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Impact of androgen deprivation therapy on mortality of prostate cancer patients with COVID-19: a propensity score-based analysis

Duarte et al., Infectious Agents and Cancer, doi:10.1186/s13027-021-00406-y
Nov 2021  
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Mortality 11% Improvement Relative Risk Antiandrogens for COVID-19  Duarte et al.  Prophylaxis Is prophylaxis with antiandrogens beneficial for COVID-19? Retrospective 199 patients in Brazil Lower mortality with antiandrogens (not stat. sig., p=0.37) c19early.org Duarte et al., Infectious Agents and C.., Nov 2021 Favorsvarious Favorscontrol 0 0.5 1 1.5 2+
7th treatment shown to reduce risk in September 2020, now with p = 0.000000056 from 49 studies.
No treatment is 100% effective. Protocols combine treatments.
5,300+ studies for 116 treatments. c19early.org
Retrospective 199 prostate cancer patients hospitalized with COVID-19 in Brazil, showing no significant difference in mortality with active ADT.
risk of death, 11.2% lower, RR 0.89, p = 0.37, treatment 100 of 156 (64.1%), control 32 of 43 (74.4%), NNT 9.7, adjusted per study, odds ratio converted to relative risk.
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
Duarte et al., 25 Nov 2021, retrospective, Brazil, peer-reviewed, 4 authors.
This PaperAntiandrogensAll
Impact of androgen deprivation therapy on mortality of prostate cancer patients with COVID-19: a propensity score-based analysis
Mateus Bringel Oliveira Duarte, Frederico Leal, Juliana Luz Passos Argenton, José Barreto Campello Carvalheira
Infectious Agents and Cancer, doi:10.1186/s13027-021-00406-y
Background: Previous studies hypothesized that androgen deprivation therapy (ADT) may reduce severe acute respiratory syndrome coronavirus 2 (SARS-COV2) infectivity. However, it is unknown whether there is an association between ADT and a higher survival in prostate cancer patients with COVID-19. Methods: We performed a retrospective analysis of prostate cancer (PC) patients hospitalized to treat COVID-19 in Brazil's public health system. We compared patients with the active use of ADT versus those with non-active ADT, past use. We constructed propensity score models of patients in active versus non-active use of ADT. All variables were used to derive propensity score estimation in both models. In the first model we performed a pair-matched propensity score model between those under active and non-active use of ADT. To the second model we initially performed a multivariate backward elimination process to select variables to a final inverse-weight adjusted with double robust estimation model. Results: We analyzed 199 PC patients with COVID-19 that received ADT. In total, 52.3% (95/199) of our patients were less than 75 years old, 78.4% (156/199) were on active ADT, and most were using a GnRH analog (80.1%; 125/156). Most of patients were in palliative treatment (89.9%; 179/199). Also, 63.3% of our cohort died from COVID-19. Fortyeight patients under active ADT were pair matched against 48 controls (non-active ADT). All patients (199) were analyzed in the double robust model. ADT active use were not protective factor in both inverse-weight based propensity score (OR 0.70, 95% CI 0.38-1.31, P = 0.263), and pair-matched propensity score (OR 0.67, 95% CI 0.27-1.63, P = 0.374) models. We noticed a significant imbalance in the propensity score of patients in active and those in non-active ADT, with important reductions in the differences after the adjustments. Conclusions: The active use of ADT was not associated with a reduced risk of death in patients with COVID-19.
Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s13027-021-00406-y. Additional file 1. Supplemental Table 1 . Baseline variables of active and non-active androgen deprivation therapy groups and standardized mean differences after propensity score-based pair matching. S1 . Standardized mean difference. The mean difference represents the difference between propensity score inside the variable before and after pair matching. Additional file 2. Figure Additional file 3. Figure S2 . Cumulative distribution of logit propensity score. The graphs summarize the cumulative distribution of logit propensity score, as well as the difference between active and non-active groups before and after matching. Additional file 4. Figure S3 . Density of propensity score distribution. The figure summarizes the distribution of propensity score applied in the double robust estimation model according to the use of androgen deprivation therapy (ADT). Authors' contributions Declarations Ethics approval and consent to participate The project was submitted and approved by our institutional ethics (Comitê de Ética em Pesquisa (CEP) da Universidade Estadual de Campinas) commitment and the consent form was waived. Consent for publication The project was submitted and approved by our institutional ethics (Comitê de Ética em Pesquisa (CEP) da Universidade Estadual de Campinas) commitment and the consent form was waived. ..
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DOI record: { "DOI": "10.1186/s13027-021-00406-y", "ISSN": [ "1750-9378" ], "URL": "http://dx.doi.org/10.1186/s13027-021-00406-y", "abstract": "<jats:title>Abstract</jats:title><jats:sec>\n <jats:title>Background</jats:title>\n <jats:p>Previous studies hypothesized that androgen deprivation therapy (ADT) may reduce severe acute respiratory syndrome coronavirus 2 (SARS-COV2) infectivity. However, it is unknown whether there is an association between ADT and a higher survival in prostate cancer patients with COVID-19.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Methods</jats:title>\n <jats:p>We performed a retrospective analysis of prostate cancer (PC) patients hospitalized to treat COVID-19 in Brazil’s public health system. We compared patients with the active use of ADT versus those with non-active ADT, past use. We constructed propensity score models of patients in active versus non-active use of ADT. All variables were used to derive propensity score estimation in both models. In the first model we performed a pair-matched propensity score model between those under active and non-active use of ADT. To the second model we initially performed a multivariate backward elimination process to select variables to a final inverse-weight adjusted with double robust estimation model.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Results</jats:title>\n <jats:p>We analyzed 199 PC patients with COVID-19 that received ADT. In total, 52.3% (95/199) of our patients were less than 75 years old, 78.4% (156/199) were on active ADT, and most were using a GnRH analog (80.1%; 125/156). Most of patients were in palliative treatment (89.9%; 179/199). Also, 63.3% of our cohort died from COVID-19. Forty-eight patients under active ADT were pair matched against 48 controls (non-active ADT). All patients (199) were analyzed in the double robust model. ADT active use were not protective factor in both inverse-weight based propensity score (OR 0.70, 95% CI 0.38–1.31, <jats:italic>P</jats:italic> = 0.263), and pair-matched propensity score (OR 0.67, 95% CI 0.27–1.63, <jats:italic>P</jats:italic> = 0.374) models. We noticed a significant imbalance in the propensity score of patients in active and those in non-active ADT, with important reductions in the differences after the adjustments.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Conclusions</jats:title>\n <jats:p>The active use of ADT was not associated with a reduced risk of death in patients with COVID-19.