Characterization and trajectories of hematological parameters prior to severe COVID-19 based on a large-scale prospective health checkup cohort in western China: a longitudinal study of 13-year follow-up.

BACKGROUND: The relaxation of the "zero-COVID" policy on Dec. 7, 2022, in China posed a major public health threat recently. Complete blood count test was discovered to have complicated relationships with COVID-19 after the infection, while very few studies could track long-term monitoring of the health status and identify the characterization of hematological parameters prior to COVID-19. METHODS: Based on a 13-year longitudinal prospective health checkup cohort of ~ 480,000 participants in West China Hospital, the largest medical center in western China, we documented 998 participants with a laboratory-confirmed diagnosis of COVID-19 during the 1 month after the policy. We performed a time-to-event analysis to explore the associations of severe COVID-19 patients diagnosed, with 34 different hematological parameters at the baseline level prior to COVID-19, including the whole and the subtypes of white and red blood cells. RESULTS: A total of 998 participants with a positive SARS-CoV-2 test were documented in the cohort, 42 of which were severe cases. For white blood cell-related parameters, a higher level of basophil percentage (HR = 6.164, 95% CI = 2.066-18.393, P = 0.001) and monocyte percentage (HR = 1.283, 95% CI = 1.046-1.573, P = 0.017) were found associated with the severe COVID-19. For lymphocyte-related parameters, a lower level of lymphocyte count (HR = 0.571, 95% CI = 0.341-0.955, P = 0.033), and a higher CD4/CD8 ratio (HR = 2.473, 95% CI = 1.009-6.059, P = 0.048) were found related to the risk of severe COVID-19. We also observed that abnormality of red cell distribution width (RDW), mean corpuscular hemoglobin concentration (MCHC), and hemoglobin might also be involved in the development of severe COVID-19. The different trajectory patterns of RDW-SD and white blood cell count, including lymphocyte and neutrophil, prior to the infection were also discovered to have significant associations with the risk of severe COVID-19 (all P < 0.05). CONCLUSIONS: Our findings might help decision-makers and clinicians to classify different risk groups of population due to outbreaks including COVID-19. They could not only optimize the allocation of medical resources, but also help them be more proactive instead of reactive to long COVID-19 or even other outbreaks in the future.

Is robotic distal pancreatectomy better than laparoscopic distal pancreatectomy after the learning curve? A systematic review and meta-analysis.

Aim: The aim of this study was to compare the safety and overall effect of robotic distal pancreatectomy (RDP) to laparoscopic distal pancreatectomy (LDP) after the learning curve, especially in perioperative outcome and short-term oncological outcome. Methods: A literature search was performed by two authors independently using PubMed, Embase, and Web of Science to identify any studies comparing the results of RDP versus LDP published until 5 January 2022. Only the studies where RDP was performed in more than 35 cases were included in this study. We performed a meta-analysis of operative time, blood loss, reoperation, readmission, hospital stay, overall complications, major complications, postoperative pancreatic fistula (POPF), blood transfusion, conversion to open surgery, spleen preservation, tumor size, R0 resection, and lymph node dissection. Results: Our search identified 15 eligible studies, totaling 4,062 patients (1,413 RDP). It seems that the RDP group had a higher rate of smaller tumor size than the LDP group (MD: -0.15; 95% CI: -0.20 to -0.09; p < 0.00001). Furthermore, compared with LPD, RDP was associated with a higher spleen preservation rate (OR: 2.19; 95% CI: 1.36-3.54; p = 0.001) and lower rate of conversion to open surgery (OR: 0.43; 95% CI: 0.33-0.55; p < 0.00001). Our study revealed that there were no significant differences in operative time, overall complications, major complications, blood loss, blood transfusion, reoperation, readmission, POPF, and lymph node dissection between RDP and LDP. Conclusions: RDP is safe and feasible for distal pancreatectomy compared with LDP, and it can reduce the rate of conversion to open surgery and increase the rate of spleen preservation, which needs to be further confirmed by quality comparative studies with large samples. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/#recordDetails.

Big Data Health Care Platform With Multisource Heterogeneous Data Integration and Massive High-Dimensional Data Governance for Large Hospitals: Design, Development, and Application.

BACKGROUND: With the advent of data-intensive science, a full integration of big data science and health care will bring a cross-field revolution to the medical community in China. The concept big data represents not only a technology but also a resource and a method. Big data are regarded as an important strategic resource both at the national level and at the medical institutional level, thus great importance has been attached to the construction of a big data platform for health care. OBJECTIVE: We aimed to develop and implement a big data platform for a large hospital, to overcome difficulties in integrating, calculating, storing, and governing multisource heterogeneous data in a standardized way, as well as to ensure health care data security. METHODS: The project to build a big data platform at West China Hospital of Sichuan University was launched in 2017. The West China Hospital of Sichuan University big data platform has extracted, integrated, and governed data from different departments and sections of the hospital since January 2008. A master-slave mode was implemented to realize the real-time integration of multisource heterogeneous massive data, and an environment that separates heterogeneous characteristic data storage and calculation processes was built. A business-based metadata model was improved for data quality control, and a standardized health care data governance system and scientific closed-loop data security ecology were established. RESULTS: After 3 years of design, development, and testing, the West China Hospital of Sichuan University big data platform was formally brought online in November 2020. It has formed a massive multidimensional data resource database, with more than 12.49 million patients, 75.67 million visits, and 8475 data variables. Along with hospital operations data, newly generated data are entered into the platform in real time. Since its launch, the platform has supported more than 20 major projects and provided data service, storage, and computing power support to many scientific teams, facilitating a shift in the data support model-from conventional manual extraction to self-service retrieval (which has reached 8561 retrievals per month). CONCLUSIONS: The platform can combine operation systems data from all departments and sections in a hospital to form a massive high-dimensional high-quality health care database that allows electronic medical records to be used effectively and taps into the value of data to fully support clinical services, scientific research, and operations management. The West China Hospital of Sichuan University big data platform can successfully generate multisource heterogeneous data storage and computing power. By effectively governing massive multidimensional data gathered from multiple sources, the West China Hospital of Sichuan University big data platform provides highly available data assets and thus has a high application value in the health care field. The West China Hospital of Sichuan University big data platform facilitates simpler and more efficient utilization of electronic medical record data for real-world research.