Fluid management in patients with acute kidney injury-A post-hoc analysis of the FINNAKI study

Purpose: Whether positive fluid balance among patients with acute kidney injury (AKI) stems from decreased urine output, overzealous fluid administration, or both is poorly characterized. Materials and methods: This was a post hoc analysis of the prospective multicenter observational Finnish Acute Kidney Injury study including 824 AKI and 1162 non-AKI critically ill patients. Results: We matched 616 AKI (diagnosed during the three first intensive care unit (ICU) days) and non-AKI patients using propensity score. During the three first ICU days, AKI patients received median [IQR] of 11.4 L [8.0-15.2]L fluids and non-AKI patients 10.2 L [7.5-13.7]L, p < 0.001 while the fluid output among AKI patients was 4.7 L [3.0-7.2]L and among non-AKI patients 5.8 L [4.1-8.0]L, p < 0.001. In AKI patients, the median [IQR] cumulative fluid balance was 2.5 L [-0.2-6.0]L compared to 0.9 L [-1.4-3.6]L among non-AKI patients, p < 0.001. Among the 824 AKI patients, smaller volumes of fluid input with a multivariable OR of 0.90 (0.88-0.93) and better fluid output (multivariable OR 1.12 (1.07-1.18)) associated with enhanced change of resolution of AKI. Conclusions: AKI patients received more fluids albeit having lower fluid output compared to matched critically ill non-AKI patients. Smaller volumes of fluid input and higher fluid output were associated with better AKI recovery. (c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).Peer reviewe

Mortality prediction models in the adult critically ill : A scoping review

Background Mortality prediction models are applied in the intensive care unit (ICU) to stratify patients into different risk categories and to facilitate benchmarking. To ensure that the correct prediction models are applied for these purposes, the best performing models must be identified. As a first step, we aimed to establish a systematic review of mortality prediction models in critically ill patients. Methods Mortality prediction models were searched in four databases using the following criteria: developed for use in adult ICU patients in high-income countries, with mortality as primary or secondary outcome. Characteristics and performance measures of the models were summarized. Performance was presented in terms of discrimination, calibration and overall performance measures presented in the original publication. Results In total, 43 mortality prediction models were included in the final analysis. In all, 15 models were only internally validated (35%), 13 externally (30%) and 10 (23%) were both internally and externally validated by the original researchers. Discrimination was assessed in 42 models (98%). Commonly used calibration measures were the Hosmer-Lemeshow test (60%) and the calibration plot (28%). Calibration was not assessed in 11 models (26%). Overall performance was assessed in the Brier score (19%) and the Nagelkerke's R-2 (4.7%). Conclusions Mortality prediction models have varying methodology, and validation and performance of individual models differ. External validation by the original researchers is often lacking and head-to-head comparisons are urgently needed to identify the best performing mortality prediction models for guiding clinical care and research in different settings and populations.Peer reviewe

Clinical examination findings as predictors of acute kidney injury in critically ill patients

Background Acute Kidney Injury (AKI) in critically ill patients is associated with a markedly increased morbidity and mortality. The aim of this study was to establish the predictive value of clinical examination for AKI in critically ill patients. Methods This was a sub-study of the SICS-I, a prospective observational cohort study of critically ill patients acutely admitted to the Intensive Care Unit (ICU). Clinical examination was performed within 24 hours of ICU admission. The occurrence of AKI was determined at day two and three after admission according to the KDIGO definition including serum creatinine and urine output. Multivariable regression modeling was used to assess the value of clinical examination for predicting AKI, adjusted for age, comorbidities and the use of vasopressors. Results A total of 1003 of 1075 SICS-I patients (93%) were included in this sub-study. 414 of 1003 patients (41%) fulfilled the criteria for AKI. Increased heart rate (OR 1.12 per 10 beats per minute increase, 98.5% CI 1.04-1.22), subjectively cold extremities (OR 1.52, 98.5% CI 1.07-2.16) and a prolonged capillary refill time on the sternum (OR 1.89, 98.5% CI 1.01-3.55) were associated with AKI. This multivariable analysis yielded an area under the receiver-operating curve (AUROC) of 0.70 (98.5% CI 0.66-0.74). The model performed better when lactate was included (AUROC of 0.72, 95%CI 0.69-0.75), P = .04. Conclusion Clinical examination findings were able to predict AKI with moderate accuracy in a large cohort of critically ill patients. Findings of clinical examination on ICU admission may trigger further efforts to help predict developing AKI.Peer reviewe

