Dataset Information

Remote monitoring for heart failure: Assessing the risks of readmission and mortality.

ABSTRACT:

Study objective

Remote monitoring (RM) can help patients with heart failure (HF) remain free of hospitalization. Our objective was to implement a patient-centered RM program that ensured timely clinical response, which would be associated with reduced mortality.

Design

This was a retrospective, observational, propensity-matched study.

Setting

A large regional health system between 9/1/2016-1/31/2018.

Participants

Patients admitted with acute HF exacerbation were matched on key variables. Up to two comparison patients were selected for each RM user.

Interventions

We used an algorithmic approach to assess daily physiologic data, assess symptoms, provide patient education, encourage patient self-management, and triage medical problems.

Main outcome measures

We assessed all-cause mortality using Kaplan-Meier and log rank analysis. We used Cox proportional hazards to compare risk of death.

Results

Our cohort of 680 RM users and 1198 comparisons were similar across baseline characteristics except age (74.7 years versus 76.6 years, p < 0.001, respectively). Having one or more admissions in the preceding 120 days was more prevalent in the RM group (35.9% versus 29.8%, p = 0.013). The 30- and 90-day all-cause readmission rates were each higher among the RM users compared with the comparison patients (p = 0.013 and p < 0.001 for 30 and 90 days, respectively). Mortality was lower in the RM group at 30 and 90 days post-discharge (p < 0.001).

Conclusions

RM that responds to biometric data and encourages patient self-management can be used in a large hospital system and is associated with decreased all-cause mortality. Our findings underscore RM technology as a method to improve HF care.

SUBMITTER: Johnson AE

PROVIDER: S-EPMC10978114 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Publications

Remote monitoring for heart failure: Assessing the risks of readmission and mortality.

Johnson Amber E AE Bilderback Andrew L AL Boninger Michael M Beatty Kathryn Y KY Bellon Johanna J Leopold Meagan C MC Watson Andrew R AR Ramani Ravi N RN

American heart journal plus : cardiology research and practice 20210911

<h4>Study objective</h4>Remote monitoring (RM) can help patients with heart failure (HF) remain free of hospitalization. Our objective was to implement a patient-centered RM program that ensured timely clinical response, which would be associated with reduced mortality.<h4>Design</h4>This was a retrospective, observational, propensity-matched study.<h4>Setting</h4>A large regional health system between 9/1/2016-1/31/2018.<h4>Participants</h4>Patients admitted with acute HF exacerbation were matc ...[more]

PMID: 38550399

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Project description:This study intends to predict in-hospital and 6-month mortality, as well as 30-day and 90-day hospital readmission, using Machine Learning (ML) approach via conventional features. A total of 737 patients remained after applying the exclusion criteria to 1101 heart failure patients. Thirty-four conventional features were collected for each patient. First, the data were divided into train and test cohorts with a 70-30% ratio. Then train data were normalized using the Z-score method, and its mean and standard deviation were applied to the test data. Subsequently, Boruta, RFE, and MRMR feature selection methods were utilized to select more important features in the training set. In the next step, eight ML approaches were used for modeling. Next, hyperparameters were optimized using tenfold cross-validation and grid search in the train dataset. All model development steps (normalization, feature selection, and hyperparameter optimization) were performed on a train set without touching the hold-out test set. Then, bootstrapping was done 1000 times on the hold-out test data. Finally, the obtained results were evaluated using four metrics: area under the ROC curve (AUC), accuracy (ACC), specificity (SPE), and sensitivity (SEN). The RFE-LR (AUC: 0.91, ACC: 0.84, SPE: 0.84, SEN: 0.83) and Boruta-LR (AUC: 0.90, ACC: 0.85, SPE: 0.85, SEN: 0.83) models generated the best results in terms of in-hospital mortality. In terms of 30-day rehospitalization, Boruta-SVM (AUC: 0.73, ACC: 0.81, SPE: 0.85, SEN: 0.50) and MRMR-LR (AUC: 0.71, ACC: 0.68, SPE: 0.69, SEN: 0.63) models performed the best. The best model for 3-month rehospitalization was MRMR-KNN (AUC: 0.60, ACC: 0.63, SPE: 0.66, SEN: 0.53) and regarding 6-month mortality, the MRMR-LR (AUC: 0.61, ACC: 0.63, SPE: 0.44, SEN: 0.66) and MRMR-NB (AUC: 0.59, ACC: 0.61, SPE: 0.48, SEN: 0.63) models outperformed the others. Reliable models were developed in 30-day rehospitalization and in-hospital mortality using conventional features and ML techniques. Such models can effectively personalize treatment, decision-making, and wiser budget allocation. Obtained results in 3-month rehospitalization and 6-month mortality endpoints were not astonishing and further experiments with additional information are needed to fetch promising results in these endpoints.

