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

Clinical Assessment of Fluid Status in Adults With Acute Kidney Injury: A Scoping Review

BACKGROUND

Acute kidney injury refers to sudden, potentially reversible, reduction in kidney function. Hypovolaemia is commonly the major risk factor. When acute kidney injury is established, fluid can accumulate leading to fluid overload. Undertaking a rigorous fluid assessment is vital in the management of a patient in hospital with acute kidney injury, as insufficient or excessive fluid can lead to increased morbidity and mortality.

OBJECTIVES

The aim of this scoping review is to identify which clinical assessments are useful when undertaking fluid assessment in a patient with acute kidney injury, and to identify signs and symptoms of fluid overload or dehydration in patients in hospital with acute kidney injury.

DESIGN

The JBI methodology for scoping reviews was followed and reported using the PRISMA-ScR checklist. PubMed, CINAHL Plus and SCOPUS were searched for research papers relating to the signs and symptoms or fluid assessments in patients with acute kidney injury.

RESULTS

Fifteen research papers were identified with four key areas being: Fluid balance/urine output and weight; early warning scores; clinical signs and symptoms; holistic assessment. The primary studies included in this scoping review have shown that hypovolaemia may be indicated by low blood pressure, orthostatic hypotension, low Mean Arterial Pressure, elevated heart rate, prolonged capillary refill time on the sternum (> 4.5 s) and subjectively reported cold peripheries. With clinical symptoms including dry mouth, increased thirst and dry skin. Accurate documentation of urine output and fluid balance is crucial in determining fluid status.

CONCLUSION

The assessment of fluid should be holistic and include history taking, diagnosis, blood tests and associated clinical signs and symptoms.

A novel real-time model for predicting acute kidney injury in critically ill patients within 12 hours

BACKGROUND

A major challenge in the prevention and early treatment of acute kidney injury (AKI) is the lack of high-performance predictors in critically ill patients. Therefore, we innovatively constructed U-AKIpredTM for predicting AKI in critically ill patients within 12 h of panel measurement.

METHODS

The prospective cohort study included 680 patients in the training set and 249 patients in the validation set. After performing inclusion and exclusion criteria, 417 patients were enrolled in the training set and 164 patients were enrolled in the validation set. AKI was diagnosed by Kidney Disease: Improving Global Outcomes (KDIGO) criteria.

RESULTS

Twelve urinary kidney injury biomarkers (mALB, IgG, TRF, α1MG, NAG, NGAL, KIM-1, L-FABP, TIMP2, IGFBP7, CAF22, and IL-18) exhibited good predictive performance for AKI within 12 h in critically ill patients. U-AKIpredTM, combined with three crucial biomarkers (α1MG, L-FABP, and IGFBP7) by multivariate logistic regression analysis, exhibited better predictive performance for AKI in critically ill patients within 12 h than the other 12 kidney injury biomarkers. The area under the curve (AUC) of the U-AKIpredTM, as a predictor of AKI within 12 h, was 0.802 (95% CI: 0.771-0.833, P < .001) in the training set and 0.844 (95% CI: 0.792-0.896, P < .001) in the validation cohort. A nomogram based on the results of the training and validation sets of U-AKIpredTM was developed that showed optimal predictive performance for AKI. The fitting effect and prediction accuracy of U-AKIpredTM was evaluated by multiple statistical indicators. To provide a more flexible predictive tool, the dynamic nomogram (https://www.xsmartanalysis.com/model/U-AKIpredTM) was constructed using a web calculator. Decision curve analysis and a clinical impact curve were used to reveal that U-AKIpredTM with the three crucial biomarkers had a higher net benefit than these 12 kidney injury biomarkers, respectively. The net reclassification index and integrated discrimination index were used to improve the significant risk reclassification of AKI compared with the 12 kidney injury biomarkers. The predictive efficiency of U-AKIpredTM was better than the NephroCheck® when testing for AKI and severe AKI.

CONCLUSION

U-AKIpredTM is an excellent predictive model of AKI in critically ill patients within 12 h and would assist clinicians in identifying those at high risk of AKI.

Development and Validation of a Clinical Prediction Model for Stages of Acute Kidney Injury in Critically Ill Patients

Introduction

Among critically ill patients, acute kidney injury (AKI) has a high incidence and leads to poor prognosis. As AKI is often only detected well after onset, early risk stratification is crucial. This study aimed to develop and internally validate the first clinical prediction model for different stages of AKI in critically ill adults.

Methods

We utilized data from the Simple Intensive Care Studies II (SICS-II), a prospective cohort study at the University Medical Center Groningen, the Netherlands. The prognostic outcome was the highest KDIGO-based stage of AKI within the first 7 days of ICU stay. Candidate predictors included fifty-nine readily available variables in critical care. Least absolute shrinkage and selection operator and proportional odds logistic regression were used for variable selection and model estimation, respectively. Receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis were applied to evaluate model performance and clinical usefulness.

Results

Of the SICS-II cohort, 976 patients were eligible for our analyses (median [interquartile range] age 64 [52-72] years, 38% female). Within 7 days after ICU admission, 29%, 23%, and 14% of patients progressed to their highest severity of AKI at stages 1, 2, and 3, respectively. We derived a 15-variable model for predicting this maximum ordinal outcome with an area under the ROC curve of 0.76 (95% CI, 0.74-0.78) in bootstrap validation. The model showed good calibration and improved net benefit in decision curve analysis over a range of clinically plausible thresholds.

