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.

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.

Validating Publicly Available Crosswalks for Translating ICD-9 to ICD-10 Diagnosis Codes for Cardiovascular Outcomes Research.

Background On October 1, 2015, the Center for Medicare and Medicaid Services transitioned from the International Classification of Diseases, Ninth Revision ( ICD-9) to the ICD, Tenth Revision ( ICD-10) compendium of codes for diagnosis and billing in health care, but translation between the two is often inexact. Here we describe a validated crosswalk to translate ICD-9 codes into ICD-10 codes, with a focus on complications after carotid revascularization and endovascular aortic aneurysm repair. Methods and Results We devised an 8-step process to derive and validate ICD-10 codes from existing ICD-9 codes. We used publicly available sources, including the General Equivalence Mapping database, to translate ICD-9 codes used in prior work to ICD-10 codes. We defined ICD-10 codes as validated if they were concordant with the initial ICD-9 codes after manual comparison by two physicians. Our primary validation measure was the percent of valid ICD-10 codes out of the total ICD-10 codes obtained during translation. We began with 126 ICD-9 diagnosis codes used for complication identification after carotid revascularization procedures, and 97 ICD-9 codes for complications after endovascular aortic aneurysm procedures. Translation generated 143 ICD-10 codes for carotid revascularization, a 14% increase from the initial 126 codes. Manual comparison demonstrated 98% concordance, with 99% agreement between the reviewers. Similarly, we identified 108 ICD-10 codes for endovascular aortic aneurysm repair, an 11% increase from the initial 97 ICD-9 codes. We again noted excellent concordance and agreement (98% and 100%, respectively). Manual review identified 4 ICD-10 codes incorrectly translated from ICD-9 codes for carotid revascularization, and 3 codes incorrectly translated for endovascular aortic aneurysm repair. Conclusions Algorithms to crosswalk lists of ICD-9 codes to ICD-10 codes can leverage electronic resources to minimize the burden of code translation. However, manual review for code validation may be necessary, with collaboration across institutions for researchers to share their efforts.

Association Between Hypertonic Saline and Hospital Length of Stay in Acute Viral Bronchiolitis: A Reanalysis of 2 Meta-analyses.

IMPORTANCE: Two previous meta-analyses of nebulized hypertonic saline (HS) on hospital length of stay (LOS) in acute viral bronchiolitis have suggested benefit. Neither study fully addressed the issue of excessive heterogeneity in the cohort of studies, indicating that it may be inappropriate to combine such dissimilar studies to estimate a common treatment effect. OBJECTIVE: To reanalyze the existing data set for sources of heterogeneity to delineate the population most likely to benefit from HS. DATA SOURCES: We used the previously analyzed cohort of randomized trials from 2 published meta-analyses comparing HS with normal saline (or, in 1 case, with standard of care) in infants hospitalized for bronchiolitis. We also repeated the search strategy used by the most recent Cochrane Review in the Medline database through September 2015. STUDY SELECTION: Eighteen randomized clinical trials of HS in infants with bronchiolitis reporting LOS as an outcome measure were included. DATA EXTRACTION AND SYNTHESIS: The guidelines used for abstracting data included LOS, study year, setting, sample size, type of control, admission/discharge criteria, adjunct medications, treatment frequency, mean day of illness at study enrollment, mean severity of illness scores, and mean age. MAIN OUTCOMES AND MEASURES: Weighted mean difference in LOS and study heterogeneity as measured by the I2 statistic. RESULTS: There were 18 studies included of 2063 infants (63% male), with a mean age of 4.2 months. The mean LOS was 3.6 days. Two main sources of heterogeneity were identified. First, the effect of HS on LOS was entirely sensitive to the removal of one study population, noted to have a widely divergent definition of the primary outcome. Second, there was a baseline imbalance in mean day of illness at presentation between treatment groups. Controlling for either of these factors resolved the heterogeneity (I2 = reduced from 78% to 45% and 0%, respectively) and produced summary estimates in support of the null hypothesis (that HS does not affect LOS). There was a weighted mean difference in LOS of -0.21 days (95% CI, -0.43 to +0.02) for the sensitivity analysis and +0.02 days (95% CI, -0.14 to +0.17) for studies without unbalanced treatment groups on presentation. CONCLUSIONS AND RELEVANCE: Prior analyses were driven by an outlier population and unbalanced treatment groups in positive trials. Once heterogeneity was accounted for, the data did not support the use of HS to decrease LOS in infants hospitalized with bronchiolitis.