Yang Li1,2,3,4,5, Xiaohong Chen1,2,3,4,5, Yimei Wang1,2,3,4,5, Jiachang Hu1,2,3,4,5, Ziyan Shen1,2,3,4,5, Xiaoqiang Ding6,7,8,9,10. 1. Department of Nephrology, Zhongshan Hospital, Fudan University, No.180 Fenglin Road, Xuhui District, Shanghai, 200032, China. 2. Shanghai Medical Center of Kidney, Shanghai, 200032, China. 3. Shanghai Key Laboratory of Kidney and Blood Purification, Shanghai, 200032, China. 4. Shanghai Institute of Kidney and Dialysis, Shanghai, 200032, China. 5. Hemodialysis Quality Control Center of Shanghai, Shanghai, 200032, China. 6. Department of Nephrology, Zhongshan Hospital, Fudan University, No.180 Fenglin Road, Xuhui District, Shanghai, 200032, China. dingxqzshospital@163.com. 7. Shanghai Medical Center of Kidney, Shanghai, 200032, China. dingxqzshospital@163.com. 8. Shanghai Key Laboratory of Kidney and Blood Purification, Shanghai, 200032, China. dingxqzshospital@163.com. 9. Shanghai Institute of Kidney and Dialysis, Shanghai, 200032, China. dingxqzshospital@163.com. 10. Hemodialysis Quality Control Center of Shanghai, Shanghai, 200032, China. dingxqzshospital@163.com.
Abstract
BACKGROUND: Patients who were diagnosed with hematologic malignancies (HM) had a higher risk of acute kidney injury (AKI). This study applies the Bayesian networks (BNs) to investigate the interrelationships between AKI and its risk factors among HM patients, and to evaluate the predictive and inferential ability of BNs model in different clinical settings. METHODS: During 2014 and 2015, a total of 2501 inpatients with HM were recruited in this retrospective study conducted in a tertiary hospital, Shanghai of China. Patients' demographics, medical history, clinical and laboratory records on admission were extracted from the electronic medical records. Candidate predictors of AKI were screened in the group-LASSO (gLASSO) regression, and then they were incorporated into BNs analysis for further interrelationship modeling and disease prediction. RESULTS: Of 2395 eligible patients with HM, 370 episodes were diagnosed with AKI (15.4%). Patients with multiple myeloma (24.1%) and leukemia (23.9%) had higher incidences of AKI, followed by lymphoma (13.4%). Screened by the gLASSO regression, variables as age, gender, diabetes, HM category, anti-tumor treatment, hemoglobin, serum creatinine (SCr), the estimated glomerular filtration rate (eGFR), serum uric acid, serum sodium and potassium level were found with significant associations with the occurrence of AKI. Through BNs analysis, age, hemoglobin, eGFR, serum sodium and potassium had directed connections with AKI. HM category and anti-tumor treatment were indirectly linked to AKI via hemoglobin and eGFR, and diabetes was connected with AKI by affecting eGFR level. BNs inferences concluded that when poor eGFR, anemia and hyponatremia occurred simultaneously, the patients' probability of AKI was up to 78.5%. The area under the receiver operating characteristic curve (AUC) of BNs model was 0.835, higher than that in the logistic score model (0.763). It also showed a robust performance in 10-fold cross-validation (AUC: 0.812). CONCLUSION: Bayesian networks can provide a novel perspective to reveal the intrinsic connections between AKI and its risk factors in HM patients. The BNs predictive model could help us to calculate the probability of AKI at the individual level, and follow the tide of e-alert and big-data realize the early detection of AKI.
BACKGROUND:Patients who were diagnosed with hematologic malignancies (HM) had a higher risk of acute kidney injury (AKI). This study applies the Bayesian networks (BNs) to investigate the interrelationships between AKI and its risk factors among HM patients, and to evaluate the predictive and inferential ability of BNs model in different clinical settings. METHODS: During 2014 and 2015, a total of 2501 inpatients with HM were recruited in this retrospective study conducted in a tertiary hospital, Shanghai of China. Patients' demographics, medical history, clinical and laboratory records on admission were extracted from the electronic medical records. Candidate predictors of AKI were screened in the group-LASSO (gLASSO) regression, and then they were incorporated into BNs analysis for further interrelationship modeling and disease prediction. RESULTS: Of 2395 eligible patients with HM, 370 episodes were diagnosed with AKI (15.4%). Patients with multiple myeloma (24.1%) and leukemia (23.9%) had higher incidences of AKI, followed by lymphoma (13.4%). Screened by the gLASSO regression, variables as age, gender, diabetes, HM category, anti-tumor treatment, hemoglobin, serum creatinine (SCr), the estimated glomerular filtration rate (eGFR), serum uric acid, serum sodium and potassium level were found with significant associations with the occurrence of AKI. Through BNs analysis, age, hemoglobin, eGFR, serum sodium and potassium had directed connections with AKI. HM category and anti-tumor treatment were indirectly linked to AKI via hemoglobin and eGFR, and diabetes was connected with AKI by affecting eGFR level. BNs inferences concluded that when poor eGFR, anemia and hyponatremia occurred simultaneously, the patients' probability of AKI was up to 78.5%. The area under the receiver operating characteristic curve (AUC) of BNs model was 0.835, higher than that in the logistic score model (0.763). It also showed a robust performance in 10-fold cross-validation (AUC: 0.812). CONCLUSION: Bayesian networks can provide a novel perspective to reveal the intrinsic connections between AKI and its risk factors in HM patients. The BNs predictive model could help us to calculate the probability of AKI at the individual level, and follow the tide of e-alert and big-data realize the early detection of AKI.
Authors: Kang Liu; Xiangzhou Zhang; Weiqi Chen; Alan S L Yu; John A Kellum; Michael E Matheny; Steven Q Simpson; Yong Hu; Mei Liu Journal: JAMA Netw Open Date: 2022-07-01