Bio

Bio


Jose Posada holds a Ph.D. and master’s degree in Biomedical Informatics from the University of Pittsburgh. He is highly skilled in the use of EHR data to answer clinical and healthcare operational questions. He co-leads the development and support of the Stanford Clinical Data Warehouse STARR-OMOP where he is responsible for increasing and ensuring the data quality of the database and leading the participation of Stanford in multicentric multinational clinical studies. His current research and development efforts focus mostly on clinical text. He led the scientific design of two clinical text pipelines, one to de-identify millions of clinical notes (TiDE) and the other to extract concepts from biomedical ontologies from them. He is uniquely qualified for developing and implementing state of the art clinical natural language processing algorithms to answer clinical questions.

Current Role at Stanford


Sr. Clinical Data Scientist

Honors & Awards


  • Fulbright Fellowship for PhD Studies, Fulbright (2014)

Education & Certifications


  • PhD, University of Pittsburgh, Biomedical Informatics (2018)
  • MSc, University of Pittsburgh, Biomedical Informatics (2016)
  • MSc, Universidad del Norte, Mechanical Engineering (2009)
  • Engineer, Universidad del Norte, Electronics Engineering (2007)

Patents


  • M. E. Sanjuan, J. R. Garcia, J. D. Posada, P. J. Villalba,. "United States Patent 8,390,446 Method and apparatus for on-line estimation and forecasting of species concentration during a reaction by measuring electrical conductivity", Universidad del Norte, Jul 1, 2013
  • M. E. Sanjuan, J. R. Garcia, J. D. Posada, P. J. Villalba,. "Germany Patent EP 2350628 B1 20130717 Method and apparatus for on-line estimation and forecasting of species concentration during a reaction", Universidad del Norte, Jul 1, 2013

Professional

Professional Interests


Clinical Natural Language Processing
Artificial Intelligence in Medicine
Electronic Health Records Data Quality

Work Experience


  • Sr. Clinical Data Scientist, Stanford University (September 15, 2018 - Present)

    Location

    Redwood City

  • Graduate Student Researcher, University of Pittsburgh (August 1, 2016 - August 31, 2018)

    Location

    Pittsburgh

  • Full time professor, Universidad Autonoma del Caribe (January 1, 2011 - August 31, 2018)

    Location

    Barranquilla

  • Research and Teaching Assistant, Universidad del Norte (January 1, 2007 - December 31, 2010)

    Location

    Barranquilla

Publications

All Publications


  • Characteristics and outcomes of over 300,000 COVID-19 individuals with history of cancer in the United States and Spain. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology Roel, E., Pistillo, A., Recalde, M., Sena, A. G., Fernandez-Bertolin, S., Aragon, M., Puente, D., Ahmed, W., Alghoul, H., Alser, O., Alshammari, T. M., Areia, C., Blacketer, C., Carter, W., Casajust, P., Culhane, A. C., Dawoud, D., DeFalco, F., DuVall, S. L., Falconer, T., Golozar, A., Gong, M., Hester, L., Hripcsak, G., Tan, E. H., Jeon, H., Jonnagaddala, J., Lai, L. Y., Lynch, K. E., Matheny, M. E., Morales, D. R., Natarajan, K., Nyberg, F., Ostropolets, A., Posada, J. D., Prats-Uribe, A., Reich, C. G., Rivera, D. R., Schilling, L. M., Soerjomataram, I., Shah, K., Shah, N. H., Shen, Y., Spotnitz, M., Subbian, V., Suchard, M. A., Trama, A., Zhang, L., Zhang, Y., Ryan, P. B., Prieto-Alhambra, D., Kostka, K., Duarte-Salles, T. 2021

    Abstract

    BACKGROUND: We described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and COVID-19. Secondly, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza.METHODS: We conducted a cohort study using eight routinely-collected healthcare databases from Spain and the US, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: i) diagnosed with COVID-19, ii) hospitalized with COVID-19, and iii) hospitalized with influenza in 2017-2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes.RESULTS: We included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5-19% and 1-14% in the diagnosed cohort, respectively). Hematological malignancies were also frequent, with non-Hodgkin's lymphoma being among the 5 most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza (n=67,743) had a similar distribution of cancer subtypes, sex, age and comorbidities but lower occurrence of adverse events.CONCLUSIONS: Patients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematological malignancies were frequent.IMPACT: This study provides epidemiologic characteristics that can inform clinical care and etiological studies.

