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Aims: This study aimed to devise and validate a clinical scoring system for risk prediction of bladder cancer to guide urgent cystoscopy evaluation among people with diabetes. Methods: People with diabetes who received cystoscopy from a large database in the Chinese population (2009–2018). We recruited a derivation cohort based on random sampling from 70% of all individuals. We used the adjusted odds ratios (aORs) for independent risk factors to devise a risk score, ranging from 0 to 5: 0–2 'average risk' (AR) and 3–5 'high risk' (HR). Results: A total of 5905 people with diabetes, among whom 123 people with BCa were included. The prevalence rate in the derivation (n = 4174) and validation cohorts (n = 1731) was 2.2% and 1.8% respectively. Using the scoring system constructed, 79.6% and 20.4% in the derivation cohort were classified as AR and HR respectively. The prevalence rate in the AR and HR groups was 1.57% and 4.58% respectively. The risk score consisted of age (18–70: 0; >70: 2), male sex (1), ever/ex‐smoker (1) and duration of diabetes (≥10 years: 1). Individuals in the HR group had 3.26‐fold (95% CI = 1.65–6.44, p = 0.025) increased prevalence of bladder than the AR group. The concordance (c‐) statistics was 0.72, implying a good discriminatory capability of the risk score to stratify high‐risk individuals who should consider earlier cystoscopy. Conclusions: The risk prediction algorithm may inform urgency of cystoscopy appointments, thus allowing a more efficient use of resources and contributing to early detection of BCa among people planned to be referred. [ABSTRACT FROM AUTHOR]

Background: Worldwide, gastric cancer is a leading cause of cancer incidence and mortality. This study aims to devise and validate a scoring system based on readily available clinical data to predict the risk of gastric cancer in a large Chinese population. Methods: We included a total of 6,209,697 subjects aged between 18 and 70 years who have received upper digestive endoscopy in Hong Kong from 1997 to 2018. A binary logistic regression model was constructed to examine the predictors of gastric cancer in a derivation cohort (n = 4,347,224), followed by model evaluation in a validation cohort (n = 1,862,473). The algorithm's discriminatory ability was evaluated as the area under the curve (AUC) of the mathematically constructed receiver operating characteristic (ROC) curve. Results: Age, male gender, history of Helicobacter pylori infection, use of proton pump inhibitors, non‐use of aspirin, non‐steroidal anti‐inflammatory drugs (NSAIDs), and statins were significantly associated with gastric cancer. A scoring of ≤8 was designated as "average risk (AR)". Scores at 9 or above were assigned as "high risk (HR)". The prevalence of gastric cancer was 1.81% and 0.096%, respectively, for the HR and LR groups. The AUC for the risk score in the validation cohort was 0.834, implying an excellent fit of the model. Conclusions: This study has validated a simple, accurate, and easy‐to‐use scoring algorithm which has a high discriminatory capability to predict gastric cancer. The score could be adopted to risk stratify subjects suspected as having gastric cancer, thus allowing prioritized upper digestive tract investigation. [ABSTRACT FROM AUTHOR]

Background: The use of digital cognitive tests is getting common nowadays. Older adults or their family members may use online tests for self-screening of dementia. However, the diagnostic performance across different digital tests is still to clarify. The objective of this study was to evaluate the diagnostic performance of digital cognitive tests for MCI and dementia in older adults., Methods: Literature searches were systematically performed in the OVID databases. Validation studies that reported the diagnostic performance of a digital cognitive test for MCI or dementia were included. The main outcome was the diagnostic performance of the digital test for the detection of MCI or dementia., Results: A total of 56 studies with 46 digital cognitive tests were included in this study. Most of the digital cognitive tests were shown to have comparable diagnostic performances with the paper-and-pencil tests. Twenty-two digital cognitive tests showed a good diagnostic performance for dementia, with a sensitivity and a specificity over 0.80, such as the Computerized Visuo-Spatial Memory test and Self-Administered Tasks Uncovering Risk of Neurodegeneration. Eleven digital cognitive tests showed a good diagnostic performance for MCI such as the Brain Health Assessment. However, all the digital tests only had a few validation studies to verify their performance., Conclusions: Digital cognitive tests showed good performances for MCI and dementia. The digital test can collect digital data that is far beyond the traditional ways of cognitive tests. Future research is suggested on these new forms of cognitive data for the early detection of MCI and dementia., (Copyright © 2021 Elsevier B.V. All rights reserved.)