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. 2023 Oct 4:10:1089087.
doi: 10.3389/fmed.2023.1089087. eCollection 2023.

Validation of automated data abstraction for SCCM discovery VIRUS COVID-19 registry: practical EHR export pathways (VIRUS-PEEP)

Diana J Valencia Morales  1 , Vikas Bansal  2 , Smith F Heavner  3 , Janna C Castro  4 , Mayank Sharma  1 , Aysun Tekin  1 , Marija Bogojevic  1 , Simon Zec  1 , Nikhil Sharma  2 , Rodrigo Cartin-Ceba  5 , Rahul S Nanchal  6 , Devang K Sanghavi  7 , Abigail T La Nou  8 , Syed A Khan  9 , Katherine A Belden  10 , Jen-Ting Chen  11 , Roman R Melamed  12 , Imran A Sayed  13 , Ronald A Reilkoff  14 , Vitaly Herasevich  1 , Juan Pablo Domecq Garces  2 , Allan J Walkey  15 , Karen Boman  16 , Vishakha K Kumar  16 , Rahul Kashyap  1
Affiliations

Validation of automated data abstraction for SCCM discovery VIRUS COVID-19 registry: practical EHR export pathways (VIRUS-PEEP)

Diana J Valencia Morales et al. Front Med (Lausanne). .

Abstract

Background: The gold standard for gathering data from electronic health records (EHR) has been manual data extraction; however, this requires vast resources and personnel. Automation of this process reduces resource burdens and expands research opportunities.

Objective: This study aimed to determine the feasibility and reliability of automated data extraction in a large registry of adult COVID-19 patients.

Materials and methods: This observational study included data from sites participating in the SCCM Discovery VIRUS COVID-19 registry. Important demographic, comorbidity, and outcome variables were chosen for manual and automated extraction for the feasibility dataset. We quantified the degree of agreement with Cohen's kappa statistics for categorical variables. The sensitivity and specificity were also assessed. Correlations for continuous variables were assessed with Pearson's correlation coefficient and Bland-Altman plots. The strength of agreement was defined as almost perfect (0.81-1.00), substantial (0.61-0.80), and moderate (0.41-0.60) based on kappa statistics. Pearson correlations were classified as trivial (0.00-0.30), low (0.30-0.50), moderate (0.50-0.70), high (0.70-0.90), and extremely high (0.90-1.00).

Measurements and main results: The cohort included 652 patients from 11 sites. The agreement between manual and automated extraction for categorical variables was almost perfect in 13 (72.2%) variables (Race, Ethnicity, Sex, Coronary Artery Disease, Hypertension, Congestive Heart Failure, Asthma, Diabetes Mellitus, ICU admission rate, IMV rate, HFNC rate, ICU and Hospital Discharge Status), and substantial in five (27.8%) (COPD, CKD, Dyslipidemia/Hyperlipidemia, NIMV, and ECMO rate). The correlations were extremely high in three (42.9%) variables (age, weight, and hospital LOS) and high in four (57.1%) of the continuous variables (Height, Days to ICU admission, ICU LOS, and IMV days). The average sensitivity and specificity for the categorical data were 90.7 and 96.9%.

Conclusion and relevance: Our study confirms the feasibility and validity of an automated process to gather data from the EHR.

Keywords: COVID-19; VIRUS COVID-19 registry; data automation; electronic health records; validation.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Study flowchart.
Figure 2
Figure 2
Agreement between manual and PEEP (Bland–Altman plot). (A) Age. (B) Weight. (C) Height. (D) Hospital Length of Stay. (E) Days to ICU admission. (F) ICU Length of Stay. (G) IMV Days.
See this image and copyright information in PMC

References

    1. Wang C, Horby PW, Hayden FG, Gao GF. A novel coronavirus outbreak of global health concern. Lancet. (2020) 395:470–3. doi: 10.1016/S0140-6736(20)30185-9, PMID: - DOI - PMC - PubMed
    1. Domecq JP, Lal A, Sheldrick CR, Kumar VK, Boman K, Bolesta S, et al. Outcomes of patients with coronavirus disease 2019 receiving organ support therapies: the international viral infection and respiratory illness universal study registry. Crit Care Med. (2021) 49:437–48. doi: 10.1097/CCM.0000000000004879, PMID: - DOI - PMC - PubMed
    1. Walkey AJ, Kumar VK, Harhay MO, Bolesta S, Bansal V, Gajic O, et al. The viral infection and respiratory illness universal study (VIRUS): an international registry of coronavirus 2019-related critical illness. Crit Care Explor. (2020) 2:e0113. doi: 10.1097/CCE.0000000000000113, PMID: - DOI - PMC - PubMed
    1. Walkey AJ, Sheldrick RC, Kashyap R, Kumar VK, Boman K, Bolesta S, et al. Guiding principles for the conduct of observational critical care research for coronavirus disease 2019 pandemics and beyond: the Society of Critical Care Medicine discovery viral infection and respiratory illness universal study registry. Crit Care Med. (2020) 48:e1038–44. doi: 10.1097/CCM.0000000000004572, PMID: - DOI - PMC - PubMed
    1. Grimm AG. Hospitals Reported That the COVID-19 Pandemic Has Significantly Strained Health Care Delivery Results of a National Pulse Survey. USA: U.S. Department of Health and Human Services Office of Inspector General. (2021). Available at: https://oig.hhs.gov/oei/reports/OEI-09-21-00140.pdf

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