Literature DB >> 24067536

How can we best predict acute kidney injury following cardiac surgery?: a prospective observational study.

Kristin S Berg1, Roar Stenseth, Alexander Wahba, Hilde Pleym, Vibeke Videm.   

Abstract

BACKGROUND: Several models for predicting acute kidney injury following cardiac surgery have been published, and various end-point definitions have been used.
OBJECTIVES: Our aim was to investigate how acute kidney injury following cardiac surgery could be most accurately predicted.
DESIGN: Single-centre prospective observational study.
SETTING: St Olav's University Hospital, Trondheim, Norway, from 2000 to 2007. PATIENTS: All 5029 adult patients undergoing cardiac surgery were considered eligible for participation. Patients who required preoperative dialysis and patients with missing information on preoperative or maximum postoperative serum creatinine concentration were excluded (n=51). A total of 4978 patients were entered into the statistical analyses. MAIN OUTCOME MEASURES: Logistic regression with bootstrapping methods was applied for model development and validation, together with the area under the receiver operating characteristic curve and Hosmer-Lemeshow test. We tested different end-points, exchanged serum creatinine concentration with creatinine clearance or estimated glomerular filtration rate and added intraoperative variables. The main end-point was at least 50% increase in serum creatinine concentration, an increase in concentration by at least 26.4 μmol l(-1) (0.3 mg dl(-1)) or a new requirement for dialysis after surgery.
RESULTS: The final model consisted of 11 preoperative predictors of acute kidney injury: age, BMI, lipid-lowering treatment, hypertension, peripheral vascular disease, chronic pulmonary disease, haemoglobin concentration, serum creatinine concentration, previous cardiac surgery, emergency operation and operation type. The area under the receiver operating characteristic curve was 0.819 (95% confidence interval 0.801 to 0.837), and the Hosmer-Lemeshow test P value was 0.17. Exchanging serum creatinine concentration with glomerular filtration rate or creatinine clearance slightly reduced model discrimination and the addition of intraoperative variables improved discrimination somewhat. Slight end-point definition changes had little impact.
CONCLUSION: The risk of acute kidney injury can be accurately predicted using preoperative variables. Serum creatinine concentration was more accurate than estimated glomerular filtration rate or creatinine clearance. Intraoperative variables slightly improved the model, but did not seem to outweigh the advantages of a preoperative model.

Entities:  

Mesh:

Substances:

Year:  2013        PMID: 24067536     DOI: 10.1097/EJA.0b013e328365ae64

Source DB:  PubMed          Journal:  Eur J Anaesthesiol        ISSN: 0265-0215            Impact factor:   4.330


  15 in total

1.  Factors associated with postoperative requirement of renal replacement therapy following off-pump coronary bypass surgery.

Authors:  Tomoko S Kato; Yoichiro Machida; Kenji Kuwaki; Taira Yamamoto; Atsushi Amano
Journal:  Heart Vessels       Date:  2016-06-06       Impact factor: 2.037

Review 2.  Sex and the Risk of AKI Following Cardio-thoracic Surgery: A Meta-Analysis.

Authors:  Joel Neugarten; Sandipani Sandilya; Beenu Singh; Ladan Golestaneh
Journal:  Clin J Am Soc Nephrol       Date:  2016-10-20       Impact factor: 8.237

3.  Development and Validation of a Web-Based Prediction Model for AKI after Surgery.

Authors:  Sang H Woo; Jillian Zavodnick; Lily Ackermann; Omar H Maarouf; Jingjing Zhang; Scott W Cowan
Journal:  Kidney360       Date:  2020-12-29

4.  Development and Validation of a Personalized Model With Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records.

Authors:  Kang Liu; Xiangzhou Zhang; Weiqi Chen; Alan S L Yu; John A Kellum; Michael E Matheny; Steven Q Simpson; Yong Hu; Mei Liu
Journal:  JAMA Netw Open       Date:  2022-07-01

5.  Mortality and complications after hip fracture among elderly patients undergoing hemodialysis.

Authors:  Jeff Chien-Fu Lin; Wen-Miin Liang
Journal:  BMC Nephrol       Date:  2015-07-07       Impact factor: 2.388

6.  A Predictive Model for Assessing Surgery-Related Acute Kidney Injury Risk in Hypertensive Patients: A Retrospective Cohort Study.

Authors:  Xing Liu; Yongkai Ye; Qi Mi; Wei Huang; Ting He; Pin Huang; Nana Xu; Qiaoyu Wu; Anli Wang; Ying Li; Hong Yuan
Journal:  PLoS One       Date:  2016-11-01       Impact factor: 3.240

7.  Latent variable modeling improves AKI risk factor identification and AKI prediction compared to traditional methods.

Authors:  Loren E Smith; Derek K Smith; Jeffrey D Blume; Edward D Siew; Frederic T Billings
Journal:  BMC Nephrol       Date:  2017-02-08       Impact factor: 2.388

Review 8.  Risk Assessment.

Authors:  Pragya Ajitsaria; Sabry Z Eissa; Ross K Kerridge
Journal:  Curr Anesthesiol Rep       Date:  2018-01-30

9.  Pulse wave velocity and neutrophil gelatinase-associated lipocalin as predictors of acute kidney injury following aortic valve replacement.

Authors:  Emaddin Kidher; Leanne Harling; Hutan Ashrafian; Hatam Naase; Andrew Chukwuemeka; Jon Anderson; Darrel P Francis; Thanos Athanasiou
Journal:  J Cardiothorac Surg       Date:  2014-05-17       Impact factor: 1.637

10.  Reduced Long-Term Relative Survival in Females and Younger Adults Undergoing Cardiac Surgery: A Prospective Cohort Study.

Authors:  Tone Bull Enger; Hilde Pleym; Roar Stenseth; Guri Greiff; Alexander Wahba; Vibeke Videm
Journal:  PLoS One       Date:  2016-09-28       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.