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      Impact of intensive care unit admission during handover on mortality: propensity matched cohort study

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          ABSTRACT

          Objective:

          To investigate the impact of intensive care unit admission during medical handover on mortality.

          Methods:

          Post-hoc analysis of data extracted from a prior study aimed at addressing the impacts of intensive care unit readmission on clinical outcomes. This retrospective, single-center, propensity-matched cohort study was conducted in a 41-bed general open-model intensive care unit. Patients were assigned to one of two cohorts according to time of intensive care unit admission: Handover Group (intensive care unit admission between 6:30 am and 7:30 am or 6:30 pm and 7:30 pm) or Control Group (intensive care unit admission between 7:31 am and 6:29 pm or 7:31 pm and 6:29 am). Patients in the Handover Group were propensity-matched to patients in the Control Group at a 1:2 ratio.

          Results:

          A total of 6,650 adult patients were admitted to the intensive care unit between June 1 st 2013 and May 31 st 2015. Following exclusion of non-eligible participants, 5,779 patients (389; 6.7% and 5,390; 93.3%, Handover and Control Group) were deemed eligible for propensity score matching. Of these, 1,166 were successfully matched (389; 33.4% and 777; 66.6%, Handover and Control Group). Following propensity-score matching, intensive care unit admission during handover was not associated with increased risk of intensive care unit (OR: 1.40; 95%CI: 0.92-2.11; p=0.113) or in-hospital (OR: 1.23; 95%CI: 0.85-1.75; p=0.265) mortality.

          Conclusion:

          Intensive care unit admission during medical handover did not affect in-hospital mortality in this propensity-matched, single-center cohort study.

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          Most cited references25

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          Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies

          In a study comparing the effects of two treatments, the propensity score is the probability of assignment to one treatment conditional on a subject's measured baseline covariates. Propensity-score matching is increasingly being used to estimate the effects of exposures using observational data. In the most common implementation of propensity-score matching, pairs of treated and untreated subjects are formed whose propensity scores differ by at most a pre-specified amount (the caliper width). There has been a little research into the optimal caliper width. We conducted an extensive series of Monte Carlo simulations to determine the optimal caliper width for estimating differences in means (for continuous outcomes) and risk differences (for binary outcomes). When estimating differences in means or risk differences, we recommend that researchers match on the logit of the propensity score using calipers of width equal to 0.2 of the standard deviation of the logit of the propensity score. When at least some of the covariates were continuous, then either this value, or one close to it, minimized the mean square error of the resultant estimated treatment effect. It also eliminated at least 98% of the bias in the crude estimator, and it resulted in confidence intervals with approximately the correct coverage rates. Furthermore, the empirical type I error rate was approximately correct. When all of the covariates were binary, then the choice of caliper width had a much smaller impact on the performance of estimation of risk differences and differences in means. Copyright © 2010 John Wiley & Sons, Ltd.
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            SAPS 3—From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission

            Objective To develop a model to assess severity of illness and predict vital status at hospital discharge based on ICU admission data. Design Prospective multicentre, multinational cohort study. Patients and setting A total of 16,784 patients consecutively admitted to 303 intensive care units from 14 October to 15 December 2002. Measurements and results ICU admission data (recorded within ±1 h) were used, describing: prior chronic conditions and diseases; circumstances related to and physiologic derangement at ICU admission. Selection of variables for inclusion into the model used different complementary strategies. For cross-validation, the model-building procedure was run five times, using randomly selected four fifths of the sample as a development- and the remaining fifth as validation-set. Logistic regression methods were then used to reduce complexity of the model. Final estimates of regression coefficients were determined by use of multilevel logistic regression. Variables selection and weighting were further checked by bootstraping (at patient level and at ICU level). Twenty variables were selected for the final model, which exhibited good discrimination (aROC curve 0.848), without major differences across patient typologies. Calibration was also satisfactory (Hosmer-Lemeshow goodness-of-fit test Ĥ=10.56, p=0.39, Ĉ=14.29, p=0.16). Customised equations for major areas of the world were computed and demonstrate a good overall goodness-of-fit. Conclusions The SAPS 3 admission score is able to predict vital status at hospital discharge with use of data recorded at ICU admission. Furthermore, SAPS 3 conceptually dissociates evaluation of the individual patient from evaluation of the ICU and thus allows them to be assessed at their respective reference levels. Electronic Supplementary Material Electronic supplementary material is included in the online fulltext version of this article and accessible for authorised users: http://dx.doi.org/10.1007/s00134-005-2763-5
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              Circulatory Shock

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                Author and article information

                Journal
                Einstein (Sao Paulo)
                Einstein (Sao Paulo)
                eins
                Einstein
                Instituto Israelita de Ensino e Pesquisa Albert Einstein
                1679-4508
                2317-6385
                10 June 2021
                2021
                : 19
                : eAO5748
                Affiliations
                [1 ] orgnameHospital Israelita Albert Einstein São Paulo SP Brazil originalHospital Israelita Albert Einstein, São Paulo, SP, Brazil.
                [2 ] orgnameHospital Israelita Albert Einstein orgdiv1Hospital Municipal Dr. Moysés Deutsch São Paulo SP Brazil originalHospital Municipal Dr. Moysés Deutsch; Hospital Israelita Albert Einstein, São Paulo, SP, Brazil.
                [1 ] São Paulo SP Brasil originalHospital Israelita Albert Einstein, São Paulo, SP, Brasil.
                [2 ] São Paulo SP Brasil originalHospital Municipal Dr. Moysés Deutsch; Hospital Israelita Albert Einstein, São Paulo, SP, Brasil.
                Author notes
                Corresponding author: Thiago Domingos Corrêa, Avenida Albert Einstein, 627/701, 5th floor – Morumbi Zip code: 05652-900 – São Paulo, SP, Brazil. Phone: (55 11) 2151-1500 E-mail: thiago.correa@ 123456einstein.br

                Conflict of interest:

                none.

                Autor correspondente: Thiago Domingos Corrêa, Avenida Albert Einstein, 627/701, 5° andar – Morumbi CEP: 05652-900 – São Paulo, SP, Brasil. Tel.: (11) 2151-1500 E-mail: thiago.correa@ 123456einstein.br

                Conflitos de interesse:

                não há

                Author information
                https://orcid.org/0000-0002-1010-3711
                https://orcid.org/0000-0002-7255-2926
                https://orcid.org/0000-0002-0522-7445
                https://orcid.org/0000-0002-2698-7873
                https://orcid.org/0000-0002-5852-7150
                https://orcid.org/0000-0003-1822-1568
                https://orcid.org/0000-0001-9546-3910
                Article
                00213
                10.31744/einstein_journal/2021AO5748
                8225264
                34161436
                4faae7a0-605d-4f45-a216-5003c443164c

                This content is licensed under a Creative Commons Attribution 4.0 International License.

                History
                : 15 April 2020
                : 06 December 2020
                Page count
                Figures: 4, Tables: 8, Equations: 0, References: 22
                Categories
                Original Article

                patient handoff,patient safety,patient outcome assessment,intensive care units/statistics & numerical data,communication,patient readmission,patient discharge,hospital mortality,health resources/statistics & numerical data

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