Kidney Res Clin Pract > Volume 43(4); 2024 > Article |
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Author | Population | Sample size | Main Predictors | Outcomes | Statistical analysis approach | C-statistics/AUC |
---|---|---|---|---|---|---|
Fan et al. [34] | Patients with sepsis in ICU from MIMIC-III database | Training: 11,008 | DM, CKD, CHF, CLD, hyperbicarbonemia, hyperglycemia, low blood pH, prolonged clotting time, hypotension, hyperlactatemia | SA-AKI | LASSO, logistic regression | Original score |
Validation: 4,718 | Training: 0.711 | |||||
Validation: 0.712 | ||||||
Simplified score | ||||||
Training: 0.712 | ||||||
Validation: 0.705 | ||||||
Xin et al. [42] | Patients aged ≥65 yr with sepsis in one hospital in China | Training 637 | Low MAP, albumin globulin ratio, prothrombin time activity, platelet, high serum procalcitonin, and creatinine | SA-AKI, MAKE30, and 30-day mortality | Logistic regression | SA-AKI |
Validation: 212 | Training: 0.852 | |||||
Validation: 0.858 | ||||||
30-day mortality | ||||||
0.813 | ||||||
MAKE30 | ||||||
0.823 | ||||||
Xie et al. [35] | Patients with sepsis in ICU in one hospital in China | Not reported | Male sex, low anti-thrombin III, high creatinine, and BUN | SA-AKI | Logistic regression | 0.986 |
Zhou et al. [36] | Patients with sepsis in ICU | Training: 1,554 | Older age, HTN, CAD, DM, CHF, COPD, acute severe pancreatitis, hypotension, hypoproteinemia, lactic acidosis, ICU length of stay, low hemoglobin, other organ failure | SA-AKI | Logistic regression | Validation: 0.857 |
Validation: 777 | ||||||
Xin et al. [37] | Patients with sepsis in one hospital in China | Training: 787 | Cardiovascular disease, high WBC, procalcitonin, thrombin time, low mean artery pressure, platelet count, prothrombin time activity | SA-AKI, MAKE-30 | Logistic regression | SA-AKI |
Validation: 264 | Training: 0.872 | |||||
Validation: 0.888 | ||||||
MAKE30 | ||||||
0.843 | ||||||
Xia et al. [39] | Patients with SA-AKI in ICU from MIMIC-IV database | Not reported | High serum creatinine, change in serum creatinine within 24 hr, CRRT within 48 hr, lactate | Persistent SA-AKI | Logistic regression | Training: 0.80 |
Validation: 0.81 | ||||||
Hu et al. [40] | Patients with SA-AKI in ICU | Training: 2,066 | Older age, admission type, liver disease, metastatic cancer, lactate, BUN/creatinine ratio, creatinine, positive culture, and AKI stage | In-hospital mortality | LASSO, Cox regression | Training: 0.73 |
Training: MIMIC-III | Validation: 102 | Validation: 0.72 | ||||
Validation: One hospital in China | ||||||
Jiang et al. [41] | Patients aged ≥65 yr with persistent SA-AKI>48 hr in ICU from MIMIC MIMIC-IV database | Training: 1,065 | Male, sex, cancer, AKI stage, low GCS score, high BUN, respiratory rate, CRRT within 48 hr, mechanical ventilation | In-hospital mortality | Logistic regression | Training: 0.78 |
Validation: 454 | Validation: 0.82 | |||||
Li et al. [38] | Patients with SA-AKD in ICU | Training: 1,779 | Age, GCS score, SBP, oxygen saturation, platelet count, WBC, bicarbonate | In-hospital mortality | Logistic regression | Training: 0.829 |
Training: MIMIC IV | Validation: 344 | Validation: 0.760 | ||||
Validation: eICU-CRD |
AKD, acute kidney disease; AKI, acute kidney injury; AUC, area under the receiver operating characteristic curve; BUN, blood urea nitrogen; CAD, coronary artery disease; CHF, congestive heart failure; CKD, chronic kidney disease; CLD, chronic liver disease; COPD, chronic obstructive pulmonary disease; CRRT, continuous renal replacement therapy; DM, diabetes mellitus; GCS, Glasgow coma scale; HTN, hypertension; ICU, intensive care unit; LASSO, least absolute shrinkage and selection operator; MAKE30, major adverse kidney event within 30 days; MAP, mean arterial pressure; MIMIC, Medical Information Mart for Intensive Care; SA-AKI, sepsis-associated acute kidney injury; SBP, systolic blood pressure; WBC, white blood cell count.