</jats:p>\n </jats:sec>", "alternative-id": [ "406" ], "article-number": "66", "assertion": [ { "group": { "label": "Article History", "name": "ArticleHistory" }, "label": "Received", "name": "received", "order": 1, "value": "9 July 2021" }, { "group": { "label": "Article History", "name": "ArticleHistory" }, "label": "Accepted", "name": "accepted", "order": 2, "value": "11 November 2021" }, { "group": { "label": "Article History", "name": "ArticleHistory" }, "label": "First Online", "name": "first_online", "order": 3, "value": "25 November 2021" }, { "group": { "label": "Declarations", "name": "EthicsHeading" }, "name": "Ethics", "order": 1 }, { "group": { "label": "Ethics approval and consent to participate", "name": "EthicsHeading" }, "name": "Ethics", "order": 2, "value": "The project was submitted and approved by our institutional ethics (Comitê de Ética em Pesquisa (CEP) da Universidade Estadual de Campinas) commitment and the consent form was waived." }, { "group": { "label": "Consent for publication", "name": "EthicsHeading" }, "name": "Ethics", "order": 3, "value": "The project was submitted and approved by our institutional ethics (Comitê de Ética em Pesquisa (CEP) da Universidade Estadual de Campinas) commitment and the consent form was waived." }, { "group": { "label": "Competing interests", "name": "EthicsHeading" }, "name": "Ethics", "order": 4, "value": "The authors declare that they have no competing interests." } ], "author": [ { "affiliation": [], "family": "Duarte", "given": "Mateus Bringel Oliveira", "sequence": "first" }, { "affiliation": [], "family": "Leal", "given": "Frederico", "sequence": "additional" }, { "affiliation": [], "family": "Argenton", "given": "Juliana Luz Passos", "sequence": "additional" }, { "ORCID": "http://orcid.org/0000-0002-0136-0943", "affiliation": [], "authenticated-orcid": false, "family": "Carvalheira", "given": "José Barreto Campello", "sequence": "additional" } ], "container-title": [ "Infectious Agents and Cancer" ], "content-domain": { "crossmark-restriction": false, "domain": [ "link.springer.com" ] }, "created": { "date-parts": [ [ 2021, 11, 30 ] ], "date-time": "2021-11-30T18:32:03Z", "timestamp": 1638297123000 }, "deposited": { "date-parts": [ [ 2021, 11, 30 ] ], "date-time": "2021-11-30T18:40:32Z", "timestamp": 1638297632000 }, "funder": [ { "DOI": "10.13039/501100001807", "award": [ "2018/23428‐0" ], "doi-asserted-by": "publisher", "name": "fundação de amparo à pesquisa do estado de são paulo" } ], "indexed": { "date-parts": [ [ 2021, 12, 10 ] ], "date-time": "2021-12-10T03:28:51Z", "timestamp": 1639106931736 }, "is-referenced-by-count": 0, "issn-type": [ { "type": "electronic", "value": "1750-9378" } ], "issue": "1", "issued": { "date-parts": [ [ 2021, 11, 25 ] ] }, "journal-issue": { "issue": "1", "published-print": { "date-parts": [ [ 2021, 12 ] ] } }, "language": "en", "license": [ { "URL": "https://creativecommons.org/licenses/by/4.0", "content-version": "tdm", "delay-in-days": 0, "start": { "date-parts": [ [ 2021, 11, 25 ] ], "date-time": "2021-11-25T00:00:00Z", "timestamp": 1637798400000 } }, { "URL": "https://creativecommons.org/licenses/by/4.0", "content-version": "vor", "delay-in-days": 0, "start": { "date-parts": [ [ 2021, 11, 25 ] ], "date-time": "2021-11-25T00:00:00Z", "timestamp": 1637798400000 } } ], "link": [ { "URL": "https://link.springer.com/content/pdf/10.1186/s13027-021-00406-y.pdf", "content-type": "application/pdf", "content-version": "vor", "intended-application": "text-mining" }, { "URL": "https://link.springer.com/article/10.1186/s13027-021-00406-y/fulltext.html", "content-type": "text/html", "content-version": "vor", "intended-application": "text-mining" }, { "URL": "https://link.springer.com/content/pdf/10.1186/s13027-021-00406-y.pdf", "content-type": "application/pdf", "content-version": "vor", "intended-application": "similarity-checking" } ], "member": "297", "original-title": [], "prefix": "10.1186", "published": { "date-parts": [ [ 2021, 11, 25 ] ] }, "published-online": { "date-parts": [ [ 2021, 11, 25 ] ] }, "published-print": { "date-parts": [ [ 2021, 12 ] ] }, "publisher": "Springer Science and Business Media LLC", "reference": [ { "author": "SH Woolf", "first-page": "123", "journal-title": "JAMA", "key": "406_CR1", "unstructured": "Woolf SH, Chapman DA, Lee JH. 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Please send us corrections, updates, or comments. c19early involves the extraction of 100,000+ datapoints from thousands of papers. Community updates help ensure high accuracy. Treatments and other interventions are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. FLCCC and WCH provide treatment protocols.
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