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      ICU-Acquired Hypernatremia Is Associated with Persistent Inflammation, Immunosuppression and Catabolism Syndrome

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          Abstract

          Developing hypernatremia while on intensive care unit (ICU) is a common problem with various undesirable effects. A link to persistent inflammation, immunosuppression and catabolism syndrome (PICS) can be established in two ways. On the one hand, hypernatremia can lead to inflammation and catabolism via hyperosmolar cell stress, and on the other, profound catabolism can lead to hypernatremia via urea-induced osmotic diuresis. In this retrospective single-center study, we examined 115 patients with prolonged ICU stays (≥14 days) and sufficient renal function. Depending on their serum sodium concentrations between ICU day 7 and 21, allocation to a hypernatremic (high) and a nonhypernatremic group (low) took place. Distinct signs of PICS were detectable within the complete cohort. Thirty-three of them (28.7%) suffered from ICU-acquired hypernatremia, which was associated with explicitly higher signs of inflammation and ongoing catabolism as well as a prolonged ICU length of stay. Catabolism was discriminated better by the urea generation rate and the urea-to-creatinine ratio than by serum albumin concentration. An assignable cause for hypernatremia was the urea-induced osmotic diuresis. When dealing with ICU patients requiring prolonged treatment, hypernatremia should at least trigger thoughts on PICS as a contributing factor. In this regard, the urea-to-creatinine ratio is an easily accessible biomarker for catabolism.

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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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            nparLD: AnRSoftware Package for the Nonparametric Analysis of Longitudinal Data in Factorial Experiments

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              SAPS 3—From evaluation of the patient to evaluation of the intensive care unit. Part 1: Objectives, methods and cohort description

              Objective Risk adjustment systems now in use were developed more than a decade ago and lack prognostic performance. Objective of the SAPS 3 study was to collect data about risk factors and outcomes in a heterogeneous cohort of intensive care unit (ICU) patients, in order to develop a new, improved model for risk adjustment. Design Prospective multicentre, multinational cohort study. Patients and setting A total of 19,577 patients consecutively admitted to 307 ICUs from 14 October to 15 December 2002. Measurements and results Data were collected at ICU admission, on days 1, 2 and 3, and the last day of the ICU stay. Data included sociodemographics, chronic conditions, diagnostic information, physiological derangement at ICU admission, number and severity of organ dysfunctions, length of ICU and hospital stay, and vital status at ICU and hospital discharge. Data reliability was tested with use of kappa statistics and intraclass-correlation coefficients, which were >0.85 for the majority of variables. Completeness of the data was also satisfactory, with 1 [0–3] SAPS II parameter missing per patient. Prognostic performance of the SAPS II was poor, with significant differences between observed and expected mortality rates for the overall cohort and four (of seven) defined regions, and poor calibration for most tested subgroups. Conclusions The SAPS 3 study was able to provide a high-quality multinational database, reflecting heterogeneity of current ICU case-mix and typology. The poor performance of SAPS II in this cohort underscores the need for development of a new risk adjustment system for critically ill patients. 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/s00134-005-2762-6
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                Author and article information

                Journal
                J Clin Med
                J Clin Med
                jcm
                Journal of Clinical Medicine
                MDPI
                2077-0383
                18 September 2020
                September 2020
                : 9
                : 9
                : 3017
                Affiliations
                [1 ]Department of General and Surgical Critical Care Medicine, Innsbruck Medical University Hospital, Anichstr. 35, 6020 Innsbruck, Austria; mathias.stroehle@ 123456tirol-kliniken.at (M.S.); benedikt.treml@ 123456tirol-kliniken.at (B.T.); mirjam.bachler@ 123456tirol-kliniken.at (M.B.); stefan.schmid@ 123456tirol-kliniken.at (S.S.); janett.kreutziger@ 123456tirol-kliniken.at (J.K.)
                [2 ]Institute for Sports Medicine, Alpine Medicine and Health Tourism, UMIT—University for Health Sciences, Medical Informatics and Technology, 6060 Hall in Tirol, Austria
                Author notes
                [* ]Correspondence: christopher.rugg@ 123456tirol-kliniken.at ; Tel.: +43-50-504-80271
                Author information
                https://orcid.org/0000-0002-7150-7357
                https://orcid.org/0000-0002-6623-7823
                https://orcid.org/0000-0002-3648-7338
                https://orcid.org/0000-0001-5363-8858
                Article
                jcm-09-03017
                10.3390/jcm9093017
                7563338
                32962124
                8d0b1041-f553-4200-af57-dd8a8edadc4f
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 13 August 2020
                : 17 September 2020
                Categories
                Article

                hypernatremia,pics,catabolism,inflammation,urea-to-creatinine ratio

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