Articles

<b>Background: </b>Cystatin C (CysC) predicts adverse outcomes in heart failure (HF), yet its prognostic value relative to underlying renal function remains undefined. We aimed to determine the association between serum CysC and 1-year readmission risk in HF patients stratified by chronic kidney disease (CKD) status. <b>Methods:</b> This retrospective cohort study analyzed 2,008 hospitalized HF patients. Based on CKD status, patients were categorized into non-CKD and CKD groups. Serum CysC levels were analyzed as both continuous variable and in tertiles. The association with 1-year readmission was assessed using multivariable Cox proportional hazards models. <b>Results:</b> Overall, each 1 mg/L increase in CysC was independently associated with an 11% higher risk of re-admission (adjusted HR: 1.11, 95% CI: 1.01-1.23, p=0.003). A significant interaction with CKD status was found (p=0.003). In the non-CKD group, elevated CysC (>1.98 mg/L) was associated with a 51% increased risk (adjusted HR: 1.51, 95% CI: 1.18-1.92, p=0.001), with a non-linear threshold effect identified at 0.998 mg/L. Conversely, no significant association was observed in CKD patients (adjusted HR: 0.96, 95% CI: 0.82-1.12, p=0.576). <b>Conclusions:</b> Elevated serum CysC is a potent predictor of 1-year readmission in HF patients without CKD, but not in those with established CKD. Its prognostic utility is therefore highly dependent on renal context, highlighting its greatest value in patients without advanced renal dysfunction.

This research is a collaboration involving a university team, a partnering corporation, and a hemodialysis clinic, which is a cross-disciplinary research initiative in the field of Artificial Intelligence of Things (AIoT) within the medical informatics domain. The research has two objectives: (1) The development of an <i>Internet of Things (IoT)</i><i>-based</i><i> Information System</i> customized for the hemodialysis machines at the clinic, including transmission bridges, clinical personnel dedicated web/app, and a backend server. The system has been deployed at the clinic and is now officially operational; (2) The research also utilized de-identified, anonymous data (collected by the officially operational system) to train, evaluate, and compare <i>Deep Learning-based Intradialytic Blood Pressure</i><i> (BP)</i><i>/Pulse </i><i>Rate (PR) </i><i>Predictive Models</i>, with subsequent suggestions provided. Both objectives were executed under the supervision of the Institutional Review Board (IRB) at Mackay Memorial Hospital in Taiwan. The system completed for objective one has introduced three significant services to the clinic, including automated hemodialysis data collection, digitized data storage, and an information-rich human-machine interface as well as graphical data displays, which replaces traditional paper-based clinical administrative operations, thereby enhancing healthcare efficiency. The graphical data presented through web and app interfaces aids in real-time, intuitive comprehension of the patients’ conditions during hemodialysis. Moreover, the data stored in the backend database is available for physicians to conduct relevant analyses, unearth insights into medical practices, and provide precise medical care for individual patients. The training and evaluation of the predictive models for objective two, along with related comparisons, analyses, and recommendations, suggest that in situations with limited computational resources and data, an Artificial Neural Network (ANN) model with six hidden layers, SELU activation function, and a focus on artery-related features can be employed for hourly intradialytic BP/PR prediction tasks. It is believed that this contributes to the collaborating clinic and relevant research communities.