Incident atrial fibrillation and its risk prediction in patients developing COVID-19: A machine learning based algorithm approach

Background The elderly multi-morbid patient is at high risk of adverse outcomes with COVID-19 complications, and in the general population, the development of incident AF is associated with worse outcomes in such patients. We therefore investigated incident AF risks in a large prospective population of elderly patients with/without incident COVID-19 cases and baseline cardiovascular/non-cardiovascular multi-morbidities. We used two approaches: main-effect modeling and secondly, a machine-learning (ML) approach accounting for complex dynamic relationships. Methods We studied a prospective elderly US cohort of 280592 patients from medical databases in a 8-month investigation of new COVID19 cases. Incident AF outcomes were examined in relationship to diverse multi-morbid conditions, COVID-19 status and demographic variables, with ML accounting for the dynamic nature of changing multimorbidity risk factors. Results Multi-morbidity contributed to the onset of confirmed COVID-19 cases with cognitive impairment (OR 1.69; 95%CI 1.52-1.88), anemia (OR 1.41; 95%CI 1.32-1.50), diabetes mellitus (OR 1.35; 95%CI 1.27-1.44) and vascular disease (OR 1.30; 95%CI 1.21-1.39) having the highest associations. A main effect model (C-index value 0.718) showed that COVID-19 had the highest association with incident AF cases (OR 3.12; 95%CI 2.61-3.710, followed by congestive heart failure (1.72; 95%CI 1.50-1.96), then coronary artery disease (OR 1.43; 95%CI 1.27-1.60) and valvular disease (1.42; 95%CI 1.26-1.60). The ML algorithm demonstrated improved discriminatory validity incrementally over the statistical main effect model (training: C-index 0.729, 95%CI 0.718-0.740; validation: C-index 0.704, 95%CI 0.687-0.72). Calibration of ML based formulation was satisfactory and better than the main-effect model. Decision curve analysis demonstrated that the clinical utility for the ML based formulation was better than the ‘treat all’ strategy and the main effect model. Conclusion COVID-19 status has major implications for incident AF in a cohort with diverse cardiovascular/non-cardiovascular multi-morbidities. Our approach accounting for dynamic multimorbidity changes had good prediction for incident AF amongst incident COVID19 cases.

On the Marginal Dependency of Cohen’s κ

Cohen’s κ (kappa) is typically used as a measure of degree of rater agreement. It is often criticized because it is marginal-dependent. In this article, this characteristic is explained and illustrated in the context of (1) nonuniform marginal probability distributions, (2) odds ratios that remain constant while κ changes in the presence of varying marginal distributions, and (3) percentages of raw agreement that remain constant while κ changes in the presence of varying marginal distributions. The meaning and interpretation of κ are explained with reference to the log-linear main effect model of variable independence. This model is used for the estimation of the expected cell frequencies of agreement tables. It is shown that the interpretation of κ as a measure of degree of agreement is incorrect. The correct interpretation is that κ assesses the degree of agreement beyond that expected based on a statistical model such as the independence or the null model. Based on Goodman’s (1991) distinction between marginal-free and marginal-dependent measures, it is shown that κ is marginal-dependent. It shares this characteristic with the well-known χ2-statistic and the correlation coefficient for cross-classifications. In contrast, the odds ratio, the unweighted log-linear interaction, and the percentage of raw agreement are marginal-free. Therefore, the expectation that marginal-dependent κ would reflect the same data characteristics as some of the marginal-free measures is misguided. It is recommended that researchers report both measures of degree of agreement and measures of agreement beyond some expectation.

Childhood Physical Abuse, Perceived Social Support, and Socioemotional Status in Adult Women

This study investigated the extent to which an interactional model, relative to a main effect model, predicts the relationship between childhood physical abuse, perceived social support, and various aspects of socioemotional functioning in adult women. The results indicated that perceived social support during childhood was significantly related to subsequent levels of adult depression, trait anxiety, and child abuse potential in a manner consistent with a main effect model. Childhood history of physical abuse was related only to adult child abuse potential. Implications and study limitations are discussed.

Soutien social et symptômes dépressifs au sein des personnes âgées

ABSTRACTThere is great interest in identifying psychological and social variables associated with depressive symptoms in older adults. The goal of this article is to review the literature on the relationship between social support and depressive symptoms in the elderly and to identify the mechanisms involved in this relationship. The review indicates that both structural and functional dimensions of social support are inversely related to depressive symptoms in elderly persons. In addition, there is evidence supporting both the main effect model and the buffering effect model of social support. It is unclear, however, whether observation of these effects depends on the type of measure used to assess social support. A better understanding of the relationship between social support and depression requires the consideration of more precise dimensions of social support as well as the nature of the Stressors experienced by older people.