Conclusion

Using readily available predictors in the ICU setting, we could develop a prediction model for different stages of AKI with good performance and promising clinical usefulness. Our findings serve as an initial step towards applying a valid and timely prediction model for AKI severity, possibly helping to limit morbidity and improve patient outcomes.

Prolonged capillary refill time and short-term mortality of critically ill patients: A meta-analysis

BACKGROUND

Prolonged capillary refill time (CRT) is an indicator of poor peripheral perfusion. The aim of the systematic review and meta-analysis was to evaluate the association of prolonged CRT and mortality of critically ill patients.

METHODS

To achieve the objective of this meta-analysis, we conducted a thorough search of PubMed, Embase, Cochrane Library, and the Web of Science to identify relevant observational studies with longitudinal follow-up. The Cochrane Q test was utilized to assess between-study heterogeneity, and the I2 statistic was calculated to estimate the degree of heterogeneity. We employed random-effects models to combine the outcomes, considering the potential influence of heterogeneity.

RESULTS

Eleven studies, encompassing 11,659 critically ill patients were included. During follow-up durations within hospitalization to 3 months, 1247 (10.7%) patients died. The pooled results indicated that a prolonged CRT at early phase of admission was significantly associated with an increased risk of all-cause mortality (risk ratio [RR]: 1.73, 95% confidence interval [CI]: 1.39 to 2.16, p < 0.001; I2 = 60%). Subgroup analyses showed that the association was not significantly modified by study design (prospective or retrospective), etiology of diseases (infection, non-infection, or mixed), or cutoff of CRT (>3 s, 3.5 s, or 4 s). The association between CRT and mortality was weaker in studies with multivariate analysis (RR: 1.43, 95% CI: 1.27 to 1.60, p < 0.001; I2 = 0%) as compared to that derived from studies of univariate analysis (RR: 6.27, 95% CI: 3.29 to 11.97, p < 0.001; I2 = 0%).

CONCLUSIONS

Prolonged CRT at admission may be a predictor of increased short-term mortality of critically ill patients.

Prognostic value of capillary refill time in adult patients: a systematic review with meta-analysis

PURPOSE

Acute circulatory failure leads to tissue hypoperfusion. Capillary refill time (CRT) has been widely studied, but its predictive value remains debated. We conducted a meta-analysis to assess the ability of CRT to predict death or adverse events in a context at risk or confirmed acute circulatory failure in adults.

METHOD

MEDLINE, EMBASE, and Google scholar databases were screened for relevant studies. The pooled area under the ROC curve (AUC ROC), sensitivity, specificity, threshold, and diagnostic odds ratio using a random-effects model were determined. The primary analysis was the ability of abnormal CRT to predict death in patients with acute circulatory failure. Secondary analysis included the ability of CRT to predict death or adverse events in patients at risk or with confirmed acute circulatory failure, the comparison with lactate, and the identification of explanatory factors associated with better accuracy.

RESULTS

A total of 60,656 patients in 23 studies were included. Concerning the primary analysis, the pooled AUC ROC of 13 studies was 0.66 (95%CI [0.59; 0.76]), and pooled sensitivity was 54% (95%CI [43; 64]). The pooled specificity was 72% (95%CI [55; 84]). The pooled diagnostic odds ratio was 3.4 (95%CI [1.4; 8.3]). Concerning the secondary analysis, the pooled AUC ROC of 23 studies was 0.69 (95%CI [0.65; 0.74]). The prognostic value of CRT compared to lactate was not significantly different. High-quality CRT was associated with a greater accuracy.

CONCLUSION

CRT poorly predicted death and adverse events in patients at risk or established acute circulatory failure. Its accuracy is greater when high-quality CRT measurement is performed.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03809-w.

Monitoring peripheral perfusion in sepsis associated acute kidney injury: Analysis of mortality

Microcirculatory disorders have been consistently linked to the pathophysiology of sepsis. One of the major organs affected is the kidneys, resulting in sepsis-associated acute kidney injury (SA-AKI) that correlates considerably with mortality. However, the potential role of clinical assessment of peripheral perfusion as a possible tool for SA-AKI management has not been established. To address this gap, the purpose of this study was to investigate the prevalence of peripheral hypoperfusion in SA-AKI, its association with mortality, and fluid balance. This observational cohort study enrolled consecutive septic patients in the Intensive Care Unit. After fluid resuscitation, peripheral perfusion was evaluated using the capillary filling time (CRT) and peripheral perfusion index (PI) techniques. The AKI was defined based on both serum creatinine and urine output criteria. One hundred and forty-one patients were included, 28 (19%) in the non-SA-AKI group, and 113 (81%) in the SA-AKI group. The study revealed higher peripheral hypoperfusion rates in the SA-AKI group using the CRT (OR 3.6; 95% CI 1.35-9.55; p < 0.05). However, this result lost significance after multivariate adjustment. Perfusion abnormalities in the SA-AKI group diagnosed by both CRT (RR 1.96; 95% CI 1.25-3.08) and PI (RR 1.98; 95% CI 1.37-2.86) methods were associated to higher rates of 28-day mortality (p < 0.01). The PI's temporal analysis showed a high predictive value for death over the first 72 h (p < 0.01). A weak correlation between PI values and the fluid balance was found over the first 24 h (r = - 0.20; p < 0.05). In conclusion, peripheral perfusion was not different intrinsically between patients with or without SA-AKI. The presence of peripheral hypoperfusion in the SA-AKI group has appeared to be a prognostic marker for mortality. This evaluation maintained its predictive value over the first 72 hours. The fluid balance possibly negatively influences peripheral perfusion in the SA-AKI.

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.