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

Wiersema, Renske; Koeze, Jacqueline; Eck, Ruben J.; Kaufmann, Thomas; Hiemstra, Bart; Koster, Geert; Franssen, Casper F. M.; Vaara, Suvi T.; Keus, Frederik; Horst, Iwan C C van der

doi:10.1111/aas.13465

Cited by 9 publications

(14 citation statements)

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“…Nevertheless, healthcare providers can take into account improbable events and take variables that are currently unavailable for algorithms. It remains essential to keep an eye on the patient at the bedside, as previous studies have shown that often simple variables can predict adverse outcomes [ 27 , 28 ]. Furthermore, the healthcare providers' “gut feeling,” or “clinical gestalt,” will probably never be fully understood but may be of crucial additive value.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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“…In fact, a very recent study reinforces this hypothesis. Wiersema et al (2020) demonstrated that a prolonged CRT, among other clinical findings, was able to predict AKI development in critically ill patients [ 38 ]. Therefore, new studies are necessary to establish the predictive value of peripheral perfusion assessment for AKI development in septic patients.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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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.

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“…The study flowchart according to the PRISMA statement is shown in Figure 1. The review included 150 studies (encompassing 14.4 million patients) with requisite data to enable sROC curves in 103 studies. The median duration of study data collection was 4 years (range, 3 months to 17 years).…”

Section: Resultsmentioning

confidence: 99%

“…Most studies reported prospective data collection (124 [82.7%]) . Based on prespecified clinical scenario subgroups, 29 studies assessed AKI associated with nephrotoxic agents, including 26 studies examining radio-contrast medium associated AKI and 3 studies that reported AKI associated with other nephrotoxins; 64 studies assessed AKI prediction in the postoperative setting; 21 studies assessed AKI prediction in intensive care units (ICU); 35 studies assessed AKI in general hospitalizations…”

Section: Resultsmentioning

confidence: 99%

Characterization of Risk Prediction Models for Acute Kidney Injury

et al. 2023

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ImportanceDespite the expansion of published prediction models for acute kidney injury (AKI), there is little evidence of uptake of these models beyond their local derivation nor data on their association with patient outcomes.ObjectiveTo systematically review published AKI prediction models across all clinical subsettings.Data SourcesMEDLINE via PubMed (January 1946 to April 2021) and Embase (January 1947 to April 2021) were searched using medical subject headings and text words related to AKI and prediction models.Study SelectionAll studies that developed a prediction model for AKI, defined as a statistical model with at least 2 predictive variables to estimate future occurrence of AKI, were eligible for inclusion. There was no limitation on study populations or methodological designs.Data Extraction and SynthesisTwo authors independently searched the literature, screened the studies, and extracted and analyzed the data following the Preferred Reporting Items for Systematic Review and Meta-analyses guideline. The data were pooled using a random-effects model, with subgroups defined by 4 clinical settings. Between-study heterogeneity was explored using multiple methods, and funnel plot analysis was used to identify publication bias.Main Outcomes and MeasuresC statistic was used to measure the discrimination of prediction models.ResultsOf the 6955 studies initially identified through literature searching, 150 studies, with 14.4 million participants, met the inclusion criteria. The study characteristics differed widely in design, population, AKI definition, and model performance assessments. The overall pooled C statistic was 0.80 (95% CI, 0.79-0.81), with pooled C statistics in different clinical subsettings ranging from 0.78 (95% CI, 0.75-0.80) to 0.82 (95% CI, 0.78-0.86). Between-study heterogeneity was high overall and in the different clinical settings (eg, contrast medium–associated AKI: I2 = 99.9%; P &lt; .001), and multiple methods did not identify any clear sources. A high proportion of models had a high risk of bias (126 [84.4%]) according to the Prediction Model Risk Of Bias Assessment Tool.Conclusions and RelevanceIn this study, the discrimination of the published AKI prediction models was good, reflected by high C statistics; however, the wide variation in the clinical settings, populations, and predictive variables likely drives the highly heterogenous findings that limit clinical utility. Standardized procedures for development and validation of prediction models are urgently needed.

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

Cited by 9 publications

References 31 publications

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

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

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

Characterization of Risk Prediction Models for Acute Kidney Injury

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