    View details for DOI 10.1158/1055-9965.EPI-21-0266

    View details for PubMedID 34272262

  • Characteristics and outcomes of 627 044 COVID-19 patients living with and without obesity in the United States, Spain, and the United Kingdom. International journal of obesity (2005) Recalde, M., Roel, E., Pistillo, A., Sena, A. G., Prats-Uribe, A., Ahmed, W., Alghoul, H., Alshammari, T. M., Alser, O., Areia, C., Burn, E., Casajust, P., Dawoud, D., DuVall, S. L., Falconer, T., Fernandez-Bertolin, S., Golozar, A., Gong, M., Lai, L. Y., Lane, J. C., Lynch, K. E., Matheny, M. E., Mehta, P. P., Morales, D. R., Natarjan, K., Nyberg, F., Posada, J. D., Reich, C. G., Rijnbeek, P. R., Schilling, L. M., Shah, K., Shah, N. H., Subbian, V., Zhang, L., Zhu, H., Ryan, P., Prieto-Alhambra, D., Kostka, K., Duarte-Salles, T. 2021

    Abstract

    BACKGROUND: A detailed characterization of patients with COVID-19 living with obesity has not yet been undertaken. We aimed to describe and compare the demographics, medical conditions, and outcomes of COVID-19 patients living with obesity (PLWO) to those of patients living without obesity.METHODS: We conducted a cohort study based on outpatient/inpatient care and claims data from January to June 2020 from Spain, the UK, and the US. We used six databases standardized to the OMOP common data model. We defined two non-mutually exclusive cohorts of patients diagnosed and/or hospitalized with COVID-19; patients were followed from index date to 30 days or death. We report the frequency of demographics, prior medical conditions, and 30-days outcomes (hospitalization, events, and death) by obesity status.RESULTS: We included 627 044 (Spain: 122 058, UK: 2336, and US: 502 650) diagnosed and 160 013 (Spain: 18 197, US: 141 816) hospitalized patients with COVID-19. The prevalence of obesity was higher among patients hospitalized (39.9%, 95%CI: 39.8-40.0) than among those diagnosed with COVID-19 (33.1%; 95%CI: 33.0-33.2). In both cohorts, PLWO were more often female. Hospitalized PLWO were younger than patients without obesity. Overall, COVID-19 PLWO were more likely to have prior medical conditions, present with cardiovascular and respiratory events during hospitalization, or require intensive services compared to COVID-19 patients without obesity.CONCLUSION: We show that PLWO differ from patients without obesity in a wide range of medical conditions and present with more severe forms of COVID-19, with higher hospitalization rates and intensive services requirements. These findings can help guiding preventive strategies of COVID-19 infection and complications and generating hypotheses for causal inference studies.

    View details for DOI 10.1038/s41366-021-00893-4

    View details for PubMedID 34267326

  • 30-Day Outcomes of Children and Adolescents With COVID-19: An International Experience. Pediatrics Talita, D., Vizcaya, D., Pistillo, A., Casajust, P., Sena, A. G., Lai, L. Y., Prats-Uribe, A., Ahmed, W., Alshammari, T. M., Alghoul, H., Alser, O., Burn, E., You, S. C., Areia, C., Blacketer, C., DuVall, S., Falconer, T., Fernandez-Bertolin, S., Fortin, S., Golozar, A., Gong, M., Tan, E. H., Huser, V., Iveli, P., Morales, D. R., Nyberg, F., Posada, J. D., Recalde, M., Roe, E., Schilling, L. M., Shah, N. H., Shah, K., Suchard, M. A., Zhang, L., Zhang, Y., Williams, A. E., Reich, C. G., Hripcsak, G., Rijnbeek, P., Ryan, P., Kostka, K., Prieto-Alhambra, D. 2021

