Heart failure hospitalization prediction in remote patient management systems

Healthcare systems are shifting from patient care in hospitals to monitored care at home. It is expected to improve the quality of care without exploding the costs. Remote patient management (RPM) systems offer a great potential in monitoring patients with chronic diseases, like heart failure or diabetes. Patient modeling in RPM systems opens opportunities in two broad directions: personalizing information services, and alerting medical personnel about the changing conditions of a patient. In this study we focus on heart failure hospitalization (HFH) prediction, which is a particular problem of patient modeling for alerting. We formulate a short term HFH prediction problem and show how to address it with a data mining approach. We emphasize challenges related to the heterogeneity, different types and periodicity of the data available in RPM systems. We present an experimental study on HFH prediction using, which results lay a foundation for further studies and implementation of alerting and personalization services in RPM systems

Heart failure hospitalization prediction in remote patient management systems

Healthcare systems are shifting from patient care in hospitals to monitored care at home. It is expected to improve the quality of care without exploding the costs. Remote patient management (RPM) systems offer a great potential in monitoring patients with chronic diseases, like heart failure or diabetes. Patient modeling in RPM systems opens opportunities in two broad directions: personalizing information services, and alerting medical personnel about the changing conditions of a patient. In this study we focus on heart failure hospitalization (HFH) prediction, which is a particular problem of patient modeling for alerting. We formulate a short term HFH prediction problem and show how to address it with a data mining approach. We emphasize challenges related to the heterogeneity, different types and periodicity of the data available in RPM systems. We present an experimental study on HFH prediction using, which results lay a foundation for further studies and implementation of alerting and personalization services in RPM systems

Patient condition modeling in remote patient management : hospitalization prediction

In order to maintain and improve the quality of care without exploding costs, healthcare systems are undergoing a paradigm shift from patient care in the hospital to patient care at home. Remote patient management (RPM) systems offer a great potential in reducing hospitalization costs and worsening of symptoms for patients with chronic diseases, e.g., heart failure and diabetes. Different types of data collected by RPM systems provide an opportunity for personalizing information services, and alerting medical personnel about the changing conditions of the patient. In this work we focus on a particular problem of patient modeling that is the hospitalization prediction. We consider the problem definition, our approach to this problem, highlight the results of the experimental study and reflect on their use in decision making

Early indication of decompensated heart failure in patients on home-telemonitoring: a comparison of prediction algorithms based on daily weight and noninvasive transthoracic bio-impedance

Background: Heart Failure (HF) is a common reason for hospitalization. Admissions might be prevented by early detection of and intervention for decompensation. Conventionally, changes in weight, a possible measure of fluid accumulation, have been used to detect deterioration. Transthoracic impedance may be a more sensitive and accurate measure of fluid accumulation. Objective: In this study, we review previously proposed predictive algorithms using body weight and noninvasive transthoracic bio-impedance (NITTI) to predict HF decompensations. Methods: We monitored 91 patients with chronic HF for an average of 10 months using a weight scale and a wearable bio-impedance vest. Three algorithms were tested using either simple rule-of-thumb differences (RoT), moving averages (MACD), or cumulative sums (CUSUM). Results: Algorithms using NITTI in the 2 weeks preceding decompensation predicted events (P<.001); however, using weight alone did not. Cross-validation showed that NITTI improved sensitivity of all algorithms tested and that trend algorithms provided the best performance for either measurement (Weight-MACD: 33%, NITTI-CUSUM: 60%) in contrast to the simpler rules-of-thumb (Weight-RoT: 20%, NITTI-RoT: 33%) as proposed in HF guidelines. Conclusions: NITTI measurements decrease before decompensations, and combined with trend algorithms, improve the detection of HF decompensation over current guideline rules; however, many alerts are not associated with clinically overt decompensation

Remote multiparametric monitoring and management of heart failure patients through cardiac implantable electronic devices

