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  • 1
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    Online Resource
    Outcomes of COVID-19 in Patients With Cancer: Report From the National COVID Cohort Collaborative (N3C)
    American Society of Clinical Oncology (ASCO) ; 2021
    In:  Journal of Clinical Oncology Vol. 39, No. 20 ( 2021-07-10), p. 2232-2246
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 39, No. 20 ( 2021-07-10), p. 2232-2246
    Abstract: Variation in risk of adverse clinical outcomes in patients with cancer and COVID-19 has been reported from relatively small cohorts. The NCATS’ National COVID Cohort Collaborative (N3C) is a centralized data resource representing the largest multicenter cohort of COVID-19 cases and controls nationwide. We aimed to construct and characterize the cancer cohort within N3C and identify risk factors for all-cause mortality from COVID-19. METHODS We used 4,382,085 patients from 50 US medical centers to construct a cohort of patients with cancer. We restricted analyses to adults ≥ 18 years old with a COVID-19–positive or COVID-19–negative diagnosis between January 1, 2020, and March 25, 2021. We followed N3C selection of an index encounter per patient for analyses. All analyses were performed in the N3C Data Enclave Palantir platform. RESULTS A total of 398,579 adult patients with cancer were identified from the N3C cohort; 63,413 (15.9%) were COVID-19–positive. Most common represented cancers were skin (13.8%), breast (13.7%), prostate (10.6%), hematologic (10.5%), and GI cancers (10%). COVID-19 positivity was significantly associated with increased risk of all-cause mortality (hazard ratio, 1.20; 95% CI, 1.15 to 1.24). Among COVID-19–positive patients, age ≥ 65 years, male gender, Southern or Western US residence, an adjusted Charlson Comorbidity Index score ≥ 4, hematologic malignancy, multitumor sites, and recent cytotoxic therapy were associated with increased risk of all-cause mortality. Patients who received recent immunotherapies or targeted therapies did not have higher risk of overall mortality. CONCLUSION Using N3C, we assembled the largest nationally representative cohort of patients with cancer and COVID-19 to date. We identified demographic and clinical factors associated with increased all-cause mortality in patients with cancer. Full characterization of the cohort will provide further insights into the effects of COVID-19 on cancer outcomes and the ability to continue specific cancer treatments.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.21.01074
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2021
    detail.hit.zdb_id: 2005181-5
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  • 2
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    Computational health economics for identification of unprofitable health care enrollees
    Oxford University Press (OUP) ; 2017
    In:  Biostatistics Vol. 18, No. 4 ( 2017-10-01), p. 682-694
    Details
    In: Biostatistics, Oxford University Press (OUP), Vol. 18, No. 4 ( 2017-10-01), p. 682-694
    Type of Medium: Online Resource
    ISSN: 1465-4644 , 1468-4357
    URL: Article
    DOI: 10.1093/biostatistics/kxx012
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2017
    detail.hit.zdb_id: 2020601-X
    SSG: 12
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  • 3
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    Evaluation of crowdsourced mortality prediction models as a framework for assessing artificial intelligence in medicine
    Oxford University Press (OUP) ; 2023
    In:  Journal of the American Medical Informatics Association ( 2023-08-18)
    Details
    In: Journal of the American Medical Informatics Association, Oxford University Press (OUP), ( 2023-08-18)
    Abstract: Applications of machine learning in healthcare are of high interest and have the potential to improve patient care. Yet, the real-world accuracy of these models in clinical practice and on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate methods that predict healthcare outcomes. We focused on the prediction of all-cause mortality as the community challenge question. Materials and methods Using a Model-to-Data framework, 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries, generated 25 accurate models all trained on a dataset of over 1.1 million patients and evaluated on patients prospectively collected over a 1-year observation of a large health system. Results The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI, 0.942-0.951) and an area under the precision-recall curve of 0.487 (95% CI, 0.458-0.499) on a prospectively collected patient cohort. Discussion Post hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data. Conclusion This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI.