    View details for DOI 10.1542/peds.2020-042929

    View details for PubMedID 34049958

  • Use of repurposed and adjuvant drugs in hospital patients with covid-19: multinational network cohort study. BMJ (Clinical research ed.) Prats-Uribe, A., Sena, A. G., Lai, L. Y., Ahmed, W., Alghoul, H., Alser, O., Alshammari, T. M., Areia, C., Carter, W., Casajust, P., Dawoud, D., Golozar, A., Jonnagaddala, J., Mehta, P. P., Gong, M., Morales, D. R., Nyberg, F., Posada, J. D., Recalde, M., Roel, E., Shah, K., Shah, N. H., Schilling, L. M., Subbian, V., Vizcaya, D., Zhang, L., Zhang, Y., Zhu, H., Liu, L., Cho, J., Lynch, K. E., Matheny, M. E., You, S. C., Rijnbeek, P. R., Hripcsak, G., Lane, J. C., Burn, E., Reich, C., Suchard, M. A., Duarte-Salles, T., Kostka, K., Ryan, P. B., Prieto-Alhambra, D. 2021; 373: n1038

    Abstract

    OBJECTIVE: To investigate the use of repurposed and adjuvant drugs in patients admitted to hospital with covid-19 across three continents.DESIGN: Multinational network cohort study.SETTING: Hospital electronic health records from the United States, Spain, and China, and nationwide claims data from South Korea.PARTICIPANTS: 303264 patients admitted to hospital with covid-19 from January 2020 to December 2020.MAIN OUTCOME MEASURES: Prescriptions or dispensations of any drug on or 30 days after the date of hospital admission for covid-19.RESULTS: Of the 303264 patients included, 290131 were from the US, 7599 from South Korea, 5230 from Spain, and 304 from China. 3455 drugs were identified. Common repurposed drugs were hydroxychloroquine (used in from <5 (<2%) patients in China to 2165 (85.1%) in Spain), azithromycin (from 15 (4.9%) in China to 1473 (57.9%) in Spain), combined lopinavir and ritonavir (from 156 (<2%) in the VA-OMOP US to 2,652 (34.9%) in South Korea and 1285 (50.5%) in Spain), and umifenovir (0% in the US, South Korea, and Spain and 238 (78.3%) in China). Use of adjunctive drugs varied greatly, with the five most used treatments being enoxaparin, fluoroquinolones, ceftriaxone, vitamin D, and corticosteroids. Hydroxychloroquine use increased rapidly from March to April 2020 but declined steeply in May to June and remained low for the rest of the year. The use of dexamethasone and corticosteroids increased steadily during 2020.CONCLUSIONS: Multiple drugs were used in the first few months of the covid-19 pandemic, with substantial geographical and temporal variation. Hydroxychloroquine, azithromycin, lopinavir-ritonavir, and umifenovir (in China only) were the most prescribed repurposed drugs. Antithrombotics, antibiotics, H2 receptor antagonists, and corticosteroids were often used as adjunctive treatments. Research is needed on the comparative risk and benefit of these treatments in the management of covid-19.

    View details for DOI 10.1136/bmj.n1038

    View details for PubMedID 33975825

  • Ontology-driven weak supervision for clinical entity classification in electronic health records. Nature communications Fries, J. A., Steinberg, E., Khattar, S., Fleming, S. L., Posada, J., Callahan, A., Shah, N. H. 2021; 12 (1): 2017

    Abstract

    In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e.g. the order of an event relative to a time index) can inform many important analyses. However, creating training data for clinical entity tasks is time consuming and sharing labeled data is challenging due to privacy concerns. The information needs of the COVID-19 pandemic highlight the need for agile methods of training machine learning models for clinical notes. We present Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules. Our approach, unlike hand-labeled notes, is easy to share and modify, while offering performance comparable to learning from manually labeled training data. In this work, we validate our framework on six benchmark tasks and demonstrate Trove's ability to analyze the records of patients visiting the emergency department at Stanford Health Care for COVID-19 presenting symptoms and risk factors.

    View details for DOI 10.1038/s41467-021-22328-4

    View details for PubMedID 33795682

  • ACE: the Advanced Cohort Engine for searching longitudinal patient records. Journal of the American Medical Informatics Association : JAMIA Callahan, A., Polony, V., Posada, J. D., Banda, J. M., Gombar, S., Shah, N. H. 2021

