An incremental updating technique is developed for maintenance of the association rules discovered by database mining. There have been many studies on efficient discovery of association rules in large databases. However, it is nontrivial to maintain such discovered rules in large databases because a database may allow frequent or occasional updates and such updates may not only invalidate some existing strong association rules but also turn some weak rules into strong ones. An incremental updating technique is proposed for efficient maintenance of discovered association rules when new transaction data are added to a transaction database.published_or_final_versio

Background: COVID-19 is a major public health concern. Given the extent of the pandemic, it is urgent to identify risk factors associated with disease severity. More accurate prediction of those at risk of developing severe infections is of high clinical importance. Objective: Based on the UK Biobank (UKBB), we aimed to build machine learning models to predict the risk of developing severe or fatal infections, and uncover major risk factors involved. Methods: We first restricted the analysis to infected individuals (n=7846), then performed analysis at a population level, considering those with no known infection as controls (ncontrols=465,728). Hospitalization was used as a proxy for severity. A total of 97 clinical variables (collected prior to the COVID-19 outbreak) covering demographic variables, comorbidities, blood measurements (eg, hematological/liver/renal function/metabolic parameters), anthropometric measures, and other risk factors (eg, smoking/drinking) were included as predictors. We also constructed a simplified (lite) prediction model using 27 covariates that can be more easily obtained (demographic and comorbidity data). XGboost (gradient-boosted trees) was used for prediction and predictive performance was assessed by cross-validation. Variable importance was quantified by Shapley values (ShapVal), permutation importance (PermImp), and accuracy gain. Shapley dependency and interaction plots were used to evaluate the pattern of relationships between risk factors and outcomes. Results: A total of 2386 severe and 477 fatal cases were identified. For analyses within infected individuals (n=7846), our prediction model achieved area under the receiving-operating characteristic curve (AUC–ROC) of 0.723 (95% CI 0.711-0.736) and 0.814 (95% CI 0.791-0.838) for severe and fatal infections, respectively. The top 5 contributing factors (sorted by ShapVal) for severity were age, number of drugs taken (cnt_tx), cystatin C (reflecting renal function), waist-to-hip ratio (WHR), and Townsend deprivation index (TDI). For mortality, the top features were age, testosterone, cnt_tx, waist circumference (WC), and red cell distribution width. For analyses involving the whole UKBB population, AUCs for severity and fatality were 0.696 (95% CI 0.684-0.708) and 0.825 (95% CI 0.802-0.848), respectively. The same top 5 risk factors were identified for both outcomes, namely, age, cnt_tx, WC, WHR, and TDI. Apart from the above, age, cystatin C, TDI, and cnt_tx were among the top 10 across all 4 analyses. Other diseases top ranked by ShapVal or PermImp were type 2 diabetes mellitus (T2DM), coronary artery disease, atrial fibrillation, and dementia, among others. For the “lite” models, predictive performances were broadly similar, with estimated AUCs of 0.716, 0.818, 0.696, and 0.830, respectively. The top ranked variables were similar to above, including age, cnt_tx, WC, sex (male), and T2DM. Conclusions: We identified numerous baseline clinical risk factors for severe/fatal infection by XGboost. For example, age, central obesity, impaired renal function, multiple comorbidities, and cardiometabolic abnormalities may predispose to poorer outcomes. The prediction models may be useful at a population level to identify those susceptible to developing severe/fatal infections, facilitating targeted prevention strategies. A risk-prediction tool is also available online. Further replications in independent cohorts are required to verify our findings.

Effective therapies for COVID-19 are still lacking, and drug repositioning is a promising approach to address this problem. Here, we adopted a medical informatics approach to repositioning. We leveraged a large prospective cohort, the UK-Biobank (UKBB, N ~ 397,000), and studied associations of prior use of all level-4 ATC drug categories (N = 819, including vaccines) with COVID-19 diagnosis and severity. Effects of drugs on the risk of infection, disease severity, and mortality were investigated separately. Logistic regression was conducted, controlling for main confounders. We observed strong and highly consistent protective associations with statins. Many top-listed protective drugs were also cardiovascular medications, such as angiotensin-converting enzyme inhibitors (ACEI), angiotensin receptor blockers (ARB), calcium channel blocker (CCB), and beta-blockers. Some other drugs showing protective associations included biguanides (metformin), estrogens, thyroid hormones, proton pump inhibitors, and testosterone-5-alpha reductase inhibitors, among others. We also observed protective associations by influenza, pneumococcal, and several other vaccines. Subgroup and interaction analyses were also conducted, which revealed differences in protective effects in various subgroups. For example, protective effects of flu/pneumococcal vaccines were weaker in obese individuals, while protection by statins was stronger in cardiovascular patients. To conclude, our analysis revealed many drug repositioning candidates, for example several cardiovascular medications. Further studies are required for validation.

