Risk prediction for acute kidney disease and adverse outcomes in patients with chronic obstructive pulmonary disease: an interpretable machine learning approach
<p>Little is known about acute kidney injury (AKI) and acute kidney disease (AKD) in patients with chronic obstructive pulmonary disease (COPD) and COPD mortality based on the acute/subacute renal injury. This study develops machine learning models to predict AKI, AKD, and mortality in COPD pa...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , , , |
| منشور في: |
2025
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1852021470016831488 |
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| author | Siqi Jiang (4092664) |
| author2 | Lingyu Xu (8845568) Xinyuan Wang (4887079) Chenyu Li (120599) Chen Guan (3108252) Lin Che (848561) Yanfei Wang (513529) Xuefei Shen (16988548) Yan Xu (14594) |
| author2_role | author author author author author author author author |
| author_facet | Siqi Jiang (4092664) Lingyu Xu (8845568) Xinyuan Wang (4887079) Chenyu Li (120599) Chen Guan (3108252) Lin Che (848561) Yanfei Wang (513529) Xuefei Shen (16988548) Yan Xu (14594) |
| author_role | author |
| dc.creator.none.fl_str_mv | Siqi Jiang (4092664) Lingyu Xu (8845568) Xinyuan Wang (4887079) Chenyu Li (120599) Chen Guan (3108252) Lin Che (848561) Yanfei Wang (513529) Xuefei Shen (16988548) Yan Xu (14594) |
| dc.date.none.fl_str_mv | 2025-04-08T06:20:07Z |
| dc.identifier.none.fl_str_mv | 10.6084/m9.figshare.28748338.v1 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Risk_prediction_for_acute_kidney_disease_and_adverse_outcomes_in_patients_with_chronic_obstructive_pulmonary_disease_an_interpretable_machine_learning_approach/28748338 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biochemistry Medicine Microbiology Immunology Developmental Biology Marine Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Acute kidney disease acute kidney injury chronic obstructive pulmonary disease machine learning mortality predictive model |
| dc.title.none.fl_str_mv | Risk prediction for acute kidney disease and adverse outcomes in patients with chronic obstructive pulmonary disease: an interpretable machine learning approach |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Little is known about acute kidney injury (AKI) and acute kidney disease (AKD) in patients with chronic obstructive pulmonary disease (COPD) and COPD mortality based on the acute/subacute renal injury. This study develops machine learning models to predict AKI, AKD, and mortality in COPD patients, utilizing web applications for clinical decisions.</p> <p>We included 2,829 inpatients from January 2016 to December 2018. Data were split into 80% for training and 20% for testing. Eight machine learning algorithms were used, and model performance was evaluated using various metrics. SHAP was used to visualize the decision process. The best models, assessed using AUROC were used to develop web applications for identifying high-risk patients.</p> <p>The incidence rates were 13.71% for AKI and 15.11% for AKD. The overall mortality rate was 4.84%. LightGBM performed best with AUROC of 0.815, 0.827, and 0.934 in AKI, AKD, and mortality, respectively. Key predictors for AKI were Scr, neutrophil percentage, cystatin c, BUN, and LDH. For AKD, the key predictors were age, AKI grade, HDL-C, Scr, and BUN. The key predictors for mortality included the use of dopamine and epinephrine drugs, cystatin c, renal function trajectory, albumin, and neutrophil percentage. Force plots visualized the prediction process for individual patients.</p> <p>The incidence of AKI and AKD is significant in patients with COPD. Renal function trajectory is crucial for predicting mortality in these patients. Web applications were developed to predict AKI, AKD, and mortality, improving prognosis by identifying high-risk patients and reducing adverse events and disease progression.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_7d893a8da43ae0d64607f2e15a7bf8fd |
