Phenotyping coronavirus disease 2019 during a global health pandemic: Lessons learned from the characterization of an early cohort

DeLozier, Sarah; Bland, Sarah; McPheeters, Melissa L; Wells, Quinn S.; Farber‐Eger, Eric; Bejan, Cosmin Adrian; Fabbri, Daniel; Rosenbloom, S. Trent; Roden, Dan M.; Johnson, Kevin B.; Wei, Wei‐Qi; Peterson, Josh F.; Bastarache, Lisa

doi:10.1016/j.jbi.2021.103777

Cited by 11 publications

(16 citation statements)

References 31 publications

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“…However, EHR phenotyping pose many challenges, which could lead to inaccurately extracting the treatment status of the patient. 50 For example, drug exposure extraction from clinical text relies on accurate identification of text expressions that are negated (e.g., “ he could not be on Coumadin because of history of GI bleed ”) or hypothetical (e.g., “ Zofran 4 mg PO once a day as needed for nausea ”). 14 This is another reason for interpreting the results of over‐the‐counter drugs with caution because they are primarily extracted from clinical notes.…”

Section: Discussionmentioning

confidence: 99%

DrugWAS: Drug‐wide Association Studies for COVID‐19 Drug Repurposing

Bejan

Cahill

Staso

et al. 2021

Clin Pharma and Therapeutics

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This study aimed to systematically investigate if any of the available drugs in the electronic health record (EHR) can be repurposed as potential treatment for coronavirus disease 2019 . Based on a retrospective cohort analysis of EHR data, drug-wide association studies (DrugWAS) were performed on 9,748 patients with COVID-19 at Vanderbilt University Medical Center (VUMC). For each drug study, multivariable logistic regression with overlap weighting using propensity score was applied to estimate the effect of drug exposure on COVID-19 disease outcomes. Patient exposure to a drug between 3-months prior to the pandemic and the COVID-19 diagnosis was chosen as the exposure of interest. All-cause of death was selected as the primary outcome. Hospitalization, admission to the intensive care unit, and need for mechanical ventilation were identified as secondary outcomes. Overall, 17 drugs were significantly associated with decreased COVID-19 severity. Previous exposure to two types of 13-valent pneumococcal conjugate vaccines, PCV13 (odds ratio (OR), 0.31, 95% confidence interval (CI), 0.12-0.81 and OR, 0.33, 95% CI, 0.15-0.73), diphtheria toxoid and tetanus toxoid vaccine (OR, 0.38, 95% CI, 0.15-0.93) were significantly associated with a decreased risk of death (primary outcome). Secondary analyses identified several other significant associations showing lower risk for COVID-19 outcomes: acellular pertussis vaccine, 23-valent pneumococcal polysaccharide vaccine (PPSV23), flaxseed extract, ethinyl estradiol, estradiol, turmeric extract, ubidecarenone, azelastine, pseudoephedrine, dextromethorphan, omega-3 fatty acids, fluticasone, and ibuprofen. In conclusion, this cohort study leveraged EHR data to identify a list of drugs that could be repurposed to improve COVID-19 outcomes. Further randomized clinical trials are needed to investigate the efficacy of the proposed drugs.Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has triggered a pandemic infection leading to unprecedented excess mortality and adverse consequences to global economy. 1,2 According to the World Health Organization (WHO), SARS-CoV-2 has spread to over 222 countries and territories resulting in > 81 million

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Section: Discussionmentioning

confidence: 99%

DrugWAS: Drug‐wide Association Studies for COVID‐19 Drug Repurposing

Bejan

Cahill

Staso

et al. 2021

Clin Pharma and Therapeutics

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“…Reliable subphenotype characterization that reflects the geographical and healthcare settings from which they are ascertained is crucial 46 . To date, variability between Mexican states and TCIs regarding severity are rarely reported 47,48,49 , nor assessed for variability independently from age and gender.…”

Section: State and Types Of Clinical Institutionmentioning

confidence: 99%

Subphenotyping of COVID-19 patients at pre-admission towards anticipated severity stratification: an analysis of 778 692 Mexican patients through an age-gender unbiased meta-clustering technique

