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Article ; Online: Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities.

Cousins, Henry C / Nayar, Gowri / Altman, Russ B

Annual review of biomedical data science

2024

Abstract: Drug repurposing refers to the inference of therapeutic relationships between a clinical indication and existing compounds. As an emerging paradigm in drug development, drug repurposing enables more efficient treatment of rare diseases, stratified ... ...

Abstract	Drug repurposing refers to the inference of therapeutic relationships between a clinical indication and existing compounds. As an emerging paradigm in drug development, drug repurposing enables more efficient treatment of rare diseases, stratified patient populations, and urgent threats to public health. However, prioritizing well-suited drug candidates from among a nearly infinite number of repurposing options continues to represent a significant challenge in drug development. Over the past decade, advances in genomic profiling, database curation, and machine learning techniques have enabled more accurate identification of drug repurposing candidates for subsequent clinical evaluation. This review outlines the major methodologic classes that these approaches comprise, which rely on (
Language	English
Publishing date	2024-04-10
Publishing country	United States
Document type	Journal Article ; Review
ISSN	2574-3414
ISSN (online)	2574-3414
DOI	10.1146/annurev-biodatasci-110123-025333
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Association between spironolactone use and COVID-19 outcomes in population-scale claims data: a retrospective cohort study.

Cousins, Henry C / Altman, Russ B

medRxiv : the preprint server for health sciences

2023

Abstract: Background: Spironolactone has been proposed as a potential modulator of SARS-CoV-2 cellular entry. We aimed to measure the effect of spironolactone use on the risk of adverse outcomes following COVID-19 hospitalization.: Methods: We performed a ... ...

Abstract	Background: Spironolactone has been proposed as a potential modulator of SARS-CoV-2 cellular entry. We aimed to measure the effect of spironolactone use on the risk of adverse outcomes following COVID-19 hospitalization. Methods: We performed a retrospective cohort study of COVID-19 outcomes for patients with or without exposure to spironolactone, using population-scale claims data from the Komodo Healthcare Map. We identified all patients with a hospital admission for COVID-19 in the study window, defining treatment status based on spironolactone prescription orders. The primary outcomes were progression to respiratory ventilation or mortality during the hospitalization. Odds ratios (OR) were estimated following either 1:1 propensity score matching (PSM) or multivariable regression. Subgroup analysis was performed based on age, gender, body mass index (BMI), and dominant SARS-CoV-2 variant. Findings: Among 898,303 eligible patients with a COVID-19-related hospitalization, 16,324 patients (1.8%) had a spironolactone prescription prior to hospitalization. 59,937 patients (6.7%) met the ventilation endpoint, and 26,515 patients (3.0%) met the mortality endpoint. Spironolactone use was associated with a significant reduction in odds of both ventilation (OR 0.82; 95% CI: 0.75-0.88; p < 0.001) and mortality (OR 0.88; 95% CI: 0.78-0.99; p = 0.033) in the PSM analysis, supported by the regression analysis. Spironolactone use was associated with significantly reduced odds of ventilation for all age groups, men, women, and non-obese patients, with the greatest protective effects in younger patients, men, and non-obese patients. Interpretation: Spironolactone use was associated with a protective effect against ventilation and mortality following COVID-19 infection, amounting to up to 64% of the protective effect of vaccination against ventilation and consistent with an androgen-dependent mechanism. The findings warrant initiation of large-scale randomized controlled trials to establish a potential therapeutic role for spironolactone in COVID-19 patients.
Language	English
Publishing date	2023-03-02
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.02.28.23286515
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Association between spironolactone use and COVID-19 outcomes in population-scale claims data: a retrospective cohort study

Cousins, Henry C. / Altman, Russ B.