    Abstract

    OBJECTIVE: To propose a paradigm for a scalable time-aware clinical data search, and to describe the design, implementation and use of a search engine realizing this paradigm.MATERIALS AND METHODS: The Advanced Cohort Engine (ACE) uses a temporal query language and in-memory datastore of patient objects to provide a fast, scalable, and expressive time-aware search. ACE accepts data in the Observational Medicine Outcomes Partnership Common Data Model, and is configurable to balance performance with compute cost. ACE's temporal query language supports automatic query expansion using clinical knowledge graphs. The ACE API can be used with R, Python, Java, HTTP, and a Web UI.RESULTS: ACE offers an expressive query language for complex temporal search across many clinical data types with multiple output options. ACE enables electronic phenotyping and cohort-building with subsecond response times in searching the data of millions of patients for a variety of use cases.DISCUSSION: ACE enables fast, time-aware search using a patient object-centric datastore, thereby overcoming many technical and design shortcomings of relational algebra-based querying. Integrating electronic phenotype development with cohort-building enables a variety of high-value uses for a learning health system. Tradeoffs include the need to learn a new query language and the technical setup burden.CONCLUSION: ACE is a tool that combines a unique query language for time-aware search of longitudinal patient records with a patient object datastore for rapid electronic phenotyping, cohort extraction, and exploratory data analyses.

    View details for DOI 10.1093/jamia/ocab027

    View details for PubMedID 33712854

  • Electrochemical Immunosensor for the Quantification of S100B at Clinically Relevant Levels Using a Cysteamine Modified Surface. Sensors (Basel, Switzerland) Rodriguez, A., Burgos-Florez, F., Posada, J. D., Cervera, E., Zucolotto, V., Sanjuan, H., Sanjuan, M., Villalba, P. J. 2021; 21 (6)

    Abstract

    Neuronal damage secondary to traumatic brain injury (TBI) is a rapidly evolving condition, which requires therapeutic decisions based on the timely identification of clinical deterioration. Changes in S100B biomarker levels are associated with TBI severity and patient outcome. The S100B quantification is often difficult since standard immunoassays are time-consuming, costly, and require extensive expertise. A zero-length cross-linking approach on a cysteamine self-assembled monolayer (SAM) was performed to immobilize anti-S100B monoclonal antibodies onto both planar (AuEs) and interdigitated (AuIDEs) gold electrodes via carbonyl-bond. Surface characterization was performed by atomic force microscopy (AFM) and specular-reflectance FTIR for each functionalization step. Biosensor response was studied using the change in charge-transfer resistance (Rct) from electrochemical impedance spectroscopy (EIS) in potassium ferrocyanide, with [S100B] ranging 10-1000 pg/mL. A single-frequency analysis for capacitances was also performed in AuIDEs. Full factorial designs were applied to assess biosensor sensitivity, specificity, and limit-of-detection (LOD). Higher Rct values were found with increased S100B concentration in both platforms. LODs were 18 pg/mL(AuES) and 6 pg/mL(AuIDEs). AuIDEs provide a simpler manufacturing protocol, with reduced fabrication time and possibly costs, simpler electrochemical response analysis, and could be used for single-frequency analysis for monitoring capacitance changes related to S100B levels.

    View details for DOI 10.3390/s21061929

    View details for PubMedID 33801798

  • COVID-19 in patients with autoimmune diseases: characteristics and outcomes in a multinational network of cohorts across three countries. Rheumatology (Oxford, England) Tan, E. H., Sena, A. G., Prats-Uribe, A. n., You, S. C., Ahmed, W. U., Kostka, K. n., Reich, C. n., Duvall, S. L., Lynch, K. E., Matheny, M. E., Duarte-Salles, T. n., Bertolin, S. F., Hripcsak, G. n., Natarajan, K. n., Falconer, T. n., Spotnitz, M. n., Ostropolets, A. n., Blacketer, C. n., Alshammari, T. M., Alghoul, H. n., Alser, O. n., Lane, J. C., Dawoud, D. M., Shah, K. n., Yang, Y. n., Zhang, L. n., Areia, C. n., Golozar, A. n., Recalde, M. n., Casajust, P. n., Jonnagaddala, J. n., Subbian, V. n., Vizcaya, D. n., Lai, L. Y., Nyberg, F. n., Morales, D. R., Posada, J. D., Shah, N. H., Gong, M. n., Vivekanantham, A. n., Abend, A. n., Minty, E. P., Suchard, M. n., Rijnbeek, P. n., Ryan, P. B., Prieto-Alhambra, D. n. 2021

