Objectives To evaluate the utility of applying the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) across multiple observational databases within an organization and to apply standardized analytics tools for conducting observational research.Materials and methods Six deidentified patient-level datasets were transformed to the OMOP CDM. We evaluated the extent of information loss that occurred through the standardization process. We developed a standardized analytic tool to replicate the cohort construction process from a published epidemiology protocol and applied the analysis to all 6 databases to assess time-to-execution and comparability of results.Results Transformation to the CDM resulted in minimal information loss across all 6 databases. Patients and observations excluded were due to identified data quality issues in the source system, 96% to 99% of condition records and 90% to 99% of drug records were successfully mapped into the CDM using the standard vocabulary. The full cohort replication and descriptive baseline summary was executed for 2 cohorts in 6 databases in less than 1 hour.Discussion The standardization process improved data quality, increased efficiency, and facilitated cross-database comparisons to support a more systematic approach to observational research. Comparisons across data sources showed consistency in the impact of inclusion criteria, using the protocol and identified differences in patient characteristics and coding practices across databases.Conclusion Standardizing data structure (through a CDM), content (through a standard vocabulary with source code mappings), and analytics can enable an institution to apply a network-based approach to observational research across multiple, disparate observational health databases.

IMPORTANCE Current guidelines recommend ticagrelor as the preferred P2Y12 platelet inhibitor for patients with acute coronary syndrome (ACS), primarily based on a single large randomized clinical trial. The benefits and risks associated with ticagrelor vs clopidogrel in routine practice merits attention.OBJECTIVE To determine the association of ticagrelor vs clopidogrel with ischemic and hemorrhagic events in patients undergoing percutaneous coronary intervention (PCI) for ACS in clinical practice. DESIGN, SETTING, AND PARTICIPANTSA retrospective cohort study of patients with ACS who underwent PCI and received ticagrelor or clopidogrel was conducted using 2 United States electronic health record-based databases and 1 nationwide South Korean database from November 2011 to March 2019. Patients were matched using a large-scale propensity score algorithm, and the date of final follow-up was March 2019.EXPOSURES Ticagrelor vs clopidogrel. MAIN OUTCOMES AND MEASURESThe primary end point was net adverse clinical events (NACE) at 12 months, composed of ischemic events (recurrent myocardial infarction, revascularization, or ischemic stroke) and hemorrhagic events (hemorrhagic stroke or gastrointestinal bleeding). Secondary outcomes included NACE or mortality, all-cause mortality, ischemic events, hemorrhagic events, individual components of the primary outcome, and dyspnea at 12 months. The database-level hazard ratios (HRs) were pooled to calculate summary HRs by random-effects meta-analysis. RESULTSAfter propensity score matching among 31 290 propensity-matched pairs (median age group, 60-64 years; 29.3% women), 95.5% of patients took aspirin together with ticagrelor or clopidogrel. The 1-year risk of NACE was not significantly different between ticagrelor and clopidogrel (15.1% [3484/23 116 person-years] vs 14.6% [3290/22 587 person-years]; summary HR, 1.05 [95% CI, 1.00-1.10]; P = .06). There was also no significant difference in the risk of all-cause mortality (2.0% for ticagrelor vs 2.1% for clopidogrel; summary HR, 0.97 [95% CI, 0.81-1.16]; P = .74) or ischemic events (13.5% for ticagrelor vs 13.4% for clopidogrel; summary HR, 1.03 [95% CI, 0.98-1.08]; P = .32). The risks of hemorrhagic events (2.1% for ticagrelor vs 1.6% for clopidogrel; summary HR, 1.35 [95% CI, 1.13-1.61]; P = .001) and dyspnea (27.3% for ticagrelor vs 22.6% for clopidogrel; summary HR, 1.21 [95% CI, 1.17-1.26]; P < .001) were significantly higher in the ticagrelor group.CONCLUSIONS AND RELEVANCE Among patients with ACS who underwent PCI in routine clinical practice, ticagrelor, compared with clopidogrel, was not associated with significant difference in the risk of NACE at 12 months. Because the possibility of unmeasured confounders cannot be excluded, further research is needed to determine whether ticagrelor is more effective than clopidogrel in this setting.

Background: Beginning March 2020, the COVID-19 pandemic has disrupted different aspects of life. The impact on children's rate of weight gain has not been analysed. Methods:In this retrospective cohort study, we used United States (US) Electronic Health Record (EHR) data from Optum® to calculate the age-and sex-adjusted change in BMI ( BMI adj ) in individual 6to-17-year-old children between two well child checks (WCCs). The mean of individual BMI adj during 2017-2020 was calculated by month. For September-December WCCs, the mean of individual BMI adj (overall and by subgroup) was reported for 2020 and 2017-2019, and the impact of 2020 vs 2017-2019 was tested by multivariable linear regression. Findings: The mean [95% Confidence Interval -CI] BMI adj in September-December of 2020 was 0 •62 [0 •59,0 •64] kg/m 2 , compared to 0 •31 [0 •29, 0 •32] kg/m 2 in previous years. The increase was most prominent in children with pre-existing obesity (1 •16 [1 •07,1 •24] kg/m 2 in 2020 versus 0 •56 [0 •52,0 •61] kg/m 2 in previous years), Hispanic children (0 •93 [0 •84,1 •02] kg/m 2 in 2020 versus 0 •41 [0 •36,0 •46] kg/m 2 in previous years), and children who lack commercial insurance (0 •88 [0 •81,0 •95] kg/m 2 in 2020 compared to 0 •43 [0 •39,0 •47] kg/m 2 in previous years). BMI adj accelerated most in ages 8-12 and least in ages 15-17.Interpretation: Children's rate of unhealthy weight gain increased notably during the COVID-19 pandemic across demographic groups, and most prominently in children already vulnerable to unhealthy weight gain. This data can inform policy decisions critical to child development and health as the pandemic continues to unfold.

