Serum sodium variability and acute kidney injury: a retrospective observational cohort study on a hospitalized population

Lombardi, Gianmarco; Ferraro, Pietro Manuel; Naticchia, Alessandro; Gambaro, Giovanni

doi:10.1007/s11739-020-02462-5

Cited by 19 publications

(18 citation statements)

References 32 publications

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“…4) The feature sodium may also become an important parameter for the prediction according to its high weight in the prediction, which is consistent with Lombardi's research results (44). The change in sodium level leads to a change of osmotic pressure and AKI may develop finally.…”

Section: Impact Weight Analysissupporting

confidence: 82%

Analysis of the Impact of Medical Features and Risk Prediction of Acute Kidney Injury for Critical Patients Using Temporal Electronic Health Record Data With Attention-Based Neural Network

Chen¹,

Chen

Sun

et al. 2021

Front. Med.

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Acute kidney injury (AKI) is one of the most severe consequences of kidney injury, and it will also cause or aggravate the complications by the fast decline of kidney excretory function. Accurate AKI prediction, including the AKI case, AKI stage, and AKI onset time interval, can provide adequate support for effective interventions. Besides, discovering how the medical features affect the AKI result may also provide supporting information for disease treatment. An attention-based temporal neural network approach was employed in this study for AKI prediction and for the analysis of the impact of medical features from temporal electronic health record (EHR) data of patients before AKI diagnosis. We used the publicly available dataset provided by the Medical Information Mart for Intensive Care (MIMIC) for model training, validation, and testing, and then the model was applied in clinical practice. The improvement of AKI case prediction is around 5% AUC (area under the receiver operating characteristic curve), and the AUC value of AKI stage prediction on AKI stage 3 is over 82%. We also analyzed the data by two steps: the associations between the medical features and the AKI case (positive or inverse) and the extent of the impact of medical features on AKI prediction result. It shows that features, such as lactate, glucose, creatinine, blood urea nitrogen (BUN), prothrombin time (PT), and partial thromboplastin time (PTT), are positively associated with the AKI case, while there are inverse associations between the AKI case and features such as platelet, hemoglobin, hematocrit, urine, and international normalized ratio (INR). The laboratory test features such as urine, glucose, creatinine, sodium, and blood urea nitrogen and the medication features such as nonsteroidal anti-inflammatory drugs, agents acting on the renin–angiotensin system, and lipid-lowering medication were detected to have higher weights than other features in the proposed model, which may imply that these features have a great impact on the AKI case.

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Section: Impact Weight Analysissupporting

confidence: 82%

Analysis of the Impact of Medical Features and Risk Prediction of Acute Kidney Injury for Critical Patients Using Temporal Electronic Health Record Data With Attention-Based Neural Network

Chen¹,

Chen

Sun

et al. 2021

Front. Med.

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“…Thus, the level of these ions and particles above mentioned may reflect and affect patient renal function. Sodium imbalance was generally associated with patient poor outcomes including the development of AKI, and previous studies had proved this finding (27,28). Relevant studies also found that early treatment for hyperkalemia could reduce potassium level and AKI, and potassium level was closely related to patient renal function (29)(30)(31)(32).…”

Section: Discussionmentioning

confidence: 96%

Association Between Serum Osmolality and Acute Kidney Injury in Critically Ill Patients: A Retrospective Cohort Study

et al. 2021

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Purposes: Acute kidney injury (AKI) is a common complication in critically ill patients and is usually associated with poor outcomes. Serum osmolality has been validated in predicting critically ill patient mortality. However, data about the association between serum osmolality and AKI is still lacking in ICU. Therefore, the purpose of the present study was to investigate the association between early serum osmolality and the development of AKI in critically ill patients.Methods: The present study was a retrospective cohort analysis based on the medical information mart for intensive care III (MIMIC-III) database. 20,160 patients were involved in this study and divided into six subgroups according to causes for ICU admission. The primary outcome was the incidence of AKI after ICU admission. The association between early serum osmolality and AKI was explored using univariate and multivariate logistic regression analyses.Results: The normal range of serum osmolality was 285–300 mmol/L. High serum osmolality was defined as serum osmolality >300 mmol/L and low serum osmolality was defined as serum osmolality <285 mmol/L. Multivariate logistic regression indicated that high serum osmolality was independently associated with increased development of AKI with OR = 1.198 (95% CL = 1.199–1.479, P < 0.001) and low serum osmolality was also independently associated with increased development of AKI with OR = 1.332 (95% CL = 1.199–1.479, P < 0.001), compared with normal serum osmolality, respectively.Conclusions: In critically ill patients, early high serum osmolality and low serum osmolality were both independently associated with an increased risk of development of AKI.

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“…It is a common and severe complication in hospitalized populations. The reported incidence is quite varied especially since it is strongly influenced by definition and study population with an incidence ranging from 7 to 57% [2][3][4][5][6] and with a significant burden on patient morbidity and mortality [7][8][9]. As reported by clinical and epidemiological studies, several diseases [10][11][12][13][14] show a defined seasonal variation.…”

Section: Introductionmentioning

confidence: 99%

Seasonality of acute kidney injury in a tertiary hospital academic center: an observational cohort study

et al. 2021

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Background The aim of our study was to describe seasonal trends of acute kidney injury (AKI) and its relationship with weather conditions in a hospitalized population. Methods We retrospectively collected demographic (age, sex), clinical (ICD-9-CM codes of diagnosis discharge) and laboratory data (creatinine values) from the inpatient population admitted to Fondazione Policlinico Universitario A. Gemelli IRCCS between January 2010 and December 2014 with inclusion of all patients ≥18 years with at least two values available for creatinine. The outcome of interest was AKI development, defined according to creatinine kinetics criteria. The exposures of interest were the months and seasons of the year; air temperature and humidity level were also evaluated. Log-binomial regression models adjusted for age, sex, eGFR, comorbidities, Charlson/Deyo index score, year of hospitalization were used to estimate risk ratios (RR) and 95% confidential intervals (CI). Results A total of 64,610 patients met the inclusion criteria. AKI occurred in 2864 (4.4%) hospital admissions. After full adjustment, winter period was associated with increased risk of AKI (RR 1.16, 95% CI 1.05, 1.29, p=0.003). Lower air temperature and higher humidity level were associated with risk of AKI, however in multivariable-adjusted models only higher humidity level showed a significant and independent association. Conclusions AKI is one of the most common complications of hospitalized populations with a defined seasonal pattern and a significant increase in incidence during wintertime; weather conditions, particularly higher humidity level, are independent predictors of AKI and could partially justify the observed seasonal variations.

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Serum sodium variability and acute kidney injury: a retrospective observational cohort study on a hospitalized population

Cited by 19 publications

References 32 publications

Analysis of the Impact of Medical Features and Risk Prediction of Acute Kidney Injury for Critical Patients Using Temporal Electronic Health Record Data With Attention-Based Neural Network

Analysis of the Impact of Medical Features and Risk Prediction of Acute Kidney Injury for Critical Patients Using Temporal Electronic Health Record Data With Attention-Based Neural Network

Association Between Serum Osmolality and Acute Kidney Injury in Critically Ill Patients: A Retrospective Cohort Study

Seasonality of acute kidney injury in a tertiary hospital academic center: an observational cohort study

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