Publications by authors named "Karthik Natarajan"

52 Publications

Comparative effectiveness of medical concept embedding for feature engineering in phenotyping.

JAMIA Open 2021 Apr 16;4(2):ooab028. Epub 2021 Jun 16.

Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York 10032, USA.

Objective: Feature engineering is a major bottleneck in phenotyping. Properly learned medical concept embeddings (MCEs) capture the semantics of medical concepts, thus are useful for retrieving relevant medical features in phenotyping tasks. We compared the effectiveness of MCEs learned from knowledge graphs and electronic healthcare records (EHR) data in retrieving relevant medical features for phenotyping tasks.

Materials And Methods: We implemented 5 embedding methods including node2vec, singular value decomposition (SVD), LINE, skip-gram, and GloVe with 2 data sources: (1) knowledge graphs obtained from the observational medical outcomes partnership (OMOP) common data model; and (2) patient-level data obtained from the OMOP compatible electronic health records (EHR) from Columbia University Irving Medical Center (CUIMC). We used phenotypes with their relevant concepts developed and validated by the electronic medical records and genomics (eMERGE) network to evaluate the performance of learned MCEs in retrieving phenotype-relevant concepts. in retrieving phenotype-relevant concepts based on a single and multiple seed concept(s) was used to evaluate MCEs.

Results: Among all MCEs, MCEs learned by using node2vec with knowledge graphs showed the best performance. Of MCEs based on knowledge graphs and EHR data, MCEs learned by using node2vec with knowledge graphs and MCEs learned by using GloVe with EHR data outperforms other MCEs, respectively.

Conclusion: MCE enables scalable feature engineering tasks, thereby facilitating phenotyping. Based on current phenotyping practices, MCEs learned by using knowledge graphs constructed by hierarchical relationships among medical concepts outperformed MCEs learned by using EHR data.
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http://dx.doi.org/10.1093/jamiaopen/ooab028DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8206403PMC
April 2021

Characterising the long-term clinical outcomes of 1190 hospitalised patients with COVID-19 in New York City: a retrospective case series.

BMJ Open 2021 06 2;11(6):e049488. Epub 2021 Jun 2.

Medicine, Infectious Diseases, Columbia University Irving Medical Center, New York City, New York, USA.

Objective: To characterise the long-term outcomes of patients with COVID-19 admitted to a large New York City medical centre at 3 and 6 months after hospitalisation and describe their healthcare usage, symptoms, morbidity and mortality.

Design: Retrospective cohort through manual chart review of the electronic medical record.

Setting: NewYork-Presbyterian/Columbia University Irving Medical Center, a quaternary care academic medical centre in New York City.

Participants: The first 1190 consecutive patients with symptoms of COVID-19 who presented to the hospital for care between 1 March and 8 April 2020 and tested positive for SARS-CoV-2 on reverse transcriptase PCR assay.

Main Outcome Measures: Type and frequency of follow-up encounters, self-reported symptoms, morbidity and mortality at 3 and 6 months after presentation, respectively; patient disposition information prior to admission, at discharge, and at 3 and 6 months after hospital presentation.

Results: Of the 1190 reviewed patients, 929 survived their initial hospitalisation and 261 died. Among survivors, 570 had follow-up encounters (488 at 3 months and 364 at 6 months). An additional 33 patients died in the follow-up period. In the first 3 months after admission, most encounters were telehealth visits (59%). Cardiopulmonary symptoms (35.7% and 28%), especially dyspnoea (22.1% and 15.9%), were the most common reported symptoms at 3-month and 6-month encounters, respectively. Additionally, a large number of patients reported generalised (26.4%) or neuropsychiatric (24.2%) symptoms 6 months after hospitalisation. Patients with severe COVID-19 were more likely to have reduced mobility, reduced independence or a new dialysis requirement in the 6 months after hospitalisation.

Conclusions: Patients hospitalised with SARS-CoV-2 infection reported persistent symptoms up to 6 months after diagnosis. These results highlight the long-term morbidity of COVID-19 and its burden on patients and healthcare resources.
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http://dx.doi.org/10.1136/bmjopen-2021-049488DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8182750PMC
June 2021

A deep database of medical abbreviations and acronyms for natural language processing.

Sci Data 2021 06 2;8(1):149. Epub 2021 Jun 2.

Department of Biomedical Informatics, Columbia University, New York, NY, USA.

