Developing a personalized remote patient monitoring algorithm: a proof-of-concept in heart failure

Mehran Moazeni, Lieke Numan, Maaike Brons, Jaco Houtgraaf, Frans H Rutten, Daniel L Oberski, Linda W van Laake, Folkert W Asselbergs, Emmeke Aarts*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

AIMS: Non-invasive remote patient monitoring is an increasingly popular technique to aid clinicians in the early detection of worsening heart failure (HF) alongside regular follow-ups. However, previous studies have shown mixed results in the performance of such systems. Therefore, we developed and evaluated a personalized monitoring algorithm aimed at increasing positive-predictive-value (PPV) (i.e. alarm quality) and compared performance with simple rule-of-thumb and moving average convergence-divergence algorithms (MACD).

METHODS AND RESULTS: In this proof-of-concept study, the developed algorithm was applied to retrospective data of daily bodyweight, heart rate, and systolic blood pressure of 74 HF-patients with a median observation period of 327 days (IQR: 183 days), during which 31 patients experienced 64 clinical worsening HF episodes. The algorithm combined information on both the monitored patients and a group of stable HF patients, and is increasingly personalized over time, using linear mixed-effect modelling and statistical process control charts. Optimized on alarm quality, heart rate showed the highest PPV (Personalized: 92%, MACD: 2%, Rule-of-thumb: 7%) with an F1 score of (Personalized: 28%, MACD: 6%, Rule-of-thumb: 8%). Bodyweight demonstrated the lowest PPV (Personalized: 16%, MACD: 0%, Rule-of-thumb: 6%) and F1 score (Personalized: 10%, MACD: 3%, Rule-of-thumb: 7%) overall compared methods.

CONCLUSION: The personalized algorithm with flexible patient-tailored thresholds led to higher PPV, and performance was more sensitive compared to common simple monitoring methods (rule-of-thumb and MACD). However, many episodes of worsening HF remained undetected. Heart rate and systolic blood pressure monitoring outperformed bodyweight in predicting worsening HF. The algorithm source code is publicly available for future validation and improvement.

Original languageEnglish
Pages (from-to)455-463
Number of pages9
JournalEuropean Heart Journal - Digital Health
Volume4
Issue number6
DOIs
Publication statusPublished - 23 Aug 2023

Keywords

  • Intensive longitudinal data
  • Remote patient monitoring
  • Process monitoring
  • Statistical process control chart
  • Heart failure
  • Dynamic monitoring

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