1,188
Views
0
CrossRef citations to date
0
Altmetric
Cardiology & Cardiovascular Disorders

Incorporating intraoperative blood pressure time-series variables to assist in prediction of acute kidney injury after type a acute aortic dissection repair: an interpretable machine learning model

, , , , , , , , , & show all
Article: 2266458 | Received 19 Jun 2023, Accepted 24 Sep 2023, Published online: 09 Oct 2023

References

  • Wang Y, Bellomo R. Cardiac surgery-associated acute kidney injury: risk factors, pathophysiology and treatment. Nat Rev Nephrol. 2017;13(11):1–14. doi: 10.1038/nrneph.2017.119.
  • Elsayed RS, Cohen RG, Fleischman F, et al. Acute type a aortic dissection. Cardiol Clin. 2017;35(3):331–345. doi: 10.1016/j.ccl.2017.03.004.
  • Howard DPJ, Banerjee A, Fairhead JF, et al. Population-based study of incidence and outcome of acute aortic dissection and premorbid risk factor control: 10-year results from the oxford vascular study. Circulation. 2013;127(20):2031–2037. doi: 10.1161/CIRCULATIONAHA.112.000483.
  • Helgason D, Helgadottir S, Ahlsson A, et al. Acute kidney injury after acute repair of type a aortic dissection. Ann Thorac Surg. 2021;111(4):1292–1298. doi: 10.1016/j.athoracsur.2020.07.019.
  • Evangelista A, Isselbacher EM, Bossone E, et al. Insights from the international registry of acute aortic dissection: a 20-Year experience of collaborative clinical ­research. Circulation. 2018;137(17):1846–1860. doi: 10.1161/CIRCULATIONAHA.117.031264.
  • Chawla LS, Bellomo R, Bihorac A, et al. Acute kidney disease and renal recovery: consensus report of the acute disease quality initiative (ADQI) 16 workgroup. Nat Rev Nephrol. 2017;13(4):241–257. doi: 10.1038/nrneph.2017.2.
  • Najafi M, Goodarzynejad H, Karimi A, et al. Is preoperative serum creatinine a reliable indicator of outcome in patients undergoing coronary artery bypass surgery? J Thorac Cardiovasc Surg. 2009;137(2):304–308. doi: 10.1016/j.jtcvs.2008.08.001.
  • Kim WH, Lee SM, Choi JW, et al. Simplified clinical risk score to predict acute kidney injury after aortic surgery. J Cardiothorac Vasc Anesth. 2013;27(6):1158–1166. doi: 10.1053/j.jvca.2013.04.007.
  • Dong N, Piao H, Du Y, et al. Development of a practical prediction score for acute renal injury after surgery for stanford type a aortic dissection. Interact Cardiovasc Thorac Surg. 2020;30(5):746–753. doi: 10.1093/icvts/ivaa011.
  • Luo C, Zhong Y, Qiao Z, et al. Development and validation of a nomogram for postoperative severe acute kidney injury in acute type a aortic dissection. JGC. 2022;19:734–742.
  • Zhang Y, Lan Y, Chen T, et al. Prediction of acute kidney injury for acute type a aortic dissection patients who underwent sun’s procedure by a perioperative nomogram. Cardiorenal Med. 2022;12(3):117–130. doi: 10.1159/000524907.
  • Li X, Wang Z, Huang X, et al. Prediction model of acute kidney injury after different types of acute aortic dissection based on machine learning. Front Cardiovasc Med. 2022;9:984772. doi: 10.3389/fcvm.2022.984772.
  • Cuocolo R, Caruso M, Perillo T, et al. Machine learning in oncology: a clinical appraisal. Cancer Lett. 2020;481:55–62. doi: 10.1016/j.canlet.2020.03.032.
  • Krittanawong C, Zhang H, Wang Z, et al. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol. 2017;69(21):2657–2664. doi: 10.1016/j.jacc.2017.03.571.
  • Bzdok D, Meyer-Lindenberg A. Machine learning for precision psychiatry: opportunities and challenges. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3(3):223–230. doi: 10.1016/j.bpsc.2017.11.007.
  • Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349(6245):255–260. doi: 10.1126/science.aaa8415.
  • Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317–1318. doi: 10.1001/jama.2017.18391.
  • Filiberto AC, Loftus TJ, Elder CT, et al. Intraoperative hypotension and complications after vascular surgery: a scoping review. Surgery. 2021;170(1):311–317. doi: 10.1016/j.surg.2021.03.054.
  • Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–612. doi: 10.7326/0003-4819-150-9-200905050-00006.