    Abstract

    Patients with autoimmune diseases were advised to shield to avoid COVID-19, but information on their prognosis is lacking. We characterised 30-day outcomes and mortality after hospitalisation with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after hospital admissions among similar patients with seasonal influenza.A multinational network cohort study was conducted using electronic health records data from Columbia University Irving Medical Center (CUIMC) (United States [US]), Optum [US], Department of Veterans Affairs (VA) (US), Information System for Research in Primary Care-Hospitalisation Linked Data (SIDIAP-H) (Spain), and claims data from IQVIA Open Claims (US) and Health Insurance and Review Assessment (HIRA) (South Korea). All patients with prevalent autoimmune diseases, diagnosed and/or hospitalised between January and June 2020 with COVID-19, and similar patients hospitalised with influenza in 2017-2018 were included. Outcomes were death and complications within 30 days of hospitalisation.We studied 133 589 patients diagnosed and 48 418 hospitalised with COVID-19 with prevalent autoimmune diseases. Most patients were female, aged ≥50 years with previous comorbidities. The prevalence of hypertension (45.5-93.2%), chronic kidney disease (14.0-52.7%) and heart disease (29.0-83.8%) was higher in hospitalised vs diagnosed patients with COVID-19. Compared with 70 660 hospitalised with influenza, those admitted with COVID-19 had more respiratory complications including pneumonia and acute respiratory distress syndrome, and higher 30-day mortality (2.2% to 4.3% vs 6.3% to 24.6%).Compared with influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality.

    View details for DOI 10.1093/rheumatology/keab250

    View details for PubMedID 33725121

  • Prediction of Major Depressive Disorder Following Beta-Blocker Therapy in Patients with Cardiovascular Diseases. Journal of personalized medicine Jin, S., Kostka, K., Posada, J. D., Kim, Y., Seo, S. I., Lee, D. Y., Shah, N. H., Roh, S., Lim, Y., Chae, S. G., Jin, U., Son, S. J., Reich, C., Rijnbeek, P. R., Park, R. W., You, S. C. 2020; 10 (4)

    Abstract

    Incident depression has been reported to be associated with poor prognosis in patients with cardiovascular disease (CVD), which might be associated with beta-blocker therapy. Because early detection and intervention can alleviate the severity of depression, we aimed to develop a machine learning (ML) model predicting the onset of major depressive disorder (MDD). A model based on L1 regularized logistic regression was trained against the South Korean nationwide administrative claims database to identify risk factors for the incident MDD after beta-blocker therapy in patients with CVD. We identified 50,397 patients initiating beta-blockers for CVD, with 774 patients developing MDD within 365 days after initiating beta-blocker therapy. An area under the receiver operating characteristic curve (AUC) of 0.74 was achieved. A history of non-selective beta-blockers and factors related to anxiety disorder, sleeping problems, and other chronic diseases were the most strong predictors. AUCs of 0.62-0.71 were achieved in the external validation conducted on six independent electronic health records and claims databases in the USA and South Korea. In conclusion, an ML model that identifies patients at high-risk for incident MDD was developed. Application of ML to identify susceptible patients for adverse events of treatment may serve as an important approach for personalized medicine.

    View details for DOI 10.3390/jpm10040288

    View details for PubMedID 33352870

  • Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study. Nature communications Burn, E., You, S. C., Sena, A. G., Kostka, K., Abedtash, H., Abrahao, M. T., Alberga, A., Alghoul, H., Alser, O., Alshammari, T. M., Aragon, M., Areia, C., Banda, J. M., Cho, J., Culhane, A. C., Davydov, A., DeFalco, F. J., Duarte-Salles, T., DuVall, S., Falconer, T., Fernandez-Bertolin, S., Gao, W., Golozar, A., Hardin, J., Hripcsak, G., Huser, V., Jeon, H., Jing, Y., Jung, C. Y., Kaas-Hansen, B. S., Kaduk, D., Kent, S., Kim, Y., Kolovos, S., Lane, J. C., Lee, H., Lynch, K. E., Makadia, R., Matheny, M. E., Mehta, P. P., Morales, D. R., Natarajan, K., Nyberg, F., Ostropolets, A., Park, R. W., Park, J., Posada, J. D., Prats-Uribe, A., Rao, G., Reich, C., Rho, Y., Rijnbeek, P., Schilling, L. M., Schuemie, M., Shah, N. H., Shoaibi, A., Song, S., Spotnitz, M., Suchard, M. A., Swerdel, J. N., Vizcaya, D., Volpe, S., Wen, H., Williams, A. E., Yimer, B. B., Zhang, L., Zhuk, O., Prieto-Alhambra, D., Ryan, P. 2020; 11 (1): 5009

