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.

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.