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.