Feasibility of cardiac output measurements in critically ill patients by medical students

Background: Critical care ultrasonography (CCUS) is increasingly applied also in the intensive care unit (ICU) and performed by non-experts, including even medical students. There is limited data on the training efforts necessary for novices to attain images of sufficient quality. There is no data on medical students performing CCUS for the measurement of cardiac output (CO), a hemodynamic variable of importance for daily critical care. Objective: The aim of this study was to explore the agreement of cardiac output measurements as well as the quality of images obtained by medical students in critically ill patients compared to the measurements obtained by experts in these images. Methods: In a prospective observational cohort study, all acutely admitted adults with an expected ICU stay over 24 h were included. CCUS was performed by students within 24 h of admission. CCUS included the images required to measure the CO, i.e., the left ventricular outflow tract (LVOT) diameter and the velocity time integral (VTI) in the LVOT. Echocardiography experts were involved in the evaluation of the quality of images obtained and the quality of the CO measurements. Results: There was an opportunity for a CCUS attempt in 1155 of the 1212 eligible patients (95%) and in 1075 of the 1212 patients (89%) CCUS examination was performed by medical students. In 871 out of 1075 patients (81%) medical students measured CO. Experts measured CO in 783 patients (73%). In 760 patients (71%) CO was measured by both which allowed for comparison; bias of CO was 0.0 L min−1 with limits of agreement of − 2.6 L min−1 to 2.7 L min−1. The percentage error was 50%, reflecting poor agreement of the CO measurement by students compared with the experts CO measurement. Conclusions: Medical students seem capable of obtaining sufficient quality CCUS images for CO measurement in the majority of critically ill patients. Measurements of CO by medical students, however, had poor agreement with expert measurements. Experts remain indispensable for reliable CO measurements. Trial registration Clinicaltrials.gov; http://www.clinicaltrials.gov; registration number NCT02912624

Clinical examination findings as predictors of acute kidney injury in critically ill patients

Background Acute Kidney Injury (AKI) in critically ill patients is associated with a markedly increased morbidity and mortality. The aim of this study was to establish the predictive value of clinical examination for AKI in critically ill patients. Methods This was a sub-study of the SICS-I, a prospective observational cohort study of critically ill patients acutely admitted to the Intensive Care Unit (ICU). Clinical examination was performed within 24 hours of ICU admission. The occurrence of AKI was determined at day two and three after admission according to the KDIGO definition including serum creatinine and urine output. Multivariable regression modeling was used to assess the value of clinical examination for predicting AKI, adjusted for age, comorbidities and the use of vasopressors. Results A total of 1003 of 1075 SICS-I patients (93%) were included in this sub-study. 414 of 1003 patients (41%) fulfilled the criteria for AKI. Increased heart rate (OR 1.12 per 10 beats per minute increase, 98.5% CI 1.04-1.22), subjectively cold extremities (OR 1.52, 98.5% CI 1.07-2.16) and a prolonged capillary refill time on the sternum (OR 1.89, 98.5% CI 1.01-3.55) were associated with AKI. This multivariable analysis yielded an area under the receiver-operating curve (AUROC) of 0.70 (98.5% CI 0.66-0.74). The model performed better when lactate was included (AUROC of 0.72, 95%CI 0.69-0.75), P = .04. Conclusion Clinical examination findings were able to predict AKI with moderate accuracy in a large cohort of critically ill patients. Findings of clinical examination on ICU admission may trigger further efforts to help predict developing AKI

Right ventricular strain measurements in critically ill patients: an observational SICS sub-study