  • Levin A, Stevens PE. Summary of KDIGO 2012 CKD guideline: behind the scenes, need for guidance, and a framework for moving forward. Kidney Int. 2014;85(1):49–61. doi: 10.1038/ki.2013.444.
  • Favia I, Vitale V, Ricci Z. The Vasoactive-Inotropic score and Levosimendan: time for LVIS? J Cardiothorac Vasc Anesth. 2013;27(2):e15–e16. doi: 10.1053/j.jvca.2012.11.009.
  • Monk TG, Bronsert MR, Henderson WG, et al. Association between intraoperative hypotension and hypertension and 30-day postoperative mortality in noncardiac surgery. Anesthesiology. 2015;123(2):307–319. doi: 10.1097/ALN.0000000000000756.
  • Epstein RH, Dexter F, Schwenk ES. Hypotension during induction of anaesthesia is neither a reliable nor a useful quality measure for comparison of anaesthetists’ performance. Br J Anaesth. 2017;119(1):106–114. doi: 10.1093/bja/aex153.
  • Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract. 2012;120(4):c179–84. doi: 10.1159/000339789.
  • Vasquez MM, Hu C, Roe DJ, et al. Least absolute shrinkage and selection operator type methods for the identification of serum biomarkers of overweight and ­obesity: simulation and application. BMC Med Res Methodol. 2016;16(1):154. doi: 10.1186/s12874-016-0254-8.
  • DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–845.
  • Van Calster B, Wynants L, Verbeek JFM, et al. Reporting and interpreting decision curve analysis: a guide for investigators. Eur Urol. 2018;74(6):796–804. doi: 10.1016/j.eururo.2018.08.038.
  • Shalabi LA, Shaaban Z. Normalization as a preprocessing engine for data mining and the approach of preference matrix. International Conference on Dependability of Computer Systems [Internet]. 2006 [cited 2023 May 18]. http://ieeexplore.ieee.org/document/4024051/.
  • Okada S, Ohzeki M, Taguchi S. Efficient partition of integer optimization problems with one-hot encoding. Sci Rep. 2019;9(1):13036. doi: 10.1038/s41598-019-49539-6.
  • Quinn KN, Wilber H, Townsend A, et al. Chebyshev approximation and the global geometry of model predictions. Phys Rev Lett. 2019;122(15):158302. doi: 10.1103/PhysRevLett.122.158302.
  • Altman NS. An introduction to kernel and Nearest-Neighbor nonparametric regression. The American Statistician. 1992;46:175–185.
  • Palevsky PM, Liu KD, Brophy PD, et al. KDOQI US commentary on the 2012 KDIGO clinical practice guideline for acute kidney injury. Am J Kidney Dis. 2013;61(5):649–672. doi: 10.1053/j.ajkd.2013.02.349.
  • Parolari A, Pesce LL, Pacini D, et al. Risk factors for perioperative acute kidney injury after adult cardiac surgery: role of perioperative management. Ann Thorac Surg. 2012;93(2):584–591. doi: 10.1016/j.athoracsur.2011.09.073.
  • Walsh M, Devereaux PJ, Garg AX, et al. Relationship between intraoperative mean arterial pressure and clinical outcomes after noncardiac surgery: toward an empirical definition of hypotension. Anesthesiology. 2013;119(3):507–515. doi: 10.1097/ALN.0b013e3182a10e26.
  • Sun LY, Wijeysundera DN, Tait GA, et al. Association of ­intraoperative hypotension with acute kidney injury after elective noncardiac surgery. Anesthesiology. 2015;123(3):515–523. doi: 10.1097/ALN.0000000000000765.
  • van Waes JAR, van Klei WA, Wijeysundera DN, et al. Association between intraoperative hypotension and myocardial injury after vascular surgery. Anesthesiology. 2016;124(1):35–44. doi: 10.1097/ALN.0000000000000922.
  • Bijker JB, Persoon S, Peelen LM, et al. Intraoperative hypotension and perioperative ischemic stroke after general surgery: a nested case-control study. Anesthesiology. 2012;116(3):658–664. doi: 10.1097/ALN.0b013e3182472320.
  • Drummond JC. The lower limit of autoregulation: time to revise our thinking? Anesthesiology. 1997;86(6):1431–1433. doi: 10.1097/00000542-199706000-00034.
  • Forni LG, Joannidis M. Blood pressure deficits in acute kidney injury: not all about the mean arterial pressure? Crit Care. 2017;21(1):102. doi: 10.1186/s13054-017-1683-4.
