scholarly journals Comparison of Prediction Models for Acute Kidney Injury Among Patients with Hepatobiliary Malignancies Based on XGBoost and LASSO-Logistic Algorithms

2021 ◽  
Vol Volume 14 ◽  
pp. 1325-1335
Author(s):  
Yunlu Zhang ◽  
Yimei Wang ◽  
Jiarui Xu ◽  
Bowen Zhu ◽  
Xiaohong Chen ◽  
...  
2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
L Lei ◽  
Y He ◽  
Z Guo ◽  
B Liu ◽  
J Liu ◽  
...  

Abstract Background Patients with congestive heart failure (CHF) are vulnerable to contrast-induced acute kidney injury (CI-AKI), but few prediction models are currently available. Objectives We aimed to establish a simple nomogram for CI-AKI risk assessment for patients with CHF undergoing coronary angiography. Methods A total of 1876 consecutive patients with CHF (defined as New York Heart Association functional class II-IV or Killip class II-IV) were enrolled and randomly (2:1) assigned to a development cohort and a validation cohort. The endpoint was CI-AKI defined as serum creatinine elevation of ≥0.3 mg/dL or 50% from baseline within the first 48–72 hours following the procedure. Predictors for the nomogram were selected by multivariable logistic regression with a stepwise approach. The discriminative power was assessed using the area under the receiver operating characteristic (ROC) curve and was compared with the classic Mehran score in the validation cohort. Calibration was assessed using the Hosmer–Lemeshow test and 1000 bootstrap samples. Results The incidence of CI-AKI was 9.06% (n=170) in the total sample, 8.64% (n=109) in the development cohort and 9.92% (n=61) in the validation cohort (p=0.367). The simple nomogram including four predictors (age, intra-aortic balloon pump, acute myocardial infarction and chronic kidney disease) demonstrated a similar predictive power as the Mehran score (area under the curve: 0.80 vs 0.75, p=0.061), as well as a well-fitted calibration curve. Conclusions The present simple nomogram including four predictors is a simple and reliable tool to identify CHF patients at risk of CI-AKI, whereas further external validations are needed. Figure 1 Funding Acknowledgement Type of funding source: None


Renal Failure ◽  
2020 ◽  
Vol 42 (1) ◽  
pp. 869-876
Author(s):  
Yang Li ◽  
Xiaohong Chen ◽  
Ziyan Shen ◽  
Yimei Wang ◽  
Jiachang Hu ◽  
...  

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Enrico Favaro ◽  
Roberta Lazzarin ◽  
Daniela Cremasco ◽  
Erika Pierobon ◽  
Marta Guizzo ◽  
...  

Abstract Background and Aims The modern development of the black box approach in clinical nephrology is inconceivable without a logical theory of renal function and a comprehension of anatomical architecture of the kidney, in health and disease: this is the undisputed contribution offered by Malpighi, Oliver and Trueta starting from the seventeenth century. The machine learning model for the prediction of acute kidney injury, progression of renal failure and tubulointerstitial nephritis is a good example of how different knowledge about kidney are an indispensable tool for the interpretation of model itself. Method Historical data were collected from literature, textbooks, encyclopedias, scientific periodicals and laboratory experimental data concerning these three authors. Results The Italian Marcello Malpighi (1628-1694), born in Crevalcore near Bologna, was Professor of anatomy at Bologna, Pisa and Messina. The historic description of the pulmonary capillaries was made in his second epistle to Borelli published in 1661 and intitled De pulmonibus, by means of the frog as “the microscope of nature” (Fig. 1). It is the first description of capillaries in any circulation. William Harvey in De motu cordis in 1628 (year of publication the same of date of birth of Italian anatomist!) could not see the capillary vessels. This thriumphant discovery will serve for the next reconnaissance of characteristic renal rete mirabile.in the corpuscle of Malpighi, lying within the capsule of Bowman. Jean Redman Oliver (1889-1976), a pathologist born and raised in Northern California, was able to bridge the gap between the nephron and collecting system through meticulous dissections, hand drawn illustrations and experiments which underpin our current understanding of renal anatomy and physiology. In the skillful lecture “When is the kidney not a kidney?” (1949) Oliver summarizes his far-sighted vision on renal physiology and disease in the following sentence: the Kidney in health, if you will, but the Nephrons in disease. Because, the “nephron” like the “kidney” is an abstraction that must be qualified in terms of its various parts, its cellular components and the molecular mechanisms involved in each discrete activity (Fig. 2). The Catalan surgeon Josep Trueta I Raspall (1897-1977) was born in the Poblenou neighborhood of Barcelona. His impact of pioneering and visionary contribution to the changes in renal circulation for the pathogenesis of acute kidney injury was pivotal for history of renal physiology. “The kidney has two potential circulatory circulations. Blood may pass either almost exclusively through one or other of two pathways, or to a varying degree through both”. (Studies of the Renal Circulation, published in 1947). Now this diversion of blood from cortex to the less resistant medullary circulation is known with the eponym Trueta shunt. Conclusion The black box approach to the kidney diseases should be considered by practitioners as a further tool to help to inform model update in many clinical setting. The number of machine learning clinical prediction models being published is rising, as new fields of application are being explored in medicine (Fig. 3). A challenge in the clinical nephrology is to explore the “kidney machine” during each therapeutic diagnostic procedure. Always, the intriguing relationship between the set of nephrological syndromes and kidney diseases cannot disregard the precious notions the specific organization of kidney microcirculation, fruit of many scientific contributions of the work by Malpighi, Oliver and Trueta (Fig. 3).


