The global prevalence of end-stage renal disease (ESRD) was estimated ranging 5.3 to 9.7 million patients in 2017 and is a major healthcare cost driver in both developed and developing economies. The majority of patients with ESRD, approximately 90%, received in-center maintenance hemodialysis (HD). Although HD patients are under the close supervision of a nephrologist, they are vulnerable to anemia and substantial hemoglobin (Hb) variability, which are controversially associated with poor clinical outcomes, such as all-cause mortality. The contemporary narrow target hemoglobin level recommended in the KDIGO and KDOQI guidelines, despite the ongoing debates, poses a crucial challenge in maintaining the optimal hemoglobin level in HD patients.
The Big Data Center at China Medical University Hospital (CMUH) has developed a tool, Hb Scope APP, that can use the color of the HD tubing to predict real-time Hb levels by leveraging the smartphone's camera capacity and machine learning (ML) technology. The performance of the Hb Scope ML algorithm in predicting Hb > 10 g/dL can reach an accuracy of 0.93 and an AUROC of 0.99 in the testing dataset. This opens an opportunity to establish a vibrant digital ecosystem for automatic anemia management.
Innovative ML tools must be appropriately regulated before these algorithms are adopted into clinical practice. Therefore, in the current validation study, we propose to do a multicenter validation trial for validating whether the Hb predicted by Hb Scope APP can achieve an area under the receiver operating curve (AUROC) of at least 0.80 in the adult HD populations from CMUH, Asia University Hospital (AUH) in Taiwan, and SEHA Kidney Care (SKC)-Central in the United Arab Emirates.
