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The aim of the study was to demonstrate the effectiveness of telemonitoring functional status and vital signs to early detect heart failure exacerbation, and to minimize readmissions and length of hospitalisations.
Patients over 75 with heart failure were included after a hospitalisation due to heart failure exacerbation. Patients were assigned randomly into intervention or control. The intervention group comprised 47 patients who were assessed through telemonitoring, while 40 followed traditional clinical pathways.
Patients were followed-up for 3 months after discharge, collecting emergency visits and readmissions due to a new heart failure exacerbation. Those patients in the intervention group used a commercial telemedicine system: Careline H@me, which was provided by the company SALUDNOVA. They system was personalized by adding the functional status monitoring capabilities. Thus, the system collected vital signs (i.e. blood pressure, heart rate, respiratory rate, oxygen saturation, glucose, and weight); symptoms of decompensated heart failure (i.e. dyspnoea and orthopnoea); and functional status (i.e. part of the Short Physical Performance Battery, SPPB) and a brief questionnaire (i.e. Do you have the medication?) every 48 hours.
Two staff geriatricians at the Hospital Universitario de Getafe accessed, during working days, a secured dedicated web-portal to assess the progression of their patients, evaluated through the monitored variables and the self reported symptoms. They reacted to the data, if needed, making a phone call or visiting the patient. Besides, patients could contact the geriatricians or attending the emergency room as usually.
After completion, we analysed the results. First, we carried out a descriptive analysis of the data. Later, we analysed the effects of the intervention with telemedicine and the predictive values of the different measured variables through logistic regressions.
Full description
Patient registries were carried out in two different Databases: one included general information on patients and epidemiological data. The other database stored the monitored data obtained every 48 hours.
Quality assurance and source data verification was achieved by retrieving general information on patients from the Hospital Electronic Medical Record. Moreover epidemiological information was assessed by the Principal Investigator who removed outlier values and assessed the occurrence of sharp changes in temporal series (e.g. changes >0.5Kg in two days).
Patients were recruited at the acute unit of the Geriatrics Service. When they were admitted due a heart failure exacerbation a trained nurse performed the initial assess. If the patient was suitable, a geriatrician explained to him /her the study and if the patient accepted we was asked to sign the informed consent. Then he was assigned randomly to a group. For those in the intervention group, they were trained by the geriatrician and the nurse on how to perform the measurements in the mobile phone. For those in the control group, the staff recorded the baseline characteristics, and during the follow-up the emergency visits, readmissions and death was recorded. These latter variables were also recorded in the patients of the intervention group.
The data dictionary included variables identifying patients and others describing their status and evolution:
Missing data were excluded from the analysis, the registry was excluded. If a patient registered more than one measurement for the same expected data, the latest remained, and the rest was deleted.
Statistical analysis:
First, we carried out a descriptive analysis of the data. Later, we analysed the effects of the intervention with telemedicine and the predictive values of the different measured variables through logistic regressions.
Descriptive data are presented as median values (IQR). Baseline characteristics between control and intervention groups were compared using non-parametric tests (Mann-Whitney).
In order to determine the effects of the intervention, the analysis was divided in two parts. First, a logistic regression model was fitted to determine if the patient monitorization (yes/no) significantly predict worsening using age and sex as covariates. Then, a new variable, hospitalization length of stay, was created with the length of stay for any readmission in each group to assess whether the use of telemonitoring affected the length of hospitalisation. Both control and intervention groups were compared using Mann-Whitney.
Finally, in the intervention group, the same logistic regression model was fitted to determine the factors that significantly predict worsening.
All analyses were made with the Statistical Package R for windows (Vienna, Austria) (http://www.r-project.org), version 3.1.1. P-value was set at level <0.05.
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90 participants in 2 patient groups
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Data sourced from clinicaltrials.gov
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