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Background Administrative healthcare claims databases are used in drug safety research but are limited for investigating the impacts of prenatal exposures on neonatal and pediatric outcomes without mother-infant pair identification. Further, existing algorithms are not transportable across data sources. We developed a transportable mother-infant linkage algorithm and evaluated it in two, large US commercially insured populations. Methods We used two US commercial health insurance claims databases during the years 2000 to 2021. Mother-infant links were constructed where persons of female sex 12–55 years of age with a pregnancy episode ending in live birth were associated with a person who was 0 years of age at database entry, who shared a common insurance plan ID, had overlapping insurance coverage time, and whose date of birth was within ± 60-days of the mother’s pregnancy episode live birth date. We compared the characteristics of linked vs. non-linked mothers and infants to assess similarity. Results The algorithm linked 3,477,960 mothers to 4,160,284 infants in the two databases. Linked mothers and linked infants comprised 73.6% of all mothers and 49.1% of all infants, respectively. 94.9% of linked infants’ dates of birth were within ± 30-days of the associated mother’s pregnancy episode end dates. Characteristics were largely similar in linked vs. non-linked mothers and infants. Differences included that linked mothers were older, had longer pregnancy episodes, and had greater post-pregnancy observation time than mothers with live births who were not linked. Linked infants had less observation time and greater healthcare utilization than non-linked infants. Conclusions We developed a mother-infant linkage algorithm and applied it to two US commercial healthcare claims databases that achieved a high linkage proportion and demonstrated that linked and non-linked mother and infant cohorts were similar. Transparent, reusable algorithms applied to large databases enable large-scale research on exposures during pregnancy and pediatric outcomes with relevance to drug safety. These features suggest studies using this algorithm can produce valid and generalizable evidence to inform clinical, policy, and regulatory decisions.

Large population-based studies examining differences in ICI-associated cardiotoxicity across cancer types and agents are limited. Data of 5518 cancer patients who received at least one cycle of ICIs were extracted from a large network of health care organizations. ICI treatment groups were classified by the first ICI agent(s) (ipilimumab, nivolumab, pembrolizumab, cemiplimab, avelumab, atezolizumab, or durvalumab) or its class (PD-1 inhibitors, PD-L1 inhibitors, CTLA4-inhibitors, or their combination (ipilimumab + nivolumab)). Time to first cardiac adverse event (CAE) (arrhythmia, acute myocardial infarction, myocarditis, cardiomyopathy, or pericarditis) developed within one year after ICI initiation was analyzed using a competing-risks regression model adjusting for ICI treatment groups, patient demographic and clinical characteristics, and cancer sites. By month 12, 12.5% developed cardiotoxicity. The most common cardiotoxicity was arrhythmia (9.3%) and 2.1% developed myocarditis. After adjusting for patient characteristics and cancer sites, patients who initiated on monotherapy with ipilimumab (adjusted Hazard Ratio (aHR): 2.00; 95% CI: 1.49–2.70; p < 0.001) or pembrolizumab (aHR: 1.21; 95% CI: 1.01–1.46; p = 0.040) had a higher risk of developing CAEs within one year compared to nivolumab monotherapy. Ipilimumab and pembrolizumab use may increase the risk of cardiotoxicity compared to other agents. Avelumab also estimated a highly elevated risk (aHR: 1.92; 95% CI: 0.85–4.34; p = 0.117) compared to nivolumab and other PD-L1 agents, although the estimate did not reach statistical significance, warranting future studies.

In recent years, the focus of facial expression recognition (FER) has gradually shifted from laboratory settings to challenging natural scenes. This requires a great deal of real-world facial expression data. However, most existing real-world databases are based on European-American cultures, and only one is for Asian cultures. This is mainly because the data on European-American expressions are more readily accessed and publicly available online. Owing to the diversity of huge data, FER in European-American cultures has recently developed rapidly. In contrast, the development of FER in Asian cultures is limited by the data. To narrow this gap, we construct a challenging real-world East Asian facial expression (EAFE) database, which contains 10,000 images collected from 113 Chinese, Japanese, and Korean movies and five search engines. We apply three neural network baselines including VGG-16, ResNet-50, and Inception-V3 to classify the images in EAFE. Then, we conduct two sets of experiments to find the optimal learning rate schedule and loss function. Finally, by training with the cosine learning rate schedule and island loss, ResNet-50 can achieve the best accuracy of 80.53% on the testing set, proving that the database is challenging. In addition, we used the Microsoft Cognitive Face API to extract facial attributes in EAFE, so that the database can also be used for facial recognition and attribute analysis. The release of the EAFE can encourage more research on Asian FER in natural scenes and can also promote the development of FER in cross-cultural domains.

