Background
The Heart Failure Association of the European Society of Cardiology defines heart failure (HF) as “a clinical syndrome characterized by typical symptoms (e.g. breathlessness, ankle swelling and fatigue) that may accompanied by signs (e.g. elevated jugular venous pressure, pulmonary crackles and peripheral edema) caused by a structural and/or functional cardiac abnormality, resulting in a reduced cardiac output and/or elevated intracardiac pressures at rest or during stress” [
1]. HF is a highly prevalent condition, associated with significant morbidity and a poor prognosis [
2‐
4]. A 2013 update from the American Heart Association estimated that there are 5.1 million people with HF in USA and 23 million worldwide [
5]. The incidence of HF also increases significantly with age, and hence, because of the aging population, the prevalence of HF can be expected to increase substantially in the future [
6]. In addition, the majority of patients with HF experience a considerable reduction in health-related quality of life (HRQoL) [
7].
The HRQoL of patients with HF is an important outcome as it reflects the impact of HF on their daily lives [
8]. HF patients experience high levels of physical, functional and emotional distress [
9]. Indeed, there is evidence that adults with HF have poorer HRQoL than those without HF [
10‐
13].
In recent decades, various specific HRQoL questionnaires for patients with HF have become regarded as important assessment tools [
14‐
17]. Among these, one of the most widely known and used is the Minnesota Living with Heart Failure Questionnaire (MLHFQ) [
16,
18]. It is a self-administered disease-specific questionnaire for patients with HF [
19], comprising 21 items. It provides a total score as well as scores for two domains, physical and emotional. The questionnaire has been translated into and validated in Spanish [
14].
There are several definitions of measurement responsiveness [
20,
21]. For this study, we defined responsiveness as the ability of an instrument to detect real changes in the concept being measured [
22]. In the Spanish version of the MLHFQ, responsiveness has not been studied in depth and it is very important to determine whether an HRQoL questionnaire is able detect changes over time in the patient that occur naturally or due to clinical intervention. In addition, HRQoL questionnaires should be easily interpretable by clinicians, and one of the most common ways to facilitate interpretation is ascertaining the minimal detectable change (MDC) and the minimal clinically important difference (MCID) [
23]. The latter may vary by patient characteristics or clinical status.
Therefore, the objective of the present study was to examine the responsiveness of the MLHFQ, using distribution and anchor-based approaches [
21,
22,
24‐
26]. Among distribution-based methods, we calculated the MDC at the individual level. We also compared the MDC with the MCID for each domain [
23,
27]. To the best of our knowledge, only one previous study has analyzed the responsiveness of the Spanish version of the MLHFQ [
14], and our study is the first to explore the responsiveness of this questionnaire by estimating MDC and MCID in Spain.
Discussion
The results of this prospective observational study with a large sample of patients with HF offer new information about the responsiveness, MCID and MDC of the MLHFQ. This questionnaire was highly responsive capturing changes in HRQoL 6 months after discharge.
There was extensive evidence supporting responsiveness of the MLHFQ and its capacity to discriminate between different magnitudes of change in patients’ HRQoL. A systemic review with meta-analyses carried out by Garin et al. evaluate and compare data on the conceptual model and metric properties of several HF specific HRQoL instruments and conclude that they would primarily support the use of the MLHFQ [
15].
The small ceiling and floor effects and the use of the full range of scores in a sample which covers the full range of severity, indicate that the instrument is likely to detect improvement or deterioration.
We have analyzed the validity and reliability of our anchor questions through correlations, as described in the literature [
42,
43]. Partial correlations between anchor question responses and change scores were nearly 0.50 for all MLHFQ domains. This could be due to the anchor question for the total score not being a direct question asked to the patient, but rather a calculated response. Specifically, it was calculated by combining the responses to the other two anchor questions, and this could be expected to affect its validity and reliability.
