The predictive value of preoperative prognostic nutritional index for postoperative acute kidney injury in 584 patients undergoing cardiac surgery_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

JING Weiwei , YU Hong , JIANG Jiali , HE Leilei , ZHAO Dailiang ,  YU Hai

Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, 610041, P.R.China;

Corresponding author：

YU Hai, Email: yuhaishan117@yahoo.com

Keywords：

Prognostic nutritional index; cardiopulmonary bypass; cardiac surgery; acute kidney injury

DOI：

10.7507/1007-4848.202010092

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Objective To determine the predictive value of the preoperative prognostic nutritional index (PNI) regarding the development of acute kidney injury (AKI) after non-coronary artery bypass grafting (CABG) cardiac surgery.Methods The clinical data of 584 patients who underwent elective non-CABG cardiac surgery with cardiopulmonary bypass (CPB) in our hospital from May to September 2019 were reviewed. There were 268 (45.9%) males and 316 (54.1%) females, with a mean age of 52.1±11.6 years. The mean cardiopulmonary time and aortic-clamp time was 124.8±50.1 min and 86.4±38.9 min, respectively. Totally 449 (76.9%) patients received isolate valve surgery. We developed the risk prediction model of AKI using multivariable logistic regression. The predictive values of preoperative PNI, Cleveland Clinic Score (CCS) and risk prediction model were estimated by the area under the receiver operating characteristic curve (AUC) and Hosmer-Lemeshow goodness-of-fit test. The improvement of preoperative PNI to predictive values of CCS or AKI risk prediction models were defined by the net reclassification index (NRI) and variation of AUC.Results The preoperative PNI could neither effectively predict the occurrence of AKI following non-CABG cardiac surgery (AUC=0.553, 95%CI 0.489-0.617, P=0.095) nor improve the predictive effect of other AKI predictive models. The risk prediction model of AKI structured by our study had high predictive value on AKI or severe AKI (stage 2-3) (AUC=0.741, 95%CI 0.686-0.796, P<0.001) and superior to CCS (AUC=0.512, 95%CI 0.449-0.576, P=0.703).Conclusion The preoperative PNI can neither predict the occurrence of AKI following elective non-CABG cardiac surgery nor improve the prediction values of other AKI prediction models.

Citation： JING Weiwei, YU Hong, JIANG Jiali, HE Leilei, ZHAO Dailiang, YU Hai. The predictive value of preoperative prognostic nutritional index for postoperative acute kidney injury in 584 patients undergoing cardiac surgery. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2021, 28(4): 467-472. doi: 10.7507/1007-4848.202010092 Copy

1.	Mao H, Katz N, Ariyanon W, et al. Cardiac surgery-associated acute kidney injury. Cardiorenal Med, 2013, 3(3): 178-199.
2.	Corredor C, Thomson R, Al-Subaie N. Long-term consequences of acute kidney injury after cardiac surgery: A systematic review and meta-analysis. J Cardiothorac Vasc Anesth, 2016, 30(1): 69-75.
3.	Thiele RH, Isbell JM, Rosner MH. AKI associated with cardiac surgery. Clin J Am Soc Nephrol, 2015, 10(3): 500-514.
4.	Palant CE, Amdur RL, Chawla LS. Long-term consequences of acute kidney injury in the perioperative setting. Curr Opin Anaesthesiol, 2017, 30(1): 100-104.
5.	Ronco C, Bellomo R, Kellum JA. Acute kidney injury. Lancet, 2019, 394(10212): 1949-1964.
6.	Ortega-Loubon C, Fernández-Molina M, Carrascal-Hinojal Y, et al. Cardiac surgery-associated acute kidney injury. Ann Card Anaesth, 2016, 19(4): 687-698.
7.	Teker Açıkel ME, Korkut AK. Impact of controlling nutritional status score (CONUT) and prognostic nutritional index (PIN) on patients undergoing coronary artery bypass graft surgery. Heart Surg Forum, 2019, 22(4): E294-E297.
8.	Dolapoglu A, Avci E, Kiris T, et al. The predictive value of the prognostic nutritional index for postoperative acute kidney injury in patients undergoing on-pump coronary bypass surgery. J Cardiothorac Surg, 2019, 14(1): 74.
9.	Wang X, Wang Y. The prognostic nutritional index is prognostic factor of gynecological cancer: A systematic review and meta-analysis. Int J Surg, 2019, 67: 79-86.
10.	Lopes JA, Jorge S. The RIFLE and AKIN classifications for acute kidney injury: a critical and comprehensive review. Clin Kidney J, 2013, 6(1): 8-14.
11.	Penny-Dimri JC, Bergmeir C, Reid CM, et al. Machine learning algorithms for predicting and risk profiling of cardiac surgery-associated acute kidney injury. Semin Thorac Cardiovasc Surg, 2020, 33(4): 1122-1138.
12.	Carter JV, Pan J, Rai SN, et al. ROC-ing along: Evaluation and interpretation of receiver operating characteristic curves. Surgery, 2016, 159(6): 1638-1645.
13.	Cuschieri S. The STROBE guidelines. Saudi J Anaesth, 2019, 13(Suppl 1): S31-S34.
14.	Hoste EAJ, Kellum JA, Selby NM, et al. Global epidemiology and outcomes of acute kidney injury. Nat Rev Nephrol, 2018, 14(10): 607-625.
15.	Chew STH, Hwang NC. Acute kidney injury after cardiac surgery: A narrative review of the literature. J Cardiothorac Vasc Anesth, 2019, 33(4): 1122-1138.
16.	Wiedermann CJ, Wiedermann W, Joannidis M. Causal relationship between hypoalbuminemia and acute kidney injury. World J Nephrol, 2017, 6(4): 176-187.
17.	Tseng PY, Chen YT, Wang CH, et al. Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Crit Care, 2020, 24(1): 478.
18.	Sampaio MC, Máximo CA, Montenegro CM, et al. Comparison of diagnostic criteria for acute kidney injury in cardiac surgery. Arq Bras Cardiol, 2013, 101(1): 18-25.

