AKIpredictor, an online prognostic calculator for acute kidney injury in adult critically ill patients: development, validation and comparison to serum neutrophil gelatinase-associated lipocalin

Flechet, Marine; Güiza, Fabián; Schetz, Miet; Wouters, Pieter; Vanhorebeek, Ilse; Derese, Inge; Gunst, Jan; Spriet, Isabel; Casaer, Michaël P; Berghe, Greet Van den; Meyfroidt, Geert

doi:10.1007/s00134-017-4678-3

Cited by 127 publications

(121 citation statements)

References 48 publications

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“…In a recent article published in Intensive Care Medicine, Flechet et al report the development of an electronic AKI prediction calculator [12]. In a post hoc analysis of the EPaNIC dataset, the authors analysed the data of 4490 patients with the aim to develop and validate a clinical model to predict the onset of AKI during the first 7 days in the ICU.…”

Section: Predicting Akimentioning

confidence: 99%

“…Furthermore, their performance is strongly dependent on the patient cohort, and a change of endpoint prevalence in the population of interest is likely to influence the diagnostic performance of the test as much as the clinical relevance. Once validated, AKI prediction scores like the one developed by Flechet and colleagues [12] may serve as powerful tools to select populations of interest for biomarker testing and future intervention studies. …”

Section: Predicting Akimentioning

confidence: 99%

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Predictions are difficult…especially about AKI

2017

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Section: Predicting Akimentioning

confidence: 99%

Section: Predicting Akimentioning

confidence: 99%

Predictions are difficult…especially about AKI

2017

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“…Further, even among the variables that predicted AKI in more than 1 study, definitions used for these variables differed from study to study. For example, CKD was determined by Kashani et al [11] based on the patient’s medical history, while it was defined as baseline estimated glomerular filtration rate < 60 mL/min/1.73 m 2 by Malhotra et al[13] In contrast, Flechet et al[14] used baseline SCr rather than CKD in their risk prediction model. Similarly, the definition of nephrotoxin exposure varied widely across studies (Table 1b).…”

Section: Comparison Of Clinical Risk Prediction Modelsmentioning

confidence: 99%

“…Flechet et al [14] conducted a retrospective study using the Early Parenteral Nutrition Completing Enteral Nutrition in Adult Critically Ill Patients (EPaNIC) database. They developed and validated a clinical risk stratification tool to predict AKI during the first 7 days of ICU stay.…”

Section: What Has Been Done In Relation To Clinical Risk Prediction Tmentioning

confidence: 99%

Risk Prediction Models for Acute Kidney Injury in Critically Ill Patients: Opus in Progressu

Neyra

Leaf

2018

Nephron

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Acute kidney injury (AKI) is a complex systemic syndrome associated with high morbidity and mortality. Among critically ill patients admitted to intensive care units (ICUs), the incidence of AKI is as high as 50% and is associated with dismal outcomes. Thus, the development and validation of clinical risk prediction tools that accurately identify patients at high risk for AKI in the ICU is of paramount importance. We provide a comprehensive review of 3 clinical risk prediction tools that have been developed for incident AKI occurring in the first few hours or days following admission to the ICU. We found substantial heterogeneity among the clinical variables that were examined and included as significant predictors of AKI in the final models. The area under the receiver operating characteristic curves was ∼0.8 for all 3 models, indicating satisfactory model performance, though positive predictive values ranged from only 23 to 38%. Hence, further research is needed to develop more accurate and reproducible clinical risk prediction tools. Strategies for improved assessment of AKI susceptibility in the ICU include the incorporation of dynamic (time-varying) clinical parameters, as well as biomarker, functional, imaging, and genomic data.

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“…The AUC for the discovery and external validation cohorts was 0.79 and 0.81, respectively, and the positive predictive value was 23% for a risk score ≥5. Similarly, Flechet et al [94] developed a tool named AKIpredictor using random forest machine-learning schemes and correlation-based ranking algorithms. This tool incorporates clinical parameters before, at the time of, and during the first day of ICU admission and was developed to predict AKI within the first 7 days following ICU admission.…”

Section: Acute Kidney Injury and Critical Care Nephrologymentioning

confidence: 99%

Impact of Recent Clinical Trials on Nephrology Practice: Are We in a Stagnant Era?

et al. 2018

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Background: Although renal replacement therapy prevents death from uremia, survival among patients with acute and chronic kidney diseases (CKD) remains an imperative concern. The expected life span of US dialysis patients 60–64 years of age is approximately 4.5 years; this is similar to that of patients with lung cancer. Despite substantial progress in many medical specialties over the past decades (e.g., notable reductions in myocardial infarction, stroke, and mortality rates in the general population), survival among dialysis patients has not improved significantly over the same period. A few decades ago, HIV infection and AIDS were pretty much a death sentence. Because of progress in HIV treatment, now it can be controlled with a daily pill, and ongoing research is pushing treatment even further and controls the virus with longer-acting treatment. A cure is no longer impossible for HIV and other viral infections such as hepatitis B and C and many malignancies, but so far there is no cure for CKD. Summary: Billions of dollars have been spent on kidney disease research in the past decades, with no tangible progress in clinical practice. The challenges of improving the quantity and quality of trials in nephrology are enormous. The number of randomized controlled trials (RCTs) published in nephrology is lower than that in other medical subspecialties, and most of the big RCTs in nephrology yield negative results. Nephrology studies evaluating hard clinical endpoints or surrogate endpoints are scarce. Key Message: Herein we discuss the slow progress in nephrology research that has impacted clinical practice over the last couple of decades and highlight the major obstacles, challenges, and potential solutions.

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AKIpredictor, an online prognostic calculator for acute kidney injury in adult critically ill patients: development, validation and comparison to serum neutrophil gelatinase-associated lipocalin

Abstract: AKI can be predicted early with models that only use routinely collected clinical information and outperform NGAL measured at ICU admission. The AKI-123 models are available at http://akipredictor.com/ . Trial registration Clinical Trials.gov NCT00512122.

Cited by 127 publications

References 48 publications

Predictions are difficult…especially about AKI

Predictions are difficult…especially about AKI

Risk Prediction Models for Acute Kidney Injury in Critically Ill Patients: Opus in Progressu

Impact of Recent Clinical Trials on Nephrology Practice: Are We in a Stagnant Era?

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