    Type of Medium: Online Resource
    ISSN: 1067-5027 , 1527-974X
    URL: Article
    DOI: 10.1093/jamia/ocad159
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2018371-9
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  • 4
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    The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens
    Springer Science and Business Media LLC ; 2019
    In:  Genome Biology Vol. 20, No. 1 ( 2019-12)
    Details
    In: Genome Biology, Springer Science and Business Media LLC, Vol. 20, No. 1 ( 2019-12)
    Abstract: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster , which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster , it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.
    Type of Medium: Online Resource
    ISSN: 1474-760X
    URL: Article
    DOI: 10.1186/s13059-019-1835-8
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
    detail.hit.zdb_id: 2040529-7
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  • 5
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    Rituximab is associated with worse COVID-19 outcomes in patients with rheumatoid arthritis: A retrospective, nationally sampled cohort study from the U.S. National COVID Cohort Collaborative (N3C)
    Elsevier BV ; 2023
    In:  Seminars in Arthritis and Rheumatism Vol. 58 ( 2023-02), p. 152149-
    Details
    In: Seminars in Arthritis and Rheumatism, Elsevier BV, Vol. 58 ( 2023-02), p. 152149-
    Type of Medium: Online Resource
    ISSN: 0049-0172
    URL: Article
    DOI: 10.1016/j.semarthrit.2022.152149
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2048942-0
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  • 6
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    Catalytic Denitrification in a Trickle Bed Reactor: Ion Exchange Waste Brine Treatment
    Wiley ; 2017
    In:  Journal AWWA Vol. 109, No. 5 ( 2017-05)
    Details
    In: Journal AWWA, Wiley, Vol. 109, No. 5 ( 2017-05)
    Abstract: Catalytic reduction of nitrate in ion exchange (IX) waste brine for reuse is a promising option for reducing IX costs and environmental impacts. A recycling trickle bed reactor (TBR) was designed and optimized using 0.5 percent by weight (wt%) palladium–0.05 wt% indium catalysts supported on US mesh size 12 × 14 or 12 × 30 activated carbon particles. Various liquid superficial velocities (U r ) and hydrogen gas superficial velocities (U g‐H2 ) were evaluated to assess performance in different flow regimes; catalyst activity increased with U g‐H2 at all U r for both catalysts and was greatest for the 12 × 30 catalyst at the lowest U r (8.9 m/h). The 12 × 30 catalyst demonstrated up to 100% higher catalytic activity and 280% higher mass transfer rate compared with the 12 × 14 catalyst. Optimal TBR performance was achieved with both catalysts in the trickle flow regime. The results indicate that the TBR is a promising step forward, and continued improvements are possible to overcome remaining mass transfer limitations.
    Type of Medium: Online Resource
    ISSN: 0003-150X , 1551-8833
    URL: Article
    DOI: 10.5942/jawwa.2017.109.0055
    RVK:
    ZM 1385
    Language: English
    Publisher: Wiley
    Publication Date: 2017
    detail.hit.zdb_id: 2144899-1
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  • 7
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    Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes
    Elsevier BV ; 2023
    In:  eBioMedicine Vol. 87 ( 2023-01), p. 104413-
    Details
    In: eBioMedicine, Elsevier BV, Vol. 87 ( 2023-01), p. 104413-
    Type of Medium: Online Resource
    ISSN: 2352-3964
    URL: Article
    DOI: 10.1016/j.ebiom.2022.104413
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2799017-5
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  • 8
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    Evaluation of the secondary use of electronic health records to detect seasonal, holiday-related, and rare events related to traumatic injury and poisoning
    Springer Science and Business Media LLC ; 2020
    In:  BMC Public Health Vol. 20, No. 1 ( 2020-12)
    Details
    In: BMC Public Health, Springer Science and Business Media LLC, Vol. 20, No. 1 ( 2020-12)