Information ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 386
Author(s):  
Sheikh S. Abdullah ◽  
Neda Rostamzadeh ◽  
Kamran Sedig ◽  
Amit X. Garg ◽  
Eric McArthur

Acute kidney injury (AKI) is a common complication in hospitalized patients and can result in increased hospital stay, health-related costs, mortality and morbidity. A number of recent studies have shown that AKI is predictable and avoidable if early risk factors can be identified by analyzing Electronic Health Records (EHRs). In this study, we employ machine learning techniques to identify older patients who have a risk of readmission with AKI to the hospital or emergency department within 90 days after discharge. One million patients’ records are included in this study who visited the hospital or emergency department in Ontario between 2014 and 2016. The predictor variables include patient demographics, comorbid conditions, medications and diagnosis codes. We developed 31 prediction models based on different combinations of two sampling techniques, three ensemble methods, and eight classifiers. These models were evaluated through 10-fold cross-validation and compared based on the AUROC metric. The performances of these models were consistent, and the AUROC ranged between 0.61 and 0.88 for predicting AKI among 31 prediction models. In general, the performances of ensemble-based methods were higher than the cost-sensitive logistic regression. We also validated features that are most relevant in predicting AKI with a healthcare expert to improve the performance and reliability of the models. This study predicts the risk of AKI for a patient after being discharged, which provides healthcare providers enough time to intervene before the onset of AKI.


2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Ao Jiao ◽  
Qingpeng Liu ◽  
Feng Li ◽  
Rui Guo ◽  
Bowen Wang ◽  
...  

Purpose. Acute kidney injury (AKI) is a major and severe complication following donation-after-circulatory-death (DCD) liver transplantation (LT) and is associated with increased postoperative morbidity and mortality. However, the risk factors and the prognosis factors of AKI still need to be further explored, and the relativity of intraoperative hepatic blood inflow (HBI) and AKI following LT has not been discussed yet. The purpose of this study was to investigate the correlation between HBI and AKI and to construct a prediction model of early acute kidney injury (EAKI) following DCD LT with the combination of HBI and other clinical parameters. Methods. Clinical data of 132 patients who underwent DCD liver transplantation at the first hospital of China Medical University from April 2005 to March 2017 were analyzed. Data of 105 patients (the first ten years of patients) were used to develop the prediction model. Then we assessed the clinical usefulness of the prediction models in the validation cohort (27 patients). EAKI according to Kidney Disease Improving Global Outcomes (KDIGO) criteria based on serum creatinine increase during 7-day of postoperative follow-up. Results. After Least Absolute Shrinkage and Selection Operator (LASSO) regression and simplification, a simplified prediction model consisting of the Child-Turcotte-Pugh (CTP) score (p=0.033), anhepatic phase (p=0.014), packed red blood cell (pRBC) transfusion (p=0.027), and the HBI indexed by height (HBI/h) (p=0.002) was established. The C-indexes of the model in the development and validation cohort were 0.823 [95% CI, 0.738-0.908] and 0.921 [95% CI, 0.816-1.000], respectively. Conclusions. In this study, we demonstrated the utility of HBI/h as a predictor for EAKI following DCD LT, as well as the clinical usefulness of the prediction model through the combination of the CTP score, anhepatic phase, pRBC transfusion and HBI/h.


