Sex-specificity of mortality risk factors among hospitalized COVID-19 patients in New York City: prospective cohort study

T, Jun; Nirenberg, Sharon; Kovatch, Patricia; Huang, Kuan‐lin

doi:10.1101/2020.07.29.20164640

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…By further restricting our analysis to age-adjusted estimates, we might have excluded evidence from countries that are now not otherwise represented in our review. Although 14 [138,145,149,171,198,207,218,232,241,259,265,273,288,293] of the primary studies in our selection have not yet been peer-reviewed (but were published on pre-print servers), they were included in our analyses to increase the evidence pool. These studies were reviewed and evaluated in the included systematic reviews; however, their inclusion might limit our work.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Pre-existing health conditions and severe COVID-19 outcomes: an umbrella review approach and meta-analysis of global evidence

Treskova-Schwarzbach

Haas

Reda

et al. 2021

BMC Med

130

119

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Background This study applies an umbrella review approach to summarise the global evidence on the risk of severe COVID-19 outcomes in patients with pre-existing health conditions. Methods Systematic reviews (SRs) were identified in PubMed, Embase/Medline and seven pre-print servers until December 11, 2020. Due to the absence of age-adjusted risk effects stratified by geographical regions, a re-analysis of the evidence was conducted. Primary studies were extracted from SRs and evaluated for inclusion in the re-analysis. Studies were included if they reported risk estimates (odds ratio (OR), hazard ratio (HR), relative risk (RR)) for hospitalisation, intensive care unit admission, intubation or death. Estimated associations were extracted from the primary studies for reported pre-existing conditions. Meta-analyses were performed stratified for each outcome by regions of the World Health Organization. The evidence certainty was assessed using GRADE. Registration number CRD42020215846. Results In total, 160 primary studies from 120 SRs contributed 464 estimates for 42 pre-existing conditions. Most studies were conducted in North America, European, and Western Pacific regions. Evidence from Africa, South/Latin America, and the Eastern Mediterranean region was scarce. No evidence was available from the South-East Asia region. Diabetes (HR range 1.2–2.0 (CI range 1.1–2.8)), obesity (OR range 1.5–1.75 (CI range 1.1–2.3)), heart failure (HR range 1.3–3.3 (CI range 0.9–8.2)), COPD (HR range 1.12–2.2 (CI range 1.1–3.2)) and dementia (HR range 1.4–7.7 (CI range 1.2–39.6)) were associated with fatal COVID-19 in different regions, although the estimates varied. Evidence from Europe and North America showed that liver cirrhosis (OR range 3.2–5.9 (CI range 0.9–27.7)) and active cancer (OR range 1.6–4.7 (CI range 0.5–14.9)) were also associated with increased risk of death. Association between HIV and undesirable COVID-19 outcomes showed regional heterogeneity, with an increased risk of death in Africa (HR 1.7 (CI 1.3–2.2)). GRADE certainty was moderate to high for most associations. Conclusion Risk of undesirable COVID-19 health outcomes is consistently increased in certain patient subgroups across geographical regions, showing high variability in others. The results can be used to inform COVID-19 vaccine prioritisation or other intervention strategies.

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Section: Strengths and Limitationsmentioning

confidence: 99%

Pre-existing health conditions and severe COVID-19 outcomes: an umbrella review approach and meta-analysis of global evidence

Treskova-Schwarzbach

Haas

Reda

et al. 2021

BMC Med

130

119

View full text Add to dashboard Cite

show abstract

“…The curation of EHR data is as previously described. 18,19 Brie y, in the setting of the COVID-19 pandemic, the Mount Sinai Data Warehouse (MSDW) developed and released a de-identi ed data set encompassing all COVID-19 related patient encounters within the Mount Sinai system, accompanied by selected demographics, comorbidities, vital signs, medications, and lab values. De-identi cation was performed in accordance with the Safe Harbor subsection of the HIPAA Privacy Rule.…”

Section: Study Setting and Data Sourcesmentioning

confidence: 99%

Prediction of COVID-19 Status Using Baseline Demographics and Lab Data

Zhang

Jun

Frank³

et al. 2021

SSRN Journal

Self Cite

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The global surge in COVID-19 cases underscores the need for fast, scalable, and reliable testing. Current COVID-19 diagnostic tests are limited by turnaround time, limited availability, or occasional false ndings. Here, we developed a machine learning-based framework for predicting COVID-19 positive test status relying only on readily-available baseline data, including patient demographics, comorbidities, and common lab values. Leveraging a cohort of 31,739 adults within an academic health system, we trained and tested multiple types of machine learning models, achieving an area under the curve of 0.75. Feature importance analyses highlighted serum calcium levels, aspartate aminotransferase levels, and oxygen saturation as key predictors. Additionally, we developed a single decision tree model that provided an operable method for stratifying sub-populations. Overall, this study provides a proof-of-concept that COVID-19 status prediction models can be developed using only baseline data. The resulting prediction can complement existing tests to enhance screening and pandemic containment work ows.

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Section: Study Setting and Data Sourcesmentioning

confidence: 99%

Prediction of COVID-19 status using baseline demographics and lab data

Zhang¹,

Jun

Frank³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

The global surge in COVID-19 cases underscores the need for fast, scalable, and reliable testing. Current COVID-19 diagnostic tests are limited by turnaround time, limited availability, or occasional false findings. Here, we developed a machine learning-based framework for predicting COVID-19 positive test status relying only on readily-available baseline data, including patient demographics, comorbidities, and common lab values. Leveraging a cohort of 31,739 adults within an academic health system, we trained and tested multiple types of machine learning models, achieving an area under the curve of 0.75. Feature importance analyses highlighted serum calcium levels, aspartate aminotransferase levels, and oxygen saturation as key predictors. Additionally, we developed a single decision tree model that provided an operable method for stratifying sub-populations. Overall, this study provides a proof-of-concept that COVID-19 status prediction models can be developed using only baseline data. The resulting prediction can complement existing tests to enhance screening and pandemic containment workflows.

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Sex-specificity of mortality risk factors among hospitalized COVID-19 patients in New York City: prospective cohort study

Cited by 7 publications

References 26 publications

Pre-existing health conditions and severe COVID-19 outcomes: an umbrella review approach and meta-analysis of global evidence

Pre-existing health conditions and severe COVID-19 outcomes: an umbrella review approach and meta-analysis of global evidence

Prediction of COVID-19 Status Using Baseline Demographics and Lab Data

Prediction of COVID-19 status using baseline demographics and lab data

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