hospital Zurich inpatient discharges in 2017, the algorithm identified 2454 ‘at-risk’ patients, reducing the number of medical records to be manually screened by 93.8%. From this subset, nvHAP was identified in 251 patients (0.64%, 95%CI: 0.57–0.73). Sensitivity, negative predictive value, and accuracy of semi-automated surveillance versus full manual surveillance were lowest in the validation sample consisting of patients with HAP according to the International Classification of Diseases (ICD-10) discharge diagnostic codes, with 97.5% (CI: 93.7–99.3%), 99.2% (CI: 97.9–99.8%), and 99.4% (CI: 98.4–99.8%), respectively. The overall incidence rate of nvHAP was 0.83/1000 patient days (95%CI: 0.73–0.94), with highest rates in haematology/oncology, cardiac and thoracic surgery, and internal medicine including subspecialties.

Conclusions

The semi-automated surveillance demonstrated a very high sensitivity, negative predictive value, and accuracy. This approach significantly reduces manual surveillance workload, thus making continuous nvHAP surveillance feasible as a pivotal element for successful prevention efforts.

Mental models: a basic concept for human factors design in infection prevention

Much of the effort devoted to promoting better hand hygiene is based on the belief that poor hand hygiene reflects poor motivation. We argue, however, that automatic unconscious behaviour driven by 'mental models' is an important contributor to what actually happens. Mental models are concepts of reality--imaginary, often blurred, and sometimes unstable. Human beings use them to reduce mental load and free up capacity in the conscious mind to focus on deliberate activities. They are pragmatic solutions to the complexity of life. Knowledge of such mental processes helps healthcare designers and clinicians overcome barriers to behavioural change. This article reviews the concept of mental models and considers how it can be used to improve hand hygiene and patient safety.