medRxiv

Abstract	Background: Spironolactone has been proposed as a potential modulator of SARS-CoV-2 cellular entry. We aimed to measure the effect of spironolactone use on the risk of adverse outcomes following COVID-19 hospitalization. Methods: We performed a retrospective cohort study of COVID-19 outcomes for patients with or without exposure to spironolactone, using population-scale claims data from the Komodo Healthcare Map. We identified all patients with a hospital admission for COVID-19 in the study window, defining treatment status based on spironolactone prescription orders. The primary outcomes were progression to respiratory ventilation or mortality during the hospitalization. Odds ratios (OR) were estimated following either 1:1 propensity score matching (PSM) or multivariable regression. Subgroup analysis was performed based on age, gender, body mass index (BMI), and dominant SARS-CoV-2 variant. Findings: Among 898,303 eligible patients with a COVID-19-related hospitalization, 16,324 patients (1.8%) had a spironolactone prescription prior to hospitalization. 59,937 patients (6.7%) met the ventilation endpoint, and 26,515 patients (3.0%) met the mortality endpoint. Spironolactone use was associated with a significant reduction in odds of both ventilation (OR 0.82; 95% CI: 0.75-0.88; p < 0.001) and mortality (OR 0.88; 95% CI: 0.78-0.99; p = 0.033) in the PSM analysis, supported by the regression analysis. Spironolactone use was associated with significantly reduced odds of ventilation for all age groups, men, women, and non-obese patients, with the greatest protective effects in younger patients, men, and non-obese patients. Interpretation: Spironolactone use was associated with a protective effect against ventilation and mortality following COVID-19 infection, amounting to up to 64% of the protective effect of vaccination against ventilation and consistent with an androgen-dependent mechanism. The findings warrant initiation of large-scale randomized controlled trials to establish a potential therapeutic role for spironolactone in COVID-19 patients.
Keywords	covid19
Language	English
Publishing date	2023-03-02
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2023.02.28.23286515
Database	COVID19

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Article ; Online: Genetic Correlations Among Corneal Biophysical Parameters and Anthropometric Traits.

Cousins, Henry C / Cousins, Clara C / Valluru, Girish / Altman, Russ B / Liu, Yutao / Pasquale, Louis R / Ahmad, Sumayya

Translational vision science & technology

2023 Volume 12, Issue 8, Page(s) 8

Abstract: Purpose: The genetic architecture of corneal dysfunction remains poorly understood. Epidemiological and clinical evidence suggests a relationship between corneal structural features and anthropometric measures. We used global and local genetic ... ...

Abstract	Purpose: The genetic architecture of corneal dysfunction remains poorly understood. Epidemiological and clinical evidence suggests a relationship between corneal structural features and anthropometric measures. We used global and local genetic similarity analysis to identify genomic features that may underlie structural corneal dysfunction. Methods: We assembled genome-wide association study summary statistics for corneal features (central corneal thickness, corneal hysteresis [CH], corneal resistance factor [CRF], and the 3 mm index of keratometry) and anthropometric traits (body mass index, weight, and height) in Europeans. We calculated global genetic correlations (rg) between traits using linkage disequilibrium (LD) score regression and local genetic covariance using ρ-HESS, which partitions the genome and performs regression with LD regions. Finally, we identified genes located within regions of significant genetic covariance and analyzed patterns of tissue expression and pathway enrichment. Results: Global LD score regression revealed significant negative correlations between height and both CH (rg = -0.12; P = 2.0 × 10-7) and CRF (rg = -0.11; P = 6.9 × 10-7). Local analysis revealed 68 genomic regions exhibiting significant local genetic covariance between CRF and height, containing 2874 unique genes. Pathway analysis of genes in regions with significant local rg revealed enrichment among signaling pathways with known keratoconus associations, including cadherin and Wnt signaling, as well as enrichment of genes modulated by copper and zinc ions. Conclusions: Corneal biophysical parameters and height share a common genomic architecture, which may facilitate identification of disease-associated genes and therapies for corneal ectasias. Translational relevance: Local genetic covariance analysis enables the identification of associated genes and therapeutic targets for corneal ectatic disease.
MeSH term(s)	Humans ; Genome-Wide Association Study ; Cornea ; Keratoconus/metabolism ; Physical Examination
Language	English
Publishing date	2023-08-10
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2674602-5
ISSN	2164-2591 ; 2164-2591
ISSN (online)	2164-2591
ISSN	2164-2591
DOI	10.1167/tvst.12.8.8
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Article ; Online: Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns.

Cousins, Henry C / Cousins, Clara C / Harris, Alon / Pasquale, Louis R

Journal of medical Internet research

2020 Volume 22, Issue 7, Page(s) e19483

Abstract: Background: Timely allocation of medical resources for coronavirus disease (COVID-19) requires early detection of regional outbreaks. Internet browsing data may predict case outbreaks in local populations that are yet to be confirmed.: Objective: We ... ...

