A Simple Scoring Method for Predicting the Low Risk of Persistent Acute Kidney Injury in Critically Ill Adult Patients

Matsuura, Ryo; Iwagami, Masao; Moriya, Hideshige; Ohtake, Takayasu; Hamasaki, Yoshifumi; Nangaku, Masaomi; Doi, Kent; Kobayashi, Shuzo; Noiri, Eisei

doi:10.1038/s41598-020-62479-w

Cited by 14 publications

(33 citation statements)

References 22 publications

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“…A growing body of evidence suggests that duration of AKI or renal recovery is associated with outcomes in critically ill septic patients 2 , 7 , 8 , 33 , 34 . Several clinical tools, including urinary indices 10 – 12 , imaging techniques 13 , 17 , prediction models 35 , 36 , and biomarkers 14 – 17 , were investigated in previous studies to predict renal recovery or its surrogate, namely progression to severe AKI. Nevertheless, they were found to be poorly effective or have not been validated in patients with sepsis 9 .…”

Section: Discussionmentioning

confidence: 99%

Machine learning for early discrimination between transient and persistent acute kidney injury in critically ill patients with sepsis

Luo

Yan

Zhang

et al. 2021

Sci Rep

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Acute kidney injury (AKI) is commonly present in critically ill patients with sepsis. Early prediction of short-term reversibility of AKI is beneficial to risk stratification and clinical treatment decision. The study sought to use machine learning methods to discriminate between transient and persistent sepsis-associated AKI. Septic patients who developed AKI within the first 48 h after ICU admission were identified from the Medical Information Mart for Intensive Care III database. AKI was classified as transient or persistent according to the Acute Disease Quality Initiative workgroup consensus. Five prediction models using logistic regression, random forest, support vector machine, artificial neural network and extreme gradient boosting were constructed, and their performance was evaluated by out-of-sample testing. A simplified risk prediction model was also derived based on logistic regression and features selected by machine learning algorithms. A total of 5984 septic patients with AKI were included, 3805 (63.6%) of whom developed persistent AKI. The artificial neural network and logistic regression models achieved the highest area under the receiver operating characteristic curve (AUC) among the five machine learning models (0.76, 95% confidence interval [CI] 0.74–0.78). The simplified 14-variable model showed adequate discrimination, with the AUC being 0.76 (95% CI 0.73–0.78). At the optimal cutoff of 0.63, the sensitivity and specificity of the simplified model were 63% and 76% respectively. In conclusion, a machine learning-based simplified prediction model including routine clinical variables could be used to differentiate between transient and persistent AKI in critically ill septic patients. An easy-to-use risk calculator can promote its widespread application in daily clinical practice.

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Section: Discussionmentioning

confidence: 99%

Machine learning for early discrimination between transient and persistent acute kidney injury in critically ill patients with sepsis

Luo

Yan

Zhang

et al. 2021

Sci Rep

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“…Few studies have predicted AKI prognosis with effective predictions to support decision-making. Several clinical tools, including prediction models [23,24], urinary indices [25,26], novel biomarkers [27,28], and imaging techniques [29] were canvassed in previous studies to predict renal recovery, namely progression to severe AKI. For example, a recent study enrolling 8,320 critical patients with AKI found a poor performance of SOFA and △Cr for predicting persistent AKI, with the AUC of 0.69 (95%CI 0.66-0.71) and 0.74 (95%CI 0.71-0.77), respectively [23].…”

Section: Discussionmentioning

confidence: 99%

“…Several clinical tools, including prediction models [23,24], urinary indices [25,26], novel biomarkers [27,28], and imaging techniques [29] were canvassed in previous studies to predict renal recovery, namely progression to severe AKI. For example, a recent study enrolling 8,320 critical patients with AKI found a poor performance of SOFA and △Cr for predicting persistent AKI, with the AUC of 0.69 (95%CI 0.66-0.71) and 0.74 (95%CI 0.71-0.77), respectively [23]. Furthermore, the renal angina index has been proposed to detect critically ill children at high risk of persistent AKI, validated as an effective screening tool.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Machine Learning Methods and Conventional Regression for Predicting Renal Function Recovery and Recovery Time among AKI Patients in ICU

