In the context of informatization and digitization, medical big data has become crucial for promoting medical research and technological innovation, posing unprecedented challenges to the construction and operation of big data research supercomputing platforms. This article systematically elaborates on the construction plan of the scientific research supercomputing platform of the West China Biomedical Big Data Center of Sichuan University, as well as the management and service models that support data research. It also compares the scale and operation of existing scientific research supercomputing platforms at home and abroad, providing a reference for the construction and management of medical big data scientific research supercomputing platforms in other institutions.
Chronic kidney disease (CKD) has become a global public health problem because of its high prevalence, low awareness, poor prognosis, and high medical costs. Effective follow-up management can facilitate timely adjustment of the treatment of the CKD patients and delay the disease progression. The application of internet of things (IoT) technology in dynamic monitoring and telemedicine is helpful for the self-management of patients with chronic diseases, and can provide convenient, intelligent, and humanized medical and health services. In the future, with the rapid growth of demands of CKD management and innovations in information technology, new medical IoT industry will accelerate the intelligent development of CKD management. Multi-disciplinary and multi-industrial collaboration should be promoted to solve current challenges, such as evaluation of actual effectiveness, the system design and construction, and the accessibility of intelligent healthcare services, to ensure that IoT products can improve clinical outcomes, reduce medical expenditure, and lower disease burden.
Medical aid to Xinjiang is an important task for large public hospitals in China. The innovative mode of team aiding in medical aid program for Xinjiang has played an important role in the national aid-Xinjiang program. West China Hospital of Sichuan University is actively exploring an aid-Xinjiang mode which combines medical aid of multi-disciplinary teams collaborated by doctors, nurses, medical technicians, and management teams with scientific and technological aid; based on the reality of Xinjiang medical healthcare, promoting the overall improvement of medical care through multi-disciplinary integration of resources; and relied on big data, promoting the innovative development of scientific and technological aid to Xinjiang. It is of great practical significance to summarize the work of medical aid to Xinjiang in West China Hospital of Sichuan University over the years and to put forward suggestions for the generalization and popularization of the medical aid to Xinjiang mode.
Objective To integrate multi-dimensional and multimodal data to develop a tool for predicting the risk of chronic kidney disease (CKD). Methods Data from the UK Biobank were utilized, involving 6561 participants recruited between 2006 and 2010, with a follow-up window from April 17, 2007 to November 30, 2022. In the development cohort (n=5248), a multimodal random survival forest (RSF) model was constructed, integrating conventional risk factors [variables from the CKD Prognosis Consortium (CKD-PC) equation], social determinants of health, Life’s Essential 8 data, and retinal optical coherence tomography imaging features. Comparative models included a base model (based solely on estimated glomerular filtration rate and urine albumin-to-creatinine ratio), the CKD-PC equation, a unimodal RSF model (conventional risk factors + social determinants of health + Life’s Essential 8 data), and an extended model (the multimodal RSF model plus a polygenic risk score). The performance of these models was compared in the validation cohort (n=1313). Results After a median follow-up of 12.7 years, 3.57% (234/6561) of the participants developed CKD. In the validation cohort, the 5-year concordance index (C-index) of the multimodal RSF model was 0.73 [95% confidence interval (CI) (0.68, 0.77)], which was significantly higher than that of the base model [C-index=0.67, 95%CI (0.65, 0.71)], the CKD-PC equation [C-index=0.64, 95%CI (0.58, 0.70)], and the unimodal RSF model [C-index=0.69, 95%CI (0.64, 0.73)], and the extended model with the inclusion of the polygenic risk score did not significantly improve predictive performance [C-index=0.72, 95%CI (0.67, 0.76)]. The results of the time-dependent area under the receiver operating characteristic curve analysis were consistent with these findings. Based on the predicted CKD risk derived from the multimodal RSF model for risk stratification, the actual proportions of individuals who developed CKD in the high-, medium-, and low-risk strata were 19.7%, 4.1%, and 1.5%, respectively. In addition to established risk factors, retinal imaging information, healthcare accessibility, and financial status were among the top-ranked predictors for CKD risk. Conclusion Integrating multi-dimensional and multimodal data—including conventional risk factors, social factors, lifestyle factors, and retinal imaging—can improve the performance of CKD risk prediction and stratification.