Prevalence and Prognostic Impact of Hypernatremia in Sepsis and Septic Shock Patients in The Intensive Care Unit: A Single Centre Experience

Shirazy, Mohamed; Omar, Islam; Abduljabbar, Duaa; Bousselmi, K; Alkhaja, Maryam; Chaari, Anis; Kauts, Vipin; Hakim, Karim Abdel

doi:10.2478/jccm-2020-0001

Cited by 14 publications

(11 citation statements)

References 37 publications

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“…Sodium_max is an interesting feature in our XGBoost-based model. Hypernatremia can be an independent predictor of poor outcome in septic patients in the ICU, which is similar to some views [ 38 ]. However, another study [ 39 ] showed the risk of death increased by 71.6% when serum sodium was < 129 mmol/L for patients with sepsis.…”

Section: Discussionsupporting

confidence: 87%

Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost

et al. 2020

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Background Sepsis is a significant cause of mortality in-hospital, especially in ICU patients. Early prediction of sepsis is essential, as prompt and appropriate treatment can improve survival outcomes. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression and scoring system. The aims of this study were to develop a machine learning approach using XGboost to predict the 30-days mortality for MIMIC-III Patients with sepsis-3 and to determine whether such model performs better than traditional prediction models. Methods Using the MIMIC-III v1.4, we identified patients with sepsis-3. The data was split into two groups based on death or survival within 30 days and variables, selected based on clinical significance and availability by stepwise analysis, were displayed and compared between groups. Three predictive models including conventional logistic regression model, SAPS-II score prediction model and XGBoost algorithm model were constructed by R software. Then, the performances of the three models were tested and compared by AUCs of the receiver operating characteristic curves and decision curve analysis. At last, nomogram and clinical impact curve were used to validate the model. Results A total of 4559 sepsis-3 patients are included in the study, in which, 889 patients were death and 3670 survival within 30 days, respectively. According to the results of AUCs (0.819 [95% CI 0.800–0.838], 0.797 [95% CI 0.781–0.813] and 0.857 [95% CI 0.839–0.876]) and decision curve analysis for the three models, the XGboost model performs best. The risk nomogram and clinical impact curve verify that the XGboost model possesses significant predictive value. Conclusions Using machine learning technique by XGboost, more significant prediction model can be built. This XGboost model may prove clinically useful and assist clinicians in tailoring precise management and therapy for the patients with sepsis-3.

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Section: Discussionsupporting

confidence: 87%

Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost

et al. 2020

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“…The exclusion of patients with insufficient renal function or requirement of renal replacement therapy, of course, partially excludes more severely ill patients and therefore influences mortality rates. A prolonged ICU LOS for patients suffering from ICU-acquired hypernatremia as shown in this study is, however, in accordance with recent literature and emphasizes the significance and importance of the observed illness [ 9 , 11 , 12 , 41 ]. Yet, whether hypernatremia, inflammation, catabolism or the combination of all three led to the observed prolonged ICU LOS cannot be determined by the presented results in this study.…”

Section: Discussionsupporting

confidence: 92%

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

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Ströhle

Treml

et al. 2020

JCM

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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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“…In contrast with hyponatremia, baseline hypernatremia has been shown to be directly associated with the diagnosis of sepsis at admission (16). Additionally, patients presenting both hypernatremia and sepsis are known to have a higher mortality rate than those presenting sepsis alone (17).…”

Section: Introductionmentioning

confidence: 99%

Prognostic Impact of Hyponatremia and Hypernatremia in COVID-19 Pneumonia. A HOPE-COVID-19 (Health Outcome Predictive Evaluation for COVID-19) Registry Analysis

et al. 2020

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Dysnatremia is associated with increased mortality in patients with community-acquired pneumonia. SARS-COV2 (Severe-acute-respiratory syndrome caused by Coronavirus-type 2) pneumonia can be fatal. The aim of this study was to ascertain whether admittance dysnatremia is associated with mortality, sepsis, or intensive therapy (IT) in patients hospitalized with SARS-COV2 pneumonia. This is a retrospective study of the HOPE-COVID-19 registry, with data collected from January 1th through April 31th, 2020. We selected all hospitalized adult patients with RT-PCR-confirmed SARS-COV2 pneumonia and a registered admission serum sodium level (SNa). Patients were classified as hyponatremic (SNa <135 mmol/L), eunatremic (SNa 135–145 mmol/L), or hypernatremic (SNa >145 mmol/L). Multivariable analyses were performed to elucidate independent relationships of admission hyponatremia and hypernatremia, with mortality, sepsis, or IT during hospitalization. Four thousand six hundred sixty-four patients were analyzed, median age 66 (52–77), 58% males. Death occurred in 988 (21.2%) patients, sepsis was diagnosed in 551 (12%) and IT in 838 (18.4%). Hyponatremia was present in 957/4,664 (20.5%) patients, and hypernatremia in 174/4,664 (3.7%). Both hyponatremia and hypernatremia were associated with mortality and sepsis. Only hyponatremia was associated with IT. In conclusion, hyponatremia and hypernatremia at admission are factors independently associated with mortality and sepsis in patients hospitalized with SARS-COV2 pneumonia.Clinical Trial Registrationhttps://clinicaltrials.gov/ct2/show/NCT04334291, NCT04334291.

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Prevalence and Prognostic Impact of Hypernatremia in Sepsis and Septic Shock Patients in The Intensive Care Unit: A Single Centre Experience

Cited by 14 publications

References 37 publications

Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost

Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost

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

Prognostic Impact of Hyponatremia and Hypernatremia in COVID-19 Pneumonia. A HOPE-COVID-19 (Health Outcome Predictive Evaluation for COVID-19) Registry Analysis

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