Background: Right ventricular (RV) dysfunction is common in critically ill patients and is associated with poor outcomes. RV function is usually evaluated by Tricuspid Annular Plane Systolic Excursion (TAPSE) which can be obtained using critical care echocardiography (CCE). Myocardial deformation imaging, measuring strain, is suitable for advanced RV function assessment and has widely been studied in cardiology. However, it is relatively new for the Intensive Care Unit (ICU) and little is known about RV strain in critically ill patients. Therefore, the objectives of this study were to evaluate the feasibility of RV strain in critically ill patients using tissue-Doppler imaging (TDI) and explore the association between RV strain and conventional CCE measurements representing RV function. Methods: This is a single-center sub-study of two prospective observational cohorts (Simple Intensive Care Studies (SICS)-I and SICS-II). All acutely admitted adults with an expected ICU stay over 24 h were included. CCE was performed within 24 h of ICU admission. In patients in which CCE was performed, TAPSE, peak systolic velocity at the tricuspid annulus (RV s’) and TDI images were obtained. RV free wall longitudinal strain (RVFWSL) and RV global four-chamber longitudinal strain (RV4CSL) were measured during offline analysis. Results: A total of 171 patients were included. Feasibility of RVFWSL and RV4CSL was, respectively, 62% and 56% in our population; however, when measurements were performed, intra- and inter-rater reliability based on the intraclass correlation coefficient were good to excellent. RV dysfunction based on TAPSE or RV s’ was found in 56 patients (33%) and 24 patients (14%) had RV dysfunction based on RVFWSL or RV4CSL. In 14 patients (8%), RVFWSL, RV4CSL, or both were reduced, despite conventional RV function measurements being preserved. These patients had significantly higher severity of illness scores. Sensitivity analysis with fractional area change showed similar results. Conclusions: TDI RV strain imaging in critically ill patients is challenging; however, good-to-excellent reproducibility was shown when measurements were adequately obtained. Future studies are needed to elucidate the diagnostic and prognostic value of RV strain in critically ill patients, especially to outweigh the difficulty and effort of imaging against the clinical value

Right ventricular strain measurements in critically ill patients:an observational SICS sub-study

Background: Right ventricular (RV) dysfunction is common in critically ill patients and is associated with poor outcomes. RV function is usually evaluated by Tricuspid Annular Plane Systolic Excursion (TAPSE) which can be obtained using critical care echocardiography (CCE). Myocardial deformation imaging, measuring strain, is suitable for advanced RV function assessment and has widely been studied in cardiology. However, it is relatively new for the Intensive Care Unit (ICU) and little is known about RV strain in critically ill patients. Therefore, the objectives of this study were to evaluate the feasibility of RV strain in critically ill patients using tissue-Doppler imaging (TDI) and explore the association between RV strain and conventional CCE measurements representing RV function. Methods: This is a single-center sub-study of two prospective observational cohorts (Simple Intensive Care Studies (SICS)-I and SICS-II). All acutely admitted adults with an expected ICU stay over 24 h were included. CCE was performed within 24 h of ICU admission. In patients in which CCE was performed, TAPSE, peak systolic velocity at the tricuspid annulus (RV s’) and TDI images were obtained. RV free wall longitudinal strain (RVFWSL) and RV global four-chamber longitudinal strain (RV4CSL) were measured during offline analysis. Results: A total of 171 patients were included. Feasibility of RVFWSL and RV4CSL was, respectively, 62% and 56% in our population; however, when measurements were performed, intra- and inter-rater reliability based on the intraclass correlation coefficient were good to excellent. RV dysfunction based on TAPSE or RV s’ was found in 56 patients (33%) and 24 patients (14%) had RV dysfunction based on RVFWSL or RV4CSL. In 14 patients (8%), RVFWSL, RV4CSL, or both were reduced, despite conventional RV function measurements being preserved. These patients had significantly higher severity of illness scores. Sensitivity analysis with fractional area change showed similar results. Conclusions: TDI RV strain imaging in critically ill patients is challenging; however, good-to-excellent reproducibility was shown when measurements were adequately obtained. Future studies are needed to elucidate the diagnostic and prognostic value of RV strain in critically ill patients, especially to outweigh the difficulty and effort of imaging against the clinical value

The simple observational critical care studies: estimations by students, nurses, and physicians of in-hospital and 6-month mortality