Zhou¹,

Romero²,

Martı́nez-Miranda

et al. 2021

Preprint

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Objective To describe COVID-19 subphenotypes regarding severity patterns including prognostic, ICU and morbimortality outcomes, through stratification based on gender and age groups, as described by inter-patient variability patterns in clinical phenotypes and demographic features. Materials and methods We used the COVID-19 open data from the Mexican Government including patient-level epidemiological and clinical data from 778 692 SARS-CoV-2 patients from January 13, 2020 to September 30, 2020. Inter-patient variability was analyzed by combining dimensionality reduction and hierarchical clustering methods. We produced cluster analyses for all combinations of gender and age groups (<18, 18-49, 50-64, and >64). For each group, the optimum number of clusters was selected combining a quantitative approach using the Silhouette coefficient, and a qualitative approach through a subgroup expert inspection via visual analytics. Using the features of the resultant age-gender clusters, we performed a meta-clustering analysis to provide an overall description of the population. Results We observed a total of 56 age-gender clusters, grouped in 11 clinically distinguishable meta-clusters with different outcomes. Meta-clusters 1 to 3 showed the highest recovery rates (90.27-95.22%). These clusters include: (1) healthy patients of all ages, (2) children with comorbidities who had priority in medical resources, (3) young patients with obesity and smoking habit. Meta-clusters 4 and 5 showed moderate recovery rates (81.3-82.81%): (4) patients with hypertension or diabetes of all ages, (5) typical obese patients with three highly correlated conditions, namely, pneumonia, hypertension and diabetes. Meta-clusters 6 to 11 had very low recovery rates (53.96-66.94%) which include: (6) immunosuppressed patients with the highest comorbidity rate in many diseases, (7) CKD patients with the worse survival length and recovery, (8) elderly smoker with mild COPD, (9) severe diabetic elderly with hypertension, (10, 11) oldest obese smokers with severe COPD and mild cardiovascular disease with the latter (11) showing a relatively higher age and smoke rate, severe COPD and shorter survival length, reinforcing a high correlation between smoking habit and COPD among elderly. Additionally, the source Mexican state and type of clinical institution proved to be an important factor for heterogeneity in severity. Discussion The proposed unsupervised learning approach successfully uncovered discriminative COVID-19 severity patterns for both genders and all age groups from clinical phenotypes and demographic features. A careful read of group outcomes showed consistent results regarding recent literature. Regarding the Mexican population, our results suggest that habits and comorbidities may play a key role in predicting mortality in older patients. Centenarians tended to fall in the groups with better outcomes repeatedly. Additionally, immunosuppression was not found as a relevant factor for severity alone but did when present along with chronic kidney disease. Further useful correlations could be found by evaluating the duration of unhealthy habits, demographic features, comorbidities, the time since diagnosis, recovery progress, readmission record, and the effect of source variability. Conclusion The resultant eleven meta-clusters provide bases to comprehend the classification of patients with COVID-19 based on comorbidities, habits, demographic characteristics, geographic data and type of clinical institutions, as well as revealing the correlations between the above characteristics thereby help to anticipate the possible clinical outcomes for every specifically characterized patient. These subphenotypes can establish target groups for automated stratification or triage systems to provide personalized therapies or treatments. Code available at: https://github.com/bdslab-upv/covid19-metaclustering Dynamic results visualization at: http://covid19sdetool.upv.es/?tab=mexicoGov

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“…The current infrastructure and complexity of EHR systems vary across hospitals, which limits the capability of using EHR data for research purposes [ 9 ]. Data quality and related issues have been studied in many contexts, and the findings can vary across different institutions and different research studies [ 9 , 25 , 26 , 27 , 28 ]. Many such issues are generated during the documentation process at the point of care [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

“…Lynch et al evaluated the performance of ICD-10 code U07.1 for identifying COVID-19 patients using a manual chart review as a gold standard, and they found that the performance was low [ 26 ]. Similarly, DeLozier et al found that using laboratory testing (sensitivity = 93%) only to define COVID-19 patients outperformed the use of ICD-10 code U07.1 (sensitivity = 46.4%), which can be improved when combining the output of both definitions of ICD-10 and laboratory testing to yield a sensitivity of 100% [ 27 ]. Lynch et al reported the use of ICD-10 codes either alone or supported with laboratory tests is not sufficient for surveillance and research [ 26 ], as ICD-10 codes do not appear to capture cases correctly [ 30 ].…”

Section: Introductionmentioning

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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Phenotyping coronavirus disease 2019 during a global health pandemic: Lessons learned from the characterization of an early cohort

Abstract: Graphical abstract

Cited by 11 publications

References 31 publications

DrugWAS: Drug‐wide Association Studies for COVID‐19 Drug Repurposing

DrugWAS: Drug‐wide Association Studies for COVID‐19 Drug Repurposing

Subphenotyping of COVID-19 patients at pre-admission towards anticipated severity stratification: an analysis of 778 692 Mexican patients through an age-gender unbiased meta-clustering technique

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

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