Author | Population | Sample size | Outcomes | Machine learning techniques | C-statistics/AUC |
---|---|---|---|---|---|
Zhang et al. [29] | Patient with sepsis in ICU | Training: 21,308 | SA-AKI | Ensemble model, combining support vector machine, random forest, neural network, XGBoost via stacking algorithm | eICU-CRD: 0.774-0.788 |
Training: MIMIC-IV | Validation | within 12-48 hr | ZG: 0.756-0.813 | ||
Validation: eICU-CRD, ZG | eICU-CRD: 24,352 | ||||
ZG: 505 | |||||
Zhou et al. [30] | Patient with SA-ARDS from MIMIC-III database | 1,085 | SA-AKI | Logistic regression, support vector machine, random forest, XGBoost | Highest C-statistics: XGBoost (0.86) |
Yue et al. [26] | Patient with sepsis in ICU from MIMIC-III database | 3,176 | SA-AKI | Logistic regression, KNN, support vector machine, decision tree, random forest, XGBoost, artifical neural network | Logistic regression: 0.737 |
KNN: 0.664 | |||||
Support vector machine: 0.735 | |||||
Decision tree: 0.749 | |||||
Random forest: 0.779 | |||||
XGBoost: 0.817 | |||||
Artifical neural network: 0.755 | |||||
Yu et al. [16] | Various hospitalized patients populations from multiple studies. | 87 to over 1 million (varying across studies) | Acute kidney injury | Regression, ensemble tree methods, SVM, neural networks, etc. | AUC ranged from 0.69 to 0.98 across studies. |
Luo et al. [23] | Patients with SA-AKI in ICU from MIMIC-III database | 5,984 (70% training, 30% validation set) | Persistent SA-AKI > 48 hours | Logistic regression, random forest, support vector machine, artificial neural network, XGBoost | Logistic regression: 0.76 |
Random forest: 0.75 | |||||
Support vector machine: 0.74 | |||||
Artificial neural network: 0.76 | |||||
XGBoost: 0.75 | |||||
He et al. [31] | Patients with SA-AKI in ICU | Training: 209 | Acute kidney disease | RNN-LSTM, decision trees, logistic regression | RNN-LSTM |
Training: one hospital in China | Validation: 509 | Training: 1.0 | |||
Validation: MIMIC-III | Validation: 1.0 | ||||
Decision trees | |||||
Training: 0.954 | |||||
Validation: 0.872 | |||||
Logistic regression | |||||
Training: 0.728 | |||||
Validation - 0.717 | |||||
Li et al. [24] | Patients with SA-AKI in ICU from MIMIC IV database | Training: 6,503 | In-hospital mortality | Logistic regression, support vector machine, KNN, decision tree, random forest, XGBoost | Logistic regression: 0.730 |
Validation: 1,626 | Support vector machine: 0.680 | ||||
KNN: 0.601 | |||||
Decision tree: 0.585 | |||||
Random forest: 0.778 | |||||
XGBoost: 0.794 | |||||
Zhou et al. [25] | Patients with SA-AKI in ICU | 16,154 (80% training, 20% validation set) external validation set: 132 | In-hospital mortality | Categorical boosting, gradient boosting decision tree, light gradient boosting, adaptive boosting, XGBoost, KNN, multilayer perception, logistic regression, naive Bayes, support vector machine | Categorical boosting: 0.83, ext - 0.75 |
Training/validation: MIMIC-IV | Gradient boosting decision tree: 0.82, ext - 0.62 | ||||
External Validation: 2 hospitals in China | Light gradient boosting: 0.8, ext - 0.61 | ||||
Adaptive boosting: 0.82, ext - 0.60) | |||||
XGBoost: 0.81, ext - 0.57 | |||||
KNN: 0.80, ext - 0.63 | |||||
Multilayer perception: 0.79, ext - 0.63 | |||||
Logistic regression: 0.79, ext - 0.71 | |||||
Naive Bayes: 0.76, ext - 0.60 | |||||
Support vector machine: 0.76, ext - 0.68 | |||||
Fan et al. [28] | Patients with SA-AKI in ICU | Training: 2,499 | 7- day mortaltiy | Logistic regression, random forest, XGBoost, multilayer perception, support vector machine | Logistic regression: 0.75, ext - 0.70 |
Training: MIMIC IV | External validation: 100 | Random forest: 0.84, ext - 0.78 | |||
Validation: one hospital in China | XGBoost: 0.91, ext - 0.81 | ||||
Multilayer perception: 0.75, ext - 0.72 | |||||
Support vector machine: 0.80, ext - 0.64 | |||||
Yang et al. [27] | Patients with SA-AKI in ICU from MIMIC IV database | 9,158 (70% training, 30% validation set) | 28-day mortality | Logistic regression, random forest, gradient boosting machine, XGBoost | Logistic regression: 0.850 |
Random forest: 0.849 | |||||
Gradient boosting machine: 0.865 | |||||
XGBoost: 0.873 |
AUC, area under the receiver operating characteristic curve; eICU-CRD, eICU Collaborative Research Database; Ext, external validation; ICUs, intensive care units; KNN, k-nearest neighbors; MIMIC, Medical Information Mart for Intensive Care; RNN-LSTM, recurrent neural network-long short-term memory; SA-AKI, sepsis-associated acute kidney injury; SA-ARDS, sepsis-associated acute respiratory distress syndrome; SVM, support vector machine; XGBoost, eXtreme Gradient Boosting; ZG, Zhejiang University’s Affiliated Hospitals’ database.
Wisit Cheungpasitporn
https://orcid.org/0000-0001-9954-9711
Charat Thongprayoon
https://orcid.org/0000-0002-8313-3604
Kianoush B. Kashani
https://orcid.org/0000-0003-2184-3683
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