Simulated case management of home telemonitoring to assess the impact of different alert algorithms on work-load and clinical decisions.

BACKGROUND: Home telemonitoring (HTM) of chronic heart failure (HF) promises to improve care by timely indications when a patient's condition is worsening. Simple rules of sudden weight change have been demonstrated to generate many alerts with poor sensitivity. Trend alert algorithms and bio-impedance (a more sensitive marker of fluid change), should produce fewer false alerts and reduce workload. However, comparisons between such approaches on the decisions made and the time spent reviewing alerts has not been studied. METHODS: Using HTM data from an observational trial of 91 HF patients, a simulated telemonitoring station was created and used to present virtual caseloads to clinicians experienced with HF HTM systems. Clinicians were randomised to either a simple (i.e. an increase of 2 kg in the past 3 days) or advanced alert method (either a moving average weight algorithm or bio-impedance cumulative sum algorithm). RESULTS: In total 16 clinicians reviewed the caseloads, 8 randomised to a simple alert method and 8 to the advanced alert methods. Total time to review the caseloads was lower in the advanced arms than the simple arm (80 ± 42 vs. 149 ± 82 min) but agreements on actions between clinicians were low (Fleiss kappa 0.33 and 0.31) and despite having high sensitivity many alerts in the bio-impedance arm were not considered to need further action. CONCLUSION: Advanced alerting algorithms with higher specificity are likely to reduce the time spent by clinicians and increase the percentage of time spent on changes rated as most meaningful. Work is needed to present bio-impedance alerts in a manner which is intuitive for clinicians.

Are there patients missing from community heart failure registers? An audit of clinical practice.

BACKGROUND: General practitioners in the UK are financially incentivised, via the Quality Outcomes Framework, to maintain a record of all patients at their practice with heart failure and manage them appropriately. The prevalence of heart failure recorded in primary care registers (0.7-1.0%) is less than reported in epidemiological studies (3-5%). Using an audit of clinical practice, we set out to investigate if there are patients 'missing' from primary care heart failure registers and what the underlying mechanisms might be. DESIGN: The design of this study was as an audit of clinical practice at a UK general practice ( n = 9390). METHODS: Audit software (ENHANCE-HF) was used to identify patients who may have heart failure via a series of hierarchical searches of electronic records. Heart failure was then confirmed or excluded based on the electronic records by a heart failure specialist nurse and patients added to the register. Outcome data for patients without heart failure was collected after two years. RESULTS: Heart failure prevalence was 0.63% at baseline and 1.12% after the audit. Inaccurate coding accounted for the majority of missing patients. Amongst patients without heart failure who were taking a loop diuretic, the rate of incident heart failure was 13% and the rate of death or hospitalization with heart failure was 25% respectively during two-year follow-up. CONCLUSION: There are many patients missing from community heart failure registers which may detriment patient outcome and practice income. Patients without heart failure who take loop diuretics are at high risk of heart failure-related events.

What impact do anxiety, depression, perceived control and technology capability have on whether patients with chronic heart failure take-up or continue to use home tele-monitoring services? Study design of ADaPT-HF.

BACKGROUND: Home tele-monitoring (HTM) is used to monitor the clinical signs and symptoms of patients with chronic heart failure (CHF) in order to reduce unplanned hospital admissions. However, not all patients who are referred will agree to use HTM, and some patients choose to withdraw early from its use. AIMS: ADaPT-HF will investigate whether depression, anxiety, low perceived control, reduced technology capability, level of education, age or the severity or complexity of a patient's illness can predict refusal of, or early withdrawal from, HTM in patients with CHF. METHODS: The study will recruit 288 patients who have been recently admitted to hospital with heart failure who have been referred for HTM. At the time of referral, patients will complete depression (nine-item Patient Health Questionnaire), anxiety (seven-item Generalised Anxiety Disorder questionnaire), perceived control (eight-item revised Controlled Attitudes Scale) and technology capability (ten-item Technology Readiness Index 2.0) screening questionnaires. In addition, data on demographics, diagnosis, clinical examination, socio-economic status, history of comorbidities, medication, biochemistry and haematology will be recorded. The primary outcome will be a composite of refusal of or early withdrawal from HTM. The principle analysis will be made using logistic regression. CONCLUSION: By establishing which factors influence a patient's decision to refuse or withdraw early from HTM, it may be possible to redesign HTM referral processes. It may be that patients with CHF who also have depression, anxiety, low control and poor technology skills should not be referred until they receive appropriate support or that they should be managed differently when they do receive HTM. The results of ADAPT-HF may provide a way of making more efficient and cost-effective use of HTM services.