• heart failure
• eHealth/telemedicine/mobile health

Heart failure (HF) manifests as a severe functional limitation and myriad physiological derangements. Divining signals from this milieu is the domain of the HF specialist among whom there is an indelible belief that more effective HF care intrinsically arises from more communication, more measurement and, increasingly, more complex methods of acquiring and processing patient-generated data. Mobile technologies and electronically integrated systems of care seem to facilitate perfectly the steps of data generation, transmission, synthesis, interpretation, action and, ultimately, benefit to the patient. Unfortunately, realising improved outcomes among persons with HF enabled by outpatient monitoring has been elusive.

In this issue of Heart, Rahimi et al1 present results of a randomised trial designed to test whether centralised decision support optimised medical therapies and improved quality of life among patients compared with monitoring without centralised decision support. This hypothesis is of paramount importance. The authors’ study employs state-of-the-art blinding, randomisation, endpoint measurement and analysis to maximise the validity of their study. Nonetheless, the study ultimately could not conclude that expert support and alerts improved the primary outcome of medication use and dosing and quality of life compared with monitoring alone. The weighted change in medication optimisation demonstrated an improvement of 3% in the control group and 8% in the intervention group (p=0.20) while Minnesota Living with Heart Failure Questionnaire values increased by 0.66 in the control group and 0.3 in the experimental group (p=0.63). What factors led to this negative finding and to the increasingly large body of work demonstrating little effect of mobile health (mHealth) technologies on patient outcomes? Some clues are available from previous work in delivery of decision support, the study design and previous trials of home monitoring in HF.

This study is an example of a provider-centred clinical decision support (CDS) intervention that takes data collected in the home, analyses that data to create actionable knowledge and provides the knowledge back to a central clinical management (CCM) team via alerts. As in this clinical trial, mHealth technologies should be thought of as CDS. Provider-centred CDS does not always improve patient outcomes, but there are several components of CDS that are critical for success.2 CDS must automate the decision support, the CDS must provide recommendations not just an assessment, the CDS must be at the time and location of decision-making and CDS must be computerised. Others have shown that medication therapy in HF can be improved if these criteria are met.3 The CDS intervention in this trial clearly meets some, but not all of these criteria. The CDS was automated, provided recommendations and was computerised. Prescriptive authority delivered at the time of interface with the patient is one essential component potentially missing from the authors’ CDS system. The CCM seems, by nature of the name, to be a team outside of the patient’s normal care team acting asynchronously, which could have led to lack of effect on medication adjustment.4

The authors’ use of an opportunity score helps set boundaries for possible improvement in medication use and dose. The trial enrolled patients with both HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF). Among patients with HFpEF, where few evidence-based treatments exist and where improving quality of life with medications is not well described, medications had little opportunity for change compared with patients with HFrEF and may have diluted the overall effect of the CCM. The HFpEF group simply had fewer opportunities for the CCM to act on. Medications were not frequently changed in the HFrEF group and, given the link between medication use and quality of life, the quality of life measure did not improve. Refining the population under study may help identify a benefit of CCMs in future trials.

The most successful study of a remote monitoring system to date used a pulmonary artery (PA) pressure monitor.5 The PA pressure monitor paired with a low-effort transmission system, centralised decision support for medication changes and demonstrated a reduction in HF readmission in both HFpEF and HFrEF demonstrated a reduction in hospitalisations. PA pressure monitoring may have achieved this success given the large lead time for anticipating HF decompensation provided by the device combined with known methods for correcting impending volume overload. Other invasive and non-invasive systems of HF monitoring have not appreciated the success of PA pressure monitoring. In the present study, the link between all aspects of the acquisition and delivery system may not have been present and affecting change in medication optimisation and quality of life may be difficult to establish when the parameters for doing so are generally known, although obtained and acted on inefficiently.

Given the prevailing view that more interaction leads to better care in HF, which has only been supported on rare occasion with evidence, the ever-expending ability to obtain data from patients, the increasing ability to synthesise those data and the myriad treatments for potential optimisation in HF, further studies similar to Rahimi et al are inevitable and, hopefully, successful. Such studies may not demonstrate effect for a number of reasons including failure of any one component in the pathway studied or the study design itself. Similar to studies of medications and device therapies that fail in late stages of study, the future for telemonitoring may be equally fraught with highly mature technologies failing to translate into clinical results.

Translating data into knowledge that can drive provider performance is arguably the defining function of HF monitoring technologies and a crux. The path between data acquisition and outcome reduction is elusive and must ultimately converge on timely delivery of treatment that affects health and well-being. The interdependence of the components of the many complex strategies tested in remote monitoring trials makes incremental knowledge difficult. Development of programmes that reward providers for achieving either optimal care or patient-centred goals may offer progress in the management of chronic HF more so than remote services.