| identifier_str_mv | 10.6084/m9.figshare.28748338.v1 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28748338 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Risk prediction for acute kidney disease and adverse outcomes in patients with chronic obstructive pulmonary disease: an interpretable machine learning approachSiqi Jiang (4092664)Lingyu Xu (8845568)Xinyuan Wang (4887079)Chenyu Li (120599)Chen Guan (3108252)Lin Che (848561)Yanfei Wang (513529)Xuefei Shen (16988548)Yan Xu (14594)BiochemistryMedicineMicrobiologyImmunologyDevelopmental BiologyMarine BiologyBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedAcute kidney diseaseacute kidney injurychronic obstructive pulmonary diseasemachine learningmortalitypredictive model<p>Little is known about acute kidney injury (AKI) and acute kidney disease (AKD) in patients with chronic obstructive pulmonary disease (COPD) and COPD mortality based on the acute/subacute renal injury. This study develops machine learning models to predict AKI, AKD, and mortality in COPD patients, utilizing web applications for clinical decisions.</p> <p>We included 2,829 inpatients from January 2016 to December 2018. Data were split into 80% for training and 20% for testing. Eight machine learning algorithms were used, and model performance was evaluated using various metrics. SHAP was used to visualize the decision process. The best models, assessed using AUROC were used to develop web applications for identifying high-risk patients.</p> <p>The incidence rates were 13.71% for AKI and 15.11% for AKD. The overall mortality rate was 4.84%. LightGBM performed best with AUROC of 0.815, 0.827, and 0.934 in AKI, AKD, and mortality, respectively. Key predictors for AKI were Scr, neutrophil percentage, cystatin c, BUN, and LDH. For AKD, the key predictors were age, AKI grade, HDL-C, Scr, and BUN. The key predictors for mortality included the use of dopamine and epinephrine drugs, cystatin c, renal function trajectory, albumin, and neutrophil percentage. Force plots visualized the prediction process for individual patients.</p> <p>The incidence of AKI and AKD is significant in patients with COPD. Renal function trajectory is crucial for predicting mortality in these patients. Web applications were developed to predict AKI, AKD, and mortality, improving prognosis by identifying high-risk patients and reducing adverse events and disease progression.</p>2025-04-08T06:20:07ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.6084/m9.figshare.28748338.v1https://figshare.com/articles/dataset/Risk_prediction_for_acute_kidney_disease_and_adverse_outcomes_in_patients_with_chronic_obstructive_pulmonary_disease_an_interpretable_machine_learning_approach/28748338CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/287483382025-04-08T06:20:07Z |
| spellingShingle | Risk prediction for acute kidney disease and adverse outcomes in patients with chronic obstructive pulmonary disease: an interpretable machine learning approach Siqi Jiang (4092664) Biochemistry Medicine Microbiology Immunology Developmental Biology Marine Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Acute kidney disease acute kidney injury chronic obstructive pulmonary disease machine learning mortality predictive model |
| status_str | publishedVersion |
| title | Risk prediction for acute kidney disease and adverse outcomes in patients with chronic obstructive pulmonary disease: an interpretable machine learning approach |
| title_full | Risk prediction for acute kidney disease and adverse outcomes in patients with chronic obstructive pulmonary disease: an interpretable machine learning approach |
| title_fullStr | Risk prediction for acute kidney disease and adverse outcomes in patients with chronic obstructive pulmonary disease: an interpretable machine learning approach |
| title_full_unstemmed | Risk prediction for acute kidney disease and adverse outcomes in patients with chronic obstructive pulmonary disease: an interpretable machine learning approach |
| title_short | Risk prediction for acute kidney disease and adverse outcomes in patients with chronic obstructive pulmonary disease: an interpretable machine learning approach |
| title_sort | Risk prediction for acute kidney disease and adverse outcomes in patients with chronic obstructive pulmonary disease: an interpretable machine learning approach |
| topic | Biochemistry Medicine Microbiology Immunology Developmental Biology Marine Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Acute kidney disease acute kidney injury chronic obstructive pulmonary disease machine learning mortality predictive model |