Acute Kidney Injury in critically ill patients:a seemingly simple syndrome

Critically ill patients are at risk of chronic organ failure. The kidneys are vulnerable organs that endure severe illnesses and treatments. Both may contribute to a sudden decrease in renal function; i.e. Acute Kidney Injury (AKI). AKI is one of the most frequently developing complications during ICU stay and reported incidences vary from 20% to 70% depending on used criteria. The exact pathophysiological mechanisms are largely unclear and there is no other treatment besides prevention and support. Risk factors for AKI include age, comorbidities, severity of illness, and the presence of (septic) shock. Decreased renal perfusion may induce local changes in the kidney and this was assumed to be an consequence of shock. However, evidence is accumulating that fluid overload or increased venous pressure (also called venous congestion, stasis of bloodflow in the venous system) may also aggravate AKI. In this thesis, we attempted to visualise and measure venous congestion using regularly obtained variables from clinical examination, biochemical analysis and ultrasonography. The associations between various measures of venous congestion and AKI were investigated, showing that indeed venous congestion may play a role. The most important result within this thesis was that variations in methodology to explicate these two complex syndromes results in significant differences in outcomes, hampering comparisons of existing research. Moreover, the observational nature of the data in this thesis provides hypotheses, but the results of randomised controlled trials need to be awaited to provide causal evidence for recommendations and guidelines for fluid management and AKI in clinical practice

Fluid balance-adjusted creatinine in diagnosing acute kidney injury in the critically ill

Background Acute kidney injury (AKI) is often diagnosed based on plasma creatinine (Cr) only. Adjustment of Cr for cumulative fluid balance due to potential dilution of Cr and subsequently missed Cr-based diagnosis of AKI has been suggested, albeit the physiological rationale for these adjustments is questionable. Furthermore, whether these adjustments lead to a different incidence of AKI when used in conjunction with urine output (UO) criteria is unknown. Methods This was a post hoc analysis of the Finnish Acute Kidney Injury study. Hourly UO and daily plasma Cr were measured during the first 5 days of intensive care unit admission. Cr values were adjusted following the previously used formula and combined with the UO criteria. Resulting incidences and mortality rates were compared with the results based on unadjusted values. Results In total, 2044 critically ill patients were analyzed. The mean difference between the adjusted and unadjusted Cr of all 7279 observations was 5 (+/- 15) mu mol/L. Using adjusted Cr in combination with UO and renal replacement therapy criteria resulted in the diagnosis of 19 (1%) additional AKI patients. The absolute difference in the incidence was 0.9% (95% confidence interval [CI]: 0.3%-1.6%). Mortality rates were not significantly different between the reclassified AKI patients using the full set of Kidney Disease: Improving Global Outcomes criteria. Conclusion Fluid balance-adjusted Cr resulted in little change in AKI incidence, and only minor differences in mortality between patients who changed category after adjustment and those who did not. Using adjusted Cr values to diagnose AKI does not seem worthwhile in critically ill patients.Peer reviewe

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

Temporal artery temperature measurements versus bladder temperature in critically ill patients, a prospective observational study

PurposeAccurate measurement of body temperature is important for the timely detection of fever or hypothermia in critically ill patients. In this prospective study, we evaluated whether the agreement between temperature measurements obtained with TAT (test method) and bladder catheter-derived temperature measurements (BT; reference method) is sufficient for clinical practice in critically ill patients.MethodsPatients acutely admitted to the Intensive Care Unit were included. After BT was recorded TAT measurements were performed by two independent researchers (TAT1; TAT2). The agreement between TAT and BT was assessed using Bland-Altman plots. Clinical acceptable limits of agreement (LOA) were defined a priori (ResultsIn total, 90 critically ill patients (64 males; mean age 62 years) were included. The observed mean difference (TAT-BT; ±SD, 95% LOA) between TAT and BT was 0.12°C (-1.08°C to +1.32°C) for TAT1 and 0.14°C (-1.05°C to +1.33°C) for TAT2. 36% (TAT1) and 42% (TAT2) of all paired measurements failed to meet the acceptable LOA of 0.5°C. Subgroup analysis showed that when patients were receiving intravenous norepinephrine, the measurements of the test method deviated more from the reference method (p = NS).ConclusionThe TAT is not sufficient for clinical practice in critically ill adults

