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      Potential benefits of incentive spirometry following a rib fracture: a propensity score analysis

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          ABSTRACT

          Objectives

          Incentive Spirometry is commonly used for respiratory recovery. The literature on incentive spirometry and its impact on patients with rib fracture is unclear and there are no recommendations regarding its use in the Emergency Department (ED), particularly in rib fracture patients, which are known for increasing the risk of pulmonary complication. Therefore, the objective of this study was to assess the use of incentive spirometry and to measure its impacts on delayed complications in patients discharged from the ED with confirmed rib fracture.

          Methods

          This is a planned sub-study of a prospective observational cohort recruited in 4 Canadians ED between November 2006 and May 2012. Non-admitted patients over 16 y.o. with at least one confirmed rib fracture on radiographs were included. Prescription of incentive spirometry was left to attending physician. Main outcomes were development of pneumonia, atelectasis, and hemothorax within 14 days. Propensity score matching analyses were performed.

          Results

          439 patients were included and 182 (41.5%) patients received incentive spirometry. There were 99 cases of hemothorax (22.6%), 103 cases of atelectasis (23.5%) and 4 cases of pneumonia (0.9%). The use of incentive spirometry was not protector for hemothorax [RR = 1.03 (0.66–1.64)] and atelectasis or pneumonia [RR = 1.07 (0.68–1.72)].

          Conclusions

          Our results suggest that unsupervised incentive spirometry use does not have a protective effect against delayed pulmonary complications after rib fracture. Further research should be conducted to assess the usefulness of incentive spirometry in specific injured population in the ED.

          Résumé

          Objectifs

          La spirométrie incitative est parfois prescrite en vue d'encourager le rétablissement de la fonction respiratoire. Toutefois, peut de littérature est disponible sur la spirométrie incitative et ses effets chez les patients avec fracture de côtes, et il n'existe pas de recommandation sur son utilisation au département des urgences (DU), tout particulièrement pour les fractures de côtes, qui sont reconnues pour accroître le risque de complications pulmonaires. Cette étude visait donc à évaluer l'utilisation de la spirométrie incitative et à mesurer son impact sur l'incidence de complications tardives chez les patients ayant été libéré de l'urgence après une confirmation de fracture de côtes.

          Méthode

          Il s'agit d'une sous-étude planifiée d'une étude observationnelle de cohorte prospective, qui a eu lieu dans 4 DU au Canada, entre novembre 2006 et mai 2012. Des patients âgés de 16 ans et plus, non hospitalisés, avec au moins une fracture de côte confirmée par radiographie ont été sélectionnés. La décision de prescrire la spirométrie incitative était laissée à la discrétion du médecin traitant. Les principaux résultats consistaient en l'apparition d'une pneumonie, d'atélectasie ou d'un hémothorax dans les 14 jours suivant le traumatisme. Des analyses d'appariement des coefficients de propension ont été réalisées.

          Résultats

          Un total de 439 patients ont participé à l’étude, dont 182 (41,5%) ont été reçu la spirométrie incitative. 99 cas d'hémothorax (22,6%), 103 cas d'atélectasie (23,5%) et 4 cas de pneumonie (0,9%) ont été observés. Nos résultats indiquent que la spirométrie incitative ne semble pas un moyen de protection contre l'hémothorax (risque relatif [RR] = 1,03 [0,66–1,64]) ni contre l'atélectasie ou la pneumonie (RR = 1,07 [0,68–1,72]).

          Conclusion

          Nos résultats suggèrent que la spirométrie incitative non supervisée n'offrirait pas d'effet protecteur contre l'apparition tardive de complications pulmonaires à la suite d'une fracture de côtes. D'autres recherches sont nécessaires afin de valider la pertinence de prescrire la spirométrie incitative au DU, chez certains groupes de blessés plus spécifiques.

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          Most cited references17

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          An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

          The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
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            • Article: not found

            Matching methods for causal inference: A review and a look forward.

            When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing well-matched samples of the original treated and control groups, thereby reducing bias due to the covariates. Since the 1970's, work on matching methods has examined how to best choose treated and control subjects for comparison. Matching methods are gaining popularity in fields such as economics, epidemiology, medicine, and political science. However, until now the literature and related advice has been scattered across disciplines. Researchers who are interested in using matching methods-or developing methods related to matching-do not have a single place to turn to learn about past and current research. This paper provides a structure for thinking about matching methods and guidance on their use, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.
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              Variable selection for propensity score models.

              Despite the growing popularity of propensity score (PS) methods in epidemiology, relatively little has been written in the epidemiologic literature about the problem of variable selection for PS models. The authors present the results of two simulation studies designed to help epidemiologists gain insight into the variable selection problem in a PS analysis. The simulation studies illustrate how the choice of variables that are included in a PS model can affect the bias, variance, and mean squared error of an estimated exposure effect. The results suggest that variables that are unrelated to the exposure but related to the outcome should always be included in a PS model. The inclusion of these variables will decrease the variance of an estimated exposure effect without increasing bias. In contrast, including variables that are related to the exposure but not to the outcome will increase the variance of the estimated exposure effect without decreasing bias. In very small studies, the inclusion of variables that are strongly related to the exposure but only weakly related to the outcome can be detrimental to an estimate in a mean squared error sense. The addition of these variables removes only a small amount of bias but can increase the variance of the estimated exposure effect. These simulation studies and other analytical results suggest that standard model-building tools designed to create good predictive models of the exposure will not always lead to optimal PS models, particularly in small studies.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                CJEM
                CJEM
                Cambridge University Press (CUP)
                1481-8035
                1481-8043
                July 2019
                February 12 2019
                July 2019
                : 21
                : 4
                : 464-467
                Article
                10.1017/cem.2018.492
                f401a708-7b11-496b-93d6-7696dd6fec0e
                © 2019

                https://www.cambridge.org/core/terms

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