1. Mao H, Katz N, Ariyanon W, et al. Cardiac surgery-associated acute kidney injury. Cardiorenal Med, 2013, 3(3): 178-199.
2. Corredor C, Thomson R, Al-Subaie N. Long-term consequences of acute kidney injury after cardiac surgery: A systematic review and meta-analysis. J Cardiothorac Vasc Anesth, 2016, 30(1): 69-75.
3. Thiele RH, Isbell JM, Rosner MH. AKI associated with cardiac surgery. Clin J Am Soc Nephrol, 2015, 10(3): 500-514.
4. Palant CE, Amdur RL, Chawla LS. Long-term consequences of acute kidney injury in the perioperative setting. Curr Opin Anaesthesiol, 2017, 30(1): 100-104.
5. Ronco C, Bellomo R, Kellum JA. Acute kidney injury. Lancet, 2019, 394(10212): 1949-1964.
6. Ortega-Loubon C, Fernández-Molina M, Carrascal-Hinojal Y, et al. Cardiac surgery-associated acute kidney injury. Ann Card Anaesth, 2016, 19(4): 687-698.
7. Teker Açıkel ME, Korkut AK. Impact of controlling nutritional status score (CONUT) and prognostic nutritional index (PIN) on patients undergoing coronary artery bypass graft surgery. Heart Surg Forum, 2019, 22(4): E294-E297.
8. Dolapoglu A, Avci E, Kiris T, et al. The predictive value of the prognostic nutritional index for postoperative acute kidney injury in patients undergoing on-pump coronary bypass surgery. J Cardiothorac Surg, 2019, 14(1): 74.
9. Wang X, Wang Y. The prognostic nutritional index is prognostic factor of gynecological cancer: A systematic review and meta-analysis. Int J Surg, 2019, 67: 79-86.
10. Lopes JA, Jorge S. The RIFLE and AKIN classifications for acute kidney injury: a critical and comprehensive review. Clin Kidney J, 2013, 6(1): 8-14.
11. Penny-Dimri JC, Bergmeir C, Reid CM, et al. Machine learning algorithms for predicting and risk profiling of cardiac surgery-associated acute kidney injury. Semin Thorac Cardiovasc Surg, 2020, 33(4): 1122-1138.
12. Carter JV, Pan J, Rai SN, et al. ROC-ing along: Evaluation and interpretation of receiver operating characteristic curves. Surgery, 2016, 159(6): 1638-1645.
13. Cuschieri S. The STROBE guidelines. Saudi J Anaesth, 2019, 13(Suppl 1): S31-S34.
14. Hoste EAJ, Kellum JA, Selby NM, et al. Global epidemiology and outcomes of acute kidney injury. Nat Rev Nephrol, 2018, 14(10): 607-625.
15. Chew STH, Hwang NC. Acute kidney injury after cardiac surgery: A narrative review of the literature. J Cardiothorac Vasc Anesth, 2019, 33(4): 1122-1138.
16. Wiedermann CJ, Wiedermann W, Joannidis M. Causal relationship between hypoalbuminemia and acute kidney injury. World J Nephrol, 2017, 6(4): 176-187.
17. Tseng PY, Chen YT, Wang CH, et al. Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Crit Care, 2020, 24(1): 478.
18. Sampaio MC, Máximo CA, Montenegro CM, et al. Comparison of diagnostic criteria for acute kidney injury in cardiac surgery. Arq Bras Cardiol, 2013, 101(1): 18-25.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

The predictive value of preoperative prognostic nutritional index for postoperative acute kidney injury in 584 patients undergoing cardiac surgery

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content