K-Means is a “de facto” standard clustering algorithm due to its simplicity and efficiency. K-Means, though, strongly depends on the initialization of the centroids (seeding method) and often gets stuck in a local sub-optimal solution. K-Means, in fact, mainly acts as a local refiner of the centroids, and it is unable to move centroids all over the data space. Random Swap was defined to go beyond K-Means, and its modus operandi integrates K-Means in a global strategy of centroids management, which can often generate a clustering solution close to the global optimum. This paper proposes an approach which extends both K-Means and Random Swap and improves the clustering accuracy through an evolutionary technique and careful seeding. Two new algorithms are proposed: the Population-Based K-Means (PB-KM) and the Population-Based Random Swap (PB-RS). Both algorithms consist of two steps: first, a population of J candidate solutions is built, and then the candidate centroids are repeatedly recombined toward a final accurate solution. The paper motivates the design of PB-KM and PB-RS, outlines their current implementation in Java based on parallel streams, and demonstrates the achievable clustering accuracy using both synthetic and real-world datasets.

Background There is an increased concern that statins may have an unintended effect of elevated lipoprotein(a) [Lp(a)]. We conducted a large sample real-world study to test the association. Methods This retrospective cohort study was conducted using data from an integrated SuValue database, which includes 221 hospitals across China covering more than 200,000 of population with longitudinal follow-up to 10 years. Propensity score matching was applied to identify two comparable cohorts with statin users and non-statin users. Detailed follow-up information such as Lp(a) levels were extracted. The hazard ratio was calculated on Lp(a) changes based on the statin usage cohorts. Detailed subgroup and different characteristic cohorts’ analyses were also conducted. Results After baseline propensity score matching, a total of 42,166 patients were included in a 1:1 matched ratio between statin users and non-statin users. In the case of no difference in low density lipoprotein (LDL-C), Lp(a) was increased significantly with the use of statins (adjusted HR 1.47; 95% confidence interval [CI] 1.43–1.50). Lp(a) increase was observed in various subgroup analyses and different cohorts. The dose intensity of statin was positively associated with the evaluated Lp(a) level. Conclusion The use of statins was associated with an increased risk of Lp(a) elevation compared with non-statin use counterparts. The clinical relevance of these increases needs to be addressed in surrogate marker trials and/or large, cardiovascular outcomes trials.

Smoking increases the cancer-specific and overall mortality risk in women with breast cancer (BC). However, the effect of smoking cessation remains controversial, and detailed research is lacking in Asia. We aimed to investigate the association between smoking status and mortality in women with BC using the population-based cancer registry. The Taiwan Cancer Registry was used to identify women with BC from 2011 to 2017. A total of 54,614 women with BC were enrolled, including 1687 smokers and 52,927 non-smokers. The outcome, mortality, was identified using Taiwan’s cause-of-death database. The association between smoking status and mortality was estimated using Cox proportional regression. Women with BC who smoked had a 1.25-fold higher (95% C.I.: 1.08–1.45; p = 0.0022) risk of overall mortality and a 1.22-fold higher (95% C.I.: 1.04–1.44; p = 0.0168) risk of cancer-specific mortality compared with non-smokers. The stratified analysis also indicated that women with BC who smoked showed a significantly higher overall mortality risk (HR: 1.20; 95% CI: 1.01–1.43; p = 0.0408) than women with BC who did not smoke among women without comorbidities. Additionally, current smokers had a 1.57-fold higher risk (95% CI: 1.02–2.42; p = 0.0407) of overall mortality compared with ever smokers among women with BC who smoked. It was shown that a current smoking status is significantly associated with an increase in overall and cancer-specific mortality risk in women with BC. Quitting smoking could reduce one’s mortality risk. Our results underscore the importance of smoking cessation for women with BC.

Purpose Long-term acetaminophen (APAP) use has poorly defined effects on renal function. We investigated these effects using a real-world database. Methods We used a database of health data routinely collected from 185 hospitals serving 20 million patients in Japan. Individuals with chronic pain were selected for the study. The primary outcome was the change in renal function, as measured by 1/serum creatinine (SCr) during the postindex period. Results After excluding individuals who did not meet the inclusion criteria, 241,167 patients were included in the analysis (median age 79.0, range 65–101 years; 111,252 were men). APAP was prescribed significantly more frequently to patients with a low renal function ( P  < 0.001). The annual changes in 1/SCr median and interquartile range (IQR) were − 0.038 (− 0.182 to 0.101) in patients receiving APAP, − 0.040 (− 0.187 to 0.082) in patients receiving non-steroidal anti-inflammatory drugs (NSAIDs), and − 0.025 (− 0.142 to 0.079) in nonmedicated control patients ( P  < 0.001). These changes were not significantly different among patients with a low renal function, with 0.003 (− 0.066 to 0.113) in the APAP group, 0.000 (− 0.089 to 0.090) in the NSAID group, and − 0.009 (− 0.086 to 0.089) in the control group ( P  = 0.327). Conclusion Physicians tended to select APAP for individuals with a low renal function. The annual changes in 1/SCr were significantly different based on APAP and NSAID use or no analgesia, but the differences were not significant among patients with a low renal function. Overall, long-term use of APAP does not appear to exacerbate the renal function in a clinical setting.

