Risk of retinal detachment and exposure to fluoroquinolones, common antibiotics, and febrile illness using a self-controlled case series study design: Retrospective analyses of three large healthcare databases in the US.

OBJECTIVE: The risk of retinal detachment (RD) following exposure to fluoroquinolone (FQ) has been assessed in multiple studies, however, results have been mixed. This study was designed to estimate the risk of RD following exposure to FQ, other common antibiotics, and febrile illness not treated with antibiotics (FINTA) using a self-controlled case series (SCCS) study design to reduce risk of confounding from unreported patient characteristics. DESIGN: Retrospective database analysis-SCCS. SETTING: Primary and Secondary Care. STUDY POPULATION: 40,981 patients across 3 US claims databases (IBM® MarketScan® commercial and Medicare databases, Optum Clinformatics). OUTCOME: RD. METHODS: Exposures included FQ as a class of drugs, amoxicillin, azithromycin, trimethoprim with and without sulfamethoxazole, and FINTA. For the primary analysis, all drug formulations were included. For the post hoc sensitivity analyses, only oral tablets were included. Risk windows were defined as exposure period (or FINTA duration) plus 30 days. Patients of all ages with RD and exposures in 3 US claims databases between 2012 to 2017 were included. Diagnostics included p value calibration and pre-exposure outcome analyses. Incidence rate ratios (IRR) and 95% confidence interval (CI) comparing risk window time with other time were calculated. RESULTS: Our primary analysis showed an increased risk for RD in the 30 days prior to exposure to FQ or trimethoprim without sulfamethoxazole. This risk decreased but remained elevated for 30 days following first exposure. Our post-hoc analysis, which excluded ophthalmic drops, showed no increased risk for RD at any time, with FQ and other antibiotics. CONCLUSION: Our results did not suggest an association between FQ and RD. Oral FQ was not associated with an increased risk for RD during the pre- or post-exposure period. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03479736-March 21, 2018.

Development and Validation of Algorithms to Identify COVID-19 Patients Using a US Electronic Health Records Database: A Retrospective Cohort Study.

Introduction: 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.

Trends in characteristics and outcomes among US adults hospitalised with COVID-19 throughout 2020: an observational cohort study.

OBJECTIVES: To 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. DESIGN: Observational cohort study. SETTING: COVID-19 subset of the Optum deidentified electronic health records, including more than 1.8 million patients from across the USA. PARTICIPANTS: There 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 MEASURES: Incident acute clinical outcomes, including in-hospital all-cause mortality. RESULTS: Respectively, 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. CONCLUSION: The 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.

Characteristics and outcomes of hospitalised adults with COVID-19 in a Global Health Research Network: a cohort study.

OBJECTIVE: To examine age, gender, and temporal differences in baseline characteristics and clinical outcomes of adult patients hospitalised with COVID-19. DESIGN: A cohort study using deidentified electronic medical records from a Global Research Network. SETTING/PARTICIPANTS: 67 456 adult patients hospitalised with COVID-19 from the USA; 7306 from Europe, Latin America and Asia-Pacific between February 2020 and January 2021. RESULTS: In 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. CONCLUSIONS: This 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.

Pediatric BMI changes during COVID-19 pandemic: An electronic health record-based retrospective cohort study.

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 (∆BMIadj) in individual 6-to-17-year-old children between two well child checks (WCCs). The mean of individual ∆BMIadj during 2017-2020 was calculated by month. For September-December WCCs, the mean of individual ∆BMIadj (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] ∆BMIadj in September-December of 2020 was 0·62 [0·59,0·64] kg/m2, compared to 0·31 [0·29, 0·32] kg/m2 in previous years. The increase was most prominent in children with pre-existing obesity (1·16 [1·07,1·24] kg/m2 in 2020 versus 0·56 [0·52,0·61] kg/m2 in previous years), Hispanic children (0·93 [0·84,1·02] kg/m2 in 2020 versus 0·41 [0·36,0·46] kg/m2 in previous years), and children who lack commercial insurance (0·88 [0·81,0·95] kg/m2 in 2020 compared to 0·43 [0·39,0·47] kg/m2 in previous years). ∆BMIadj 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. FUNDING: Amgen, Inc.

Risk of aortic aneurysm and dissection following exposure to fluoroquinolones, common antibiotics, and febrile illness using a self-controlled case series study design: Retrospective analyses of three large healthcare databases in the US.

OBJECTIVE: Recent observational studies suggest increased aortic aneurysm or dissection (AAD) risk following fluoroquinolone (FQ) exposure but acknowledge potential for residual bias from unreported patient characteristics. The objective of our study is to evaluate the potential association between FQ, other common antibiotics and febrile illness with risk of AAD using a self-controlled case series (SCCS) study design. DESIGN: Retrospective database analysis-SCCS. SETTING: Primary and Secondary Care. STUDY POPULATION: 51,898 patients across 3 US claims databases (IBM® MarketScan® commercial and Medicare databases, Optum Clinformatics). EXPOSURE: FQ or other common antibiotics or febrile illness. OUTCOME: AAD. METHODS: We studied patients with exposures and AAD between 2012 and 2017 in 3 databases. Risk windows were defined as exposure period plus 30 days. Diagnostic analyses included p-value calibration to account for residual error using negative control exposures (NCE), and pre-exposure outcome analyses to evaluate exposure-outcome timing. The measure of association was the incidence rate ratio (IRR) comparing exposed and unexposed time. RESULTS: Most NCEs produced effect estimates greater than the hypothetical null, indicating positive residual error; calibrated p (Cp) values were therefore used. The IRR following FQ exposure ranged from 1.13 (95% CI: 1.04-1.22 -Cp: 0.503) to 1.63 (95% CI: 1.45-1.84 -Cp: 0.329). An AAD event peak was identified 60 days before first FQ exposure, with IRR increasing between the 60- to 30- and 29- to 1-day pre-exposure periods. It is uncertain how much this pre-exposure AAD event peak reflects confounding versus increased antibiotic use after a surgical correction of AADs. CONCLUSION: This study does not confirm prior studies. Using Cp values to account for residual error, the observed FQ-AAD association cannot be interpreted as significant. Additionally, an AAD event surge in the 60 days before FQ exposure is consistent with confounding by indication, or increased use of antibiotics post-surgery. REGISTRATION: NCT03479736.

Association of Ticagrelor vs Clopidogrel With Net Adverse Clinical Events in Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention.

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 Participants: A 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 Measures: The 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. Results: After 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.

Extending Achilles Heel Data Quality Tool with New Rules Informed by Multi-Site Data Quality Comparison.

Large healthcare datasets of Electronic Health Record data became indispensable in clinical research. Data quality in such datasets recently became a focus of many distributed research networks. Despite the fact that data quality is specific to a given research question, many existing data quality platform prove that general data quality assessment on dataset level (given a spectrum of research questions) is possible and highly requested by researchers. We present comparison of 12 datasets and extension of Achilles Heel data quality software tool with new rules and data characterization measures.

Feasibility and utility of applications of the common data model to multiple, disparate observational health databases.

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.