Personalized Monitoring and Prediction of Heart Failure

This research demonstrates that personalized, real-time monitoring significantly outperforms traditional population-based thresholds in cardiac care, addressing a critical gap in how healthcare systems detect and respond to patient deterioration. By developing statistical models that adapt to individual patient baselines rather than relying on fixed, one-size-fits-all thresholds, this work provides a practical pathway toward implementing precision medicine in routine cardiac monitoring. The core innovation lies in combining linear mixed-effects models with statistical process control charts to create dynamic, patient-specific monitoring thresholds. This approach was systematically validated through multiple studies. Initial simulations using synthetic data from a left ventricular patient model revealed that the system excels at identifying both sudden mean shifts and gradual linear drifts in physiological measurements, making it particularly suitable for tracking progressive disease processes like heart failure. The simulations also provided crucial insights into optimizing parameter settings: short calibration windows yielded high sensitivity but increased false-alarm rates, while longer calibration periods reduced false-positives with only slightly slower response times. Importantly, the algorithm demonstrated robustness to normal physiological variability, effectively filtering out benign fluctuations that commonly occur in heart failure telemetry. When applied to real-world heart failure patients, the personalized approach substantially reduced false-positive rates compared to current rule-of-thumb strategies used in clinical practice. A key finding was that incorporating multiple biomarkers—including heart rate and blood pressure alongside traditional weight measurements—enabled the system to more accurately distinguish between genuine clinical deterioration and benign fluctuations. This enhanced specificity not only reduced unnecessary alarms and potential alarm fatigue among clinicians but also enabled more targeted and timely clinical interventions. The research was further extended to patients with left ventricular assist devices, representing the first retrospective evaluation of a patient-tailored monitoring algorithm specifically for these complex cases. The personalized model significantly outperformed standard device monitoring methods and common hospital algorithms by reducing false-positives while detecting critical adverse events such as cardiac arrhythmia and major bleeding, though some admissions remained undetected, indicating room for further improvement. Additionally, a deep learning model using meta-learning techniques was developed to predict atrial fibrillation within a two-hour window in intensive care settings. Notably, this model achieved meaningful predictions without relying on ECG data, instead using readily available numerical inputs such as medication records and vital signs. External validation confirmed the model's generalizability across diverse clinical settings, with SHAP analysis providing transparency into the model's decision-making process. The overarching conclusion is that personalized, adaptive monitoring systems represent a transformative approach to cardiac patient management. These systems require minimal patient-specific training data yet remain explainable, interpretable, and maintainable—crucial factors for clinical adoption. By enabling earlier and more accurate detection of cardiac events while reducing false alarms, this research demonstrates how integrating advanced statistical methods with routine monitoring can improve patient outcomes, streamline clinical workflows, and reduce unnecessary hospitalizations in an era of increasing pressure on healthcare systems. The work establishes a foundation for next-generation remote patient monitoring that moves beyond population averages to deliver truly personalized care.