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 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 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 Phenotyping Cardiogenic Shock

2021 ◽  
Author(s):  
Elric Zweck ◽  
Katherine L. Thayer ◽  
Ole K. L. Helgestad ◽  
Manreet Kanwar ◽  
Mohyee Ayouty ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hospital Mortality ◽  
Cardiogenic Shock ◽  
Treatment Strategies ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Tailored Treatment ◽  
Machine Learning Approach ◽  
Clinical Profiles ◽  
Patient Enrollment

Background Cardiogenic shock (CS) is a heterogeneous syndrome with varied presentations and outcomes. We used a machine learning approach to test the hypothesis that patients with CS have distinct phenotypes at presentation, which are associated with unique clinical profiles and in‐hospital mortality. Methods and Results We analyzed data from 1959 patients with CS from 2 international cohorts: CSWG (Cardiogenic Shock Working Group Registry) (myocardial infarction [CSWG‐MI; n=410] and acute‐on‐chronic heart failure [CSWG‐HF; n=480]) and the DRR (Danish Retroshock MI Registry) (n=1069). Clusters of patients with CS were identified in CSWG‐MI using the consensus k means algorithm and subsequently validated in CSWG‐HF and DRR. Patients in each phenotype were further categorized by their Society of Cardiovascular Angiography and Interventions staging. The machine learning algorithms revealed 3 distinct clusters in CS: "non‐congested (I)", "cardiorenal (II)," and "cardiometabolic (III)" shock. Among the 3 cohorts (CSWG‐MI versus DDR versus CSWG‐HF), in‐hospital mortality was 21% versus 28% versus 10%, 45% versus 40% versus 32%, and 55% versus 56% versus 52% for clusters I, II, and III, respectively. The "cardiometabolic shock" cluster had the highest risk of developing stage D or E shock as well as in‐hospital mortality among the phenotypes, regardless of cause. Despite baseline differences, each cluster showed reproducible demographic, metabolic, and hemodynamic profiles across the 3 cohorts. Conclusions Using machine learning, we identified and validated 3 distinct CS phenotypes, with specific and reproducible associations with mortality. These phenotypes may allow for targeted patient enrollment in clinical trials and foster development of tailored treatment strategies in subsets of patients with CS.

scholarly journals Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach

2016 ◽  
Vol 23 (3) ◽  
pp. 269-278 ◽  
Author(s):  
R. Andrew Taylor ◽  
Joseph R. Pare ◽  
Arjun K. Venkatesh ◽  
Hani Mowafi ◽  
Edward R. Melnick ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Big Data ◽  
Hospital Mortality ◽  
Data Driven ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Emergency Department Patients
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