The recognition, disambiguation, and expansion of medical abbreviations and acronyms is of upmost importance to prevent medically-dangerous misinterpretation in natural language processing. To support recognition, disambiguation, and expansion, we present the Medical Abbreviation and Acronym Meta-Inventory, a deep database of medical abbreviations. A systematic harmonization of eight source inventories across multiple healthcare specialties and settings identified 104,057 abbreviations with 170,426 corresponding senses. Automated cross-mapping of synonymous records using state-of-the-art machine learning reduced redundancy, which simplifies future application. Additional features include semi-automated quality control to remove errors. The Meta-Inventory demonstrated high completeness or coverage of abbreviations and senses in new clinical text, a substantial improvement over the next largest repository (6-14% increase in abbreviation coverage; 28-52% increase in sense coverage). To our knowledge, the Meta-Inventory is the most complete compilation of medical abbreviations and acronyms in American English to-date. The multiple sources and high coverage support application in varied specialties and settings. This allows for cross-institutional natural language processing, which previous inventories did not support. The Meta-Inventory is available at https://bit.ly/github-clinical-abbreviations .
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http://dx.doi.org/10.1038/s41597-021-00929-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172575PMC
June 2021

Association between procurement biopsy findings and deceased donor kidney outcomes: a paired kidney analysis.

Transpl Int 2021 May 8. Epub 2021 May 8.

Division of Nephrology, Department of Medicine, Columbia University College of Physicians & Surgeons and New York Presbyterian Hospital, New York, NY, USA.

Unfavourable procurement biopsy findings are the most common reason for deceased donor kidney discard in the United States. We sought to assess the association between biopsy findings and post-transplant outcomes when donor characteristics are accounted for. We used registry data to identify 1566 deceased donors of 3132 transplanted kidneys (2015-2020) with discordant right/left procurement biopsy classification and performed time-to-event analyses to determine the association between optimal histology and hazard of death-censored graft failure or death. We then repeated all analyses using a local cohort of 147 donors of kidney pairs with detailed procurement histology data available (2006-2016). Among transplanted kidney pairs in the national cohort, there were no significant differences in incidence of delayed graft function or primary nonfunction. Time to death-censored graft failure was not significantly different between recipients of optimal versus suboptimal kidneys. Results were similar in analyses using the local cohort. Regarding recipient survival, analysis of the national, but not local, cohort showed optimal kidneys were associated with a lower hazard of death (adjusted HR 0.68, 95% CI 0.52-0.90, P = 0.006). In conclusion, in a large national cohort of deceased donor kidney pairs with discordant right/left procurement biopsy findings, we found no association between histology and death-censored graft survival.
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http://dx.doi.org/10.1111/tri.13899DOI Listing
May 2021

Normalizing Clinical Document Titles to LOINC Document Ontology: an Initial Study.

AMIA Annu Symp Proc 2020 25;2020:1441-1450. Epub 2021 Jan 25.

School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.

The normalization of clinical documents is essential for health information management with the enormous amount of clinical documentation generated each year. The LOINC Document Ontology (DO) is a universal clinical document standard in a hierarchical structure. The objective of this study is to investigate the feasibility and generalizability of LOINC DO by mapping from clinical note titles across five institutions to five DO axes. We first developed an annotation framework based on the definition of LOINC DO axes and manually mapped 4,000 titles. Then we introduced a pre-trained deep learning model named Bidirectional Encoder Representations from Transformers (BERT) to enable automatic mapping from titles to LOINC DO axes. The results showed that the BERT-based automatic mapping achieved improved performance compared with the baseline model. By analyzing both manual annotations and predicted results, ambiguities in LOINC DO axes definition were discussed.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075502PMC
January 2021

Phenotype Concept Set Construction from Concept Pair Likelihoods.

AMIA Annu Symp Proc 2020 25;2020:1080-1089. Epub 2021 Jan 25.

Columbia University, New York, NY.

Phenotyping algorithms are essential tools for conducting clinical research on observational data. Manually devel- oped phenotyping algorithms, such as those curated within the eMERGE (electronic Medical Records and Genomics) Network, represent the gold standard but are time consuming to create. In this work, we propose a framework for learning from the structure of eMERGE phenotype concept sets to assist construction of novel phenotype definitions. We use eMERGE phenotypes as a source of reference concept sets and engineer rich features characterizing the con- cept pairs within each set. We treat these pairwise relationships as edges in a concept graph, train models to perform edge prediction, and identify candidate phenotype concept sets as highly connected subgraphs. Candidate concept sets may then be interrogated and composed to construct novel phenotype definitions.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075469PMC
June 2021

Characterizing database granularity using SNOMED-CT hierarchy.

AMIA Annu Symp Proc 2020 25;2020:983-992. Epub 2021 Jan 25.

Columbia University, New York, NY, USA.

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.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075504PMC
June 2021

Clinical Features and Outcomes of Patients with Dementia Compared to an Aging Cohort Hospitalized During the Initial New York City COVID-19 Wave.

J Alzheimers Dis 2021 ;81(2):679-690

Columbia University Irving Medical Center, New York, NY, USA.

Background: Patients with dementia are vulnerable during the coronavirus disease 2019 (COVID-19) pandemic, yet few studies describe their hospital course and outcomes.

Objective: To describe and compare the hospital course for COVID-19 patients with dementia to an aging cohort without dementia in a large New York City academic medical center.