    Abstract

    Comorbid conditions appear to be common among individuals hospitalised with coronavirus disease 2019 (COVID-19) but estimates of prevalence vary and little is known about the prior medication use of patients. Here, we describe the characteristics of adults hospitalised with COVID-19 and compare them with influenza patients. We include 34,128 (US: 8362, South Korea: 7341, Spain: 18,425) COVID-19 patients, summarising between 4811 and 11,643 unique aggregate characteristics. COVID-19 patients have been majority male in the US and Spain, but predominantly female in South Korea. Age profiles vary across data sources. Compared to 84,585 individuals hospitalised with influenza in 2014-19, COVID-19 patients have more typically been male, younger, and with fewer comorbidities and lower medication use. While protecting groups vulnerable to influenza is likely a useful starting point in the response to COVID-19, strategies will likely need to be broadened to reflect the particular characteristics of individuals being hospitalised with COVID-19.

    View details for DOI 10.1038/s41467-020-18849-z

    View details for PubMedID 33024121

  • An international characterisation of patients hospitalised with COVID-19 and a comparison with those previously hospitalised with influenza. medRxiv : the preprint server for health sciences Burn, E. n., You, S. C., Sena, A. G., Kostka, K. n., Abedtash, H. n., Abrahão, M. T., Alberga, A. n., Alghoul, H. n., Alser, O. n., Alshammari, T. M., Areia, C. n., Banda, J. M., Cho, J. n., Culhane, A. C., Davydov, A. n., DeFalco, F. J., Duarte-Salles, T. n., DuVall, S. n., Falconer, T. n., Gao, W. n., Golozar, A. n., Hardin, J. n., Hripcsak, G. n., Huser, V. n., Jeon, H. n., Jing, Y. n., Jung, C. Y., Kaas-Hansen, B. S., Kaduk, D. n., Kent, S. n., Kim, Y. n., Kolovos, S. n., Lane, J. C., Lee, H. n., Lynch, K. E., Makadia, R. n., Matheny, M. E., Mehta, P. n., Morales, D. R., Natarajan, K. n., Nyberg, F. n., Ostropolets, A. n., Park, R. W., Park, J. n., Posada, J. D., Prats-Uribe, A. n., Rao, G. n., Reich, C. n., Rho, Y. n., Rijnbeek, P. n., Sathappan, S. M., Schilling, L. M., Schuemie, M. n., Shah, N. H., Shoaibi, A. n., Song, S. n., Spotnitz, M. n., Suchard, M. A., Swerdel, J. N., Vizcaya, D. n., Volpe, S. n., Wen, H. n., Williams, A. E., Yimer, B. B., Zhang, L. n., Zhuk, O. n., Prieto-Alhambra, D. n., Ryan, P. n. 2020

    Abstract

    To better understand the profile of individuals with severe coronavirus disease 2019 (COVID-19), we characterised individuals hospitalised with COVID-19 and compared them to individuals previously hospitalised with influenza.We report the characteristics (demographics, prior conditions and medication use) of patients hospitalised with COVID-19 between December 2019 and April 2020 in the US (Columbia University Irving Medical Center [CUIMC], STAnford Medicine Research data Repository [STARR-OMOP], and the Department of Veterans Affairs [VA OMOP]) and Health Insurance Review & Assessment [HIRA] of South Korea. Patients hospitalised with COVID-19 were compared with patients previously hospitalised with influenza in 2014-19.6,806 (US: 1,634, South Korea: 5,172) individuals hospitalised with COVID-19 were included. Patients in the US were majority male (VA OMOP: 94%, STARR-OMOP: 57%, CUIMC: 52%), but were majority female in HIRA (56%). Age profiles varied across data sources. Prevalence of asthma ranged from 7% to 14%, diabetes from 18% to 43%, and hypertensive disorder from 22% to 70% across data sources, while between 9% and 39% were taking drugs acting on the renin-angiotensin system in the 30 days prior to their hospitalisation. Compared to 52,422 individuals hospitalised with influenza, patients admitted with COVID-19 were more likely male, younger, and, in the US, had fewer comorbidities and lower medication use.Rates of comorbidities and medication use are high among individuals hospitalised with COVID-19. However, COVID-19 patients are more likely to be male and appear to be younger and, in the US, generally healthier than those typically admitted with influenza.

