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Columbia Open Health Data for COVID-19 Research: Database Analysis.
Lee, Junghwan; Kim, Jae Hyun; Liu, Cong; Hripcsak, George; Natarajan, Karthik; Ta, Casey; Weng, Chunhua.
  • Lee J; Columbia University, New York, NY, United States.
  • Kim JH; Columbia University, New York, NY, United States.
  • Liu C; Columbia University, New York, NY, United States.
  • Hripcsak G; Columbia University, New York, NY, United States.
  • Natarajan K; Columbia University, New York, NY, United States.
  • Ta C; Columbia University, New York, NY, United States.
  • Weng C; Columbia University, New York, NY, United States.
J Med Internet Res ; 23(9): e31122, 2021 09 30.
Article in English | MEDLINE | ID: covidwho-1459209
ABSTRACT

BACKGROUND:

COVID-19 has threatened the health of tens of millions of people all over the world. Massive research efforts have been made in response to the COVID-19 pandemic. Utilization of clinical data can accelerate these research efforts to combat the pandemic since important characteristics of the patients are often found by examining the clinical data. Publicly accessible clinical data on COVID-19, however, remain limited despite the immediate need.

OBJECTIVE:

To provide shareable clinical data to catalyze COVID-19 research, we present Columbia Open Health Data for COVID-19 Research (COHD-COVID), a publicly accessible database providing clinical concept prevalence, clinical concept co-occurrence, and clinical symptom prevalence for hospitalized patients with COVID-19. COHD-COVID also provides data on hospitalized patients with influenza and general hospitalized patients as comparator cohorts.

METHODS:

The data used in COHD-COVID were obtained from NewYork-Presbyterian/Columbia University Irving Medical Center's electronic health records database. Condition, drug, and procedure concepts were obtained from the visits of identified patients from the cohorts. Rare concepts were excluded, and the true concept counts were perturbed using Poisson randomization to protect patient privacy. Concept prevalence, concept prevalence ratio, concept co-occurrence, and symptom prevalence were calculated using the obtained concepts.

RESULTS:

Concept prevalence and concept prevalence ratio analyses showed the clinical characteristics of the COVID-19 cohorts, confirming the well-known characteristics of COVID-19 (eg, acute lower respiratory tract infection and cough). The concepts related to the well-known characteristics of COVID-19 recorded high prevalence and high prevalence ratio in the COVID-19 cohort compared to the hospitalized influenza cohort and general hospitalized cohort. Concept co-occurrence analyses showed potential associations between specific concepts. In case of acute lower respiratory tract infection in the COVID-19 cohort, a high co-occurrence ratio was obtained with COVID-19-related concepts and commonly used drugs (eg, disease due to coronavirus and acetaminophen). Symptom prevalence analysis indicated symptom-level characteristics of the cohorts and confirmed that well-known symptoms of COVID-19 (eg, fever, cough, and dyspnea) showed higher prevalence than the hospitalized influenza cohort and the general hospitalized cohort.

CONCLUSIONS:

We present COHD-COVID, a publicly accessible database providing useful clinical data for hospitalized patients with COVID-19, hospitalized patients with influenza, and general hospitalized patients. We expect COHD-COVID to provide researchers and clinicians quantitative measures of COVID-19-related clinical features to better understand and combat the pandemic.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Influenza, Human / COVID-19 Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: 31122

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Influenza, Human / COVID-19 Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: 31122