BACKGROUND: It has become evident that some women develop preeclampsia despite aspirin. This study aimed to examine how such aspirin nonresponsiveness develops in high-risk preeclampsia pregnancies by exploring the role of genetic polymorphisms and aspirin metabolism. METHODS: The study involved pregnant women who developed preeclampsia despite low-dose aspirin and those who did not. First, we conducted a pharmacogenomic association study exploring the association of potential genetic variants with aspirin nonresponsiveness. Next, we analyzed the rate of enzymatic aspirin hydrolysis in maternal plasma. The extent of placental exposure to acetylsalicylic acid and its bioactive metabolites, that is, salicylic acid and gentisic acid, was determined by liquid chromatography-mass spectrometry. The expressions of placental AMEs (aspirin metabolizing enzymes), that is, GLYAT (glycine-N-acyltransferase), UGT1A6 (UDP glucuronosyltransferase family 1 member A6), CYP2E1 (cytochrome P450 family 2 subfamily E member 1), and NAT2 (N-acetyltransferase 2) were analyzed by quantitative reverse transcription polymerase chain reaction, immunohistochemistry staining, and enzyme-linked immunosorbent assay. Finally, the effects of AMEs were further studied on HTR-8/SVneo (an immortalized human trophoblast cell line derived from first-trimester placenta) and hPCTB (human primary cytotrophoblast) cells. RESULTS: Our genetic study showed that single-nucleotide polymorphisms (SNPs) of genes involved in aspirin pharmacokinetics and pharmacodynamics were not associated with aspirin nonresponsiveness in preeclampsia. Rates of aspirin hydrolysis in maternal plasma and the concentrations of acetylsalicylic acid, salicylic acid, and gentisic acid in the placenta did not differ between aspirin-responsive and aspirin-nonresponsive women. Intriguingly, GLYAT was significantly upregulated in the aspirin-nonresponsive placenta and associated with aspirin nonresponsiveness. This overexpression of GLYAT was found to diminish the proangiogenic, anti-inflammatory, and antisenescence effects of salicylic acid in HTR-8/SVneo and hPCTB cells. CONCLUSIONS: Our study revealed that maternal genetic factors and plasma aspirin hydrolysis are not among the decisive factors in determining the effectiveness of low-dose aspirin in preventing preeclampsia among high-risk women. Instead, placental GLYAT appears to play a key role by limiting the effect of salicylic acid in the placenta.

Background: Second-generation antipsychotics (SGAs) cause metabolic side effects. However, patients’ metabolic profiles were influenced by time-invariant and time-varying confounders. Real-world evidence on the long-term, dynamic effects of SGAs (e.g. different treatment sequences) are limited. We employed advanced causal inference methods to evaluate the metabolic impact of SGAs in a naturalistic cohort. Methods: We followed 696 Chinese patients with schizophrenia-spectrum disorders receiving SGAs. Longitudinal targeted maximum likelihood estimation (LTMLE) was used to estimate the average treatment effects (ATEs) of continuous SGA treatment versus ‘no treatment’ on metabolic outcomes, including total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglyceride (TG), fasting glucose (FG), and body mass index (BMI), over 6–18 months at 3-month intervals. LTMLE accounted for time-invariant and time-varying confounders. Post-SGA discontinuation side effects were also assessed. Results: The ATEs of continuous SGA treatment on BMI and TG showed an inverted U-shaped pattern, peaking at 12 months and declining afterwards. Similar patterns were observed for TC and LDL, albeit the ATEs peaked at 15 months. For FG and HDL, the ATEs peaked at ~6 months. The adverse impact of SGAs on BMI persisted even after medication discontinuation, yet other metabolic parameters did not show such lingering side effects. Clozapine and olanzapine exhibited greater metabolic side effects compared to other SGAs. Conclusions: Our real-world study suggests that metabolic side effects may stabilize with prolonged continuous treatment. Clozapine and olanzapine confer higher cardiometabolic risks than other SGAs. The side effects of SGAs on BMI may persist after drug discontinuation. These insights may guide antipsychotic choice and improve management of metabolic side effects.