Background Hydroxychloroquine has recently received Emergency Use Authorization by the FDA and is currently prescribed in combination with azithromycin for COVID-19 pneumonia. We studied the safety of hydroxychloroquine, alone and in combination with azithromycin.Methods New user cohort studies were conducted including 16 severe adverse events (SAEs).Rheumatoid arthritis patients aged 18+ and initiating hydroxychloroquine were compared to those initiating sulfasalazine and followed up over 30 days. Self-controlled case series (SCCS) were conducted to further establish safety in wider populations. Separately, SAEs associated with hydroxychloroquineazithromycin (compared to hydroxychloroquine-amoxicillin) were studied. Data comprised 14 sources of claims data or electronic medical records from Germany, Japan, Netherlands, Spain, UK, and USA.Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate calibrated hazard ratios (CalHRs) according to drug use. Estimates were pooled where I2<40%. ResultsOverall, 956,374 and 310,350 users of hydroxychloroquine and sulfasalazine, and 323,122 and 351,956 users of hydroxychloroquine-azithromycin and hydroxychloroquine-amoxicillin were included.No excess risk of SAEs was identified when 30-day hydroxychloroquine and sulfasalazine use were compared. SCCS confirmed these findings. However, when azithromycin was added to hydroxychloroquine, we observed an increased risk of 30-day cardiovascular mortality (CalHR2.19 [1.22-3.94]), chest pain/angina (CalHR 1.15 [95% CI 1.05-1.26]), and heart failure (CalHR 1.22 [95% CI 1.02-1.45])

Introduction: Non-cytotoxic therapy has changed the treatment paradigm for advanced non-small cell lung cancer (NSCLC) patients and significantly improved outcomes. As survival outcomes continue to improve, quality of life (QOL) has become an important measure of success, and QOL analysis is increasingly being incorporated in clinical trial design. The objective of this systematic review was to assess the methodological quality of QOL analysis in NSCLC Randomized Clinical Trials (RCTs) involving biologic agents to treat NSCLC. Additionally, we assessed whether QOL measures included specific tools for neurological and cognitive measures for patients with central nervous system (CNS) involvement. Methods: A systematic literature search was performed using Medline, Embase, and Web of Science databases to identify NSCLC RCTs published between January 1 st ,1996 and January 10 th , 2020 reporting Quality of Life (QOL) measures. Only RCTs that both enrolled previously untreated patients with advanced NSCLC and had QOL analysis were included. Full-text analysis and data extraction was performed in duplicate. The CONSORT-PRO Extension checklist was used to evaluate the quality of included trials. Results: 4,439 abstracts were screened, of which only 96 RCTs met inclusion and exclusion criteria for analysis. 41,927 patients were enrolled in the included trials. The majority of trials included patients with treated brain metastases, but only 19 RCTs (8,444 patients) specified the incidence of CNS metastases at baseline with a median proportion of 11.57% (IQR 9.2 -23.2%). 33 trials involved biologic agents irrespective of tumor mutational status, and 17 studies only included patients that harbored a targetable mutation. The QOL assessment tools that were most commonly applied were EORTC-QLQ-LC13 (43 RCTs) and EORTC-QLQ-C30 (55 RCTs). The median number of verified CONSORT-PRO Extension criteria in the included trials was 3, and only in 10 trials were all 6 criteria well-documented. The least verified criteria were the following: report of statistical approaches for missing data (27 RCTs) and the discussion of limitations for QOL generalizability to clinical practice (41 RCTs). Only 13 RCTs performed subgroup analyses to specifically evaluate QOL. Better methodological quality of QOL reporting, defined by the number of fulfilled CONSORT-PRO Extension criteria, was observed in trials that included and specified the incidence of CNS metastases at baseline (p¼0.046) and in studies that only included patients with targetable mutations (p¼0.02) Conclusion: Considering the improving survival of lung cancer patients, more efforts should be pursued to analyze QOL in these patients. Further developing, standardizing, and implementing specific tools to analyze QOL is necessary to reflect the real-life experience of lung cancer patients in the era of personalized medicine.

