Use of an EHR to inform an administrative data algorithm to categorize inpatient COVID-19 severity
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Abstract
Importance
Algorithms for classification of inpatient COVID-19 severity are necessary for confounding control in studies using real-world data (RWD).
Objective
To explore use of electronic health record (EHR) data to inform an administrative data algorithm for classification of supplemental oxygen or noninvasive ventilation (O2/NIV) and invasive mechanical ventilation (IMV) to assess disease severity in hospitalized COVID-19 patients.
Design
In this retrospective cohort study, we developed an initial procedure-based algorithm to identify O2/NIV, IMV, and NEITHER O2/NIV nor IMV in two inpatient RWD sources. We then expanded the algorithm to explore the impact of adding diagnoses indicative of clinical need for O2/NIV (hypoxia, hypoxemia) or IMV (acute respiratory distress syndrome) and O2-related patient vitals available in the EHR. Observed changes in severity categorization were used to augment the administrative algorithm.
Setting
Optum de-identified COVID-19 EHR data and HealthVerity claims and chargemaster data (March – August 2020).
Participants
Among patients hospitalized with COVID-19 in each RWD source, our motivating example selected dexamethasone (DEX+) initiators and a random selection of patients who were non-initiators of a corticosteroid of interest (CSI-) matched on date of DEX initiation, age, sex, baseline comorbidity score, days since admission, and COVID-19 severity level (NEITHER, O2/NIV, IMV) on treatment index.
Main Outcome and Measures
Inpatient COVID-19 severity was defined using the algorithms developed to classify respiratory support requirements among hospitalized COVID-19 patients (NEITHER, O2/NIV, IMV). Measures were reported as the treatment-specific distributions of patients in each severity level, and as observed changes in severity categorization between the initial procedure-based and expanded algorithms.
Results
In the administrative data cohort with 5,524 DEX+ and CSI- patient pairs matched using the initial procedure-based algorithm, 30% were categorized as O2/NIV, 5% as IMV, and 65% as NEITHER. Among patients assigned NEITHER via the initial algorithm, use of an expanded algorithm informed by the EHR-based algorithm shifted 54% DEX+ and 28% CSI- to O2/NIV, and 2% DEX+ and 1% CSI- to IMV. Among patients initially assigned O2/NIV, 7% DEX+ and 3% CSI- shifted to IMV.
Conclusions and Relevance
Application of learnings from an EHR-based exploration to our administrative algorithm minimized treatment-differential misclassification of COVID-19 severity.
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SciScore for 10.1101/2021.10.04.21264513: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization From these populations, we selected primary cohorts of DEX initiators (DEX+) and a random selection of patients who had not or had not yet initiated any CSI (CSI-) matched 1:1 on date of DEX initiation, age, sex, Charlson-Quan comorbidity score over the 183-day baseline period (Quan 2005), days since admission, and the mWHO severity level (NEITHER, O2/NIV, IMV) on treatment index (see NCT04926571 protocol for additional detail). Blinding not detected. Power Analysis not detected. Cell Line Authentication Authentication: All analyses were conducted using the Aetion Evidence Platform® (2021), a software for real-world data … SciScore for 10.1101/2021.10.04.21264513: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization From these populations, we selected primary cohorts of DEX initiators (DEX+) and a random selection of patients who had not or had not yet initiated any CSI (CSI-) matched 1:1 on date of DEX initiation, age, sex, Charlson-Quan comorbidity score over the 183-day baseline period (Quan 2005), days since admission, and the mWHO severity level (NEITHER, O2/NIV, IMV) on treatment index (see NCT04926571 protocol for additional detail). Blinding not detected. Power Analysis not detected. Cell Line Authentication Authentication: All analyses were conducted using the Aetion Evidence Platform® (2021), a software for real-world data analysis, validated for a range of studies. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: We found the following clinical trial numbers in your paper:
Identifier Status Title NCT04926571 Active, not recruiting Dexamethasone and COVID-19 Inpatient Mortality Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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