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Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication which may result in adverse impact on short- and long-term mortality. The investigatorshere developed several prediction models based on machine learning technique to allow early identification of patients who at the high risk of unfavorable kidney outcomes.
The retrospective study comprised 2108 consecutive patients who underwent cardiac surgery from January 2017 to December 2020.
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| Measure | Description | Time Frame |
|---|---|---|
| acute kidney injury | postoperative AKI was defined according to KDIGO criteria during the first 7 days after operation. Postoperative AKI was defined as either at an increase of at least 50% within 7 days or 0.3 mg/dL elevation within 48 h compared with the reference serum creatinine level. | 7 days |
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Inclusion Criteria:
Exclusion Criteria:
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age over 18 years who underwent cardiac surgery
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Chinese PLA General hospital | Beijing | Beijing Municipality | 100853 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 39076716 | Derived | Shao J, Liu F, Ji S, Song C, Ma Y, Shen M, Sun Y, Zhu S, Guo Y, Liu B, Wu Y, Qin H, Lai S, Fan Y. Development, External Validation, and Visualization of Machine Learning Models for Predicting Occurrence of Acute Kidney Injury after Cardiac Surgery. Rev Cardiovasc Med. 2023 Aug 9;24(8):229. doi: 10.31083/j.rcm2408229. eCollection 2023 Aug. |
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| ID | Term |
|---|---|
| D058186 | Acute Kidney Injury |
| ID | Term |
|---|---|
| D051437 | Renal Insufficiency |
| D007674 | Kidney Diseases |
| D014570 | Urologic Diseases |
| D052776 | Female Urogenital Diseases |
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| D005261 |
| Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D052801 | Male Urogenital Diseases |