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      Association of time of day and extubation success in very low birthweight infants: a multicenter cohort study

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          Abstract

          Objective

          To determine the association of overnight extubation (OE) with extubation success.

          Study design

          Retrospective cohort study in three NICUs from 2016 to 2020. Infants without congenital anomalies, less than 1500 grams at birth, who were ventilated and received an extubation attempt were included. Primary exposure was OE (7:00 pm–6:59 am) and outcome was extubation success defined as no mechanical ventilation for at least 7 days after extubation.

          Results

          A total of 76/379 (20%) infants received OE. Infants extubated during the daytime were older and had higher illness severity markers. Extubation success rates did not differ for overnight (57/76, 75%) versus daytime extubations (231/303, 76%) after adjusting for confounders (adjusted relative risk 0.95, 95% CI 0.82–1.11).

          Conclusion

          Though infants in our cohort undergoing daytime and OE were dissimilar, extubation success rates did not differ. Larger multicenter studies are needed to test our findings and identify markers of extubation readiness in preterm infants.

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          Most cited references27

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          A modified poisson regression approach to prospective studies with binary data.

          G Zou (2004)
          Relative risk is usually the parameter of interest in epidemiologic and medical studies. In this paper, the author proposes a modified Poisson regression approach (i.e., Poisson regression with a robust error variance) to estimate this effect measure directly. A simple 2-by-2 table is used to justify the validity of this approach. Results from a limited simulation study indicate that this approach is very reliable even with total sample sizes as small as 100. The method is illustrated with two data sets.
          Article 0 comments Cited 1675 times – based on 0 reviews     Review now
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            Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples

            Peter C Austin (2009)
            The propensity score is a subject's probability of treatment, conditional on observed baseline covariates. Conditional on the true propensity score, treated and untreated subjects have similar distributions of observed baseline covariates. Propensity-score matching is a popular method of using the propensity score in the medical literature. Using this approach, matched sets of treated and untreated subjects with similar values of the propensity score are formed. Inferences about treatment effect made using propensity-score matching are valid only if, in the matched sample, treated and untreated subjects have similar distributions of measured baseline covariates. In this paper we discuss the following methods for assessing whether the propensity score model has been correctly specified: comparing means and prevalences of baseline characteristics using standardized differences; ratios comparing the variance of continuous covariates between treated and untreated subjects; comparison of higher order moments and interactions; five-number summaries; and graphical methods such as quantile–quantile plots, side-by-side boxplots, and non-parametric density plots for comparing the distribution of baseline covariates between treatment groups. We describe methods to determine the sampling distribution of the standardized difference when the true standardized difference is equal to zero, thereby allowing one to determine the range of standardized differences that are plausible with the propensity score model having been correctly specified. We highlight the limitations of some previously used methods for assessing the adequacy of the specification of the propensity-score model. In particular, methods based on comparing the distribution of the estimated propensity score between treated and untreated subjects are uninformative. Copyright © 2009 John Wiley & Sons, Ltd.
            Article 0 comments Cited 1251 times – based on 0 reviews     Review now
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              Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies

              Peter C Austin, Elizabeth A. Stuart (2015)
              The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in which we found that the use of IPTW has increased rapidly in recent years, but that in the most recent year, a majority of studies did not formally examine whether weighting balanced measured covariates between treatment groups. We then proceed to describe a suite of quantitative and qualitative methods that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample. The quantitative methods use the weighted standardized difference to compare means, prevalences, higher‐order moments, and interactions. The qualitative methods employ graphical methods to compare the distribution of continuous baseline covariates between treated and control subjects in the weighted sample. Finally, we illustrate the application of these methods in an empirical case study. We propose a formal set of balance diagnostics that contribute towards an evolving concept of ‘best practice’ when using IPTW to estimate causal treatment effects using observational data. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
              Article 0 comments Cited 937 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                L. Dupree Hatch III:
                ORCID: http://orcid.org/0000-0002-8735-1887
                Leon.d.hatch@vumc.org
                Journal
                Journal ID (nlm-ta): J Perinatol
                Journal ID (iso-abbrev): J Perinatol
                Title: Journal of Perinatology
                Publisher: Nature Publishing Group US (New York )
                ISSN (Print): 0743-8346
                ISSN (Electronic): 1476-5543
                Publication date (Electronic): 24 July 2021
                Pages: 1-5
                Affiliations
                [1 ]GRID grid.259870.1, ISNI 0000 0001 0286 752X, Meharry Medical College, ; Nashville, TN USA
                [2 ]GRID grid.412807.8, ISNI 0000 0004 1936 9916, Division of Neonatology, Department of Pediatrics, , Vanderbilt University Medical Center, ; Nashville, TN USA
                [3 ]GRID grid.152326.1, ISNI 0000 0001 2264 7217, Vanderbilt University School of Nursing, ; Nashville, TN USA
                [4 ]GRID grid.490236.a, ISNI 0000 0004 0454 2149, Department of Pediatrics, Jackson-Madison County General Hospital, ; Jackson, TN USA
                [5 ]GRID grid.412807.8, ISNI 0000 0004 1936 9916, Center for Child Health Policy, , Vanderbilt University Medical Center, ; Nashville, TN USA
                [6 ]GRID grid.412807.8, ISNI 0000 0004 1936 9916, Critical Illness, Brain Dysfunction, and Survivorship (CIBS) Center, , Vanderbilt University Medical Center, ; Nashville, TN USA
                Author information
                http://orcid.org/0000-0001-9703-5266
                http://orcid.org/0000-0002-8735-1887
                Article
                Publisher ID: 1168
                DOI: 10.1038/s41372-021-01168-6
                PMC ID: 8308074
                PubMed ID: 34304243
                SO-VID: 2370861c-9ea7-414b-8cb6-8d1787ba6f13
                Copyright © © The Author(s), under exclusive licence to Springer Nature America, Inc. 2021
                License:

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                Date received : 15 November 2020
                Date revision received : 7 July 2021
                Date accepted : 14 July 2021
                Funding
                Funded by: Vanderbilt-Meharry James P. Carter Scholars program
                Funded by: FundRef https://doi.org/10.13039/100001130, Gerber Foundation;
                Categories
                Subject: Article

                ScienceOpen disciplines: Pediatrics
                Keywords: epidemiology,outcomes research
                Data availability:
                ScienceOpen disciplines: Pediatrics
                Keywords: epidemiology, outcomes research

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