Methods: This was a single-center retrospective cohort study describing all consecutive patients age 65 or older with confirmed COVID-19 who presented to the emergency department or were hospitalized at New York-Presbyterian/Columbia University Irving Medical Center between March 6 and April 7, 2020.

Results: A total of 531 patients were evaluated, including 116 (21.8%) with previously diagnosed dementia, and 415 without dementia. Patients with dementia had higher mortality (50.0%versus 35.4%, p = 0.006); despite similar comorbidities and complications, multivariate analysis indicated the association was dependent on age, sex, comorbidities, and code status. Patients with dementia more often presented with delirium (36.2%versus 11.6%, p < 0.001) but less often presented with multiple other COVID-19 symptoms, and these findings remained after adjusting for age and sex.

Conclusion: Hospitalized COVID-19 patients with dementia had higher mortality, but dementia was not an independent risk factor for death. These patients were approximately 3 times more likely to present with delirium but less often manifested or communicated other common COVID-19 symptoms. For this high-risk population in a worsening pandemic, understanding the unique manifestations and course in dementia and aging populations may help guide earlier diagnosis and optimize medical management.
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http://dx.doi.org/10.3233/JAD-210050DOI Listing
June 2021

Factors Affecting the Quality of Person-Generated Wearable Device Data and Associated Challenges: Rapid Systematic Review.

JMIR Mhealth Uhealth 2021 03 19;9(3):e20738. Epub 2021 Mar 19.

Department of Biomedical informatics, Columbia University, New York, NY, United States.

Background: There is increasing interest in reusing person-generated wearable device data for research purposes, which raises concerns about data quality. However, the amount of literature on data quality challenges, specifically those for person-generated wearable device data, is sparse.

Objective: This study aims to systematically review the literature on factors affecting the quality of person-generated wearable device data and their associated intrinsic data quality challenges for research.

Methods: The literature was searched in the PubMed, Association for Computing Machinery, Institute of Electrical and Electronics Engineers, and Google Scholar databases by using search terms related to wearable devices and data quality. By using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, studies were reviewed to identify factors affecting the quality of wearable device data. Studies were eligible if they included content on the data quality of wearable devices, such as fitness trackers and sleep monitors. Both research-grade and consumer-grade wearable devices were included in the review. Relevant content was annotated and iteratively categorized into semantically similar factors until a consensus was reached. If any data quality challenges were mentioned in the study, those contents were extracted and categorized as well.

Results: A total of 19 papers were included in this review. We identified three high-level factors that affect data quality-device- and technical-related factors, user-related factors, and data governance-related factors. Device- and technical-related factors include problems with hardware, software, and the connectivity of the device; user-related factors include device nonwear and user error; and data governance-related factors include a lack of standardization. The identified factors can potentially lead to intrinsic data quality challenges, such as incomplete, incorrect, and heterogeneous data. Although missing and incorrect data are widely known data quality challenges for wearable devices, the heterogeneity of data is another aspect of data quality that should be considered for wearable devices. Heterogeneity in wearable device data exists at three levels: heterogeneity in data generated by a single person using a single device (within-person heterogeneity); heterogeneity in data generated by multiple people who use the same brand, model, and version of a device (between-person heterogeneity); and heterogeneity in data generated from multiple people using different devices (between-person heterogeneity), which would apply especially to data collected under a bring-your-own-device policy.

Conclusions: Our study identifies potential intrinsic data quality challenges that could occur when analyzing wearable device data for research and three major contributing factors for these challenges. As poor data quality can compromise the reliability and accuracy of research results, further investigation is needed on how to address the data quality challenges of wearable devices.
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http://dx.doi.org/10.2196/20738DOI Listing
March 2021

COVID-19 in patients with autoimmune diseases: characteristics and outcomes in a multinational network of cohorts across three countries.

Rheumatology (Oxford) 2021 Mar 16. Epub 2021 Mar 16.

Real-World Evidence, Trial, Barcelona, Spain, Form Support.

Objective: 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.

Methods: 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.

Results: 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%).

Conclusions: Compared with influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality.
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http://dx.doi.org/10.1093/rheumatology/keab250DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7989171PMC
March 2021

Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patients.

J Am Med Inform Assoc 2021 Mar 11. Epub 2021 Mar 11.

Department of Biomedical Informatics, Columbia University, New York, New York, USA.

Objective: Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020.

Materials And Methods: For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)-positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model's calibration and evaluated feature importances to interpret model output.

Results: The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve-MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve-MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition.

Discussion: Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients.

Conclusions: We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results.
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http://dx.doi.org/10.1093/jamia/ocab029DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7989331PMC
March 2021

Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS.

Res Sq 2021 Mar 1. Epub 2021 Mar 1.

Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response [1,2]. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) [3] Characterizing Health Associated Risks, and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD. We conducted a descriptive cohort study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11 June 2020 and are iteratively updated via GitHub [4]. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical 886,193 , and 113,627 . All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts, and are available in an interactive website: https://data.ohdsi.org/Covid19CharacterizationCharybdis/. CHARYBDIS findings provide benchmarks that contribute to our understanding of COVID-19 progression, management and evolution over time. This can enable timely assessment of real-world outcomes of preventative and therapeutic options as they are introduced in clinical practice.
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http://dx.doi.org/10.21203/rs.3.rs-279400/v1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7941629PMC
March 2021

High rate of renal recovery in survivors of COVID-19 associated acute renal failure requiring renal replacement therapy.

PLoS One 2020 28;15(12):e0244131. Epub 2020 Dec 28.

Division of Nephrology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States of America.

Introduction: A large proportion of patients with COVID-19 develop acute kidney injury (AKI). While the most severe of these cases require renal replacement therapy (RRT), little is known about their clinical course.

Methods: We describe the clinical characteristics of COVID-19 patients in the ICU with AKI requiring RRT at an academic medical center in New York City and followed patients for outcomes of death and renal recovery using time-to-event analyses.

Results: Our cohort of 115 patients represented 23% of all ICU admissions at our center, with a peak prevalence of 29%. Patients were followed for a median of 29 days (2542 total patient-RRT-days; median 54 days for survivors). Mechanical ventilation and vasopressor use were common (99% and 84%, respectively), and the median Sequential Organ Function Assessment (SOFA) score was 14. By the end of follow-up 51% died, 41% recovered kidney function (84% of survivors), and 8% still needed RRT (survival probability at 60 days: 0.46 [95% CI: 0.36-0.56])). In an adjusted Cox model, coronary artery disease and chronic obstructive pulmonary disease were associated with increased mortality (HRs: 3.99 [95% CI 1.46-10.90] and 3.10 [95% CI 1.25-7.66]) as were angiotensin-converting-enzyme inhibitors (HR 2.33 [95% CI 1.21-4.47]) and a SOFA score >15 (HR 3.46 [95% CI 1.65-7.25).

Conclusions And Relevance: Our analysis demonstrates the high prevalence of AKI requiring RRT among critically ill patients with COVID-19 and is associated with a high mortality, however, the rate of renal recovery is high among survivors and should inform shared-decision making.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0244131PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7769434PMC
January 2021

Use of dialysis, tracheostomy, and extracorporeal membrane oxygenation among 240,392 patients hospitalized with COVID-19 in the United States.

medRxiv 2020 Nov 27. Epub 2020 Nov 27.

Objective: To estimate the proportion of patients hospitalized with COVID-19 who undergo dialysis, tracheostomy, and extracorporeal membrane oxygenation (ECMO).

Design: A network cohort study.

Setting: Six databases from the United States containing routinely-collected patient data: HealthVerity, Premier, IQVIA Open Claims, Optum EHR, Optum SES, and VA-OMOP.

Patients: Patients hospitalized with a clinical diagnosis or a positive test result for COVID-19.

Interventions: Dialysis, tracheostomy, and ECMO.

Measurements And Main Results: 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.

Conclusion: 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.
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http://dx.doi.org/10.1101/2020.11.25.20229088DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7709172PMC
November 2020

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 2020 Nov 27. Epub 2020 Nov 27.

Real-World Evidence, Trial Form Support, Barcelona, Spain.

Objective: 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.

Design: Multinational network cohort study.

Setting: 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).

Participants: 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.

Main Outcome Measures: 30-day complications during hospitalisation and death.

Results: 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%).

Conclusions: 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.

What Is Already Known About This Topic: 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.

What This Study Adds: 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.
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http://dx.doi.org/10.1101/2020.11.24.20236802DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7709171PMC
November 2020

Impact of Deceased Donor Kidney Procurement Biopsy Technique on Histologic Accuracy.

Kidney Int Rep 2020 Nov 14;5(11):1906-1913. Epub 2020 Aug 14.

Department of Medicine, Division of Nephrology, Columbia University College of Physicians and Surgeons and New York Presbyterian Hospital, New York, New York, USA.

Introduction: The factors that influence deceased donor kidney procurement biopsy reliability are not well established. We examined the impact of biopsy technique and pathologist training on procurement biopsy accuracy.

Methods: We retrospectively identified all deceased donor kidney-only transplants at our center from 2006 to 2016 with both procurement and reperfusion biopsies performed and information available on procurement biopsy technique and pathologist (n = 392). Biopsies were scored using a previously validated system, classifying "suboptimal" histology as the presence of at least 1 of the following: glomerulosclerosis ≥11%, moderate/severe interstitial fibrosis/tubular atrophy, or moderate/severe vascular disease. We calculated relative risk ratios (RRR) to determine the influence of technique (core vs. wedge) and pathologist (renal vs. nonrenal) on concordance between procurement and reperfusion biopsy histologic classification.

