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Usefulness of combining clinical and biochemical parameters for prediction of postoperative pulmonary complications after lung resection surgery

  • Ignacio GaruttiEmail author
  • Francisco De la Gala
  • Patricia Piñeiro
  • Lisa Rancan
  • Elena Vara
  • Almudena Reyes
  • Luis Puente-Maestu
  • Jose María Bellón
  • Carlos Simón
Original Research
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Abstract

Early detection of patients with a high risk of postoperative pulmonary complications (PPCs) could improve postoperative strategies. We investigated the role of monitoring systemic and lung inflammatory biomarkers during surgery and the early postoperative period to detect patients at high risk of PPCs after lung resection surgery (LRS). This is a substudy of a randomized control trial on the inflammatory effects of anaesthetic drugs during LRS. We classified patients into two groups, depending on whether or not they developed PPCs. We constructed three multivariate logistic regression models to analyse the power of the biomarkers to predict PPCs. Model 1 only included the usual clinical variables; Model 2 included lung and systemic inflammatory biomarkers; and Model 3 combined Models 1 and 2. Comparisons between mathematical models were based on the area under the receiver operating characteristic curve (AUROC) and tests of integrated discrimination improvement (IDI). Statistical significance was set at p < 0.05. PPCs were detected in 37 (21.3%) patients during admission. The AUROC for Models 1, 2, and 3 was 0.79 (95% CI 0.71–0.87), 0.80 (95% CI 0.72–0.88), and 0.93 (95% CI 0.88–0.97), respectively. Comparison of the AUROC between Models 1 and 2 did not reveal statistically significant values (p = 0.79). However, Model 3 was superior to Model 1 (p < 0.001). Model 3 had had an IDI of 0.29 (p < 0.001) and a net reclassification index of 0.28 (p = 0.007). A mathematical model combining inflammation biomarkers with clinical variables predicts PPCs after LRS better than a model that includes only clinical data. Clinical registration number Clinical Trial Registration NCT 02168751; EudraCT 2011-002294-29.

Keywords

Perioperative period Pulmonary inflammation Postoperative complications Cytokines bronchoalveolar lavage Cytokines blood 

