Application of group LASSO regression based Bayesian networks in risk factors exploration and disease prediction for acute kidney injury in hospitalized patients with hematologic malignancies
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 a...
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BMC
2020-05-01
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| Series: | BMC Nephrology |
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| Online Access: | http://link.springer.com/article/10.1186/s12882-020-01786-w |
| _version_ | 1818143687438237696 |
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| author | Yang Li Xiaohong Chen Yimei Wang Jiachang Hu Ziyan Shen Xiaoqiang Ding |
| author_facet | Yang Li Xiaohong Chen Yimei Wang Jiachang Hu Ziyan Shen Xiaoqiang Ding |
| author_sort | Yang Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T11:35:38Z |
| format | Article |
| id | doaj.art-df0db4b2f9e34f06817c44d108f4cb1f |
| institution | Directory Open Access Journal |
| issn | 1471-2369 |
| language | English |
| last_indexed | 2024-12-11T11:35:38Z |
| publishDate | 2020-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Nephrology |
| spelling | doaj.art-df0db4b2f9e34f06817c44d108f4cb1f2022-12-22T01:08:45ZengBMCBMC Nephrology1471-23692020-05-0121111110.1186/s12882-020-01786-wApplication of group LASSO regression based Bayesian networks in risk factors exploration and disease prediction for acute kidney injury in hospitalized patients with hematologic malignanciesYang Li0Xiaohong Chen1Yimei Wang2Jiachang Hu3Ziyan Shen4Xiaoqiang Ding5Department of Nephrology, Zhongshan Hospital, Fudan UniversityDepartment of Nephrology, Zhongshan Hospital, Fudan UniversityDepartment of Nephrology, Zhongshan Hospital, Fudan UniversityDepartment of Nephrology, Zhongshan Hospital, Fudan UniversityDepartment of Nephrology, Zhongshan Hospital, Fudan UniversityDepartment of Nephrology, Zhongshan Hospital, Fudan UniversityAbstract 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.http://link.springer.com/article/10.1186/s12882-020-01786-wAcute kidney injuryHematologic malignancyBayesian networksDisease predictionClinical epidemiology |
| spellingShingle | Yang Li Xiaohong Chen Yimei Wang Jiachang Hu Ziyan Shen Xiaoqiang Ding Application of group LASSO regression based Bayesian networks in risk factors exploration and disease prediction for acute kidney injury in hospitalized patients with hematologic malignancies BMC Nephrology Acute kidney injury Hematologic malignancy Bayesian networks Disease prediction Clinical epidemiology |
| title | Application of group LASSO regression based Bayesian networks in risk factors exploration and disease prediction for acute kidney injury in hospitalized patients with hematologic malignancies |
| title_full | Application of group LASSO regression based Bayesian networks in risk factors exploration and disease prediction for acute kidney injury in hospitalized patients with hematologic malignancies |
| title_fullStr | Application of group LASSO regression based Bayesian networks in risk factors exploration and disease prediction for acute kidney injury in hospitalized patients with hematologic malignancies |
| title_full_unstemmed | Application of group LASSO regression based Bayesian networks in risk factors exploration and disease prediction for acute kidney injury in hospitalized patients with hematologic malignancies |
| title_short | Application of group LASSO regression based Bayesian networks in risk factors exploration and disease prediction for acute kidney injury in hospitalized patients with hematologic malignancies |
| title_sort | application of group lasso regression based bayesian networks in risk factors exploration and disease prediction for acute kidney injury in hospitalized patients with hematologic malignancies |
| topic | Acute kidney injury Hematologic malignancy Bayesian networks Disease prediction Clinical epidemiology |
| url | http://link.springer.com/article/10.1186/s12882-020-01786-w |
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