Abstract	Background: Timely allocation of medical resources for coronavirus disease (COVID-19) requires early detection of regional outbreaks. Internet browsing data may predict case outbreaks in local populations that are yet to be confirmed. Objective: We investigated whether search-engine query patterns can help to predict COVID-19 case rates at the state and metropolitan area levels in the United States. Methods: We used regional confirmed case data from the New York Times and Google Trends results from 50 states and 166 county-based designated market areas (DMA). We identified search terms whose activity precedes and correlates with confirmed case rates at the national level. We used univariate regression to construct a composite explanatory variable based on best-fitting search queries offset by temporal lags. We measured the raw and z-transformed Pearson correlation and root-mean-square error (RMSE) of the explanatory variable with out-of-sample case rate data at the state and DMA levels. Results: Predictions were highly correlated with confirmed case rates at the state (mean r=0.69, 95% CI 0.51-0.81; median RMSE 1.27, IQR 1.48) and DMA levels (mean r=0.51, 95% CI 0.39-0.61; median RMSE 4.38, IQR 1.80), using search data available up to 10 days prior to confirmed case rates. They fit case-rate activity in 49 of 50 states and in 103 of 166 DMA at a significance level of .05. Conclusions: Identifiable patterns in search query activity may help to predict emerging regional outbreaks of COVID-19, although they remain vulnerable to stochastic changes in search intensity.
MeSH term(s)	Algorithms ; Betacoronavirus ; COVID-19 ; Coronavirus Infections/epidemiology ; Humans ; Internet ; Models, Statistical ; Pandemics ; Pneumonia, Viral/epidemiology ; Population Surveillance/methods ; Public Health Informatics/methods ; SARS-CoV-2 ; Search Engine/trends ; United States
Keywords	covid19
Language	English
Publishing date	2020-07-30
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2028830-X
ISSN	1438-8871 ; 1439-4456
ISSN (online)	1438-8871
ISSN	1439-4456
DOI	10.2196/19483
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Assessment of Team Dynamics and Operative Efficiency in Hip and Knee Arthroplasty.

Cousins, Henry C / Cahan, Eli M / Steere, Joshua T / Maloney, William J / Goodman, Stuart B / Miller, Matthew D / Huddleston, James I / Amanatullah, Derek F

JAMA surgery

2023 Volume 158, Issue 6, Page(s) 603–608

Abstract: Importance: Surgical team communication is a critical component of operative efficiency. The factors underlying optimal communication, including team turnover, role composition, and mutual familiarity, remain underinvestigated in the operating room.: ... ...

Abstract	Importance: Surgical team communication is a critical component of operative efficiency. The factors underlying optimal communication, including team turnover, role composition, and mutual familiarity, remain underinvestigated in the operating room. Objective: To assess staff turnover, trainee involvement, and surgeon staff preferences in terms of intraoperative efficiency. Design, setting, and participants: Retrospective analysis of staff characteristics and operating times for all total joint arthroplasties was performed at a tertiary academic medical center by 5 surgeons from January 1 to December 31, 2018. Data were analyzed from May 1, 2021, to February 18, 2022. The study included cases with primary total hip arthroplasties (THAs) and primary total knee arthroplasties (TKAs) comprising all primary total joint arthroplasties performed over the 1-year study interval. Exposures: Intraoperative turnover among nonsurgical staff, presence of trainees, and presence of surgeon-preferred staff. Main outcomes and measures: Incision time, procedure time, and room time for each surgery. Multivariable regression analyses between operative duration, presence of surgeon-preferred staff, and turnover among nonsurgical personnel were conducted. Results: A total of 641 cases, including 279 THAs (51% female; median age, 64 [IQR, 56.3-71.5] years) and 362 TKAs (66% [238] female; median age, 68 [IQR, 61.1-74.1] years) were considered. Turnover among circulating nurses was associated with a significant increase in operative duration in both THAs and TKAs, with estimated differences of 19.6 minutes (SE, 3.5; P < .001) of room time in THAs and 14.0 minutes (SE, 3.1; P < .001) of room time in TKAs. The presence of a preferred anesthesiologist or surgical technician was associated with significant decreases of 26.5 minutes (SE, 8.8; P = .003) of procedure time and 12.6 minutes (SE, 4.0; P = .002) of room time, respectively, in TKAs. The presence of a surgeon-preferred vendor was associated with a significant increase in operative duration in both THAs (26.3 minutes; SE, 7.3; P < .001) and TKAs (29.6 minutes; SE, 9.6; P = .002). Conclusions and relevance: This study found that turnover among operative staff is associated with procedural inefficiency. In contrast, the presence of surgeon-preferred staff may facilitate intraoperative efficiency. Administrative or technologic support of perioperative communication and team continuity may help improve operative efficiency.
MeSH term(s)	Humans ; Female ; Middle Aged ; Aged ; Male ; Arthroplasty, Replacement, Knee ; Retrospective Studies ; Arthroplasty, Replacement, Hip ; Surgeons ; Operating Rooms
Language	English
Publishing date	2023-03-22
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Comment
ZDB-ID	2701841-6
ISSN	2168-6262 ; 2168-6254
ISSN (online)	2168-6262
ISSN	2168-6254
DOI	10.1001/jamasurg.2023.0168
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Article: Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns

