Added value of frailty and social support in predicting risk of 30-day unplanned re-admission or death for patients with heart failure: An analysis from OPERA-HF.

BACKGROUND: Models for predicting the outcome of patients hospitalized for heart failure (HF) rarely take a holistic view. We assessed the ability of measures of frailty and social support in addition to demographic, clinical, imaging and laboratory variables to predict short-term outcome for patients discharged after a hospitalization for HF. METHODS: OPERA-HF is a prospective observational cohort, enrolling patients hospitalized for HF in a single center in Hull, UK. Variables were combined in a logistic regression model after multiple imputation of missing data to predict the composite outcome of death or readmission at 30 days. Comparisons were made to a model using clinical variables alone. The discriminative performance of each model was internally validated with bootstrap re-sampling. RESULTS: 1094 patients were included (mean age 77 [interquartile range 68-83] years; 40% women; 56% with moderate to severe left ventricular systolic dysfunction) of whom 213 (19%) had an unplanned re-admission and 60 (5%) died within 30 days. For the composite outcome, a model containing clinical variables alone had an area under the receiver-operating characteristic curve (AUC) of 0.68 [95% CI 0.64-0.72]. Adding marital status, support from family and measures of physical frailty increased the AUC (p < 0.05) to 0.70 [95% CI 0.66-0.74]. CONCLUSIONS: Measures of physical frailty and social support improve prediction of 30-day outcome after an admission for HF but predicting near-term events remains imperfect. Further external validation and improvement of the model is required.

Depression as an independent prognostic factor for all-cause mortality after a hospital admission for worsening heart failure.

BACKGROUND: Depression is associated with increased mortality amongst patients with chronic heart failure (HF). Whether depression is an independent predictor of outcome in patients admitted for worsening of HF is unclear. METHODS: OPERA-HF is an observational study enrolling patients hospitalized with worsening HF. Depression was assessed by the Hospital Anxiety and Depression Scale (HADS-D) questionnaire. Comorbidity was assessed by the Charlson Comorbidity Index (CCI). Kaplan-Meier and Cox regression analyses were used to estimate the association between depression and all-cause mortality. RESULTS: Of 242 patients who completed the HADS-D questionnaire, 153, 54 and 35 patients had no (score 0-7), mild (score 8-10) or moderate-to-severe (score 11-21) depression, respectively. During follow-up, 35 patients died, with a median time follow-up of 360days amongst survivors (interquartile range, IQR 217-574days). In univariable analysis, moderate-to-severe depression was associated with an increased risk of death (HR: 4.9; 95% CI: 2.3 to 10.2; P<0.001) compared to no depression. Moderate-to-severe depression also predicted all-cause mortality after controlling for age, CCI score, NYHA class IV, NT-proBNP and treatment with mineralocorticoid receptor antagonist, beta-blocker and diuretics (HR: 3.0; 95% CI: 1.3 to 7.0; P<0.05). CONCLUSIONS: Depression is strongly associated with an adverse outcome in the year following discharge after an admission to hospital for worsening HF. The association is only partly explained by the severity of HF or comorbidity. Further research is required to demonstrate whether recognition and treatment of depression improves patient outcomes.

Depression and anxiety as predictors of mortality among heart failure patients: systematic review and meta-analysis.

Several studies suggest that psychological factors are associated with negative outcomes and in particular higher mortality rates among heart failure (HF) patients. We aimed to evaluate the effect sizes of depression and anxiety on all-cause mortality in HF patients. We conducted a systematic review according to the PRISMA methodology. We searched for studies on depression or anxiety effects on all-cause mortality among HF patients published up to June 2015. A number of 26 and 6 articles met inclusion criteria for depression (total 80,627 patients) and anxiety (total 17,214 patients), respectively. The effect estimates were pooled using random-effect meta-analysis. Depression has significant and moderately heterogeneous effect on all-cause mortality (HR = 1.57; 95%CI 1.30-1.89, p < 0.001); adjustment for confounders led to a similar effect estimate (HR = 1.40; 95%CI 1.22-1.60; p < 0.001). Larger studies and higher study prevalence of depression were associated with smaller effect size. The effect of anxiety on mortality outcome was small and not conclusive given the low number of studies (n = 6) (HR = 1.02; 95% CI 1.00-1.04, p < 0.05). This systematic review and meta-analysis suggests that depression is an important and independent predictor of all-cause mortality among HF patients, while anxiety does not appear to have a strong effect. Further research is recommended toward the detection and treatment of depression.

Emotional sweating across the body: comparing 16 different skin conductance measurement locations.

Skin conductance (SC) is one of the most commonly used measures in psychophysiological studies involving emotional arousal and is traditionally measured at the fingers or the palms (i.e., the palmar locations) of the hand. Palmar skin conductance recording positions are, however, not always preferred for ambulatory recordings in real-life situations. This study quantifies the responsiveness and similarity with the finger of 16 different recording positions of skin conductance while watching emotional film fragments. Findings indicated foot, fingers and shoulders being most responsive, whereas arm, back, armpit, and thighbone were least responsive. The measurements at the foot were most similar with those of the finger. In contrast, arm, back, and armpit traces differed most from the finger trace. Taken together, foot and shoulders are the best alternatives to the finger for ambulatory measurement of skin conductance to reflect emotional arousal. These findings can help new applications using skin conductance, like automated emotion measurements, to come to fruition.

Window-based example selection in learning vector quantization.

A variety of modifications have been employed to learning vector quantization (LVQ) algorithms using either crisp or soft windows for selection of data. Although these schemes have been shown in practice to improve performance, a theoretical study on the influence of windows has so far been limited. Here we rigorously analyze the influence of windows in a controlled environment of gaussian mixtures in high dimensions. Concepts from statistical physics and the theory of online learning allow an exact description of the training dynamics, yielding typical learning curves, convergence properties, and achievable generalization abilities. We compare the performance and demonstrate the advantages of various algorithms, including LVQ 2.1, generalized LVQ (GLVQ), Learning from Mistakes (LFM) and Robust Soft LVQ (RSLVQ). We find that the selection of the window parameter highly influences the learning curves but not, surprisingly, the asymptotic performances of LVQ 2.1 and RSLVQ. Although the prototypes of LVQ 2.1 exhibit divergent behavior, the resulting decision boundary coincides with the optimal decision boundary, thus yielding optimal generalization ability.