In this review we focus on heart failure (HF) which, as known, is associated with a substantial risk of hospitalizations and adverse cardiovascular outcomes, including death. In recent years, systems to monitor cardiac function and patient parameters have been developed with the aim to detect subclinical pathophysiological changes that precede worsening HF. Several patient-specific parameters can be remotely monitored through cardiac implantable electronic devices (CIED) and can be combined in multiparametric scores predicting patients’ risk of worsening HF with good sensitivity and moderate specificity. Early patient management at the time of pre-clinical alerts remotely transmitted by CIEDs to physicians might prevent hospitalizations. However, it is not clear yet which is the best diagnostic pathway for HF patients after a CIED alert, which kind of medications should be changed or escalated, and in which case in-hospital visits or in-hospital admissions are required. Finally, the specific role of healthcare professionals involved in HF patient management under remote monitoring is still matter of definition. We analyzed recent data on multiparametric monitoring of patients with HF through CIEDs. We provided practical insights on how to timely manage CIED alarms with the aim to prevent worsening HF. We also discussed the role of biomarkers and thoracic echo in this context, and potential organizational models including multidisciplinary teams for remote care of HF patients with CIEDs

Remote multiparametric monitoring and management of heart failure patients through cardiac implantable electronic devices

In this review we focus on heart failure (HF) which, as known, is associated with a substantial risk of hospitalizations and adverse cardiovascular outcomes, including death. In recent years, systems to monitor cardiac function and patient parameters have been developed with the aim to detect subclinical pathophysiological changes that precede worsening HF. Several patient-specific parameters can be remotely monitored through cardiac implantable electronic devices (CIED) and can be combined in multiparametric scores predicting patients' risk of worsening HF with good sensitivity and moderate specificity. Early patient management at the time of pre-clinical alerts remotely transmitted by CIEDs to physicians might prevent hospitalizations. However, it is not clear yet which is the best diagnostic pathway for HF patients after a CIED alert, which kind of medications should be changed or escalated, and in which case in-hospital visits or in-hospital admissions are required. Finally, the specific role of healthcare professionals involved in HF patient management under remote monitoring is still matter of definition. We analyzed recent data on multiparametric monitoring of patients with HF through CIEDs. We provided practical insights on how to timely manage CIED alarms with the aim to prevent worsening HF. We also discussed the role of biomarkers and thoracic echo in this context, and potential organizational models including multidisciplinary teams for remote care of HF patients with CIEDs

Potential value of automated daily screening of cardiac resynchronization therapy defibrillator diagnostics for prediction of major cardiovascular events: results from Home-CARE (Home Monitoring in Cardiac Resynchronization Therapy) study

Aim To investigate whether diagnostic data from implanted cardiac resynchronization therapy defibrillators (CRT-Ds) retrieved automatically at 24 h intervals via a Home Monitoring function can enable dynamic prediction of cardiovascular hospitalization and death. Methods and results Three hundred and seventy-seven heart failure patients received CRT-Ds with Home Monitoring option. Data on all deaths and hospitalizations due to cardiovascular reasons and Home Monitoring data were collected prospectively during 1-year follow-up to develop a predictive algorithm with a predefined specificity of 99.5%. Seven parameters were included in the algorithm: mean heart rate over 24 h, heart rate at rest, patient activity, frequency of ventricular extrasystoles, atrial–atrial intervals (heart rate variability), right ventricular pacing impedance, and painless shock impedance. The algorithm was developed using a 25-day monitoring window ending 3 days before hospitalization or death. While the retrospective sensitivities of the individual parameters ranged from 23.6 to 50.0%, the combination of all parameters was 65.4% sensitive in detecting cardiovascular hospitalizations and deaths with 99.5% specificity (corresponding to 1.83 false-positive detections per patient-year of follow-up). The estimated relative risk of an event was 7.15-fold higher after a positive predictor finding than after a negative predictor finding. Conclusion We developed an automated algorithm for dynamic prediction of cardiovascular events in patients treated with CRT-D devices capable of daily transmission of their diagnostic data via Home Monitoring. This tool may increase patients’ quality of life and reduce morbidity, mortality, and health economic burden, it now warrants prospective studies

