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- Brons, Maaike, et al.
(författare)
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Patterns in the Use of Heart Failure Telemonitoring: Post Hoc Analysis of the e-Vita Heart Failure Trial
- 2023
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Ingår i: JMIR cardio. - : JMIR Publications. - 2561-1011. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- Background: Research on the use of home telemonitoring data and adherence to it can provide new insights into telemonitoring for the daily management of patients with heart failure (HF). Objective: We described the use of a telemonitoring platform—including remote patient monitoring of blood pressure, pulse, and weight—and the use of the electronic personal health record. Patient characteristics were assessed in both adherent and nonadherent patients to weight transmissions. Methods: We used the data of the e-Vita HF study, a 3-arm parallel randomized trial performed in stable patients with HF managed in outpatient clinics in the Netherlands. In this study, data were analyzed from the participants in the intervention arm (ie, e-Vita HF platform). Adherence to weight transmissions was defined as transmitting weight ≥3 times per week for at least 42 weeks during a year. Results: Data from 150 patients (mean age 67, SD 11 years; n=37, 25% female; n=123, 82% self-assessed New York Heart Association class I-II) were analyzed. One-year adherence to weight transmissions was 74% (n=111). Patients adherent to weight transmissions were less often hospitalized for HF in the 6 months before enrollment in the study compared to those who were nonadherent (n=9, 8% vs n=9, 23%; P=.02). The percentage of patients visiting the personal health record dropped steadily over time (n=140, 93% vs n=59, 39% at one year). With univariable analyses, there was no significant correlation between patient characteristics and adherence to weight transmissions. Conclusions: Adherence to remote patient monitoring was high among stable patients with HF and best for weighing; however, adherence decreased over time. Clinical and demographic variables seem not related to adherence to transmitting weight.Trial Registration: ClinicalTrials.gov NCT01755988; https://clinicaltrials.gov/ct2/show/NCT01755988
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| 2. |
- de Koning, Enrico, et al.
(författare)
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AI Algorithm to Predict Acute Coronary Syndrome in Prehospital Cardiac Care: Retrospective Cohort Study
- 2023
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Ingår i: JMIR Cardio. - : JMIR Publications. - 2561-1011. ; 7:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Overcrowding of hospitals and emergency departments (EDs) is a growing problem. However, not all ED consultations are necessary. For example, 80% of patients in the ED with chest pain do not have an acute coronary syndrome (ACS). Artificial intelligence (AI) is useful in analyzing (medical) data, and might aid health care workers in prehospital clinical decision-making before patients are presented to the hospital.Objective: The aim of this study was to develop an AI model which would be able to predict ACS before patients visit the ED. The model retrospectively analyzed prehospital data acquired by emergency medical services' nurse paramedics.Methods: Patients presenting to the emergency medical services with symptoms suggestive of ACS between September 2018 and September 2020 were included. An AI model using a supervised text classification algorithm was developed to analyze data. Data were analyzed for all 7458 patients (mean 68, SD 15 years, 54% men). Specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for control and intervention groups. At first, a machine learning (ML) algorithm (or model) was chosen; afterward, the features needed were selected and then the model was tested and improved using iterative evaluation and in a further step through hyperparameter tuning. Finally, a method was selected to explain the final AI model.Results: The AI model had a specificity of 11% and a sensitivity of 99.5% whereas usual care had a specificity of 1% and a sensitivity of 99.5%. The PPV of the AI model was 15% and the NPV was 99%. The PPV of usual care was 13% and the NPV was 94%.Conclusions: The AI model was able to predict ACS based on retrospective data from the prehospital setting. It led to an increase in specificity (from 1% to 11%) and NPV (from 94% to 99%) when compared to usual care, with a similar sensitivity. Due to the retrospective nature of this study and the singular focus on ACS it should be seen as a proof-of-concept. Other (possibly life-threatening) diagnoses were not analyzed. Future prospective validation is necessary before implementation.
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| 4. |
- Johansson, Peter, 1962-, et al.
(författare)
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Internet-Based Cognitive Behavioral Therapy and its Association With Self-efficacy, Depressive Symptoms, and Physical Activity : Secondary Analysis of a Randomized Controlled Trial in Patients With Cardiovascular Disease
- 2022
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Ingår i: JMIR Cardio. - Toronto, Canada : JMIR Publications, Inc.. - 2561-1011. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: In patients with cardiovascular disease (CVD), knowledge about the associations among changes in depressivesymptoms, self-efficacy, and self-care activities has been requested. This is because such knowledge can be helpful in the designof behavioral interventions aimed to improve self-efficacy, reduce depressive symptoms, and improve performance of self-careactivities in CVD patients.Objective: We aim to evaluate if internet-based cognitive behavioral therapy (iCBT) improves self-efficacy and explore therelationships among changes in depressive symptoms, self-efficacy, and physical activity, as well as the influence of iCBT onthese relationships.Methods: This study received funding in January 2015. Participant recruitment took place between January 2017 and February2018, and the main findings were published in 2019. This study is a secondary analysis of data collected in a randomized controlledstudy evaluating the effects of a 9-week iCBT program compared to an online discussion forum (ODF) on depressive symptomsin patients with CVD (N=144). Data were collected at baseline and at the 9-week follow-up. Analysis of covariance was used toevaluate the differences in self-efficacy between the iCBT and ODF groups. Structural equation modeling explored the relationshipsamong changes in depressive symptoms, self-efficacy, and physical activity, as well as the influence of iCBT on these relationships.Results: At follow-up, a significant difference in the increase in self-efficacy favoring iCBT was found (P=.04, Cohen d=0.27).We found an indirect association between changes in depressive symptoms and physical activity (ß=–.24, P<.01), with the changein self-efficacy acting as a mediator. iCBT had a direct effect on the changes in depressive symptoms, which in turn influencedthe changes in self-efficacy (ß=.23, P<.001) and physical activity (ß=.12, P<.001).Conclusions: Self-efficacy was improved by iCBT. However, the influence of iCBT on self-efficacy and physical activity wasmostly mediated by improvements in depressive symptoms.Trial Registration: ClinicalTrials.gov NCT02778074; https://clinicaltrials.gov/ct2/show/NCT02778074
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