Results: A total of 171 (44%) procurement biopsies used wedge technique, and 221 (56%) used core technique. Results of only 36 biopsies (9%) were interpreted by renal pathologists. Correlation between procurement and reperfusion glomerulosclerosis was poor for both wedge ( = 0.11) and core ( = 0.14) biopsies. Overall, 34% of kidneys had discordant classification on procurement versus reperfusion biopsy. Neither biopsy technique nor pathologist training was associated with concordance between procurement and reperfusion histology, but a larger number of sampled glomeruli was associated with a higher likelihood of concordance (adjusted RRR = 1.12 per 10 glomeruli, 95% confidence interval = 1.04-1.22).

Conclusions: Biopsy technique and pathologist training were not associated with procurement biopsy histologic accuracy in this retrospective study. Prospective trials are needed to determine how to optimize procurement biopsy practices.
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http://dx.doi.org/10.1016/j.ekir.2020.08.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7609887PMC
November 2020

Baseline phenotype and 30-day outcomes of people tested for COVID-19: an international network cohort including >3.32 million people tested with real-time PCR and >219,000 tested positive for SARS-CoV-2 in South Korea, Spain and the United States.

medRxiv 2020 Oct 27. Epub 2020 Oct 27.

Early identification of symptoms and comorbidities most predictive of COVID-19 is critical to identify infection, guide policies to effectively contain the pandemic, and improve health systems' response. Here, we characterised socio-demographics and comorbidity in 3,316,107persons tested and 219,072 persons tested positive for SARS-CoV-2 since January 2020, and their key health outcomes in the month following the first positive test. Routine care data from primary care electronic health records (EHR) from Spain, hospital EHR from the United States (US), and claims data from South Korea and the US were used. The majority of study participants were women aged 18-65 years old. Positive/tested ratio varied greatly geographically (2.2:100 to 31.2:100) and over time (from 50:100 in February-April to 6.8:100 in May-June). Fever, cough and dyspnoea were the most common symptoms at presentation. Between 4%-38% required admission and 1-10.5% died within a month from their first positive test. Observed disparity in testing practices led to variable baseline characteristics and outcomes, both nationally (US) and internationally. Our findings highlight the importance of large scale characterization of COVID-19 international cohorts to inform planning and resource allocation including testing as countries face a second wave.
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http://dx.doi.org/10.1101/2020.10.25.20218875DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7605581PMC
October 2020

Content Coverage Evaluation of the OMOP Vocabulary on the Transplant Domain Focusing on Concepts Relevant for Kidney Transplant Outcomes Analysis.

Appl Clin Inform 2020 08 7;11(4):650-658. Epub 2020 Oct 7.

Department of Biomedical Informatics, Columbia University, New York, New York, United States.

Background: Improving outcomes of transplant recipients within and across transplant centers is important with the increasing number of organ transplantations being performed. The current practice is to analyze the outcomes based on patient level data submitted to the United Network for Organ Sharing (UNOS). Augmenting the UNOS data with other sources such as the electronic health record will enrich the outcomes analysis, for which a common data model (CDM) can be a helpful tool for transforming heterogeneous source data into a uniform format.

Objectives: In this study, we evaluated the feasibility of representing concepts from the UNOS transplant registry forms with the Observational Medical Outcomes Partnership (OMOP) CDM vocabulary to understand the content coverage of OMOP vocabulary on transplant-specific concepts.

Methods: Two annotators manually mapped a total of 3,571 unique concepts extracted from the UNOS registry forms to concepts in the OMOP vocabulary. Concept mappings were evaluated by (1) examining the agreement among the initial two annotators and (2) investigating the number of UNOS concepts not mapped to a concept in the OMOP vocabulary and then classifying them. A subset of mappings was validated by clinicians.

Results: There was a substantial agreement between annotators with a kappa score of 0.71. We found that 55.5% of UNOS concepts could not be represented with OMOP standard concepts. The majority of unmapped UNOS concepts were categorized into transplant, measurement, condition, and procedure concepts.

Conclusion: We identified categories of unmapped concepts and found that some transplant-specific concepts do not exist in the OMOP vocabulary. We suggest that adding these missing concepts to OMOP would facilitate further research in the transplant domain.
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http://dx.doi.org/10.1055/s-0040-1716528DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7557323PMC
August 2020

Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study.

Nat Commun 2020 10 6;11(1):5009. Epub 2020 Oct 6.

Clinical Pharmacology Unit, Zealand University Hospital, Køge, Denmark.

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.
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http://dx.doi.org/10.1038/s41467-020-18849-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7538555PMC
October 2020

The Prognostic Value of Electrocardiogram at Presentation to Emergency Department in Patients With COVID-19.

Mayo Clin Proc 2020 10 15;95(10):2099-2109. Epub 2020 Aug 15.

Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY; Department of Medicine, Columbia University Irving Medical Center, New York, NY.

Objective: To study whether combining vital signs and electrocardiogram (ECG) analysis can improve early prognostication.

