TY - JOUR
T1 - Validation of automated data abstraction for SCCM discovery VIRUS COVID-19 registry
T2 - practical EHR export pathways (VIRUS-PEEP)
AU - Valencia Morales, Diana J.
AU - Bansal, Vikas
AU - Heavner, Smith F.
AU - Castro, Janna C.
AU - Sharma, Mayank
AU - Tekin, Aysun
AU - Bogojevic, Marija
AU - Zec, Simon
AU - Sharma, Nikhil
AU - Cartin-Ceba, Rodrigo
AU - Nanchal, Rahul S.
AU - Sanghavi, Devang K.
AU - La Nou, Abigail T.
AU - Khan, Syed A.
AU - Belden, Katherine A.
AU - Chen, Jen Ting
AU - Melamed, Roman R.
AU - Sayed, Imran A.
AU - Reilkoff, Ronald A.
AU - Herasevich, Vitaly
AU - Domecq Garces, Juan Pablo
AU - Walkey, Allan J.
AU - Boman, Karen
AU - Kumar, Vishakha K.
AU - Kashyap, Rahul
N1 - Publisher Copyright:
Copyright © 2023 Valencia Morales, Bansal, Heavner, Castro, Sharma, Tekin, Bogojevic, Zec, Sharma, Cartin-Ceba, Nanchal, Sanghavi, La Nou, Khan, Belden, Chen, Melamed, Sayed, Reilkoff, Herasevich, Domecq Garces, Walkey, Boman, Kumar and Kashyap.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - COVID-19
KW - VIRUS COVID-19 registry
KW - data automation
KW - electronic health records
KW - validation
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UR - http://www.scopus.com/inward/citedby.url?scp=85174588848&partnerID=8YFLogxK
U2 - 10.3389/fmed.2023.1089087
DO - 10.3389/fmed.2023.1089087
M3 - Article
C2 - 37859860
AN - SCOPUS:85174588848
SN - 2296-858X
VL - 10
JO - Frontiers in Medicine
JF - Frontiers in Medicine
M1 - 1089087
ER -