Cousins, Henry C / Cousins, Clara C / Harris, Alon / Pasquale, Louis R

J Med Internet Res

Abstract: BACKGROUND: Timely allocation of medical resources for coronavirus disease (COVID-19) requires early detection of regional outbreaks. Internet browsing data may predict case outbreaks in local populations that are yet to be confirmed. OBJECTIVE: We ... ...

Abstract	BACKGROUND: Timely allocation of medical resources for coronavirus disease (COVID-19) requires early detection of regional outbreaks. Internet browsing data may predict case outbreaks in local populations that are yet to be confirmed. OBJECTIVE: We investigated whether search-engine query patterns can help to predict COVID-19 case rates at the state and metropolitan area levels in the United States. METHODS: We used regional confirmed case data from the New York Times and Google Trends results from 50 states and 166 county-based designated market areas (DMA). We identified search terms whose activity precedes and correlates with confirmed case rates at the national level. We used univariate regression to construct a composite explanatory variable based on best-fitting search queries offset by temporal lags. We measured the raw and z-transformed Pearson correlation and root-mean-square error (RMSE) of the explanatory variable with out-of-sample case rate data at the state and DMA levels. RESULTS: Predictions were highly correlated with confirmed case rates at the state (mean r=0.69, 95% CI 0.51-0.81; median RMSE 1.27, IQR 1.48) and DMA levels (mean r=0.51, 95% CI 0.39-0.61; median RMSE 4.38, IQR 1.80), using search data available up to 10 days prior to confirmed case rates. They fit case-rate activity in 49 of 50 states and in 103 of 166 DMA at a significance level of .05. CONCLUSIONS: Identifiable patterns in search query activity may help to predict emerging regional outbreaks of COVID-19, although they remain vulnerable to stochastic changes in search intensity.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #658765
Database	COVID19

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Article ; Online: Regional Infoveillance of COVID-19 Case Rates

Cousins, Henry C / Cousins, Clara C / Harris, Alon / Pasquale, Louis R

Journal of Medical Internet Research, Vol 22, Iss 7, p e

Analysis of Search-Engine Query Patterns

2020 Volume 19483

Abstract: BackgroundTimely allocation of medical resources for coronavirus disease (COVID-19) requires early detection of regional outbreaks. Internet browsing data may predict case outbreaks in local populations that are yet to be confirmed. ObjectiveWe ... ...

Abstract	BackgroundTimely allocation of medical resources for coronavirus disease (COVID-19) requires early detection of regional outbreaks. Internet browsing data may predict case outbreaks in local populations that are yet to be confirmed. ObjectiveWe investigated whether search-engine query patterns can help to predict COVID-19 case rates at the state and metropolitan area levels in the United States. MethodsWe used regional confirmed case data from the New York Times and Google Trends results from 50 states and 166 county-based designated market areas (DMA). We identified search terms whose activity precedes and correlates with confirmed case rates at the national level. We used univariate regression to construct a composite explanatory variable based on best-fitting search queries offset by temporal lags. We measured the raw and z-transformed Pearson correlation and root-mean-square error (RMSE) of the explanatory variable with out-of-sample case rate data at the state and DMA levels. ResultsPredictions were highly correlated with confirmed case rates at the state (mean r=0.69, 95% CI 0.51-0.81; median RMSE 1.27, IQR 1.48) and DMA levels (mean r=0.51, 95% CI 0.39-0.61; median RMSE 4.38, IQR 1.80), using search data available up to 10 days prior to confirmed case rates. They fit case-rate activity in 49 of 50 states and in 103 of 166 DMA at a significance level of .05. ConclusionsIdentifiable patterns in search query activity may help to predict emerging regional outbreaks of COVID-19, although they remain vulnerable to stochastic changes in search intensity.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7 ; Public aspects of medicine ; RA1-1270
Subject code	006
Language	English
Publishing date	2020-07-01T00:00:00Z
Publisher	JMIR Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Integrative analysis of functional genomic screening and clinical data identifies a protective role for spironolactone in severe COVID-19.