Abbreviations

AUROC

Area under receiver operating characteristic

BAL

Bronchoalveolar lavage fluid

CPAP

Continuous positive airway pressure

FEV1

Forced expiratory volume in the first second

FVC

Forced vital capacity

ICU

Intensive care unit

IL

Interleukin

LRS

Lung resection surgery

MMP

Matrix metalloproteinase

OLV

One-lung ventilation

PPC

Postoperative pulmonary complication

Notes

Funding

This work was supported by a Grant of €91,056.02 from the Spanish Ministry of Health (“Aid to independent clinical research”) in the 2011 call. The trial was also funded in 2013 with €20,000 from AbbVie S.L.U. through “Proyecto InvestigAR” in the area of cardiac and thoracic surgery.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Sabaté S, Mazo V, Canet J. Predicting postoperative pulmonary complications: implications for outcomes and costs. Curr Opin Anaesthesiol. 2014;27:201–9.CrossRefGoogle Scholar
  2. 2.
    Agostini P, Cieslik H, Rathinam S, et al. Postoperative pulmonary complications following thoracic surgery: are there any modifiable risk factors? Thorax. 2010;65:815–8.CrossRefGoogle Scholar
  3. 3.
    Baudouin S. Lung injury after thoracotomy. Br J Anaesth. 2003;91:132–42.CrossRefGoogle Scholar
  4. 4.
    Taylor A, DeBoard Z, Gauvin JM. Prevention of postoperative pulmonary complications. Surg Clin North Am. 2015;95:237–54.CrossRefGoogle Scholar
  5. 5.
    Smetana GW. Strategies to reduce postoperative pulmonary complications in adults. King TE, Aronson MD, Hines R, ed. UpToDate. Waltham, MA, 2018.Google Scholar
  6. 6.
    Yepes-Temiño MJ, Monedero P, Pérez-Valdivieso JR. Grupo Español de Anestesia Toracica. Risk prediction model for respiratory complications after lung resection: an observational multicentre study. Eur J Anaesthesiol. 2016;33:326–33.CrossRefGoogle Scholar
  7. 7.
    Li Y, Ma YL, Gao YY, Wang DD, Chen Q. Analysis of the risk factors of postoperative cardiopulmonary complications and ability to predicate the risk in patients after lung cancer surgery. J Thorac Dis. 2017;9:1565–73.CrossRefGoogle Scholar
  8. 8.
    Lohser J, Slinger P. Lung injury after one-lung ventilation: a review of the pathophysiologic mechanisms affecting the ventilated and the collapsed lung. Anesth Analg. 2015;121:302–18.CrossRefGoogle Scholar
  9. 9.
    D’Journo XB, Michelet P, Marin V, et al. An early inflammatory response to oesophagectomy predicts the occurrence of pulmonary complications. Eur J Cardiothorac Surg. 2010;37:1144–51.CrossRefGoogle Scholar
  10. 10.
    Miskovic A, Lumb AB. Postoperative pulmonary complications. Br J Anaesth. 2017;118:317–34.CrossRefGoogle Scholar
  11. 11.
    Misthos P, Katsaragakis S, Theodorou D, Milingos N, Skottis I. The degree of oxidative stress is associated with major adverse effects after lung resection: a prospective study. Eur J Cardiothorac Surg. 2006;29:591–5.CrossRefGoogle Scholar
  12. 12.
    de la Gala F, Piñeiro; P, Reyes A, Vara E, Olmedilla L, Cruz P. I. Garutti. Postoperative pulmonary complications, pulmonary and systemic inflammatory responses after lung resection surgery with prolonged one-lung ventilation. Randomized controlled trial comparing intravenous and inhalational anaesthesia. Br J Anaesth. 2017;119:655–63.CrossRefGoogle Scholar
  13. 13.
    Sander M, Irwin M, Sinha P, Naumann E, Kox WJ, Spies CD. Suppression of interleukin-6 to interleukin-10 ratio in chronic alcoholics: association with postoperative infections. Intensive Care Med. 2002;28:285–92.CrossRefGoogle Scholar
  14. 14.
    Sun J, Su J, Xie Y, Yin MT, Huang Y, Xu L, Zhou Q, Zhu B. Plasma IL-6/IL-10 Ratio and IL-8, LDH, and HBDH level predict the severity and the risk of death in AIDS patients with pneumocystis pneumonia. J Immunol Res. 2016.  https://doi.org/10.1155/2016/1583951.Google Scholar
  15. 15.
    Mehta RL, Kellum JA, Shah SV, et al. Acute kidney injury network: report of an initiative to improve outcomes in acute kidney injury. Crit Care. 2007;11:R31.CrossRefGoogle Scholar
  16. 16.
    Dindo D, Demartines N, Clavien PA. Classification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey. Ann Surg. 2004;240:205–13.CrossRefGoogle Scholar
  17. 17.
    Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3:32–5.CrossRefGoogle Scholar
  18. 18.
    Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 1983;148:839–43.CrossRefGoogle Scholar
  19. 19.