Methods: This study analyzed 1258 adults with coronavirus disease 2019 who were seen at three hospitals in New York in March and April 2020. Electrocardiograms at presentation to the emergency department were systematically read by electrophysiologists. The primary outcome was a composite of mechanical ventilation or death 48 hours from diagnosis. The prognostic value of ECG abnormalities was assessed in a model adjusted for demographics, comorbidities, and vital signs.

Results: At 48 hours, 73 of 1258 patients (5.8%) had died and 174 of 1258 (13.8%) were alive but receiving mechanical ventilation with 277 of 1258 (22.0%) patients dying by 30 days. Early development of respiratory failure was common, with 53% of all intubations occurring within 48 hours of presentation. In a multivariable logistic regression, atrial fibrillation/flutter (odds ratio [OR], 2.5; 95% CI, 1.1 to 6.2), right ventricular strain (OR, 2.7; 95% CI, 1.3 to 6.1), and ST segment abnormalities (OR, 2.4; 95% CI, 1.5 to 3.8) were associated with death or mechanical ventilation at 48 hours. In 108 patients without these ECG abnormalities and with normal respiratory vitals (rate <20 breaths/min and saturation >95%), only 5 (4.6%) died or required mechanical ventilation by 48 hours versus 68 of 216 patients (31.5%) having both ECG and respiratory vital sign abnormalities.

Conclusion: The combination of abnormal respiratory vital signs and ECG findings of atrial fibrillation/flutter, right ventricular strain, or ST segment abnormalities accurately prognosticates early deterioration in patients with coronavirus disease 2019 and may assist with patient triage.
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http://dx.doi.org/10.1016/j.mayocp.2020.07.028DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7428764PMC
October 2020

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

J Am Med Inform Assoc 2021 03;28(3):427-443

IQVIA, Durham, North Carolina, USA.

Objective: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers.

Materials And Methods: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics.

Results: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access.

Conclusions: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.
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http://dx.doi.org/10.1093/jamia/ocaa196DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7454687PMC
March 2021

Body Mass Index and Risk for Intubation or Death in SARS-CoV-2 Infection : A Retrospective Cohort Study.

Ann Intern Med 2020 11 29;173(10):782-790. Epub 2020 Jul 29.

Columbia University Irving Medical Center, New York, New York (M.R.A., J.G., J.Z., Y.R.N., D.F., J.S., K.N.R., S.C., K.N., D.R., E.E., A.P., A.W.F., M.R.B.).

Background: Obesity is a risk factor for pneumonia and acute respiratory distress syndrome.

Objective: To determine whether obesity is associated with intubation or death, inflammation, cardiac injury, or fibrinolysis in coronavirus disease 2019 (COVID-19).

Design: Retrospective cohort study.

Setting: A quaternary academic medical center and community hospital in New York City.

Participants: 2466 adults hospitalized with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 infection over a 45-day period with at least 47 days of in-hospital observation.

Measurements: Body mass index (BMI), admission biomarkers of inflammation (C-reactive protein [CRP] level and erythrocyte sedimentation rate [ESR]), cardiac injury (troponin level), and fibrinolysis (D-dimer level). The primary end point was a composite of intubation or death in time-to-event analysis.

Results: Over a median hospital length of stay of 7 days (interquartile range, 3 to 14 days), 533 patients (22%) were intubated, 627 (25%) died, and 59 (2%) remained hospitalized. Compared with overweight patients, patients with obesity had higher risk for intubation or death, with the highest risk among those with class 3 obesity (hazard ratio, 1.6 [95% CI, 1.1 to 2.1]). This association was primarily observed among patients younger than 65 years and not in older patients ( for interaction by age = 0.042). Body mass index was not associated with admission levels of biomarkers of inflammation, cardiac injury, or fibrinolysis.

Limitations: Body mass index was missing for 28% of patients. The primary analyses were conducted with multiple imputation for missing BMI. Upper bounding factor analysis suggested that the results are robust to possible selection bias.

Conclusion: Obesity is associated with increased risk for intubation or death from COVID-19 in adults younger than 65 years, but not in adults aged 65 years or older.

Primary Funding Source: National Institutes of Health.
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http://dx.doi.org/10.7326/M20-3214DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7397550PMC
November 2020

COVID-19 TestNorm: A tool to normalize COVID-19 testing names to LOINC codes.

J Am Med Inform Assoc 2020 07;27(9):1437-1442

School of Biomedical Informatics, University of Texas, Houston, Texas, USA.

Large observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC (Logical Observation Identifiers Names and Codes) codes. COVID-19 TestNorm was developed and evaluated using 568 test names collected from 8 healthcare systems. Our results show that it could achieve an accuracy of 97.4% on an independent test set. COVID-19 TestNorm is available as an open-source package for developers and as an online Web application for end users (https://clamp.uth.edu/covid/loinc.php). We believe that it will be a useful tool to support secondary use of EHRs for research on COVID-19.
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http://dx.doi.org/10.1093/jamia/ocaa145DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7337837PMC
July 2020

Characterization and clinical course of 1000 Patients with COVID-19 in New York: retrospective case series.

medRxiv 2020 Apr 22. Epub 2020 Apr 22.