Zhao

et al. 2022

Preprint

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Background: Acute kidney injury (AKI) is one of the most frequent complications of critical illness. Early predicted renal function recovery can improve AKI prognosis with appropriate interventions. We aimed to derivate and validate two prediction models with high prediction performance and clinical interpretability for renal function recovery and recovery time after AKI, respectively.Methods: We reviewed patients diagnosed with acute kidney disease in intensive care (MIMIC-III) database between 2001 and 2012. The conventional logistic regression and machine algorithm were used to develop prediction models. The performance measures included the area under the receiver operating characteristic curve (AU-ROC), Precision-Recall (PR) curves, and the classification matrix.Results: A total of 3,989 critically-ill AKI adult patients corresponding with Kidney Disease Improving Global Outcomes (KDIGO) with the length of stay in ICU more than 48 hours were included in our analysis cohort. Comparing the model performance, the gradient boosting machine model for predicting renal function recovery had a higher AU-ROC (0.92 [95%CI 0.89-0.94]) than the logistic regression model (0.88 [95%CI 0.84-0.92]). The ExtraTrees machine learning-based model (R2=0.2367) was developed to predict recovery time after AKI, which yielded a better result than the linear regression (R2=0.2006). Conclusions: In this cohort study, machine learning algorithms showed superior ability to predict the prognosis of AKI patients in ICU compared with the traditional logistic and linear regression models. These models may prove clinically helpful and assist clinicians in providing intervention in time, potentially leading to improved prognosis.

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“…A pesar de la escasa información disponible sobre el comportamiento de la LRA en República Dominicana 12,13 , la implementación del IAR posibilita la detección oportuna de LRA y por añadidura los beneficios potenciales de esta acción: control en el uso de recursos (días de hospitalización, secuelas, costos de atención, etc.) 4,14 . -Este esfuerzo se enfoca en responder a las necesidades de países de Latinoamérica, en donde se precisa aprovechar al máximo los recursos disponibles.…”

Section: Recomendacionesunclassified

“…Nuestra región se caracteriza por gran desigualdad social e inequitativa repartición de recursos 13 , afectando a los servicios de salud y puede constituir una barrera para la introducción de nuevas tecnologías tanto para diagnóstico como tratamiento. -Consideramos al IAR descrito por Matsuura et al 14 como el sistema de estratificación de riesgo para LRA de mayor potencial para su implementación dentro de las UCI polivalentes dentro de República Dominicana. C: 8 de 8 presentes (100%); GE moderada; GR: fuerte.…”

Section: Recomendacionesunclassified

Consenso para el uso del índice de angina renal en adultos de República Dominicana

Granado¹,

Rizo-Topete²,

Molano-Triviño³

et al. 2022

NEFRO

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El poder predecir la lesión renal aguda severa puede optimizar el tiempo de detección y el tratamiento y con ello la posibilidad de mejorar desenlaces a corto y largo plazo. Con ese objetivo, en 2010, se diseñó un modelo conceptual para identificar tempranamente a los pacientes en estado crítico con riesgo de sufrir una lesión renal aguda grave al ingresar en la unidad de cuidados intensivos; este modelo se denominó índice de angina renal (IAR). El IAR desde sus trabajos iniciales ha mostrado ser clínicamente factible y fácilmente aplicable para identificar a los pacientes pediátricos en estado crítico con riesgo de lesión renal aguda grave que persiste más allá de la lesión funcional. Si bien en sus orígenes el IAR se diseñó en población pediátrica, su aplicación en adultos críticamente enfermos es enteramente factible. Para familiarizar a intensivistas y nefrólogos con esta herramienta diagnóstica y facilitar su aplicación en pacientes adultos, se realizó una revisión de la literatura y se convocó a ocho especialistas a una reunión de consenso, que dio como resultado 10 recomendaciones para la aplicación del IAR en pacientes adultos en estado crítico. Estas recomendaciones se elaboraron teniendo en cuenta las características de los sistemas de salud no solo de República Dominicana, sino también de Latinoamérica.

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A Simple Scoring Method for Predicting the Low Risk of Persistent Acute Kidney Injury in Critically Ill Adult Patients

Cited by 14 publications

References 22 publications

Machine learning for early discrimination between transient and persistent acute kidney injury in critically ill patients with sepsis

Machine learning for early discrimination between transient and persistent acute kidney injury in critically ill patients with sepsis

Comparison of Machine Learning Methods and Conventional Regression for Predicting Renal Function Recovery and Recovery Time among AKI Patients in ICU

Consenso para el uso del índice de angina renal en adultos de República Dominicana

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