Response shift after coronary revascularization.

PURPOSE: The aims of this study were to investigate (1) the extent to which response shift occurs among patients with coronary artery disease (CAD) after coronary revascularization, (2) whether the assessment of changes in health-related quality of life (HRQoL), controlled for response shift, yield more valid estimates of changes in HRQoL, as indicated by stronger associations with criterion measures of change, than without controlling for response shift, and (3) if occurrences of response shift are related to patient characteristics. METHODS: Patients with CAD completed the SF-36 and the Seattle Angina Questionnaire (SAQ7) at baseline and 3 months after coronary revascularization. Sociodemographic, clinical and psychosocial variables were measured with the patient version of the New York Heart Association-class, Subjective Significance Questionnaire, Reconstruction of Life Events Questionnaire (RE-LIFE), and HEXACO personality inventory. Oort's Structural Equation Modeling (SEM) approach was used to investigate response shift. RESULTS: 191 patient completed questionnaires at baseline and at 3 months after treatment. The SF-36 showed recalibration and reprioritization response shift and the SAQ7 reconceptualization response shift. Controlling for these response shift effects did not result in more valid estimates of change. One significant association was found between reprioritization response shift and complete integration of having CAD into their life story, as indicated by the RE-LIFE. CONCLUSION: Results indicate response shift in HRQoL following coronary revascularization. While we did not find an impact of response shift on the estimates of change, the SEM approach provides a more comprehensive insight into the different types of change in HRQoL following coronary revascularization.

Influence of response shift and disposition on patient-reported outcomes may lead to suboptimal medical decisions: a medical ethics perspective.

BACKGROUND: Patient-reported outcomes (PROs) are frequently used for medical decision making, at the levels of both individual patient care and healthcare policy. Evidence increasingly shows that PROs may be influenced by patients' response shifts (changes in interpretation) and dispositions (stable characteristics). MAIN TEXT: We identify how response shifts and dispositions may influence medical decisions on both the levels of individual patient care and health policy. We provide examples of these influences and analyse the consequences from the perspectives of ethical principles and theories of just distribution. CONCLUSION: If influences of response shift and disposition on PROs and consequently medical decision making are not considered, patients may not receive optimal treatment and health insurance packages may include treatments that are not the most effective or cost-effective. We call on healthcare practitioners, researchers, policy makers, health insurers, and other stakeholders to critically reflect on why and how such patient reports are used.

Correction: Gender differences in quality of life in coronary artery disease patients with comorbidities undergoing coronary revascularization.

[This corrects the article DOI: 10.1371/journal.pone.0234543.].

Gender differences in quality of life in coronary artery disease patients with comorbidities undergoing coronary revascularization.

In comparison to male patients with coronary artery disease, female patients suffer from more comorbidities, experience symptoms of coronary artery disease differently and report poorer health-related quality of life (HRQoL) after coronary revascularization. However, there is limited data on the impact of comorbidity burden on the recovery in HRQoL in female and male patients. We investigated the impact of comorbidity burden on the change in HRQoL following coronary revascularization in female patients versus male patients. 230 patients (60 female) with coronary artery disease were assessed before, and two weeks, three months and six months after coronary revascularization. Disease-specific HRQoL was measured with the Short-Form Seattle Angina Questionnaire. Physical and mental health was measured with the Short-Form Health Survey. Comorbidity burden was assessed by the total number of identified comorbidity conditions and by the Charlson comorbidity score. Linear mixed models were used to estimate the effects of time, gender and comorbidity burden on HRQoL. Whereas HRQoL improved after coronary revascularization in all patients, female patients reported poorer physical health and disease-specific HRQoL and their physical health improved more slowly than male patients. A higher comorbidity burden was related with poorer physical health and disease-specific HRQoL in male patients, but not in female patients. A higher comorbidity burden was associated with slower improvement in HRQoL for both female and male patients. Female patients reported poorer HRQoL and their physical health improved more slowly after coronary revascularization, irrespective of comorbidity burden. Higher comorbidity burden was associated with poorer physical health and disease-specific HRQoL in male patients only. Our results indicate that female and male patients recover differently after coronary revascularization. These findings highlight the importance of comorbidity- and gender-specific approaches for evaluating coronary artery disease and coronary revascularization procedures.

Ecological momentary assessment versus retrospective assessment for measuring change in health-related quality of life following cardiac intervention.

BACKGROUND: Measuring change in health-related quality-of-life (HRQoL) is important to assess the impact of disease and/or treatment. Ecological momentary assessment (EMA) comprises the repeated assessment of momentary HRQoL in the natural environment and is particularly suited to capture daily experiences. Our objective was to study whether change in momentary measures or retrospective measures of HRQoL are more strongly associated with criterion measures of change in HRQoL. Twenty-six coronary artery disease patients completed momentary and retrospective HRQoL questionnaires before and after coronary revascularization. Momentary HRQoL was assessed with 14 items which were repeatedly presented 9 times a day for 7 consecutive days. Each momentary assessment period was followed by a retrospective HRQoL questionnaire that used the same items, albeit phrased in the past tense and employing a one-week time frame. Criterion measures of change comprised the New York Heart Association functioning classification system and the Subjective Significance Change Questionnaire. Regression analysis was used to determine the association of momentary and retrospective HRQoL change with the criterion measures of change. RESULTS: Change according to momentary HRQoL items was more strongly associated with criterion measures of change than change according to retrospective HRQoL items. Five of 14 momentary items were significantly associated with the criterion measures. One association was found for the retrospective items, however, in the unexpected direction. CONCLUSION: Momentary HRQoL measures better captured change in HRQoL after cardiac intervention than retrospective HRQoL measures. EMA is a valuable expansion of the armamentarium of psychometrically sound HRQoL measures.

The dynamics in health-related quality of life of patients with stable coronary artery disease were revealed: a network analysis.

OBJECTIVE: Health-related quality of life (HRQoL) is a dynamic construct. Experience sampling methods (ESM) are becoming increasingly popular to capture within-person fluctuations in HRQoL. An emerging approach to analyze such momentary data is network analysis. Our aim was to explore the use of network analysis for investigating the dynamics within individual's HRQoL. STUDY DESIGN AND SETTING: We analyzed ESM data of 30 patients with stable coronary artery disease (CAD). Patients completed eight HRQoL items representing four scales (i.e., positive mood, negative mood, CAD symptoms, and physical state) at nine times a day for seven consecutive days. Network analysis was used to analyze the data at group level to estimate the average HRQoL dynamics and at patient level to estimate HRQoL dynamics of individual patients. RESULTS: Group-level analysis showed that, on average, feeling "tired" and feeling "anxious" are the most central items in patients' HRQoL. Patient-level analysis revealed differences in patients' network structures, indicating within-person differences in HRQoL dynamics. CONCLUSION: This study is one of the first to apply network analysis to momentary HRQoL data. To the extent that network models are meaningful representations of HRQoL dynamics, they may help deepening our insight into experienced HRQoL and provide targets for personalized treatment.