Objective: To characterize patients with coronavirus disease 2019 (COVID-19) in a large New York City (NYC) medical center and describe their clinical course across the emergency department (ED), inpatient wards, and intensive care units (ICUs).

Design: Retrospective manual medical record review.

Setting: NewYork-Presbyterian/Columbia University Irving Medical Center (NYP/CUIMC), a quaternary care academic medical center in NYC.

Participants: The first 1000 consecutive patients with laboratory-confirmed COVID-19.

Methods: We identified the first 1000 consecutive patients with a positive RT-SARS-CoV-2 PCR test who first presented to the ED or were hospitalized at NYP/CUIMC between March 1 and April 5, 2020. Patient data was manually abstracted from the electronic medical record.

Main Outcome Measures: We describe patient characteristics including demographics, presenting symptoms, comorbidities on presentation, hospital course, time to intubation, complications, mortality, and disposition.

Results: Among the first 1000 patients, 150 were ED patients, 614 were admitted without requiring ICU-level care, and 236 were admitted or transferred to the ICU. The most common presenting symptoms were cough (73.2%), fever (72.8%), and dyspnea (63.1%). Hospitalized patients, and ICU patients in particular, most commonly had baseline comorbidities including of hypertension, diabetes, and obesity. ICU patients were older, predominantly male (66.9%), and long lengths of stay (median 23 days; IQR 12 to 32 days); 78.0% developed AKI and 35.2% required dialysis. Notably, for patients who required mechanical ventilation, only 4.4% were first intubated more than 14 days after symptom onset. Time to intubation from symptom onset had a bimodal distribution, with modes at 3-4 and 9 days. As of April 30, 90 patients remained hospitalized and 211 had died in the hospital.

Conclusions: Hospitalized patients with COVID-19 illness at this medical center faced significant morbidity and mortality, with high rates of AKI, dialysis, and a bimodal distribution in time to intubation from symptom onset.
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http://dx.doi.org/10.1101/2020.04.20.20072116DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273275PMC
April 2020

An international characterisation of patients hospitalised with COVID-19 and a comparison with those previously hospitalised with influenza.

medRxiv 2020 Apr 25. Epub 2020 Apr 25.

Science Policy and Research, National Institute for Health and Care Excellence, UK.

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.
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http://dx.doi.org/10.1101/2020.04.22.20074336DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7239064PMC
April 2020

Characterization and clinical course of 1000 patients with coronavirus disease 2019 in New York: retrospective case series.

BMJ 2020 05 29;369:m1996. Epub 2020 May 29.

Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA.

Objective: To characterize patients with coronavirus disease 2019 (covid-19) in a large New York City medical center and describe their clinical course across the emergency department, hospital wards, and intensive care units.

Design: Retrospective manual medical record review.

Setting: NewYork-Presbyterian/Columbia University Irving Medical Center, a quaternary care academic medical center in New York City.

Participants: The first 1000 consecutive patients with a positive result on the reverse transcriptase polymerase chain reaction assay for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) who presented to the emergency department or were admitted to hospital between 1 March and 5 April 2020. Patient data were manually abstracted from electronic medical records.

Main Outcome Measures: Characterization of patients, including demographics, presenting symptoms, comorbidities on presentation, hospital course, time to intubation, complications, mortality, and disposition.

Results: Of the first 1000 patients, 150 presented to the emergency department, 614 were admitted to hospital (not intensive care units), and 236 were admitted or transferred to intensive care units. The most common presenting symptoms were cough (732/1000), fever (728/1000), and dyspnea (631/1000). Patients in hospital, particularly those treated in intensive care units, often had baseline comorbidities including hypertension, diabetes, and obesity. Patients admitted to intensive care units were older, predominantly male (158/236, 66.9%), and had long lengths of stay (median 23 days, interquartile range 12-32 days); 78.0% (184/236) developed acute kidney injury and 35.2% (83/236) needed dialysis. Only 4.4% (6/136) of patients who required mechanical ventilation were first intubated more than 14 days after symptom onset. Time to intubation from symptom onset had a bimodal distribution, with modes at three to four days, and at nine days. As of 30 April, 90 patients remained in hospital and 211 had died in hospital.

Conclusions: Patients admitted to hospital with covid-19 at this medical center faced major morbidity and mortality, with high rates of acute kidney injury and inpatient dialysis, prolonged intubations, and a bimodal distribution of time to intubation from symptom onset.
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http://dx.doi.org/10.1136/bmj.m1996DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256651PMC
May 2020

In Reply.

Obstet Gynecol 2020 06;135(6):1487

Columbia University Medical Center, New York, New York.

