TRENDS IN PARATHYROID HORMONE PLASMA CONCENTRATION IN CRITICALLY ILL PATIENTS: PROSPECTIVE OBSERVATIONAL STUDY

Introduction: There is no data in the literature regarding trends in parathormone serum concentration assessment in critically ill patients. The aim: To assess the parathyroid hormone plasma concentrations and kinetics in critically ill patients admitted to the intensive care unit due to multiorgan failure. Materials and methods: Thirty multiorgan failure (at least circulatory and respiratory failure) patients were included. Patients who met any of the following criteria were excluded: acute liver failure, end stage renal disease, hypercalcemia, parathyroid gland disease, severe vitamin D deficiency, admission from another ICU or readmission, age younger than 18 years, or lack of consent from relatives. We performed the parathyroid hormone plasma measurements in 12-hour time intervals. Results: The initial parathyroid hormone plasma concentration levels in the study group were rather variable and medians exceeded laboratory reference values. Especially in the acute kidney injury subpopulation treated with continuous renal replacement therapy these trends were emphasized. The initial parathyroid hormone plasma concentration levels in this group significantly exceeded laboratory reference values in 80% of patients. After initial spike we observed subsequent drop between second and third measurement. The distribution of plasma levels was rather variable between second and third measurement in this group of patients. Conclusions: The parathyroid hormone plasma concentration levels in the critically ill patients are variable. In the acute kidney injury subpopulation treated with continuous renal replacement therapy after initial significant spike we observed subsequent drop between second and third measurement.

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Safety of Argatroban in critically ill patients with acute kidney injury and renal replacement therapy after cardiac surgery

The Thoracic and Cardiovascular Surgeon ◽

10.1055/s-0030-1269141 ◽

2011 ◽

Vol 59 (S 01) ◽

Author(s):

M Klingele ◽

A Lerner-Gräber ◽

D Fliser ◽

HJ Schäfers ◽

H Groesdonk

Keyword(s):

Acute Kidney Injury ◽

Cardiac Surgery ◽

Renal Replacement Therapy ◽

Replacement Therapy ◽

Critically Ill ◽

Critically Ill Patients ◽

Kidney Injury

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Renal replacement therapy in critically ill patients with acute kidney injury: 2020 nephrologist’s perspective

Nefrología (English Edition) ◽

10.1016/j.nefroe.2021.05.003 ◽

2021 ◽

Author(s):

María Valdenebro ◽

Leyre Martín-Rodríguez ◽

Blanca Tarragón ◽

Paula Sánchez-Briales ◽

Jose Portolés

Keyword(s):

Acute Kidney Injury ◽

Renal Replacement Therapy ◽

Replacement Therapy ◽

Critically Ill ◽

Critically Ill Patients ◽

Kidney Injury

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Comparison of Approaches for Prediction of Renal Replacement Therapy-Free Survival in Patients with Acute Kidney Injury

Blood Purification ◽

10.1159/000513700 ◽

2021 ◽

pp. 1-7

Author(s):

Pattharawin Pattharanitima ◽

Akhil Vaid ◽

Suraj K. Jaladanki ◽

Ishan Paranjpe ◽

Ross O’Hagan ◽

...

Keyword(s):

Acute Kidney Injury ◽

Renal Replacement Therapy ◽

Replacement Therapy ◽

Critically Ill ◽

Critically Ill Patients ◽

Kidney Injury ◽

Support Vector ◽

Adaptive Boosting ◽

Free Survival ◽

Extreme Gradient Boosting

Background/Aims: Acute kidney injury (AKI) in critically ill patients is common, and continuous renal replacement therapy (CRRT) is a preferred mode of renal replacement therapy (RRT) in hemodynamically unstable patients. Prediction of clinical outcomes in patients on CRRT is challenging. We utilized several approaches to predict RRT-free survival (RRTFS) in critically ill patients with AKI requiring CRRT. Methods: We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify patients ≥18 years old with AKI on CRRT, after excluding patients who had ESRD on chronic dialysis, and kidney transplantation. We defined RRTFS as patients who were discharged alive and did not require RRT ≥7 days prior to hospital discharge. We utilized all available biomedical data up to CRRT initiation. We evaluated 7 approaches, including logistic regression (LR), random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), and MLP with long short-term memory (MLP + LSTM). We evaluated model performance by using area under the receiver operating characteristic (AUROC) curves. Results: Out of 684 patients with AKI on CRRT, 205 (30%) patients had RRTFS. The median age of patients was 63 years and their median Simplified Acute Physiology Score (SAPS) II was 67 (interquartile range 52–84). The MLP + LSTM showed the highest AUROC (95% CI) of 0.70 (0.67–0.73), followed by MLP 0.59 (0.54–0.64), LR 0.57 (0.52–0.62), SVM 0.51 (0.46–0.56), AdaBoost 0.51 (0.46–0.55), RF 0.44 (0.39–0.48), and XGBoost 0.43 (CI 0.38–0.47). Conclusions: A MLP + LSTM model outperformed other approaches for predicting RRTFS. Performance could be further improved by incorporating other data types.

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