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 for the Prediction of Mortality in the Critically Ill : The Simple Intensive Care Studies-I

Objectives: Caregivers use clinical examination to timely recognize deterioration of a patient, yet data on the prognostic value of clinical examination are inconsistent. In the Simple Intensive Care Studies-I, we evaluated the association of clinical examination findings with 90-day mortality in critically ill patients. Design: Prospective single-center cohort study. Setting: ICU of a single tertiary care level hospital between March 27, 2015, and July 22, 2017. Patients: All consecutive adults acutely admitted to the ICU and expected to stay for at least 24 hours. Interventions: A protocolized clinical examination of 19 clinical signs conducted within 24 hours of admission. Measurements Main Results: Independent predictors of 90-day mortality were identified using multivariable logistic regression analyses. Model performance was compared with established prognostic risk scores using area under the receiver operating characteristic curves. Robustness of our findings was tested by internal bootstrap validation and adjustment of the threshold for statistical significance. A total of 1,075 patients were included, of whom 298 patients (28%) had died at 90-day follow-up. Multivariable analyses adjusted for age and norepinephrine infusion rate demonstrated that the combination of higher respiratory rate, higher systolic blood pressure, lower central temperature, altered consciousness, and decreased urine output was independently associated with 90-day mortality (area under the receiver operating characteristic curves = 0.74; 95% CI, 0.71-0.78). Clinical examination had a similar discriminative value as compared with the Simplified Acute Physiology Score-II (area under the receiver operating characteristic curves = 0.76; 95% CI, 0.73-0.79; p = 0.29) and Acute Physiology and Chronic Health Evaluation-IV (using area under the receiver operating characteristic curves = 0.77; 95% CI, 0.74-0.80; p = 0.16) and was significantly better than the Sequential Organ Failure Assessment (using area under the receiver operating characteristic curves = 0.67; 95% CI, 0.64-0.71; p <0.001). Conclusions: Clinical examination has reasonable discriminative value for assessing 90-day mortality in acutely admitted ICU patients. In our study population, a single, protocolized clinical examination had similar prognostic abilities compared with the Simplified Acute Physiology Score-II and Acute Physiology and Chronic Health Evaluation-IV and outperformed the Sequential Organ Failure Assessment score.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

Two subphenotypes of septic acute kidney injury are associated with different 90-day mortality and renal recovery

Background The pathophysiology of septic acute kidney injury is inadequately understood. Recently, subphenotypes for sepsis and AKI have been derived. The objective of this study was to assess whether a combination of comorbidities, baseline clinical data, and biomarkers could classify meaningful subphenotypes in septic AKI with different outcomes. Methods We performed a post hoc analysis of the prospective Finnish Acute Kidney Injury (FINNAKI) study cohort. We included patients admitted with sepsis and acute kidney injury during the first 48 h from admission to intensive care (according to Kidney Disease Improving Global Outcome criteria). Primary outcomes were 90-day mortality and renal recovery on day 5. We performed latent class analysis using 30 variables obtained on admission to classify subphenotypes. Second, we used logistic regression to assess the association of derived subphenotypes with 90-day mortality and renal recovery on day 5. Results In total, 301 patients with septic acute kidney injury were included. Based on the latent class analysis, a two-class model was chosen. Subphenotype 1 was assigned to 133 patients (44%) and subphenotype 2 to 168 patients (56%). Increased levels of inflammatory and endothelial injury markers characterized subphenotype 2. At 90 days, 29% of patients in subphenotype 1 and 41% of patients in subphenotype 2 had died. Subphenotype 2 was associated with a lower probability of short-term renal recovery and increased 90-day mortality. Conclusions In this post hoc analysis, we identified two subphenotypes of septic acute kidney injury with different clinical outcomes. Future studies are warranted to validate the suggested subphenotypes of septic acute kidney injury.Peer reviewe