Cousins, Henry C / Kline, Adrienne Sarah / Wang, Chengkun / Qu, Yuanhao / Zengel, James / Carette, Jan / Wang, Mengdi / Altman, Russ B / Luo, Yuan / Cong, Le

Cell reports methods

2023 Volume 3, Issue 7, Page(s) 100503

Abstract: We demonstrate that integrative analysis of CRISPR screening datasets enables network-based prioritization of prescription drugs modulating viral entry in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by developing a network-based approach ...

Abstract	We demonstrate that integrative analysis of CRISPR screening datasets enables network-based prioritization of prescription drugs modulating viral entry in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by developing a network-based approach called Rapid proXimity Guidance for Repurposing Investigational Drugs (RxGRID). We use our results to guide a propensity-score-matched, retrospective cohort study of 64,349 COVID-19 patients, showing that a top candidate drug, spironolactone, is associated with improved clinical prognosis, measured by intensive care unit (ICU) admission and mechanical ventilation rates. Finally, we show that spironolactone exerts a dose-dependent inhibitory effect on viral entry in human lung epithelial cells. Our RxGRID method presents a computational framework, implemented as an open-source software package, enabling genomics researchers to identify drugs likely to modulate a molecular phenotype of interest based on high-throughput screening data. Our results, derived from this method and supported by experimental and clinical analysis, add additional supporting evidence for a potential protective role of the potassium-sparing diuretic spironolactone in severe COVID-19.
MeSH term(s)	Humans ; COVID-19 ; SARS-CoV-2/genetics ; Spironolactone/pharmacology ; Retrospective Studies ; Genomics
Chemical Substances	Spironolactone (27O7W4T232)
Language	English
Publishing date	2023-05-29
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't
ISSN	2667-2375
ISSN (online)	2667-2375
DOI	10.1016/j.crmeth.2023.100503
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Deep Learning for Localized Detection of Optic Disc Hemorrhages.

Brown, Aaron / Cousins, Henry / Cousins, Clara / Esquenazi, Karina / Elze, Tobias / Harris, Alon / Filipowicz, Artur / Barna, Laura / Yonwook, Kim / Vinod, Kateki / Chadha, Nisha / Altman, Russ B / Coote, Michael / Pasquale, Louis R

American journal of ophthalmology

2023 Volume 255, Page(s) 161–169

Abstract: Purpose: To develop an automated deep learning system for detecting the presence and location of disc hemorrhages in optic disc photographs.: Design: Development and testing of a deep learning algorithm.: Methods: Optic disc photos (597 images ... ...

Abstract	Purpose: To develop an automated deep learning system for detecting the presence and location of disc hemorrhages in optic disc photographs. Design: Development and testing of a deep learning algorithm. Methods: Optic disc photos (597 images with at least 1 disc hemorrhage and 1075 images without any disc hemorrhage from 1562 eyes) from 5 institutions were classified by expert graders based on the presence or absence of disc hemorrhage. The images were split into training (n = 1340), validation (n = 167), and test (n = 165) datasets. Two state-of-the-art deep learning algorithms based on either object-level detection or image-level classification were trained on the dataset. These models were compared to one another and against 2 independent glaucoma specialists. We evaluated model performance by the area under the receiver operating characteristic curve (AUC). AUCs were compared with the Hanley-McNeil method. Results: The object detection model achieved an AUC of 0.936 (95% CI = 0.857-0.964) across all held-out images (n = 165 photographs), which was significantly superior to the image classification model (AUC = 0.845, 95% CI = 0.740-0.912; P = .006). At an operating point selected for high specificity, the model achieved a specificity of 94.3% and a sensitivity of 70.0%, which was statistically indistinguishable from an expert clinician (P = .7). At an operating point selected for high sensitivity, the model achieves a sensitivity of 96.7% and a specificity of 73.3%. Conclusions: An autonomous object detection model is superior to an image classification model for detecting disc hemorrhages, and performed comparably to 2 clinicians.
MeSH term(s)	Humans ; Optic Disk/diagnostic imaging ; Deep Learning ; Optic Nerve Diseases/diagnosis ; Glaucoma/diagnosis ; ROC Curve ; Algorithms ; Retinal Hemorrhage/diagnosis
Language	English
Publishing date	2023-07-23
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	80030-2
ISSN	1879-1891 ; 0002-9394
ISSN (online)	1879-1891
ISSN	0002-9394
DOI	10.1016/j.ajo.2023.07.007
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