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

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

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

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

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

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

An intelligent mHealth-based adjunct to improve the management of patients with cardiovascular disease

Regular recording of vital signs, modification of lifestyle behaviour and monitoring of health progress has been shown to be effective to better manage patients with cardiovascular disease (CVD). Despite this, there remain significant hospital readmissions due to CVD exacerbations. This thesis investigated if the readmission rate of CVD patients could be reduced through remote longitudinal monitoring of physiological measurements and by offering a mobile health (mHealth)-based adjunct to assist in lifestyle modification. The thesis also investigated if there was a relationship between patient engagement and their clinical outcomes. To improve the remote management of CVD patients, the architecture of an intelligent mHealth adjunct called Total Cardiac Care (TCC) was developed based around a smartphone app and wireless peripherals to record physiological data and patient activity. The system also enabled the clinician to regularly monitor the patients’ condition using a web portal, facilitating the timely interventions when deemed necessary. The proposed system feasibility was investigated in a pilot trial, where it was widely accepted by both younger and older CVD patients with a high satisfaction rate (89.5%). The participants also had a high engagement rate with the different monitoring features (BP 77.2%, weight 74.3% and activity 84.8%). The results of a randomised controlled trial in which CVD patients (n = 164) were randomly assigned to either the mHealth intervention group or a traditional care control group identified a significant reduction in the 6-month all-cause (21 vs 41, risk reduction 49%, p = 0.015) and cardiac readmission (11 vs 25, risk reduction 56%, p = 0.025) risk when comparing the intervention cohort against the control cohort. These results suggest that the mHealth adjunct could increase the CVD patient’s engagement and the monitoring of physiological measurements and activity along with modified lifestyle behaviour over the long term could improve their cardiac health and decrease adverse events. To predict the CVD patient’s exacerbation, a model capable of detecting worsening events based on the critical change in the longitudinal physiological trends was developed using telemonitoring data collected from the intervention cohort. The model correctly predicted the CVD exacerbation events with 86.4% sensitivity, 58.4% specificity and 59.7% accuracy. This highlights that the integration of an exacerbation prediction model with the mHealth adjunct could enhance the quality of remote monitoring care provided to CVD patients