{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T04:32:28Z","timestamp":1782880348992,"version":"3.54.5"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Infect Dis"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Causal inference from observational studies is an area of interest to researchers, advancing rapidly over the years and with it, the methods for causal effect estimation. Among them, Targeted Maximum Likelihood estimation (TMLE) possesses arguably the most outstanding statistical properties, and with no outright treatment for COVID-19, there was an opportunity to estimate the causal effect of dexamethasone versus hydrocortisone upon the neutrophil-lymphocyte ratio (NLR), a vital indicator for disease progression among critically ill COVID-19 patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>TMLE variations were used in the analysis. Super Learner (SL), Bayesian Additive Regression Trees (BART) and parametric regression (PAR) were implemented to estimate the average treatment effect (ATE).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The study had 168 participants, 128 on dexamethasone and 40 on hydrocortisone. The mean causal difference in NLR on day 5; ATE [95% CI]: from SL-TMLE was \u2212\u20090.309 [-3.800, 3.182] BART-TMLE 0.246 [-3.399, 3.891] and PAR-TMLE 1.245 [-1.882, 4372]. The ATE of dexamethasone versus hydrocortisone on NLR was not statistically significant since the confidence interval included zero.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The effect of dexamethasone is not significantly different from that of hydrocortisone on NLR in critically ill COVID-19 patients admitted to ICU. This implies that the difference in effect on NLR between the two drugs is due to random chance. TMLE remains an outstanding approach for causal analysis of observational studies with the ability to be augmented with multiple prediction approaches.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12879-024-10112-w","type":"journal-article","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T18:36:25Z","timestamp":1732818985000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Estimating the causal effect of dexamethasone versus hydrocortisone on the neutrophil- lymphocyte ratio in critically ill COVID-19 patients from Tygerberg Hospital ICU using TMLE method"],"prefix":"10.1186","volume":"24","author":[{"given":"Ivan Nicholas","family":"Nkuhairwe","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tonya Marianne","family":"Esterhuizen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lovemore Nyasha","family":"Sigwadhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacques Lukenze","family":"Tamuzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rhoderick","family":"Machekano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter S.","family":"Nyasulu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"10112_CR1","doi-asserted-by":"publisher","unstructured":"Rothman KJ, Greenland S. Causation and Causal Inference in Epidemiology. 95. Epub ahead of print 10 October 2011. https:\/\/doi.org\/10.2105\/AJPH.2004.059204","DOI":"10.2105\/AJPH.2004.059204"},{"key":"10112_CR2","unstructured":"Hern\u00e1n M, Robins J. CausalInference: What If., https:\/\/cdn1.sph.harvard.edu\/wp-content\/uploads\/sites\/1268\/2021\/03\/ciwhatif_hernanrobins_30mar21.pdf (2020)."},{"key":"10112_CR3","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1093\/aje\/kww165","volume":"185","author":"MS Schuler","year":"2017","unstructured":"Schuler MS, Rose S. Targeted maximum likelihood estimation for causal inference in observational studies. Am J Epidemiol. 2017;185:65\u201373.","journal-title":"Am J Epidemiol"},{"key":"10112_CR4","doi-asserted-by":"publisher","unstructured":"Pinz\u00f3n MA, Ortiz S, Holgu\u00edn H et al. Dexamethasone vs methylprednisolone high dose for Covid-19 pneumonia. PLoS One; 16. Epub ahead of print 1 May 2021. https:\/\/doi.org\/10.1371\/journal.pone.0252057","DOI":"10.1371\/journal.pone.0252057"},{"key":"10112_CR5","first-page":"550","volume":"111","author":"EM Du Plessis","year":"2021","unstructured":"Du Plessis EM, Lalla U, Allwood BW, et al. Corticosteroids in critical COVID-19: are all corticosteroids equal? South Afr Med J. 2021;111:550\u20133.","journal-title":"South Afr Med J"},{"key":"10112_CR6","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1016\/S0140-6736(20)30628-0","volume":"395","author":"P Mehta","year":"2020","unstructured":"Mehta P, McAuley DF, Brown M, et al. COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet. 2020;395:1033\u20134.","journal-title":"Lancet"},{"key":"10112_CR7","doi-asserted-by":"publisher","first-page":"1793","DOI":"10.1016\/j.chest.2020.11.050","volume":"159","author":"H Chen","year":"2021","unstructured":"Chen H, Xie J, Su N, et al. Corticosteroid therapy is Associated with Improved Outcome in critically ill patients with COVID-19 with Hyperinflammatory phenotype. Chest. 