    View details for DOI 10.1101/2020.04.22.20074336

    View details for PubMedID 32511443

    View details for PubMedCentralID PMC7239064

  • Characterizing database granularity using SNOMED-CT hierarchy. AMIA ... Annual Symposium proceedings. AMIA Symposium Ostropolets, A., Reich, C., Ryan, P., Weng, C., Molinaro, A., DeFalco, F., Jonnagaddala, J., Liaw, S., Jeon, H., Park, R. W., Spotnitz, M. E., Natarajan, K., Argyriou, G., Kostka, K., Miller, R., Williams, A., Minty, E., Posada, J., Hripcsak, G. 2020; 2020: 983–92

    Abstract

    Multi-center observational studies require recognition and reconciliation of differences in patient representations arising from underlying populations, disparate coding practices and specifics of data capture. This leads to different granularity or detail of concepts representing the clinical facts. For researchers studying certain populations of interest, it is important to ensure that concepts at the right level are used for the definition of these populations. We studied the granularity of concepts within 22 data sources in the OHDSI network and calculated a composite granularity score for each dataset. Three alternative SNOMED-based approaches for such score showed consistency in classifying data sources into three levels of granularity (low, moderate and high), which correlated with the provenance of data and country of origin. However, they performed unsatisfactorily in ordering data sources within these groups and showed inconsistency for small data sources. Further studies on examining approaches to data source granularity are needed.

    View details for PubMedID 33936474

  • Use of dialysis, tracheostomy, and extracorporeal membrane oxygenation among 240,392 patients hospitalized with COVID-19 in the United States. medRxiv : the preprint server for health sciences Burn, E., Sena, A. G., Prats-Uribe, A., Spotnitz, M., DuVall, S., Lynch, K. E., Matheny, M. E., Nyberg, F., Ahmed, W. U., Alser, O., Alghoul, H., Alshammari, T., Zhang, L., Casajust, P., Areia, C., Shah, K., Reich, C., Blacketer, C., Andryc, A., Fortin, S., Natarajan, K., Gong, M., Golozar, A., Morales, D., Rijnbeek, P., Subbian, V., Roel, E., Recalde, M., Lane, J. C., Vizcaya, D., Posada, J. D., Shah, N. H., Jonnagaddala, J., Lai, L. Y., Avilés-Jurado, F. X., Hripcsak, G., Suchard, M. A., Ranzani, O. T., Ryan, P., Prieto-Alhambra, D., Kostka, K., Duarte-Salles, T. 2020

    Abstract

    To estimate the proportion of patients hospitalized with COVID-19 who undergo dialysis, tracheostomy, and extracorporeal membrane oxygenation (ECMO).A network cohort study.Six databases from the United States containing routinely-collected patient data: HealthVerity, Premier, IQVIA Open Claims, Optum EHR, Optum SES, and VA-OMOP.Patients hospitalized with a clinical diagnosis or a positive test result for COVID-19.Dialysis, tracheostomy, and ECMO.240,392 patients hospitalized with COVID-19 were included (22,887 from HealthVerity, 139,971 from IQVIA Open Claims, 29,061 from Optum EHR, 4,336 from OPTUM SES, 36,019 from Premier, and 8,118 from VA-OMOP). Across the six databases, 9,703 (4.04% [95% CI: 3.96% to 4.11%]) patients received dialysis, 1,681 (0.70% [0.67% to 0.73%]) had a tracheostomy, and 398 (0.17% [95% CI: 0.15% to 0.18%]) patients underwent ECMO over the 30 days following hospitalization. Use of ECMO was generally concentrated among patients who were younger, male, and with fewer comorbidities except for obesity. Tracheostomy was used for a similar proportion of patients regardless of age, sex, or comorbidity. While dialysis was used for a similar proportion among younger and older patients, it was more frequent among male patients and among those with chronic kidney disease.Use of dialysis among those hospitalized with COVID-19 is high at around 4%. Although less than one percent of patients undergo tracheostomy and ECMO, the absolute numbers of patients who have undergone these interventions is substantial and can be expected to continue grow given the continuing spread of the COVID-19.