BACKGROUND: Prognostic assessments of the mortality of critically ill patients are frequently performed in daily clinical practice and provide prognostic guidance in treatment decisions. In contrast to several sophisticated tools, prognostic estimations made by healthcare providers are always available and accessible, are performed daily, and might have an additive value to guide clinical decision-making. The aim of this study was to evaluate the accuracy of students', nurses', and physicians' estimations and the association of their combined estimations with in-hospital mortality and 6-month follow-up. METHODS: The Simple Observational Critical Care Studies is a prospective observational single-center study in a tertiary teaching hospital in the Netherlands. All patients acutely admitted to the intensive care unit were included. Within 3 h of admission to the intensive care unit, a medical or nursing student, a nurse, and a physician independently predicted in-hospital and 6-month mortality. Logistic regression was used to assess the associations between predictions and the actual outcome; the area under the receiver operating characteristics (AUROC) was calculated to estimate the discriminative accuracy of the students, nurses, and physicians. RESULTS: In 827 out of 1,010 patients, in-hospital mortality rates were predicted to be 11%, 15%, and 17% by medical students, nurses, and physicians, respectively. The estimations of students, nurses, and physicians were all associated with in-hospital mortality (OR 5.8, 95% CI [3.7, 9.2], OR 4.7, 95% CI [3.0, 7.3], and OR 7.7 95% CI [4.7, 12.8], respectively). Discriminative accuracy was moderate for all students, nurses, and physicians (between 0.58 and 0.68). When more estimations were of non-survival, the odds of non-survival increased (OR 2.4 95% CI [1.9, 3.1]) per additional estimate, AUROC 0.70 (0.65, 0.76). For 6-month mortality predictions, similar results were observed. CONCLUSIONS: Based on the initial examination, students, nurses, and physicians can only moderately predict in-hospital and 6-month mortality in critically ill patients. Combined estimations led to more accurate predictions and may serve as an example of the benefit of multidisciplinary clinical care and future research efforts

Plasma neutrophil gelatinase-associated lipocalin at intensive care unit admission as a predictor of acute kidney injury progression

Background. Acute kidney injury (AKI) is a common complication in patients during intensive care unit (ICU) admission. AKI is defined as an increase in serum creatinine (SCr) and/or a reduction in urine output. SCr is a marker of renal function with several limitations, which led to the search for biomarkers for earlier AKI detection. Our aim was to study the predictive value of plasma neutrophil gelatinase-associated lipocalin (NGAL) at admission as a biomarker for AKI progression during the first 48h of ICU admission in an unselected, heterogeneous ICU patient population. Methods. We conducted a prospective observational study in an academic tertiary referral ICU population. We recorded AKI progression in all ICU patients during the first 48h of ICU admission in a 6-week period. Plasma NGAL was measured at admission but levels were not reported to the attending clinicians. As possible predictors of AKI progression, pre-existing AKI risk factors were recorded. We examined the association of clinical parameters and plasma NGAL levels at ICU admission with the incidence and progression of AKI within the first 48h of the ICU stay. Results. A total of 361 patients were included. Patients without AKI progression during the first 48h of ICU admission had median NGAL levels at admission of 115 ng/mL [interquartile range (IQR) 81-201]. Patients with AKI progression during the first 48h of ICU admission had median NGAL levels at admission of 156 ng/mL (IQR 97-267). To predict AKI progression, a multivariant model with age, sex, diabetes mellitus, body mass index, admission type, Acute Physiology and Chronic Health Evaluation score and SCr at admission had an area under the receiver operating characteristics (ROC) curve of 0.765. Adding NGAL to this model showed a small increase in the area under the ROC curve to 0.783 (95% confidence interval 0.714-0.853). Conclusions. NGAL levels at admission were higher in patients with progression of AKI during the first 48h of ICU admission, but adding NGAL levels at admission to a model predicting this AKI progression showed no significant additive value

Mortality prediction models in the adult critically ill:A scoping review

Background Mortality prediction models are applied in the intensive care unit (ICU) to stratify patients into different risk categories and to facilitate benchmarking. To ensure that the correct prediction models are applied for these purposes, the best performing models must be identified. As a first step, we aimed to establish a systematic review of mortality prediction models in critically ill patients. Methods Mortality prediction models were searched in four databases using the following criteria: developed for use in adult ICU patients in high-income countries, with mortality as primary or secondary outcome. Characteristics and performance measures of the models were summarized. Performance was presented in terms of discrimination, calibration and overall performance measures presented in the original publication. Results In total, 43 mortality prediction models were included in the final analysis. In all, 15 models were only internally validated (35%), 13 externally (30%) and 10 (23%) were both internally and externally validated by the original researchers. Discrimination was assessed in 42 models (98%). Commonly used calibration measures were the Hosmer-Lemeshow test (60%) and the calibration plot (28%). Calibration was not assessed in 11 models (26%). Overall performance was assessed in the Brier score (19%) and the Nagelkerke's R-2 (4.7%). Conclusions Mortality prediction models have varying methodology, and validation and performance of individual models differ. External validation by the original researchers is often lacking and head-to-head comparisons are urgently needed to identify the best performing mortality prediction models for guiding clinical care and research in different settings and populations