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      Trends in Incidence and Outcomes of Cardiac Arrest Occurring in Swedish ICUs

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          Abstract

          Objective:

          To determine temporal trends in the incidence of cardiac arrest occurring in the ICU (ICU-CA) and its associated long-term mortality.

          Design:

          Retrospective observational study.

          Setting:

          Swedish ICUs, between 2011 and 2017.

          Patients:

          Adult patients (≥18 yr old) recorded in the Swedish Intensive Care Registry (SIR).

          Interventions:

          None.

          Measurements and Main Results:

          ICU-CA was defined as a first episode of cardiopulmonary resuscitation and/or defibrillation following an ICU admission, as recorded in SIR or the Swedish Cardiopulmonary Resuscitation Registry. Annual adjusted ICU-CA incidence trend (all admissions) was estimated using propensity score-weighted analysis. Six-month mortality trends (first admissions) were assessed using multivariable mixed-effects logistic regression. Analyses were adjusted for pre-admission characteristics (sex, age, socioeconomic status, comorbidities, medications, and healthcare utilization), illness severity on ICU admission, and admitting unit. We included 231,427 adult ICU admissions. Crude ICU-CA incidence was 16.1 per 1,000 admissions, with no significant annual trend in the propensity score-weighted analysis. Among 186,530 first admissions, crude 6-month mortality in ICU-CA patients was 74.7% (95% CI, 70.1–78.9) in 2011 and 68.8% (95% CI, 64.4–73.0) in 2017. When controlling for multiple potential confounders, the adjusted 6-month mortality odds of ICU-CA patients decreased by 6% per year (95% CI, 2–10). Patients admitted after out-of-hospital or in-hospital cardiac arrest had the highest ICU-CA incidence (136.1/1,000) and subsequent 6-month mortality (76.0% [95% CI, 73.6–78.4]).

          Conclusions:

          In our nationwide Swedish cohort, the adjusted incidence of ICU-CA remained unchanged between 2011 and 2017. More than two-thirds of patients with ICU-CA did not survive to 6 months following admission, but a slight improvement appears to have occurred over time.

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          Most cited references35

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          The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.

          Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalisability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September, 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles.18 items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies.A detailed explanation and elaboration document is published separately and is freely available on the websites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE statement will contribute to improving the quality of reporting of observational studies
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            Is Open Access

            The Swedish cause of death register

            Sweden has a long tradition of recording cause of death data. The Swedish cause of death register is a high quality virtually complete register of all deaths in Sweden since 1952. Although originally created for official statistics, it is a highly important data source for medical research since it can be linked to many other national registers, which contain data on social and health factors in the Swedish population. For the appropriate use of this register, it is fundamental to understand its origins and composition. In this paper we describe the origins and composition of the Swedish cause of death register, set out the key strengths and weaknesses of the register, and present the main causes of death across age groups and over time in Sweden. This paper provides a guide and reference to individuals and organisations interested in data from the Swedish cause of death register. Electronic supplementary material The online version of this article (doi:10.1007/s10654-017-0316-1) contains supplementary material, which is available to authorized users.
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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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                Author and article information

                Journal
                Critical Care Medicine
                Ovid Technologies (Wolters Kluwer Health)
                0090-3493
                September 25 2023
                Affiliations
                [1 ]Perioperative Medicine and Intensive Care, Karolinska University Hospital, Stockholm, Sweden.
                [2 ]Section of Anesthesiology and Intensive Care Medicine, Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.
                [3 ]Department of Clinical Science and Education, South General Hospital, Karolinska Institutet, Stockholm, Sweden.
                [4 ]Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
                [5 ]Department of Emergency Care and Services, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
                [6 ]Medical Unit Acute/Emergency Department, Karolinska University Hospital, Stockholm, Sweden.
                [7 ]Division of Clinical Medicine, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
                [8 ]Department of Anesthesiology and Intensive Care, South General Hospital, Stockholm, Sweden.
                Article
                10.1097/CCM.0000000000006067
                b33d0bd2-c1ac-42dd-8192-81cf39aa88aa
                © 2023
                History

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