    View details for DOI 10.1101/2020.11.25.20229088

    View details for PubMedID 33269356

    View details for PubMedCentralID PMC7709172

  • Characteristics, outcomes, and mortality amongst 133,589 patients with prevalent autoimmune diseases diagnosed with, and 48,418 hospitalised for COVID-19: a multinational distributed network cohort analysis. medRxiv : the preprint server for health sciences Tan, E. H., Sena, A. G., Prats-Uribe, A., You, S. C., Ahmed, W. U., Kostka, K., Reich, C., Duvall, S. L., Lynch, K. E., Matheny, M. E., Duarte-Salles, T., Bertolin, S. F., Hripcsak, G., Natarajan, K., Falconer, T., Spotnitz, M., Ostropolets, A., Blacketer, C., Alshammari, T. M., Alghoul, H., Alser, O., Lane, J. C., Dawoud, D. M., Shah, K., Yang, Y., Zhang, L., Areia, C., Golozar, A., Relcade, M., Casajust, P., Jonnagaddala, J., Subbian, V., Vizcaya, D., Lai, L. Y., Nyberg, F., Morales, D. R., Posada, J. D., Shah, N. H., Gong, M., Vivekanantham, A., Abend, A., Minty, E. P., Suchard, M., Rijnbeek, P., Ryan, P. B., Prieto-Alhambra, D. 2020

    Abstract

    Patients with autoimmune diseases were advised to shield to avoid COVID-19, but information on their prognosis is lacking. We characterised 30-day outcomes and mortality after hospitalisation with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after hospital admissions among similar patients with seasonal influenza.Multinational network cohort study.Electronic health records data from Columbia University Irving Medical Center (CUIMC) (NYC, United States [US]), Optum [US], Department of Veterans Affairs (VA) (US), Information System for Research in Primary Care-Hospitalisation Linked Data (SIDIAP-H) (Spain), and claims data from IQVIA Open Claims (US) and Health Insurance and Review Assessment (HIRA) (South Korea).All patients with prevalent autoimmune diseases, diagnosed and/or hospitalised between January and June 2020 with COVID-19, and similar patients hospitalised with influenza in 2017-2018 were included.30-day complications during hospitalisation and death.We studied 133,589 patients diagnosed and 48,418 hospitalised with COVID-19 with prevalent autoimmune diseases. The majority of participants were female (60.5% to 65.9%) and aged ≥50 years. The most prevalent autoimmune conditions were psoriasis (3.5 to 32.5%), rheumatoid arthritis (3.9 to 18.9%), and vasculitis (3.3 to 17.6%). Amongst hospitalised patients, Type 1 diabetes was the most common autoimmune condition (4.8% to 7.5%) in US databases, rheumatoid arthritis in HIRA (18.9%), and psoriasis in SIDIAP-H (26.4%).Compared to 70,660 hospitalised with influenza, those admitted with COVID-19 had more respiratory complications including pneumonia and acute respiratory distress syndrome, and higher 30-day mortality (2.2% to 4.3% versus 6.3% to 24.6%).Patients with autoimmune diseases had high rates of respiratory complications and 30-day mortality following a hospitalization with COVID-19. Compared to influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality. Future studies should investigate predictors of poor outcomes in COVID-19 patients with autoimmune diseases.Patients with autoimmune conditions may be at increased risk of COVID-19 infection andcomplications.There is a paucity of evidence characterising the outcomes of hospitalised COVID-19 patients with prevalent autoimmune conditions.Most people with autoimmune diseases who required hospitalisation for COVID-19 were women, aged 50 years or older, and had substantial previous comorbidities.Patients who were hospitalised with COVID-19 and had prevalent autoimmune diseases had higher prevalence of hypertension, chronic kidney disease, heart disease, and Type 2 diabetes as compared to those with prevalent autoimmune diseases who were diagnosed with COVID-19.A variable proportion of 6% to 25% across data sources died within one month of hospitalisation with COVID-19 and prevalent autoimmune diseases.For people with autoimmune diseases, COVID-19 hospitalisation was associated with worse outcomes and 30-day mortality compared to admission with influenza in the 2017-2018 season.

    View details for DOI 10.1101/2020.11.24.20236802

    View details for PubMedID 33269355

    View details for PubMedCentralID PMC7709171

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