ABSTRACT
Objective: To construct a cervical cancer risk prediction model based on nested case-control study design and Yinzhou Health Information Platform in Ningbo, and provide reliable reference for self-risk assessment of cervical cancer in local women. Methods: In local women aged 25-75 years old who had no history of cervical cancer registered in Yinzhou before October 31, 2018, a follow up was conducted for at least three years, the patients who developed cervical cancer during the follow up period were selected as the case group and matched with a control group at a ratio of 1∶10. The prediction indicators before the onset was used in model construction. Variables were selected by Lasso-logistic regression, the variables with non-zero β were selected to fit the logistic regression model and Bootstrap was used for internal validation. The discrimination of the model was evaluated by area under the receiver operating characteristic curve(AUROC), and the calibration was evaluated by calibration curve and Hosmer-Lemeshow test. Results: The prediction indicators included in the final model were age, smoking status, history of cervicitis, history of adenomyosis, HPV testing, and thinprep cytologic test. The AUROC calculated in the internal validation was 0.740 (95%CI:0.739-0.740), and the calibration curve was almost identical with the ideal curve, P=0.991 in Hosmer-Lemeshow test, indicating that the model discrimination and calibration were good. Conclusions: In this study, a simple and practical cervical cancer risk prediction model was developed. The model can be used in general population with strong interpretability, good discrimination and calibration in internal validation, which can provide a reference for women to assess their risk of cervical cancer.
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Objective: To establish a sustainable updated literature data warehouse for global vaccine safety assessment, and provide data support for evidence-based vaccine safety assessment. Methods: Semi-automated construction and updating of a literature data warehouse were achieved through the continuous integration of standard operating steps of evidence-based reviews with artificial intelligence technologies. Following the standard procedure of a systematic literature review, the literatures about vaccine safety assessment published before November 29, 2020 were retrieved from 9 databases including OVID, Scopus, Web of Science, Cochrane Library, and ClinicalTrails.org in English and Wanfang, CNKI, VIP, and SinoMed in Chinese. Literatures were screened for two rounds in a semi-automatic manner (by artificial intelligence literature processing system and manual work) according to the inclusion/exclusion criteria. Furthermore, the literatures were classified according to the types of vaccines and adverse events. The updating strategy was established, and the literature data warehouse was updated regularly. Experts were organized to select specific vaccine safety topics and carry out special demonstration studies. Results: More than 0.41 million articles were retrieved. According to the inclusion/exclusion criteria, 23 304 articles were included after two rounds of screening. At present, we have selected and completed three prior topics as demonstration studies, including the systematic review of "DPT (diphtheria, pertussis and tetanus) vaccine and encephalopathy/encephalitis", and the classified management of literatures about allergic purpura and brachial plexus neuritis. Conclusions: The sustainable updated literature data warehouse of vaccine safety can provide high-quality research data for vaccine safety research, including evidence support for immunization related policy-making and adjustment and vaccine safety-related methodological research or clinical tool development; and further demonstration studies can provide references for building a new methodological framework system for timely and efficient completion of the evidence-based assessment of vaccine safety.
Subject(s)
Humans , Artificial Intelligence , Data Warehousing , Tetanus , Tetanus Toxoid , Whooping Cough/prevention & controlABSTRACT
Cohort study is one of the important epidemiological methods which plays an irreplaceable status and role in etiological study. Using cohort study design, we can accurately and continuously collect genetic and environmental information, and identify and validate omics biomarkers to provide evidences for precision public health and medicine. However, results from a new cohort would not be available for at least ten years, as five years would be needed for funding, planning and enrolment, and another five for following up even the earliest analyses of the most common diseases; results for most cancers would take longer, with an unaffordable budget for many research investigators or institutions. That brings an alternative strategy of using existing cohort studies by sharing data between each other. Data sharing of cohort studies would be beneficial in many ways. Data sharing of cohort studies has the potential to make large samples unattainable in a single study, increase statistical power, enable more accurate and detailed subgroup analysis, increase the generalizability of results. It would also facilitate exchange of experiences and learning from each other, avoid for duplicated research and effectively promote the second use of existing data (i.e. using old data to discover new results). The data sharing would save staff recruitment, follow-up, laboratory analysis of the cost, with a high cost-benefit returns and economies of scale. Data sharing enables cross-validation and repeated verification across different data. Many international research funding agencies or leading research groups have also reached consensus on the principles and goals for promoting the sharing of medical research data. Due to rapid development of cohort studies in the past decades, China already has the basis for data sharing of cohort studies. Unfortunately, most of the existing cohort studies are self-contained, independent, lack of visibility, with insufficient co-operation and data sharing between each other. The academic value of the existing data collected in these cohort studies have not been fully exploited and utilized so far. Therefore, the China Cohort Consortium is trying to establish a multi-level three-dimensional cooperation and data sharing strategy. We hope that it will encourage researchers from public health, clinical and other related fields to work more closely through providing data management, data integration, data interaction, tools development, data repositories and other functions.
Subject(s)
China , Cohort Studies , Information Dissemination , NeoplasmsABSTRACT
<p><b>OBJECTIVE</b>To discuss the diagnostic value and possibility to be a dynamic monitoring index of serum sialic acid (SA) in nasopharyngeal carcinoma (NPC) patients.</p><p><b>METHODS</b>Serum SA and Epstein-Barr virus-viral coat antigens-IgA (EBV-VCA-IgA) were detected in 50 cases of NPC before treatment, after clinical recovery and recurrence. Healthy adult and patients of benign lesions of head and neck were also detected as controls.</p><p><b>RESULTS</b>SA and EBV-VCA-IgA were positively related significantly in different periods of NPC patients. SA was significantly varied dynamically before and after radiation and chemical therapy in NPC patients. The positive rate of SA was 94.0% (47/50) before treatment, 2.0% (1/50) after clinical recovery, 96.2% (25/26) in recurrent patient and 4.2% (1/24) in patients without recurrence. The reaction of EBV-VCA-IgA was slow and its corresponding positive rates were 90.0%, 90.0%, 84.6%, 0% and 75.0% respectively. The sensitivity of SA in pre-treated NPC patients was 94.0%, higher than EBV-VCA-IgA (90.0%). The specificity of serum SA was 93.0% in this series, lower than that of EBV-VCA-IgA (96.0%).</p><p><b>CONCLUSION</b>Dynamic detections of serum SA combined with EBV-VCA-IgA can be used as indices in dictating the changes in NPC patients and screening of high-risk population, judgment of curative effect and prediction of prognosis.</p>