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http://dx.doi.org/10.1097/AOG.0000000000003912DOI Listing
June 2020

Treatment Patterns for Chronic Comorbid Conditions in Patients With Cancer Using a Large-Scale Observational Data Network.

JCO Clin Cancer Inform 2020 03;4:171-183

Department of Biomedical Informatics, Columbia University, New York, NY.

Purpose: Patients with cancer are predisposed to developing chronic, comorbid conditions that affect prognosis, quality of life, and mortality. While treatment guidelines and care variations for these comorbidities have been described for the general noncancer population, less is known about real-world treatment patterns in patients with cancer. We sought to characterize the prevalence and distribution of initial treatment patterns across a large-scale data network for depression, hypertension, and type II diabetes mellitus (T2DM) among patients with cancer.

Methods: We used the Observational Health Data Sciences and Informatics network, an international collaborative implementing the Observational Medical Outcomes Partnership Common Data Model to standardize more than 2 billion patient records. For this study, we used 8 databases across 3 countries-the United States, France, and Germany-with 295,529,655 patient records. We identified patients with cancer using SNOMED (Systematized Nomenclature of Medicine) codes validated via manual review. We then characterized the treatment patterns of these patients initiating treatment of depression, hypertension, or T2DM with persistent treatment and at least 365 days of observation.

Results: Across databases, wide variations exist in treatment patterns for depression (n = 1,145,510), hypertension (n = 3,178,944), and T2DM (n = 886,766). When limited to 6-node (6-drug) sequences, we identified 61,052 unique sequences for depression, 346,067 sequences for hypertension, and 40,629 sequences for T2DM. These variations persisted across sites, databases, countries, and conditions, with the exception of metformin (73.8%) being the most common initial T2DM treatment. The most common initial medications were sertraline (17.5%) and escitalopram (17.5%) for depression and hydrochlorothiazide (20.5%) and lisinopril (19.6%) for hypertension.

Conclusion: We identified wide variations in the treatment of common comorbidities in patients with cancer, similar to the general population, and demonstrate the feasibility of conducting research on patients with cancer across a large-scale observational data network using a common data model.
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http://dx.doi.org/10.1200/CCI.19.00107DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7113074PMC
March 2020

Using the "Who, What, and When" of free text documentation to improve hospital infectious disease surveillance.

Am J Infect Control 2020 10 15;48(10):1261-1263. Epub 2020 Feb 15.

Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY.

We demonstrate a novel method of using unstructured health data for infectious disease surveillance. A model incorporating the dynamics of documentation of a test diagnosis (UTI) in free text, without using grammatical or syntactic analysis, achieved performance comparable to ICD-10 codes (sensitivity 57.3, positive predictive value 69.5%, negative predictive value 95.9%) and detected missed cases (15% of total).
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http://dx.doi.org/10.1016/j.ajic.2020.01.001DOI Listing
October 2020

Reproducibility of Deceased Donor Kidney Procurement Biopsies.

Clin J Am Soc Nephrol 2020 02 23;15(2):257-264. Epub 2020 Jan 23.

Division of Nephrology, Department of Medicine and.

Background And Objectives: Unfavorable histology on procurement biopsies is the most common reason for deceased donor kidney discard. We sought to assess the reproducibility of procurement biopsy findings.

Design, Setting, Participants, & Measurements: We compiled a continuous cohort of deceased donor kidneys transplanted at our institution from 1/1/2006 to 12/31/2016 that had at least one procurement biopsy performed, and excluded cases with missing biopsy reports and those used in multiorgan transplants. Suboptimal histology was defined as the presence of advanced sclerosis in greater than or equal to one biopsy compartment (glomeruli, tubules/interstitium, vessels). We calculated coefficients to assess agreement in optimal versus suboptimal classification between sequential biopsy reports for kidneys that underwent multiple procurement biopsies and used time-to-event analysis to evaluate the association between first versus second biopsies and patient and allograft survival.

Results: Of the 1011 kidneys included in our cohort, 606 (60%) had multiple procurement biopsies; 98% had first biopsy performed at another organ procurement organization and their second biopsy performed locally. Categorical agreement was highest for vascular disease (=0.17) followed by interstitial fibrosis and tubular atrophy (=0.12) and glomerulosclerosis (=0.12). Overall histologic agreement (optimal versus suboptimal) was =0.15. First biopsy histology had no association with allograft survival in unadjusted or adjusted analyses. However, second biopsy optimal histology was associated with a higher probability of death-censored allograft survival, even after adjusting for donor and recipient factors (adjusted hazard ratio, 0.50; 95% confidence interval, 0.34 to 0.75; =0.001).

Conclusions: Deceased donor kidneys that underwent multiple procurement biopsies often displayed substantial differences in histologic categorization in sequential biopsies, and there was no association between first biopsy findings and post-transplant outcomes.
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http://dx.doi.org/10.2215/CJN.09170819DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7015101PMC
February 2020