    Abstract: The increasing adoption of electronic health record (EHR) systems enables automated, large scale, and meaningful analysis of regional population health. We explored how EHR systems could inform surveillance of trauma-related emergency department visits arising from seasonal, holiday-related, and rare environmental events. Methods We analyzed temporal variation in diagnosis codes over 24 years of trauma visit data at the three hospitals in the University of Washington Medicine system in Seattle, Washington, USA. We identified seasons and days in which specific codes and categories of codes were statistically enriched, meaning that a significantly greater than average proportion of trauma visits included a given diagnosis code during that time period. Results We confirmed known seasonal patterns in emergency department visits for trauma. As expected, cold weather-related incidents (e.g. frostbite, snowboarding injury) were enriched in the winter, whereas fair weather-related incidents (e.g. bug bites, boating accidents, bicycle accidents) were enriched in the spring and summer. Our analysis of specific days of the year found that holidays were enriched for alcohol poisoning, assaults, and firework accidents. We also detected one time regional events such as the 2001 Nisqually earthquake and the 2006 Hanukkah Eve Windstorm. Conclusions Though EHR systems were developed to prioritize operational rather than analytic priorities and have consequent limitations for surveillance, our EHR enrichment analysis nonetheless re-identified expected temporal population health patterns. EHRs are potentially a valuable source of information to inform public health policy, both in retrospective analysis and in a surveillance capacity.
    Type of Medium: Online Resource
    ISSN: 1471-2458
    URL: Article
    DOI: 10.1186/s12889-020-8153-7
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2041338-5
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  • 9
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    COVID-19 omicron variants demonstrated different virulence in infected patients with cancer: The real-world evidence from the National COVID Cohort Collaborative (N3C).
    American Society of Clinical Oncology (ASCO) ; 2022
    In:  Journal of Clinical Oncology Vol. 40, No. 16_suppl ( 2022-06-01), p. e18672-e18672
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 40, No. 16_suppl ( 2022-06-01), p. e18672-e18672
    Abstract: e18672 Background: Comprehensive real-world evidence of the virulence of COVID-19 Omicron, Delta, and Alpha variants as well as the effectiveness of booster vaccinations in patients with cancer are lacking. We aimed to fill in these gaps for cancer patients and provide essential insights on the management of the fast-evolving pandemic by leveraging the nationally-representative electronic medical records from the National COVID Cohort Collaborative (N3C) registry. Methods: The virulence of COVID-19 variants was examined according to severe outcomes of infected patients with cancer, compared with non-cancer patients, using the N3C data between 12/01/2020 and 02/03/2022. Variants were inferred according to the time periods of variant dominance at 〉 95% accuracy. The Cox proportional hazards model was employed to evaluate the effects of COVID-19 variants, adjusting for age, gender, race/ethnicity, geographic regions, vaccination status, cancer types, smoking status, cancer treatments, and adjusted Charlson Comorbidity Index (CCI). Results: Our study cohort included 114,195 COVID-19 patients with cancer and 160,493 without cancer as control. Among them, 52,539 (21%) were infected by Omicron, 82,579 (33%) by Delta, and 115,200 (46%) by Alpha variants. Prior to the COVID-19 breakthrough infection, 7%, 22%, 3%, and 69% were vaccinated with 1 dose, 2 doses, a booster, or unvaccinated respectively. The proportions of hospitalization and death among patients with vs without cancer were 40% and 7% vs 18% and 0.4%, respectively. Characteristics of the cancer subcohort are summarized in the Table. Our analysis showed dramatically lower risks of severe outcomes for patients who were infected by Omicron (HR 0.42, 95%CI: 0.38 – 0.46) and slightly lower risks for Delta (HR 0.93, 95%CI: 0.89 – 0.98) compared with those infected by Alpha, after adjusting for other demographic clinical risk factors, and vaccination status. This trend remained similar in subgroups of patients with solid tumors, hematologic malignancies, or without cancer. Similar associations were observed when virulence was evaluated in association with mortality. The effectiveness of booster vaccinations varied across sub-cohorts stratified by variants and cancer types. Booster shots reduced the risk of severe outcomes for patients with solid tumors infected by Omicron variant or hematologic malignancies infected by Delta variants. Conclusions: Our work provides up-to-date and comprehensive real-world evidence of the virulence of COVID-19 variants in patients with cancer. Omicron variant showed significantly reduced virulence for different cancer types.[Table: see text]
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2022.40.16_suppl.e18672
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2022
    detail.hit.zdb_id: 2005181-5
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  • 10
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    Outcomes of COVID-19 in cancer patients: Report from the National COVID Cohort Collaborative (N3C).