2021;159:1793\u2013802.","journal-title":"Chest"},{"key":"10112_CR8","doi-asserted-by":"publisher","first-page":"1553","DOI":"10.1080\/14787210.2021.1933437","volume":"19","author":"R Crisan Dabija","year":"2021","unstructured":"Crisan Dabija R, Antohe I, Trofor A, et al. Corticosteroids in SARS-COV2 infection: certainties and uncertainties in clinical practice. Expert Rev Anti Infect Ther. 2021;19:1553\u201362.","journal-title":"Expert Rev Anti Infect Ther"},{"key":"10112_CR9","doi-asserted-by":"publisher","unstructured":"Welte T, Ambrose LJ, Sibbring GC et al. Current evidence for COVID-19 therapies: a systematic literature review. Eur Respir Rev; 30. Epub ahead of print 31 March 2021. https:\/\/doi.org\/10.1183\/16000617.0384-2020","DOI":"10.1183\/16000617.0384-2020"},{"key":"10112_CR10","unstructured":"NHS, Corticosteroids | NHS. inform. NHS webpage, https:\/\/www.nhsinform.scot\/tests-and-treatments\/medicines-and-medical-aids\/types-of-medicine\/corticosteroids (2022, accessed 29 November 2022)."},{"key":"10112_CR11","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1111\/ijlh.13374","volume":"43","author":"L Liu","year":"2021","unstructured":"Liu L, Zheng Y, Cai L, et al. Neutrophil-to-lymphocyte ratio, a critical predictor for assessment of disease severity in patients with COVID-19. Int J Lab Hematol. 2021;43:329\u201335.","journal-title":"Int J Lab Hematol"},{"key":"10112_CR12","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.cmet.2021.01.002","volume":"33","author":"J Cai","year":"2021","unstructured":"Cai J, Li HH, Zhang C, et al. The neutrophil-to-lymphocyte ratio determines clinical efficacy of corticosteroid therapy in patients with COVID-19. Cell Metab. 2021;33:258\u2013e2693.","journal-title":"Cell Metab"},{"key":"10112_CR13","first-page":"e6","volume":"81","author":"Y Liu","year":"2020","unstructured":"Liu Y, Du X, Chen J, et al. Neutrophil-to-lymphocyte ratio as an independent risk factor for mortality in hospitalized patients with COVID-19. J Infect. 2020;81:e6\u201312.","journal-title":"J Infect"},{"key":"10112_CR14","doi-asserted-by":"publisher","unstructured":"Li X, Liu C, Mao Z et al. Predictive values of neutrophil-to-lymphocyte ratio on disease severity and mortality in COVID-19 patients: a systematic review and meta-analysis. Crit Care; 24. Epub ahead of print 1 December 2020. https:\/\/doi.org\/10.1186\/s13054-020-03374-8","DOI":"10.1186\/s13054-020-03374-8"},{"key":"10112_CR15","unstructured":"CCSA. Allocation of Scarce Critical Care Resources During the COVID-19 Public Health Emergency in South Africa, https:\/\/www.criticalcare.org.za. (2020, accessed 27 January 2023)."},{"key":"10112_CR16","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.ijregi.2022.01.012","volume":"2","author":"AE Zemlin","year":"2022","unstructured":"Zemlin AE, Allwood B, Erasmus RT, et al. Prognostic value of biochemical parameters among severe COVID-19 patients admitted to an intensive care unit of a tertiary hospital in South Africa. IJID Reg. 2022;2:191\u20137.","journal-title":"IJID Reg"},{"key":"10112_CR17","doi-asserted-by":"publisher","first-page":"e0275832","DOI":"10.1371\/journal.pone.0275832","volume":"17","author":"ZC Chapanduka","year":"2022","unstructured":"Chapanduka ZC, Abdullah I, Allwood B, et al. Haematological predictors of poor outcome among COVID-19 patients admitted to an intensive care unit of a tertiary hospital in South Africa. PLoS ONE. 2022;17:e0275832.","journal-title":"PLoS ONE"},{"key":"10112_CR18","doi-asserted-by":"crossref","unstructured":"van der Laan MJ. Rose S. Targeted Learning - preface. 2011.","DOI":"10.1007\/978-1-4419-9782-1"},{"key":"10112_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12874-023-01835-6","volume":"23","author":"J Ren","year":"2023","unstructured":"Ren J, Cislo P, Cappelleri JC, et al. Comparing g-computation, propensity score-based weighting, and targeted maximum likelihood estimation for analyzing externally controlled trials with both measured and unmeasured confounders: a simulation study. BMC Med Res Methodol. 2023;23:1\u201311.","journal-title":"BMC Med Res Methodol"},{"key":"10112_CR20","doi-asserted-by":"crossref","unstructured":"Rosenblum M, Laan MJ, Van Der. Estimating Causal Eff Using Target Maximum Likelihood Estimation. 2010; 1\u20136.","DOI":"10.2202\/1557-4679.1181"},{"key":"10112_CR21","unstructured":"Van der Laan M, Coyle J, Hejazi N et al. Introduction | Targeted Learning in R. GitHub repository, https:\/\/tlverse.org\/tlverse-handbook\/introduction.html (2022, accessed 17 August 2022)."