Burden of acute kidney injury and 90-day mortality in critically ill patients

Background Mortality rates associated with acute kidney injury (AKI) vary among critically ill patients. Outcomes are often not corrected for severity or duration of AKI. Our objective was to analyse whether a new variable, AKI burden, would outperform 1) presence of AKI, 2) highest AKI stage, or 3) AKI duration in predicting 90-day mortality. Methods Kidney Diseases: Improving Global Outcomes (KDIGO) criteria using creatinine, urine output and renal replacement therapy were used to diagnose AKI. AKI burden was defined as AKI stage multiplied with the number of days that each stage was present (maximum five), divided by the maximum possible score yielding a proportion. The AKI burden as a predictor of 90-day mortality was assessed in two independent cohorts (Finnish Acute Kidney Injury, FINNAKI and Simple Intensive Care Studies I, SICS-I) by comparing four multivariate logistic regression models that respectively incorporated either the presence of AKI, the highest AKI stage, the duration of AKI, or the AKI burden. Results In the FINNAKI cohort 1096 of 2809 patients (39%) had AKI and 90-day mortality of the cohort was 23%. Median AKI burden was 0.17 (IQR 0.07-0.50), 1.0 being the maximum. The model including AKI burden (area under the receiver operator curve (AUROC) 0.78, 0.76-0.80) outperformed the models using AKI presence (AUROC 0.77, 0.75-0.79, p = 0.026) or AKI severity (AUROC 0.77, 0.75-0.79, p = 0.012), but not AKI duration (AUROC 0.77, 0.75-0.79, p = 0.06). In the SICS-I, 603 of 1075 patients (56%) had AKI and 90-day mortality was 28%. Median AKI burden was 0.19 (IQR 0.08-0.46). The model using AKI burden performed better (AUROC 0.77, 0.74-0.80) than the models using AKI presence (AUROC 0.75, 0.71-0.78, p = 0.001), AKI severity (AUROC 0.76, 0.72-0.79. p = 0.008) or AKI duration (AUROC 0.76, 0.73-0.79, p = 0.009). Conclusion AKI burden, which appreciates both severity and duration of AKI, was superior to using only presence or the highest stage of AKI in predicting 90-day mortality. Using AKI burden or other more granular methods may be helpful in future epidemiological studies of AKI.Peer reviewe

Burden of acute kidney injury and 90-day mortality in critically ill patients

Background Mortality rates associated with acute kidney injury (AKI) vary among critically ill patients. Outcomes are often not corrected for severity or duration of AKI. Our objective was to analyse whether a new variable, AKI burden, would outperform 1) presence of AKI, 2) highest AKI stage, or 3) AKI duration in predicting 90-day mortality. Methods Kidney Diseases: Improving Global Outcomes (KDIGO) criteria using creatinine, urine output and renal replacement therapy were used to diagnose AKI. AKI burden was defined as AKI stage multiplied with the number of days that each stage was present (maximum five), divided by the maximum possible score yielding a proportion. The AKI burden as a predictor of 90-day mortality was assessed in two independent cohorts (Finnish Acute Kidney Injury, FINNAKI and Simple Intensive Care Studies I, SICS-I) by comparing four multivariate logistic regression models that respectively incorporated either the presence of AKI, the highest AKI stage, the duration of AKI, or the AKI burden. Results In the FINNAKI cohort 1096 of 2809 patients (39%) had AKI and 90-day mortality of the cohort was 23%. Median AKI burden was 0.17 (IQR 0.07-0.50), 1.0 being the maximum. The model including AKI burden (area under the receiver operator curve (AUROC) 0.78, 0.76-0.80) outperformed the models using AKI presence (AUROC 0.77, 0.75-0.79, p = 0.026) or AKI severity (AUROC 0.77, 0.75-0.79, p = 0.012), but not AKI duration (AUROC 0.77, 0.75-0.79, p = 0.06). In the SICS-I, 603 of 1075 patients (56%) had AKI and 90-day mortality was 28%. Median AKI burden was 0.19 (IQR 0.08-0.46). The model using AKI burden performed better (AUROC 0.77, 0.74-0.80) than the models using AKI presence (AUROC 0.75, 0.71-0.78, p = 0.001), AKI severity (AUROC 0.76, 0.72-0.79. p = 0.008) or AKI duration (AUROC 0.76, 0.73-0.79, p = 0.009). Conclusion AKI burden, which appreciates both severity and duration of AKI, was superior to using only presence or the highest stage of AKI in predicting 90-day mortality. Using AKI burden or other more granular methods may be helpful in future epidemiological studies of AKI.Peer reviewe