    American Society of Clinical Oncology (ASCO) ; 2021
    In:  Journal of Clinical Oncology Vol. 39, No. 15_suppl ( 2021-05-20), p. 1500-1500
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 39, No. 15_suppl ( 2021-05-20), p. 1500-1500
    Abstract: 1500 Background: The impact of COVID-19 has disproportionately affected every aspect of cancer care and research—from introducing new risks for patients to disrupting the delivery of treatment and continuity of research. Variation in risk of adverse clinical outcomes in COVID-19 patients by cancer type has been reported from relatively small cohorts. Gaps in understanding effects of COVID-19 on cancer patients can be addressed through the study of a well-constructed representative cohort. The NCATS’ National COVID Cohort Collaborative (N3C) is a centralized data resource representing the largest multi-center cohort of COVID-19 cases and controls nationwide. We aimed to construct and characterize the cohort of cancer patients within N3C and identify risk factors for all-cause mortality from COVID-19. Methods: From the harmonized N3C clinical dataset, we used 3,295,963 patients from 39 medical US centers to construct a cancer patient cohort. We restricted analyses to adults ≥18 yo with a COVID-19 positive PCR or antigen test or ICD-10-CM diagnostic code for COVID-19 between 1/1/2020 and 2/14/2021. We followed N3C definitions where each lab-confirmed positive patient has one single index encounter. A modified WHO Clinical Progression Scale was used to determine clinical severity. All analyses were performed in the N3C Data Enclave on the Palantir platform. Results: A total of 372,883 adult patients with cancer were identified from the N3C cohort; 54,642 (14.7%) were COVID-19 positive. Most common represented cancers were skin (11.5%), breast (10.2%), prostate (8%), and lung cancer (5.6%). Mean age of COVID-19 positive patients was 61.6 years (SD 16.7), 47.3% over 65yo, 53.7% females, 67.2% non-Hispanic White, 21.0% Black, and 7.7% Hispanic or Latino. A total of 14.6% were current or former smokers, 22.3% had a Charlson Comorbidity Index (CCI) score of 0, 4.6% score of 1 and 28.1% score of 2. Among hospitalized COVID-19 positive patients, average length of stay in the hospital was 6 days (SD 23.1 days), 7.0% patients had died while in their initial COVID-19 hospitalization, 4.5% required invasive ventilation, and 0.1% extracorporeal membrane oxygenation. Survival probability was 86.4% at 10 days and 63.6% at 30 days. Older age over 65yo (Hazard ratio (HR) = 6.1, 95%CI: 4.3, 8.7), male gender (HR = 1.2, 95%CI: 1.1, 1.2), a CCI score of 2 or more (HR = 1.15, 95%CI: 1.1, 1.2), and acute kidney injury during hospitalization (HR = 1.3, 95%CI: 1.2, 1.4) were associated with increased risk of all-cause mortality. Conclusions: Using the N3C cohort we assembled the largest nationally representative cohort on patients with cancer and COVID-19 to date. We identified demographic and clinical factors associated with increased all-cause mortality in cancer patients. Full characterization of the cohort will provide further insights on the effects of COVID-19 on cancer outcomes and the ability to continue specific cancer treatments.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2021.39.15_suppl.1500
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2021
    detail.hit.zdb_id: 2005181-5
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