Hospital-based registry data, including patients’ information collected by academic societies or government based research groups, were previously used for clinical research in Japan. Now, real-world data routinely obtained in healthcare settings are being used in clinical epidemiology and pharmacoepidemiology. Real-world data include a database of claims originating from health insurance associations for reimbursement of medical fees, diagnosis procedure combinations databases for acute inpatient care in hospitals, a drug prescription database, and electronic medical records, including patients’ medical information obtained by doctors, derived from electronic records of hospitals. In the past ten years, much evidence of clinical epidemiology and pharmacoepidemiology studies using real-world data has been accumulated. The purpose of this review was to introduce clinical epidemiology and pharmacoepidemiology approaches and studies using real-world data in Japan.

This longitudinal, retrospective cohort study of patients with COPD describes baseline characteristics, adherence, and persistence following initiation of inhaled corticosteroids (ICS)/long-acting β -agonists (LABA)/long-acting muscarinic antagonists (LAMA) from multiple inhaler triple therapy (MITT). Patients aged ≥40 years receiving MITT between January 2012 and September 2015 were identified from the IQVIA™ Real-world Data Adjudicated Claims-USA database. MITT was defined as subjects with ≥1 overlapping days' supply of three COPD medications (ICS, LABA, and LAMA). Adherence (proportion of days covered, PDC) and discontinuation (defined as a gap of 1, 30, 60, or 90 days of supply in any of the three components of the triple therapy) were calculated for each patient over 12 months of follow-up. In addition, analyses were stratified by number of inhalers. In total, 14,635 MITT users were identified (mean age, 62 years). Mean PDC for MITT at 12 months was 0.37%. Mean PDC for the ICS/LABA and LAMA component at 12 months was 49% (0.49±0.31; median, 0.47) and 54% (0.54±0.33; 0.56), respectively. The proportion of adherent patients (PDC ≥0.8) at 12 months was 14% for MITT. Allowing for a 30-day gap from last day of therapy, 86% of MITT users discontinued therapy during follow-up. Patients with COPD had low adherence to and persistence with MITT in a real-world setting. Mean PDC for each single inhaler component was higher than the mean PDC observed with MITT. Reducing the number of inhalers may improve overall adherence to intended triple therapy.

IntroductionThe antipsychotic dosage of Chinese schizophrenia patients has rarely been studied, although nonstandard dosage has impact on prognosis.ObjectivesTo describe the dosage of antipsychotics in China routine practice.MethodsThis was a retrospective cohort study using de-identified data from a Chinese mental health hospital. The included patients were adults (≥18 years) with at least one diagnosis of schizophrenia (ICD-10: F20) and one prescription of any antipsychotic between 2014 and 2019. Date of first identified antipsychotic prescription was defined as index date, patients were followed up until last prescription of antipsychotics, end of 2019, or discontinuation (>60 days without antipsychotic prescription), whichever was earliest. Dosage was summarized using defined daily dose (DDD), calculated by cumulative average daily dose (CAD) with a unit of DDDs/day, i.e., total DDDs of all antipsychotics in follow-up period divided by total days of follow-up. CAD was categorized into low (<0.5 DDDs/day), moderate (0.5-1.5 DDDs/day), and high (>1.5 DDDs/day) groups.Results13554 patients were included with an average follow-up of 269.9 days. Median CAD was 0.8 DDDs/day (IQR=0.5-1.3), patients with hospitalization during follow-up and used multiple antipsychotics at the same time had larger median CAD, 1.0 DDDs/day and 1.2 DDDs/days, respectively. There were 3245 (23.9%), 7627 (56.3%), and 2682 (19.8%) patients in low, moderate, and high groups, respectively. The median CAD of high dosage group was 2.5 DDDs/day (IQR=1.9-10.5).ConclusionsCAD of most Chinese schizophrenia patients was low or moderate. Association between CAD and hospitalization and multiple concurrent antipsychotics merit further research.