2018 ◽  
Vol 35 (5) ◽  
pp. 836-845 ◽  
Author(s):  
Simon Sawhney ◽  
Monica Beaulieu ◽  
Corri Black ◽  
Ognjenka Djurdjev ◽  
Gabriela Espino-Hernandez ◽  
...  

Abstract Background Outcomes after acute kidney injury (AKI) are well described, but not for those already under nephrology clinic care. This is where discussions about kidney failure risk are commonplace. We evaluated whether the established kidney failure risk equation (KFRE) should account for previous AKI episodes when used in this setting. Methods This observational cohort study included 7491 people referred for nephrology clinic care in British Columbia in 2003–09 followed to 2016. Predictors were previous Kidney Disease: Improving Global Outcomes–based AKI, age, sex, proteinuria, estimated glomerular filtration rate (eGFR) and renal diagnosis. Outcomes were 5-year kidney failure and death. We developed cause-specific Cox models (AKI versus no AKI) for kidney failure and death, stratified by eGFR (</≥30 mL/min/1.73 m2). We also compared prediction models comparing the 5-year KFRE with two refitted models, one with and one without AKI as a predictor. Results AKI was associated with increased kidney failure (33.1% versus 26.3%) and death (23.8% versus 16.8%) (P  < 0.001). In Cox models, AKI was independently associated with increased kidney failure in those with an eGFR ≥30 mL/min/1.73 m2 {hazard ratio [HR] 1.35 [95% confidence interval (CI) 1.07–1.70]}, no increase in those with eGFR <30 mL/min/1.73 m2 ([HR 1.05 95% CI 0.91–1.21)] and increased mortality in both subgroups [respective HRs 1.89 (95% CI 1.56–2.30) and 1.43 (1.16–1.75)]. Incorporating AKI into a refitted kidney failure prediction model did not improve predictions on comparison of receiver operating characteristics (P = 0.16) or decision curve analysis. The original KFRE calibrated poorly in this setting, underpredicting risk. Conclusions AKI carries a poorer long-term prognosis among those already under nephrology care. AKI may not alter kidney failure risk predictions, but the use of prediction models without appreciating the full impact of AKI, including increased mortality, would be simplistic. People with kidney diseases have risks beyond simply kidney failure. This complexity and variability of outcomes of individuals is important.


JAMIA Open ◽  
2018 ◽  
Vol 2 (1) ◽  
pp. 115-122 ◽  
Author(s):  
Jianqin He ◽  
Yong Hu ◽  
Xiangzhou Zhang ◽  
Lijuan Wu ◽  
Lemuel R Waitman ◽  
...  

Abstract Objectives Acute kidney injury (AKI) in hospitalized patients puts them at much higher risk for developing future health problems such as chronic kidney disease, stroke, and heart disease. Accurate AKI prediction would allow timely prevention and intervention. However, current AKI prediction researches pay less attention to model building strategies that meet complex clinical application scenario. This study aims to build and evaluate AKI prediction models from multiple perspectives that reflect different clinical applications. Materials and Methods A retrospective cohort of 76 957 encounters and relevant clinical variables were extracted from a tertiary care, academic hospital electronic medical record (EMR) system between November 2007 and December 2016. Five machine learning methods were used to build prediction models. Prediction tasks from 4 clinical perspectives with different modeling and evaluation strategies were designed to build and evaluate the models. Results Experimental analysis of the AKI prediction models built from 4 different clinical perspectives suggest a realistic prediction performance in cross-validated area under the curve ranging from 0.720 to 0.764. Discussion Results show that models built at admission is effective for predicting AKI events in the next day; models built using data with a fixed lead time to AKI onset is still effective in the dynamic clinical application scenario in which each patient’s lead time to AKI onset is different. Conclusion To our best knowledge, this is the first systematic study to explore multiple clinical perspectives in building predictive models for AKI in the general inpatient population to reflect real performance in clinical application.


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