ObjectiveTo examine age, gender, and temporal differences in baseline characteristics and clinical outcomes of adult patients hospitalised with COVID-19.DesignA cohort study using deidentified electronic medical records from a Global Research Network.Setting/Participants67 456 adult patients hospitalised with COVID-19 from the USA; 7306 from Europe, Latin America and Asia-Pacific between February 2020 and January 2021.ResultsIn the US cohort, compared with patients 18–34 years old, patients ≥65 had a greater risk of intensive care unit (ICU) admission (adjusted HR (aHR) 1.73, 95% CI 1.58 to 1.90), acute respiratory distress syndrome(ARDS)/respiratory failure (aHR 1.86, 95% CI 1.76 to 1.96), invasive mechanical ventilation (IMV, aHR 1.93, 95% CI, 1.73 to 2.15), and all-cause mortality (aHR 5.6, 95% CI 4.36 to 7.18). Men appeared to be at a greater risk for ICU admission (aHR 1.34, 95% CI 1.29 to 1.39), ARDS/respiratory failure (aHR 1.24, 95% CI1.21 to 1.27), IMV (aHR 1.38, 95% CI 1.32 to 1.45), and all-cause mortality (aHR 1.16, 95% CI 1.08 to 1.24) compared with women. Moreover, we observed a greater risk of adverse outcomes during the early pandemic (ie, February–April 2020) compared with later periods. In the ex-US cohort, the age and gender trends were similar; for the temporal trend, the highest proportion of patients with all-cause mortality were also in February–April 2020; however, the highest percentages of patients with IMV and ARDS/respiratory failure were in August–October 2020 followed by February–April 2020.ConclusionsThis study provided valuable information on the temporal trends of characteristics and outcomes of hospitalised adult COVID-19 patients in both USA and ex-USA. It also described the population at a potentially greater risk for worse clinical outcomes by identifying the age and gender differences. Together, the information could inform the prevention and treatment strategies of COVID-19. Furthermore, it can be used to raise public awareness of COVID-19’s impact on vulnerable populations.

ObjectivesTo examine the temporal patterns of patient characteristics, treatments used and outcomes associated with COVID-19 in patients who were hospitalised for the disease between January and 15 November 2020.DesignObservational cohort study.SettingCOVID-19 subset of the Optum deidentified electronic health records, including more than 1.8 million patients from across the USA.ParticipantsThere were 51 510 hospitalised patients who met the COVID-19 definition, with 37 617 in the laboratory positive cohort and 13 893 in the clinical cohort.Primary and secondary outcome measuresIncident acute clinical outcomes, including in-hospital all-cause mortality.ResultsRespectively, 48% and 49% of the laboratory positive and clinical cohorts were women. The 50– 65 age group was the median age group for both cohorts. The use of antivirals and dexamethasone increased over time, fivefold and twofold, respectively, while the use of hydroxychloroquine declined by 98%. Among adult patients in the laboratory positive cohort, absolute age/sex standardised incidence proportion for in-hospital death changed by −0.036 per month (95% CI −0.042 to –0.031) from March to June 2020, but remained fairly flat from June to November, 2020 (0.001 (95% CI −0.001 to 0.003), 17.5% (660 deaths /3986 persons) in March and 10.2% (580/5137) in October); in the clinical cohort, the corresponding changes were −0.024 (95% CI −0.032 to –0.015) and 0.011 (95% CI 0.007 0.014), respectively (14.8% (175/1252) in March, 15.3% (189/1203) in October). Declines in the cumulative incidence of most acute clinical outcomes were observed in the laboratory positive cohort, but not for the clinical cohort.ConclusionThe incidence of adverse clinical outcomes remains high among COVID-19 patients with clinical diagnosis only. Patients with COVID-19 entering the hospital are at elevated risk of adverse outcomes.

In order to identify and evaluate candidate algorithms to detect COVID-19 cases in an electronic health record (EHR) database, this study examined and compared the utilization of acute respiratory disease codes from February to August 2020 versus the corresponding time period in the 3 years preceding. Methods: De-identified EHR data were used to identify codes of interest for candidate algorithms to identify COVID-19 patients. The number and proportion of patients who received a SARS-CoV-2 reverse transcriptase polymerase chain reaction (RT-PCR) within ±10 days of the occurrence of the diagnosis code and patients who tested positive among those with a test result were calculated, resulting in 11 candidate algorithms. Sensitivity, specificity, and likelihood ratios assessed the candidate algorithms by clinical setting and time period. We adjusted for potential verification bias by weighting by the reciprocal of the estimated probability of verification. Results: From January to March 2020, the most commonly used diagnosis codes related to COVID-19 diagnosis were R06 (dyspnea) and R05 (cough). On or after April 1, 2020, the code with highest sensitivity for COVID-19, U07.1, had near perfect adjusted sensitivity (1.00 [95% CI 1.00, 1.00]) but low adjusted specificity (0.32 [95% CI 0.31, 0.33]) in hospitalized patients. Discussion: Algorithms based on the U07.1 code had high sensitivity among hospitalized patients, but low specificity, especially after April 2020. None of the combinations of ICD-10-CM codes assessed performed with a satisfactory combination of high sensitivity and high specificity when using the SARS-CoV-2 RT-PCR as the reference standard.