  • Busse LW, Ostermann M. Vasopressor therapy and blood pressure management in the setting of acute kidney injury. Semin Nephrol. 2019;39(5):462–472. doi: 10.1016/j.semnephrol.2019.06.006.
  • Ono M, Brady K, Easley RB, et al. Duration and magnitude of blood pressure below cerebral autoregulation threshold during cardiopulmonary bypass is associated with major morbidity and operative mortality. J Thorac Cardiovasc Surg. 2014;147(1):483–489. doi: 10.1016/j.jtcvs.2013.07.069.
  • Evans L, Rhodes A, Alhazzani W, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med. 2021;47(11):1181–1247. doi: 10.1007/s00134-021-06506-y.
  • Azau A, Markowicz P, Corbeau JJ, et al. Increasing mean arterial pressure during cardiac surgery does not reduce the rate of postoperative acute kidney injury. Perfusion. 2014;29(6):496–504. doi: 10.1177/0267659114527331.
  • McEwen CC, Amir T, Qiu Y, et al. Morbidity and mortality in patients managed with high compared with low blood pressure targets during on-pump cardiac surgery: a systematic review and meta-analysis of randomized controlled trials. Can J Anaesth. 2022;69(3):374–386. doi: 10.1007/s12630-021-02171-3.
  • Chen L, Hong L, Ma A, et al. Intraoperative venous congestion rather than hypotension is associated with acute adverse kidney events after cardiac surgery: a retrospective cohort study. Br J Anaesth. 2022;128(5):785–795. doi: 10.1016/j.bja.2022.01.032.
  • Sessler DI, Bloomstone JA, Aronson S, et al. Perioperative quality initiative consensus statement on intraoperative blood pressure, risk and outcomes for elective surgery. Br J Anaesth. 2019;122(5):563–574. doi: 10.1016/j.bja.2019.01.013.
  • Vandenberghe W, Bové T, De Somer F, et al. Impact of mean perfusion pressure and vasoactive drugs on occurrence and reversal of cardiac surgery-associate acute kidney injury: a cohort study. J Crit Care. 2022;71:154101. doi: 10.1016/j.jcrc.2022.154101.
  • Tseng P-Y, Chen Y-T, Wang C-H, et al. Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Crit Care. 2020;24(1):478. doi: 10.1186/s13054-020-03179-9.
  • Zeger SL, Irizarry R, Peng RD. On time series analysis of public health and biomedical data. Annu Rev Public Health. 2006;27(1):57–79. doi: 10.1146/annurev.publhealth.26.021304.144517.
  • Sun LY, Chung AM, Farkouh ME, et al. Defining an Intraoperative Hypotension Threshold in Association with Stroke in Cardiac Surgery. Anesthesiology. 2018;129(3):440–447. doi: 10.1097/ALN.0000000000002298.
  • Löffel LM, Bachmann KF, Furrer MA, et al. Impact of intraoperative hypotension on early postoperative acute kidney injury in cystectomy patients – a retrospective cohort analysis. J Clin Anesth. 2020;66:109906. doi: 10.1016/j.jclinane.2020.109906.
  • Karamchandani K, Dave S, Hoffmann U, et al. Intraoperative arterial pressure management: knowns and unknowns. Br J Anaesth. 2023;131(3):445–451. doi: 10.1016/j.bja.2023.05.027.
  • De La Hoz MA, Rangasamy V, Bastos AB, et al. Intraoperative hypotension and acute kidney injury, stroke, and mortality during and outside cardiopulmonary bypass: a retrospective observational cohort study. Anesthesiology. 2022;136(6):927–939. doi: 10.1097/ALN.0000000000004175.
  • Ngu JMC, Jabagi H, Chung AM, et al. Defining an intraoperative hypotension threshold in association with De novo renal replacement therapy after cardiac surgery. Anesthesiology. 2020;132(6):1447–1457. doi: 10.1097/ALN.0000000000003254.
  • Jain U. Myocardial ischemia after cardiopulmonary ­bypass. J Card Surg. 1995;10(4 Suppl):520–526. doi: 10.1111/j.1540-8191.1995.tb00688.x.
  • Demirjian S, Schold JD, Navia J, et al. Predictive models for acute kidney injury following cardiac surgery. Am J Kidney Dis. 2012;59(3):382–389. doi: 10.1053/j.ajkd.2011.10.046.
  • Liu Y, Shang Y, Long D, et al. Intraoperative blood transfusion volume is an independent risk factor for postoperative acute kidney injury in type a acute aortic dissection. BMC Cardiovasc Disord. 2020;20(1):446. doi: 10.1186/s12872-020-01727-3.