},{"key":"10112_CR22","doi-asserted-by":"publisher","unstructured":"Jeremy C, Nima H, Ivana M. Rachael POS. sl3: Pipelines for Machine Learning and Super Learning. Epub ahead of print 2024. https:\/\/doi.org\/10.5281\/zenodo.1342293","DOI":"10.5281\/zenodo.1342293"},{"key":"10112_CR23","doi-asserted-by":"publisher","unstructured":"Phillips RV, Van Der Laan MJ, Lee H et al. Practical considerations for specifying a super learner, https:\/\/doi.org\/10.48550\/arXiv.2204.06139 (2022).","DOI":"10.48550\/arXiv.2204.06139"},{"key":"10112_CR24","doi-asserted-by":"crossref","unstructured":"Dorie V, Hill JR. Causal Inference using Bayesian Additive Regression Trees. Package \u2018bartCause\u2019, https:\/\/search.r-project.org\/CRAN\/refmans\/bartCause\/html\/bartc.html (2020, accessed 30 November 2022).","DOI":"10.32614\/CRAN.package.bartCause"},{"key":"10112_CR25","unstructured":"Karim E, Frank H. Chapter 6 TMLE | R Guide for TMLE in Medical Research, https:\/\/ehsanx.github.io\/TMLEworkshop\/tmle.html#tmle-steps (2021, accessed 30 November 2022)."},{"key":"10112_CR26","doi-asserted-by":"publisher","first-page":"2530","DOI":"10.1002\/sim.7628","volume":"37","author":"MA Luque-Fernandez","year":"2018","unstructured":"Luque-Fernandez MA, Schomaker M, Rachet B, et al. Targeted maximum likelihood estimation for a binary treatment: a tutorial. Stat Med. 2018;37:2530\u201346.","journal-title":"Stat Med"},{"key":"10112_CR27","doi-asserted-by":"publisher","unstructured":"Jeremy C. tmle3: the Extensible TMLE Framework. Epub ahead of print 2024. https:\/\/doi.org\/10.5281\/zenodo.4603358","DOI":"10.5281\/zenodo.4603358"},{"key":"10112_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v045.i03","volume":"45","author":"S van Buuren","year":"2011","unstructured":"van Buuren S, Groothuis-Oudshoorn K. Mice: multivariate imputation by chained equations in R. J Stat Softw. 2011;45:1\u201367.","journal-title":"J Stat Softw"},{"key":"10112_CR29","unstructured":"R Core Team. R: A Language and Environment for Statistical Computing, https:\/\/www.r-project.org\/ (2024, accessed 4 June 2024)."},{"key":"10112_CR30","unstructured":"Posit team. RStudio: Integrated Development Environment for R, http:\/\/www.posit.co\/ (2024, accessed 4 June 2024)."},{"key":"10112_CR31","unstructured":"CSRF. Different Steroid Replacement Medications - CSRF - Cushing\u2019s Support & Research Foundation, https:\/\/csrf.net\/doctors-answers\/steroid-replacement-qa\/different-steroid-replacement-medications\/ (accessed 18 February 2023)."},{"key":"10112_CR32","doi-asserted-by":"publisher","unstructured":"Lo YT, Lim VY, Ng M et al. A Prognostic Model Using Post-Steroid Neutrophil-Lymphocyte Ratio Predicts Overall Survival in Primary Central Nervous System Lymphoma. Cancers (Basel); 14. Epub ahead of print 2022. https:\/\/doi.org\/10.3390\/cancers14071818","DOI":"10.3390\/cancers14071818"},{"key":"10112_CR33","first-page":"43","volume":"34","author":"V Dorie","year":"2017","unstructured":"Dorie V, Hill J, Shalit U, et al. Automated versus do-it-yourself methods for causal inference: lessons learned from a data analysis competition. Stat Sci. 2017;34:43\u201368.","journal-title":"Stat Sci"},{"key":"10112_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12874-017-0414-5","volume":"17","author":"TR Sullivan","year":"2017","unstructured":"Sullivan TR, Lee KJ, Ryan P, et al. Multiple imputation for handling missing outcome data when estimating the relative risk. BMC Med Res Methodol. 2017;17:1\u201310.","journal-title":"BMC Med Res Methodol"},{"key":"10112_CR35","unstructured":"Berkeley UC, Gruber S, Van Der Laan MJ. Targeted Maximum Likelihood Estimation: A Gentle Introduction. UC Berkley Div Biostat Work Pap Ser, http:\/\/biostats.bepress.com\/ucbbiostat\/paper252 (2009, accessed 25 August 2022)."}],"container-title":["BMC Infectious Diseases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12879-024-10112-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12879-024-10112-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12879-024-10112-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T19:03:26Z","timestamp":1732820606000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcinfectdis.biomedcentral.com\/articles\/10.1186\/s12879-024-10112-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,29]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["10112"],"URL":"https:\/\/doi.org\/10.1186\/s12879-024-10112-w","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3443461\/v1","asserted-by":"object"}]},"ISSN":["1471-2334"],"issn-type":[{"value":"1471-2334","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,29]]},"assertion":[{"value":"13 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study received ethical clearance from the Health Research Ethics Committee of the Faculty of Medicine and Health Sciences, Stellenbosch University. Reference Number S22\/10\/192_Sub Study N20\/04\/002_COVID-19. Patient confidentiality was maintained by marking data with a unique patient identifying number. Furthermore, all study participants provided informed consent to participate.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1365"}}