Analysis of risk factors for cardiovascular surgery-associated acute kidney injury
ObjectiveTo explore the risk factors and establish a prediction model of cardiovascular surgery （CVS）-associated acute kidney injury （AKI）.MethodsFrom June 2016 to December 2019，retrospective analysis was conducted for 269 patients undergoing cardiovascular surgery with the aid of cardiopulmonary by...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal of Clinical Nephrology
2022-01-01
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| Series: | Linchuang shenzangbing zazhi |
| Subjects: | |
| Online Access: | http://www.lcszb.com/thesisDetails#10.3969/j.issn.1671-2390.2022.01.002 |
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| Summary: | ObjectiveTo explore the risk factors and establish a prediction model of cardiovascular surgery （CVS）-associated acute kidney injury （AKI）.MethodsFrom June 2016 to December 2019，retrospective analysis was conducted for 269 patients undergoing cardiovascular surgery with the aid of cardiopulmonary bypass （CPB）. The incidence and risk factors of CVS-AKI were analyzed based upon the criteria for AKI of 2012 KDIGO. A prediction model was established and validated in a prospective cohort of 25 patients since April 2020.ResultsThe incidence of CVS-AKI was 62.5%（168/269），the incidence of AKI stage 1/2/3 40.1%（108/269），11.9%（32/269） and 10.4%（28/269） respectively and the rate of renal replacement therapy 5.6%（15/269）. Logistic stepwise regression indicated that advanced age，higher body mass index （BMI） and longer CPB and mechanical ventilation were independent risk factors of CVS-AKI. The predictive incidence of CVS-AKI =<inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mfrac><mrow><msup><mrow><mi>e</mi></mrow><mrow><mi>L</mi><mi>o</mi><mi>g</mi><mi>i</mi><mi>t</mi><mfenced separators="|"><mrow><mi>P</mi></mrow></mfenced></mrow></msup></mrow><mrow><msup><mrow><mi>e</mi></mrow><mrow><mi>L</mi><mi>o</mi><mi>g</mi><mi>i</mi><mi>t</mi><mfenced separators="|"><mrow><mi>P</mi></mrow></mfenced></mrow></msup><mo>+</mo><mn mathvariant="normal">1</mn></mrow></mfrac></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4E167DCD-9714-4982-8AB4-0523D8C4822F-M001.jpg"><?fx-imagestate width="14.90133286" height="7.78933382"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4E167DCD-9714-4982-8AB4-0523D8C4822F-M001c.jpg"><?fx-imagestate width="14.90133286" height="7.78933382"?></graphic></alternatives></inline-formula>，Logit（P）=0.027× age （year） +0.109×BMI （kg/m<sup>2</sup>） +0.006× CPB time （min）+ 0.009×mechanical ventilation time （h） after surgery （h） -4.834. The model had an excellent discrimination with 0.675 AUC （area under the curve） （95<italic>CI</italic>：0.407-0.944，<italic>P</italic>=0.203）.ConclusionThe incidence of CVS-AKI at our single center was 62.5%. The independent risk factors of CVS-AKI were advanced age，higher BMI，longer CPB and postoperative mechanical ventilation. The established model may predict the incidence of CVS-AKI properly. |
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| ISSN: | 1671-2390 |