Project description:BackgroundHeart failure (HF) is one of the most common reasons for hospital admission and is a major cause of morbidity, mortality, and increasing health care costs. The EMPOWER study was a randomized trial that used remote monitoring technology to track patients' weight and diuretic adherence and a state-of-the-art approach derived from behavioral economics to motivate adherence to the reverse monitoring technology.ObjectiveThe goal was to explore patient and clinician perceptions of the program and its impact on perceived health outcomes and better understand why some patients or clinicians did better or worse than others in response to the intervention.ApproachThis was a retrospective qualitative study utilizing semi-structured interviews with 43 patients and 16 clinicians to understand the trial's processes, reflecting on successes and areas for improvement for future iterations of behavioral economic interventions.Key resultsMany patients felt supported, and they appreciated the intervention. Many also appreciated the lottery intervention, and while it was not an incentive for enrolling for many respondents, it may have increased adherence during the study. Clinicians felt that the intervention integrated well into their workflow, but the number of alerts was burdensome. Additionally, responses to alerts varied considerably by provider, perhaps because there are no professional guidelines for alerts unaccompanied by severe symptoms.ConclusionOur qualitative analysis indicates potential areas for additional exploration and consideration to design better behavioral economic interventions to improve cardiovascular health outcomes for patients with HF. Patients appreciated lottery incentives for adhering to program requirements; however, many were too far along in their disease progression to benefit from the intervention. Clinicians found the amount and frequency of electronic alerts burdensome and felt they did not improve patient outcomes.Trial registrationClinicalTrials.gov Identifier: NCT02708654.

Project description:AimsThis study aimed to review the performance of machine learning (ML) methods compared with conventional statistical models (CSMs) for predicting readmission and mortality in patients with heart failure (HF) and to present an approach to formally evaluate the quality of studies using ML algorithms for prediction modelling.Methods and resultsFollowing Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we performed a systematic literature search using MEDLINE, EPUB, Cochrane CENTRAL, EMBASE, INSPEC, ACM Library, and Web of Science. Eligible studies included primary research articles published between January 2000 and July 2020 comparing ML and CSMs in mortality and readmission prognosis of initially hospitalized HF patients. Data were extracted and analysed by two independent reviewers. A modified CHARMS checklist was developed in consultation with ML and biostatistics experts for quality assessment and was utilized to evaluate studies for risk of bias. Of 4322 articles identified and screened by two independent reviewers, 172 were deemed eligible for a full-text review. The final set comprised 20 articles and 686 842 patients. ML methods included random forests (n = 11), decision trees (n = 5), regression trees (n = 3), support vector machines (n = 9), neural networks (n = 12), and Bayesian techniques (n = 3). CSMs included logistic regression (n = 16), Cox regression (n = 3), or Poisson regression (n = 3). In 15 studies, readmission was examined at multiple time points ranging from 30 to 180 day readmission, with the majority of studies (n = 12) presenting prediction models for 30 day readmission outcomes. Of a total of 21 time-point comparisons, ML-derived c-indices were higher than CSM-derived c-indices in 16 of the 21 comparisons. In seven studies, mortality was examined at 9 time points ranging from in-hospital mortality to 1 year survival; of these nine, seven reported higher c-indices using ML. Two of these seven studies reported survival analyses utilizing random survival forests in their ML prediction models. Both reported higher c-indices when using ML compared with CSMs. A limitation of studies using ML techniques was that the majority were not externally validated, and calibration was rarely assessed. In the only study that was externally validated in a separate dataset, ML was superior to CSMs (c-indices 0.913 vs. 0.835).ConclusionsML algorithms had better discrimination than CSMs in most studies aiming to predict risk of readmission and mortality in HF patients. Based on our review, there is a need for external validation of ML-based studies of prediction modelling. We suggest that ML-based studies should also be evaluated using clinical quality standards for prognosis research. Registration: PROSPERO CRD42020134867.

Project description:BackgroundAround 50% of hospital readmissions due to heart failure are preventable, with lack of adherence to prescribed self-care as a driving factor. Remote tracking and reminders issued by mobile health devices could help to promote self-care, which could potentially reduce these readmissions.ObjectiveWe sought to investigate two factors: (1) feasibility of enrolling heart failure patients in a remote monitoring regimen that uses wireless sensors and patient-reported outcome measures; and (2) their adherence to using the study devices and completing patient-reported outcome measures.MethodsTwenty heart failure patients participated in piloting a remote monitoring regimen. Data collection included: (1) physical activity using wrist-worn activity trackers; (2) body weight using bathroom scales; (3) medication adherence using smart pill bottles; and (4) patient -reported outcomes using patient-reported outcome measures.ResultsWe evaluated 150 hospitalized heart failure patients and enrolled 20 individuals. Two factors contributed to 50% (65/130) being excluded from the study: smartphone ownership and patient discharge. Over the course of the study, 60.0% of the subjects wore the activity tracker for at least 70% of the hours, and 45.0% used the scale for more than 70% of the days. The pill bottle was used less than 10% of the days by 55.0% of the subjects.ConclusionsOur method of recruiting heart failure patients prior to hospital discharge may not be feasible as the enrollment rate was low. Once enrolled, the majority of subjects maintained a high adherence to wearing the activity tracker but low adherence to using the pill bottle and completing the follow-up surveys. Scale usage was fair, but it received positive reviews from most subjects. Given the observed usage and feedback, we suggest mobile health-driven interventions consider including an activity tracker and bathroom scale. We also recommend administering a shorter survey more regularly and through an easier interface.

Dataset Information

Remote monitoring for heart failure: Assessing the risks of readmission and mortality.

Study objective

Design

Setting

Participants

Interventions

Main outcome measures

Results

Conclusions

Publications

Remote monitoring for heart failure: Assessing the risks of readmission and mortality.

Similar Datasets

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