Predicting worsening heart failure hospitalizations in patients with implantable cardioverter defibrillators: Is it all about alerts? A pooled analysis of nine trials

Background and aims: To predict worsening heart failure hospitalizations (WHFH) in patients with implantable defibrillators and remote monitoring (RM), the HeartInsight algorithm (Biotronik, Berlin, Germany) calculates a heart failure (HF) score combining seven physiologic parameters: 24-hour heart rate (HR), nocturnal HR, HR variability, atrial tachyarrhythmia, ventricular extrasystoles, patient activity, and thoracic impedance. We compared temporal trends of the HF score and its components 12 weeks before a WHFH with 12-week trends in patients without WHFH, to assess whether trends indicate deteriorating HF regardless of alert status. Methods: Data from nine clinical trials were pooled, including 2,050 patients with a defibrillator capable of atrial sensing, ejection fraction ≤ 35%, NYHA class II/III, no long-standing atrial fibrillation, and 369 WHFH from 259 patients. Results: The mean HF score was higher in the WHFH group than in the no WHFH group (42.3 ± 26.1 versus 30.7 ± 20.6, p < 0.001) already at the beginning of 12 weeks. The mean HF score further increased to 51.6 ± 26.8 until WHFH (+22% versus no WHFH group, p = 0.003). As compared to the no WHFH group, the algorithm components either were already higher 12 weeks before WHFH (24 h HR, HR variability, thoracic impedance) or significantly increased until WHFH (nocturnal HR, atrial tachyarrhythmia, ventricular extrasystoles, patient activity). Conclusion: The HF score was significantly higher at, and further increased during 12 weeks before WHFH, as compared to the no WHFH group, with seven components showing different behavior and contribution. Temporal trends of HF score may serve as a quantitative estimate of HF condition and evolution prior to WHFH

Big Data Applications &amp; Risk Stratification in Cardiovascular Disease

This thesis aimed to explore potential tools for improving cardiovascular (CVD) management, focusing on heart failure (HF) and acute coronary syndrome (ACS). The first part examined big data applications, ranging from subgroup identification to monitoring. Using retrospective health insurance claims data, risk factors for adverse outcomes in chronic HF were investigated, revealing sex-specific differences in comorbidities but not in medication adherence, further denoting the value of such databases. Innovative machine learning techniques were then deployed, demonstrating their superiority in predictive value for adverse outcomes compared to traditional methods. Additionally, a meta-analysis on home telemonitoring systems (hTMS) showed a significant reduction in adverse outcomes, particularly in non-invasive hTMS studies, advocating their integration into outpatient management. Furthermore, a study protocol for a randomized controlled trial (RCT) aimed to promote physical activity in HF patients was developed. Lastly, PCKS9 inhibitors were found to be well-tolerated in real-world populations, with an adverse events profile comparable to RCTs. The second part of this thesis focused on risk stratification primarily through the analysis of serial measurements of blood biomarkers in both HF and post-ACS patients. The prognostic value of growth differentiation factor 15 (GDF-15) and other biomarkers was explored. Serial measurements of GDF-15 emerged as a strong predictor of adverse outcomes. Interestingly, concentrations rose before an adverse outcome during follow-up. Additionally, the prognostic value of iron deficiency in post-ACS patients was investigated, highlighting its association with an increased risk for adverse outcomes and its potential as a target in post-ACS management. Lastly, in a heart transplantation database, pre-transplant chronic kidney disease was identified as a significant risk factor for the incidence of malignancy post-transplantation, emphasizing strategies to mitigate these risks pre-transplantation. Overall, this thesis provides valuable insights into utilizing big data analysis and serial biomarker measurements to enhance clinical decision-making in CVD, specifically focusing on HF and ACS. These findings contribute to advancing personalized medicine approaches that could revolutionize CVD management and mitigate the growing healthcare burden associated with this condition.<br/