In our study, the anchor question responses indicated that patients who reported improvement gained more points than patients who remained the same or worsened in all domains of the MLHFQ. In line with this, a large effect (SES > 0.90) was found in “improved” patients in the physical domain and total score, and a moderate effect (SES 0.57) in the emotional domain. Taken together, these findings suggest that all domains of the MLHFQ have a good sensitivity to change in our population. These results are similar to those of previous responsiveness studies in other languages [
14,
18,
30,
44,
45], and what is more, our SES and SRM for “improved” patients and our change scores for improvement in all domains are larger than those reported in the other studies analyzed [
14,
18,
44,
45]. The SES has also been found to be lower in the emotional domain than in the other domains in several MLHFQ responsiveness studies [
14,
30,
45]. Nevertheless, in our case, this effect was moderate, being larger than SES values considered non-significant or small in other responsiveness studies.
On the other hand, in “worsened” patients, the effect size is small or not significant in nearly all domains. That is, the instrument is more responsive to improvement than worsening, not reflecting well changes in patients whose health deteriorates. These results are similar to the outcomes in the Spanish validation of the MLHFQ, the effect sizes for the patients showing “deterioration” only being > 0.26 for all domains [
14]. In line with this, other studies carried out in US [
45,
46] also found this questionnaire to lack discriminative power for detecting negative changes. For this reason, the MCID for “worsened” patients was not calculated. Nevertheless, for patients whose response to the anchor question was “somewhat better”, we found that changes were large enough to exceed the MDC at the individual level with a 95% level of confidence in the physical domain and total score of the MLHFQ. That is, in both cases, the change observed was greater than that required to be considered a true change, and hence, the MLHFQ can be considered responsive to detect true changes in HF patients at the individual level in the physical domain and total score.
Considering cut-off values determined by the ROC analysis, we found less conservative values for MCID, patients needing fewer points to detect such a difference. In addition, the ratio obtained from ROC analysis was not greater than 1, meaning that the change could not be distinguished from measurement error. We conclude that the MCID based on our anchor questions is more appropriate for detecting true changes.
Just like in our study, the findings of responsiveness and sensitivity of MLHFQ in other language versions show similar results. In general, patients with an improvement (measured by different ways), on overage, experienced large improvements in HRQoL. However patients with no change still experienced a moderate improvement, and those who worsened, on average, had little to no change in HRQoL [
14,
18,
44‐
48]. Likewise, these studies confirm that the MCID in the MLHFQ exceeded predefined criteria and be more clinically valid for patients with HF than other instruments [
48].
This study has various limitations that should be taken into account. Comparing responders and non-responders, those who did not respond were found to have poorer baseline MLHFQ. This might have skewed the result but any such bias would have been in our favor, as patients with a poorer health status at baseline tend to have more gains in HRQoL in follow-up. Hence, the effect size might have been smaller in our responders than it would have been in the non-responders. In addition, the differences identified may have been statistically significant due to the large sample size, while not seeming to be clinically relevant. ES are measures of the magnitude of the change scores, rather than the validity of the change scores. Therefore, ES should be considered inappropriate as parameters of the responsiveness. However, we have included these measures because they are frequently used and easily identifiable in the literature. On the other hand, we have used the Spanish version of the questionnaire, and therefore, the results may not be generalizable to other population or languages.
Nevertheless, the current study also has a number of strengths. Despite there being other studies of MLHFQ responsiveness, to the best of our knowledge, this is the first study that explores the responsiveness of the MLHFQ by estimating MDC and MCID in Spain. The present study provides data on responsiveness of the MLHFQ that could help to interpret changes detected by the questionnaire. In particular, the analysis included calculation of the MDC and MCID, a type of anchor-based method that is considered important for patients and clinicians and directly reflects their points of view [
49].
Recently, Bilbao et al. [
50] compared different factor structures of the MLHFQ proposed by several authors and found that their results supported the existence of a third factor, a social dimension, with good validity and reliability. They concluded that Munyombwe’s [
34] model had the best psychometric properties among the social factor proposed. Unfortunately, we have not analyzed the responsiveness of this factor because we did not have an appropriate anchor question to measure it. Further studies are needed to explore the responsiveness of the social factor proposed by Munyombwe [
34].