Psychological and Social Factors That Correlate With Dyspnea in Heart Failure

Ramasamy, Ranjith; Hildebrandt, Tom; O’Hea, Erin L.; Patel, Manan H.; Clemow, Lynn; Freudenberger, Ronald S

doi:10.1176/appi.psy.47.5.430

Cited by 38 publications

(38 citation statements)

References 26 publications

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“…Our findings not only support previous literature showing that depressive symptoms are associated with dyspnea complaints in patients with HF (12)(13)(14), but also suggest that this covariate-independent association is driven by somatic depressive symptoms, predominantly fatigue, lethargia and health worries. Our uncorrected model (Model 3) is in accordance with a recent cross-sectional study in which dyspnea was found to be associated to both somatic and cognitive depressive symptoms, and in which the authors did not adjust for disease severity and BMI (27).…”

Section: Accepted Manuscriptsupporting

confidence: 91%

“…It is unclear from this latter study whether anxiety or depressive symptoms drove the association. In contrast, there is one negative report, showing that anxiety was not associated with dyspnea (14). The negative results are corroborated by results from a large study in patients with COPD entering a pulmonary rehabilitation program, reporting HADS-assessed depression, but not anxiety, to be associated with dyspnea (31).…”

Section: Accepted Manuscriptmentioning

confidence: 83%

“…Studies in patients with chronic HF suggest that depression and dyspnea are moderately correlated (12)(13)(14), while for anxiety null-findings have been reported (14). The current study set out to examine the relative importance of disease severity, inflammation and psychological factors as potential contributors to the experience of dyspnea in a large prospective cohort of patients with chronic systolic HF.…”

Section: Introductionmentioning

confidence: 95%

See 2 more Smart Citations

Determinants of Dyspnea in Chronic Heart Failure

Kupper

Bonhof

Westerhuis

et al. 2016

Journal of Cardiac Failure

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Section: Accepted Manuscriptsupporting

confidence: 91%

Section: Accepted Manuscriptmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 95%

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Determinants of Dyspnea in Chronic Heart Failure

Kupper

Bonhof

Westerhuis

et al. 2016

Journal of Cardiac Failure

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“…Onco-cardiology assessment of patients may also help in reducing the readmission rates in high-risk patients. Assessment of cardiovascular patients for psychosocial aspects and careful evaluation of individual comorbidities could help to reduce the readmission rates and adherence to the medications 25–28 . d) Laboratory values: laboratory values were least predictive in the individual modeling stage.…”

Section: Resultsmentioning

confidence: 99%

Predictive Modeling of Hospital Readmission Rates Using Electronic Medical Record-Wide Machine Learning: A Case-Study Using Mount Sinai Heart Failure Cohort

et al. 2016

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Reduction of preventable hospital readmissions that result from chronic or acute conditions like stroke, heart failure, myocardial infarction and pneumonia remains a significant challenge for improving the outcomes and decreasing the cost of healthcare delivery in the United States. Patient readmission rates are relatively high for conditions like heart failure (HF) despite the implementation of high-quality healthcare delivery operation guidelines created by regulatory authorities. Multiple predictive models are currently available to evaluate potential 30-day readmission rates of patients. Most of these models are hypothesis driven and repetitively assess the predictive abilities of the same set of biomarkers as predictive features. In this manuscript, we discuss our attempt to develop a data-driven, electronic-medical record-wide (EMR-wide) feature selection approach and subsequent machine learning to predict readmission probabilities. We have assessed a large repertoire of variables from electronic medical records of heart failure patients in a single center. The cohort included 1,068 patients with 178 patients were readmitted within a 30-day interval (16.66% readmission rate). A total of 4,205 variables were extracted from EMR including diagnosis codes (n=1,763), medications (n=1,028), laboratory measurements (n=846), surgical procedures (n=564) and vital signs (n=4). We designed a multistep modeling strategy using the Naïve Bayes algorithm. In the first step, we created individual models to classify the cases (readmitted) and controls (non-readmitted). In the second step, features contributing to predictive risk from independent models were combined into a composite model using a correlation-based feature selection (CFS) method. All models were trained and tested using a 5-fold cross-validation method, with 70% of the cohort used for training and the remaining 30% for testing. Compared to existing predictive models for HF readmission rates (AUCs in the range of 0.6–0.7), results from our EMR-wide predictive model (AUC=0.78; Accuracy=83.19%) and phenome-wide feature selection strategies are encouraging and reveal the utility of such data-driven machine learning. Fine tuning of the model, replication using multi-center cohorts and prospective clinical trial to evaluate the clinical utility would help the adoption of the model as a clinical decision system for evaluating readmission status.

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“…COPD, congestive heart failure, and fluid build-up in renal failure can cause dyspnea. (Ramasamy et al,2006). Fatigue is almost a universal complaint in patients with autoimmune deficiency syndrome (AIDS), congestive heart failure, myocardial infarction, and progressive neurologic disorders such as multiple sclerosis, and autoimmune diseases such as rheumatoid arthritis, and dialysis patients receiving either hemodialysis or peritoneal dialysis, and cancer (Appels & Mulder, 1988;Brunier & Graydon, 1996;O'Brain & Pheifer, 1993;Tel et al, 2011).…”

Section: Introductionmentioning

confidence: 99%