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      Hypothermia related to continuous renal replacement therapy: incidence and associated factors Translated title: Hipotermia relacionada à terapia renal substitutiva contínua: incidência e fatores associados

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          Abstract

          Objective

          To evaluate the incidence of hypothermia in patients undergoing continuous renal replacement therapy in the intensive care unit. As secondary objectives, we determined associated factors and compared the occurrence of hypothermia between two modalities of continuous renal replacement therapy.

          Methods

          A prospective cohort study was conducted with adult patients who were admitted to a clinical-surgical intensive care unit and underwent continuous renal replacement therapy in a high-complexity public university hospital in southern Brazil from April 2017 to July 2018. Hypothermia was defined as a body temperature ≤ 35ºC. The patients included in the study were followed for the first 48 hours of continuous renal replacement therapy. The researchers collected data from medical records and continuous renal replacement therapy records.

          Results

          A total of 186 patients were equally distributed between two types of continuous renal replacement therapy: hemodialysis and hemodiafiltration. The incidence of hypothermia was 52.7% and was higher in patients admitted for shock (relative risk of 2.11; 95%CI 1.21 - 3.69; p = 0.009) and in those who underwent hemodiafiltration with heating in the return line (relative risk of 1.50; 95%CI 1.13 - 1.99; p = 0.005).

          Conclusion

          Hypothermia in critically ill patients with continuous renal replacement therapy is frequent, and the intensive care team should be attentive, especially when there are associated risk factors.

          Translated abstract

          Objetivo

          Avaliar a incidência de hipotermia em pacientes em terapia renal substitutiva contínua na unidade de terapia intensiva. Como objetivos secundários, determinar fatores associados e comparar a ocorrência de hipotermia entre duas modalidades de terapia renal substitutiva contínua.

          Métodos

          Estudo de coorte, prospectivo, realizado com pacientes adultos internados em uma unidade de terapia intensiva clínico-cirúrgica, que realizaram terapia renal substitutiva contínua em um hospital universitário público de alta complexidade do Sul do Brasil, de abril de 2017 a julho de 2018. A hipotermia foi definida como queda da temperatura corporal ≤ 35ºC. Os pacientes incluídos no estudo foram acompanhados nas 48 horas iniciais de terapia renal substitutiva contínua. Os dados foram coletados pelos pesquisadores por meio da consulta aos prontuários e às fichas de registro das terapias renais substitutivas contínuas.

          Resultados

          Foram avaliados 186 pacientes distribuídos igualmente entre dois tipos de terapia renal substitutiva contínua: hemodiálise e hemodiafiltração. A incidência de hipotermia foi de 52,7%, sendo maior nos pacientes que internaram por choque (risco relativo de 2,11; IC95% 1,21 - 3,69; p = 0,009) e nos que fizeram hemodiafiltração com aquecimento por mangueira na linha de retorno (risco relativo de 1,50; IC95% 1,13 - 1,99; p = 0,005).

          Conclusão

          A hipotermia em pacientes críticos com terapia renal substitutiva contínua é frequente, e a equipe intensivista deve estar atenta, em especial quando há fatores de risco associados.

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          Most cited references29

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          The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3).

          Definitions of sepsis and septic shock were last revised in 2001. Considerable advances have since been made into the pathobiology (changes in organ function, morphology, cell biology, biochemistry, immunology, and circulation), management, and epidemiology of sepsis, suggesting the need for reexamination.
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            A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study.

            To develop and validate a new Simplified Acute Physiology Score, the SAPS II, from a large sample of surgical and medical patients, and to provide a method to convert the score to a probability of hospital mortality. The SAPS II and the probability of hospital mortality were developed and validated using data from consecutive admissions to 137 adult medical and/or surgical intensive care units in 12 countries. The 13,152 patients were randomly divided into developmental (65%) and validation (35%) samples. Patients younger than 18 years, burn patients, coronary care patients, and cardiac surgery patients were excluded. Vital status at hospital discharge. The SAPS II includes only 17 variables: 12 physiology variables, age, type of admission (scheduled surgical, unscheduled surgical, or medical), and three underlying disease variables (acquired immunodeficiency syndrome, metastatic cancer, and hematologic malignancy). Goodness-of-fit tests indicated that the model performed well in the developmental sample and validated well in an independent sample of patients (P = .883 and P = .104 in the developmental and validation samples, respectively). The area under the receiver operating characteristic curve was 0.88 in the developmental sample and 0.86 in the validation sample. The SAPS II, based on a large international sample of patients, provides an estimate of the risk of death without having to specify a primary diagnosis. This is a starting point for future evaluation of the efficiency of intensive care units.
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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
                Rev Bras Ter Intensiva
                Rev Bras Ter Intensiva
                rbti
                Revista Brasileira de Terapia Intensiva
                Associação de Medicina Intensiva Brasileira - AMIB
                0103-507X
                1982-4335
                Jan-Mar 2021
                Jan-Mar 2021
                : 33
                : 1
                : 111-118
                Affiliations
                [1 ] Intensive Care Center, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul - Porto Alegre (RS), Brazil.
                Author notes
                Corresponding author: Cássia Maria Frediani Morsch, Centro de Tratamento Intensivo, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul , Rua Ramiro Barcelos, 2.350, Zip code: 90035-903 - Porto Alegre (RS), Brazil. E-mail: cmorsch@ 123456hcpa.edu.br
                Author information
                http://orcid.org/0000-0001-8227-2197
                http://orcid.org/0000-0001-5856-769X
                Article
                10.5935/0103-507X.20210012
                8075327
                33886860
                a412e292-b932-410c-974a-1a3f1cd71b10

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 May 2019
                : 28 May 2020
                Categories
                Original Article

                renal dialysis,hemodiafiltration,hypothermia,incidence,risk factors,intensive care units,diálise renal,hemodiafiltração,hipotermia,incidência,fatores de risco,unidades de terapia intensiva

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