Identifying Patients with Heart Failure in Susceptible to De Novo Acute Kidney Injury: A Machine Learning Approach

2020 ◽  
Author(s):  
Zhoujian Sun ◽  
Hui Chen ◽  
Wei Dong ◽  
Jinlong Shi ◽  
Huilong Duan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Acute Kidney Injury ◽  
De Novo ◽  
Kidney Injury ◽  
Learning Approach ◽  
Patients With Heart Failure ◽  
Machine Learning Approach

scholarly journals Development of a Prediction Score for In-Hospital Mortality in COVID-19 Patients with Acute Kidney Injury: A Machine Learning Approach

2021 ◽  
Author(s):  
Daniela Ponce ◽  
Luis Gustavo Modelli Andrade ◽  
Rolando Claure Granado ◽  
Alejandro Ferrero ◽  
Raul Lombardi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Hospital Mortality ◽  
Kidney Injury ◽  
Prediction Score ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Machine learning approach to predict acute kidney injury after liver surgery

2021 ◽  
Vol 9 (36) ◽  
pp. 11255-11264
Author(s):  
Jun-Feng Dong ◽  
Qiang Xue ◽  
Ting Chen ◽  
Yuan-Yu Zhao ◽  
Hong Fu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Liver Surgery ◽  
Kidney Injury ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Development of a Prediction Score for In-Hospital Mortality in COVID-19 Patients with Acute Kidney Injury: A Machine Learning Approach

2021 ◽  
Author(s):  
Daniela Ponce ◽  
Luis Gustavo Modelli de Andrade ◽  
Rolando Claure-Del Granado ◽  
Alejandro Ferrero Fuentes ◽  
Raul Lombardi
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Latin America ◽  
Hospital Mortality ◽  
Validation Cohort ◽  
Demographic Data ◽  
Kidney Injury ◽  
Learning Approach ◽  
Risk Of Death ◽  
Machine Learning Approach

Abstract Introduction: Acute kidney injury (AKI) is frequently associated to COVID-19, and is considered an indicator of severity of disease and is thus associated with increased mortality risk”. Objective: The aim of the study was to develop and validate a prognostic score at hospital admission for predicting in-hospital mortality in COVID-19 patients with AKI (AKI-COV score).Design: Cross-sectional multicenter prospective cohort study.Setting: The Latin America AKI COVID-19 Registry has been conducted in 57 cities in 12 countries from Latin America. Model training was performed on a cohort of patients admitted from May 1 to December 31, 2020. Participants: Eight hundred and seventy COVID-19 patients with AKI defined according KDIGO serum creatinine criteria were included between 01 May to 31 December 2020.Material and Methods: We evaluated four categories of predictor variables available at the time of AKI diagnosis: (1) demographic data; (2) comorbidities and condition at admission; (3) laboratory exams at admission; (4) characteristics and causes of AKI. We used a machine learning approach to fit models in the training set using 10-fold-cross validation. Predictors with more than 30% missing were removed. We select the best model and confirm the accuracy in a validation cohort using the area under the receiver operating characteristic curve (AUC-ROC). Main Outcome Measured: In-hospital mortality.Results: There were 544 (62.5%) in-hospital deaths. Increasing age, mechanical ventilation, use of vasopressors, leukocytes number[RC1] transaminases levels, hypertension, severe condition at admission, AKI ethiology, and need kidney replacement therapies (KRT) were associated with increased risk of death. Longer time from symptoms to hospitalization or to AKI diagnosis, and higher urine output were associated with reduced risk of death. The coefficients of the best model (Elastic Net) were used to build the predictive ImAgeS score. The final model has an AUC-ROC of 0.823 [95% CI 0.761 – 0.885] in the validation cohort. Conclusion: We developed a predictive model using only demographic data, comorbidities, hospital admission condition, laboratory variables and causes of AKI that shows good accuracy and is easily applicable. The use of AKI-COV score may assist health-care workers in identifying hospitalized COVID-19 patients with AKI that may require more intensive monitoring and can be used for resource allocation.

scholarly journals Development of a prediction score for in-hospital mortality in COVID-19 patients with acute kidney injury: a machine learning approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daniela Ponce ◽  
Luís Gustavo Modelli de Andrade ◽  
Rolando Claure-Del Granado ◽  
Alejandro Ferreiro-Fuentes ◽  
Raul Lombardi
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Hospital Mortality ◽  
Demographic Data ◽  
Characteristic Curve ◽  
Prognostic Score ◽  
Kidney Injury ◽  
Learning Approach ◽  
Cross Sectional ◽  
Machine Learning Approach

AbstractAcute kidney injury (AKI) is frequently associated with COVID-19 and it is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting in-hospital mortality in COVID-19 patients with AKI (AKI-COV score). This was a cross-sectional multicentre prospective cohort study in the Latin America AKI COVID-19 Registry. A total of 870 COVID-19 patients with AKI defined according to the KDIGO were included between 1 May 2020 and 31 December 2020. We evaluated four categories of predictor variables that were available at the time of the diagnosis of AKI: (1) demographic data; (2) comorbidities and conditions at admission; (3) laboratory exams within 24 h; and (4) characteristics and causes of AKI. We used a machine learning approach to fit models in the training set using tenfold cross-validation and validated the accuracy using the area under the receiver operating characteristic curve (AUC-ROC). The coefficients of the best model (Elastic Net) were used to build the predictive AKI-COV score. The AKI-COV score had an AUC-ROC of 0.823 (95% CI 0.761–0.885) in the validation cohort. The use of the AKI-COV score may assist healthcare workers in identifying hospitalized COVID-19 patients with AKI that may require more intensive monitoring and can be used for resource allocation.

scholarly journals CARDIORENAL SYNDROME IN PATIENTS WITH CHRONIC HEART FAILURE AS A STAGE OF THE CARDIORENAL CONTINUUM (PART I): DEFINITION, CLASSIFICATION, PATHOGENESIS, DIAGNOSIS, EPIDEMIOLOGY

2019 ◽  
Vol 9 (1) ◽  
pp. 5-22 ◽  
Author(s):  
E. V. Reznik ◽  
I. G. Nikitin
Keyword(s):  
Heart Failure ◽  
Acute Kidney Injury ◽  
Chronic Heart Failure ◽  
Impaired Renal Function ◽  
Prognostic Value ◽  
Kidney Injury ◽  
Cardiorenal Syndrome ◽  
Syndrome Type ◽  
Patients With Heart Failure

The combination of heart failure and renal failure is called cardiorenal syndrome. It is a stage of the cardiorenal continuum and, possibly, a small link of the cardiorenal-cerebral-metabolic axis. Despite the fact that the phrase “cardiorenal syndrome” and its five types have become a part of the medical lexicon, many aspects of this problem are still not clear. Cardiorenal syndrome can be diagnosed in 32-90.3% of patients with heart failure. Cardiorenal syndrome type 1 or 2 develops in most cases of heart failure: cardiorenal syndrome presents with the development ofchronic kidney disease in patients with chronic heart failure and acute kidney injury in patients with acute heart failure. Impaired renal function has an unfavorable prognostic value. It leads to an increase in the mortality of patients with heart failure. It is necessary to timely diagnose the presence of cardiorenal syndrome and take into account its presence when managing patients with heart failure. Further researches are needed on ways toprevent the development and prevent the progression of kidney damage in patients with heart failure, to which the efforts of the multidisciplinary team should be directed. The first part of this review examines the currently definition, classification, pathogenesis, epidemiology and prognosis of cardiorenal syndrome in patients with heart failure.

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