Phenotyping COVID-19 Patients by Ventilation Therapy: Data Quality Challenges and Cohort Characterization

Essay, Patrick; Mosier, Jarrod; Subbian, Vignesh

doi:10.3233/shti210148

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Our study showed that the identification of COVID-19 cases (confirmed, probable, susceptible) can be challenging and time-consuming as it requires an extensive amount of manual review. The quality assurance of data and accurate use of standardized terminologies are important components for developing future phenotyping algorithms to identify COVID-19 cases with high performance for secondary-use research [ 51 ]. On an international level, lessons learned from the COVID-19 pandemic showed that there is a need to improve international research utilizing clinical data through connecting efforts from multiple countries to expand the capability of dealing with pandemic emergencies worldwide [ 21 ].…”

Section: Discussionmentioning

confidence: 99%

Real-World Evidence of COVID-19 Patients’ Data Quality in the Electronic Health Records

et al. 2021

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Despite the importance of electronic health records data, less attention has been given to data quality. This study aimed to evaluate the quality of COVID-19 patients’ records and their readiness for secondary use. We conducted a retrospective chart review study of all COVID-19 inpatients in an academic healthcare hospital for the year 2020, which were identified using ICD-10 codes and case definition guidelines. COVID-19 signs and symptoms were higher in unstructured clinical notes than in structured coded data. COVID-19 cases were categorized as 218 (66.46%) “confirmed cases”, 10 (3.05%) “probable cases”, 9 (2.74%) “suspected cases”, and 91 (27.74%) “no sufficient evidence”. The identification of “probable cases” and “suspected cases” was more challenging than “confirmed cases” where laboratory confirmation was sufficient. The accuracy of the COVID-19 case identification was higher in laboratory tests than in ICD-10 codes. When validating using laboratory results, we found that ICD-10 codes were inaccurately assigned to 238 (72.56%) patients’ records. “No sufficient evidence” records might indicate inaccurate and incomplete EHR data. Data quality evaluation should be incorporated to ensure patient safety and data readiness for secondary use research and predictive analytics. We encourage educational and training efforts to motivate healthcare providers regarding the importance of accurate documentation at the point-of-care.

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Section: Discussionmentioning

confidence: 99%

Real-World Evidence of COVID-19 Patients’ Data Quality in the Electronic Health Records

et al. 2021

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Clustering of clinical and echocardiographic phenotypes of covid-19 patients

Shpigelman

Hochstadt

Coster

et al. 2023

Sci Rep

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We sought to divide COVID-19 patients into distinct phenotypical subgroups using echocardiography and clinical markers to elucidate the pathogenesis of the disease and its heterogeneous cardiac involvement. A total of 506 consecutive patients hospitalized with COVID-19 infection underwent complete evaluation, including echocardiography, at admission. A k-prototypes algorithm applied to patients' clinical and imaging data at admission partitioned the patients into four phenotypical clusters: Clusters 0 and 1 were younger and healthier, 2 and 3 were older with worse cardiac indexes, and clusters 1 and 3 had a stronger inflammatory response. The clusters manifested very distinct survival patterns (C-index for the Cox proportional hazard model 0.77), with survival best for cluster 0, intermediate for 1–2 and worst for 3. Interestingly, cluster 1 showed a harsher disease course than cluster 2 but with similar survival. Clusters obtained with echocardiography were more predictive of mortality than clusters obtained without echocardiography. Additionally, several echocardiography variables (E′ lat, E′ sept, E/e average) showed high discriminative power among the clusters. The results suggested that older infected males have a higher chance to deteriorate than older infected females. In conclusion, COVID-19 manifests differently for distinctive clusters of patients. These clusters reflect different disease manifestations and prognoses. Although including echocardiography improved the predictive power, its marginal contribution over clustering using clinical parameters only does not justify the burden of echocardiography data collection.

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Phenotyping COVID-19 Patients by Ventilation Therapy: Data Quality Challenges and Cohort Characterization

Cited by 2 publications

References 12 publications

Real-World Evidence of COVID-19 Patients’ Data Quality in the Electronic Health Records

Real-World Evidence of COVID-19 Patients’ Data Quality in the Electronic Health Records

Clustering of clinical and echocardiographic phenotypes of covid-19 patients

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