  • Lee H-C, Yoon H-K, Nam K, et al. Derivation and validation of machine learning approaches to predict acute kidney injury after cardiac surgery. J Clin Med. 2018;7(10):322. doi: 10.3390/jcm7100322.
  • Ng SY, Sanagou M, Wolfe R, et al. Prediction of acute kidney injury within 30 days of cardiac surgery. J Thorac Cardiovasc Surg. 2014;147(6):1875–1883.e1. doi: 10.1016/j.jtcvs.2013.06.049.
  • Joannidis M, Druml W, Forni LG, et al. Prevention of acute kidney injury and protection of renal function in the intensive care unit. Expert opinion of the working group for nephrology, ESICM. Intensive Care Med. 2010;36(3):392–411. doi: 10.1007/s00134-009-1678-y.
  • Redfors B, Bragadottir G, Sellgren J, et al. Effects of norepinephrine on renal perfusion, filtration and oxygenation in vasodilatory shock and acute kidney injury. Intensive Care Med. 2011;37(1):60–67. doi: 10.1007/s00134-010-2057-4.
  • Di Giantomasso D, Morimatsu H, May CN, et al. Intrarenal blood flow distribution in hyperdynamic septic shock: effect of norepinephrine. Crit Care Med. 2003;31(10):2509–2513. doi: 10.1097/01.CCM.0000084842.66153.5A.
  • Futier E, Lefrant J-Y, Guinot P-G, et al. Effect of individualized vs standard blood pressure management strategies on postoperative organ dysfunction among high-risk patients undergoing major surgery: a randomized clinical trial. JAMA. 2017;318(14):1346–1357. doi: 10.1001/jama.2017.14172.
  • Bragadottir G, Redfors B, Nygren A, et al. Low-dose vasopressin increases glomerular filtration rate, but impairs renal oxygenation in post-cardiac surgery patients. Acta Anaesthesiol Scand. 2009;53(8):1052–1059. doi: 10.1111/j.1399-6576.2009.02037.x.
  • Vedel AG, Holmgaard F, Rasmussen LS, et al. High-Target versus Low-Target blood pressure management during cardiopulmonary bypass to prevent cerebral injury in cardiac surgery patients: a randomized controlled trial. Circulation. 2018;137(17):1770–1780. doi: 10.1161/CIRCULATIONAHA.117.030308.
  • Chen T, Guestrin C. XGBoost: a scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining [Internet]. San Francisco California USA: ACM; 2016. p. 785–94. [cited 2023 May 12]. https://dl.acm.org/doi/10.1145/2939672.2939785.
  • Sheridan RP, Wang WM, Liaw A, et al. Extreme gradient boosting as a method for quantitative structure–activity relationships. J Chem Inf Model. 2016;56(12):2353–2360. doi: 10.1021/acs.jcim.6b00591.
  • Štrumbelj E, Kononenko I. Explaining prediction models and individual predictions with feature contributions. Knowl Inf Syst. 2014;41(3):647–665. doi: 10.1007/s10115-013-0679-x.
  • Lundberg S, Lee S-I. A unified approach to interpreting model predictions [Internet]. arXiv; 2017 [cited 2022 Sep 29]. http://arxiv.org/abs/1705.07874.
  • Meyer A, Zverinski D, Pfahringer B, et al. Machine learning for real-time prediction of complications in critical care: a retrospective study. Lancet Respir Med. 2018;6(12):905–914. doi: 10.1016/S2213-2600(18)30300-X.
  • Jiang W, Xu J, Shen B, et al. Validation of four prediction scores for cardiac surgery-associated acute kidney injury in Chinese patients. Braz J Cardiovasc Surg. 2017;32(6):481–486. doi: 10.21470/1678-9741-2017-0116.
  • Pannu N, Graham M, Klarenbach S, et al. A new model to predict acute kidney injury requiring renal replacement therapy after cardiac surgery. CMAJ. 2016;188(15):1076–1083. doi: 10.1503/cmaj.151447.
  • Echarri G, Duque-Sosa P, Callejas R, et al. External validation of predictive models for acute kidney injury following cardiac surgery: a prospective multicentre cohort study. Eur J Anaesthesiol. 2017;34(2):81–88. doi: 10.1097/EJA.0000000000000580.
  • Md Ralib A, Pickering JW, Shaw GM, et al. The urine output definition of acute kidney injury is too liberal. Crit Care. 2013;17(3):R112. doi: 10.1186/cc12784.