    Pencina MJ, D’ Agostino RB, Sr D’ Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27:157–72.CrossRefGoogle Scholar
  20. 20.
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna 2016. https://www.R-project.org/.
  21. 21.
    Stéphan F, Boucheseiche S, Hollande J, Flahault A, Cheffi A, Bazelly B, Bonnet F. Pulmonary complications following lung resection* a comprehensive analysis of incidence and possible risk factors. Chest. 2000;118:1263–70.CrossRefGoogle Scholar
  22. 22.
    Sun B, Wang J, Bo L, et al. Effects of volatile vs. propofol-based intravenous anesthetics on the alveolar inflammatory responses to one-lung ventilation: a meta-analysis of randomized controlled trials. J Anesth. 2015;29:570–9.CrossRefGoogle Scholar
  23. 23.
    Angele MK, Faist E. Clinical review: immunodepression in the surgical patient and increased susceptibility to infection. Crit Care. 2002;6:298–305.CrossRefGoogle Scholar
  24. 24.
    Lee WL, Downey GP. Neutrophil activation and acute lung injury. Curr Opin Crit Care. 2001;7:1–7.CrossRefGoogle Scholar
  25. 25.
    Von Dossow V, Rotard K, Redlich U, Hein OV, Spies CD. Circulating immune parameters predicting the progression from hospital-acquired pneumonia to septic shock in surgical patients. Crit Care. 2005;9:R662-9.Google Scholar
  26. 26.
    Donnelly TJ, Meade P, Jagels M, et al. Cytokine, complement, and endotoxin profiles associated with the development of the adult respiratory distress syndrome after severe injury. Crit Care Med. 1994;22:768–76.CrossRefGoogle Scholar
  27. 27.
    Parsons PE, Eisner MD, Thompson BT, et al. Lower tidal volume ventilation and plasma cytokine markers of inflammation in patients with acute lung injury. Crit Care Med. 2005;33:1–6.CrossRefGoogle Scholar
  28. 28.
    Shaw AD, Vaporciyan AA, Wu X, et al. Inflammatory gene polymorphisms influence risk of postoperative morbidity after lung resection. Ann Thorac Surg. 2005;79:1704–10.CrossRefGoogle Scholar
  29. 29.
    Hildebrandt MA, Roth JA, Vaporciyan AA, Pu X, Ye Y, Correa AM, Kim JY, Swisher SG, Wu X. Genetic variation in the TNF/TRAF2/ASK1/p38 kinase signaling pathway as markers for postoperative pulmonary complications in lung cancer patients. Sci Rep. 2015;13:5:12068.CrossRefGoogle Scholar
  30. 30.
    Kooguchi K, Kobayashi A, Kitamura Y, et al. Elevated expression of inducible nitric oxide synthase and inflammatory cytokines in the alveolar macrophages after esophagectomy. Crit Care Med. 2002;30:71–6.CrossRefGoogle Scholar
  31. 31.
    Tsukada K, Hasegawa T, Miyazaki T, et al. Predictive value of interleukin-8 and granulocyte elastase in pulmonary complication after esophagectomy. Am J Surg. 2001;181:167–71.CrossRefGoogle Scholar
  32. 32.
    Jones RO, Brittan M, Anderson NH, et al. Serial characterisation of monocyte and neutrophil function after lung resection. BMJ Open Respir Res. 2014;16:1:e000045.CrossRefGoogle Scholar
  33. 33.
    Nakamura H, Ishizaka A, Sawafuji M, et al. Elevated levels of interleukin-8 and leukotrien B4 in pulmonary edema fluid of a patient with reexpansion pulmonary edema. Am J Respir Crit Care Med. 1994;149:1037–40.CrossRefGoogle Scholar
  34. 34.
    De Perrot M, Sekine Y, Fischer S, et al. Interleukin-8 release during ischemia-reperfusion correlates with early graft function in human lung transplantation. J Heart Lung Transplant. 2001;20:175–6.CrossRefGoogle Scholar
  35. 35.
    Fisher AJ, Donnelly SC, Hirani N, et al. Elevated levels of interleukin-8 in donor lungs is associated with early graft failure after lung transplantation. Am J Respir Crit Care Med. 2001;163:259–65.CrossRefGoogle Scholar
  36. 36.
    Verhage RJ, Boone J, Rijkers GT, et al. Reduced local immune response with continuous positive airway pressure during one-lung ventilation for oesophagectomy. Br J Anaesth. 2014;112:920–8.CrossRefGoogle Scholar
  37. 37.
    Sánchez-Pedrosa G, Vara Ameigeiras E, Casanova Barea J, Rancan L, Simón Adiego CM. Garutti Martínez I. Role of surgical manipulation in lung inflammatory response in a model of lung resection surgery. Interact Cardiovasc Thorac Surg. 2018.  https://doi.org/10.1093/icvts/ivy198.Google Scholar
  38. 38.
    Duflo F, Debon R, Goudable J, Chassard D, Allaouchiche B. Alveolar and serum oxidative stress in ventilator-associated pneumonia. Br J Anaesth. 2002;89:231–6.CrossRefGoogle Scholar
  39. 39.
    de la Gala F, Piñeiro P, Garutti I, et al. Systemic and alveolar inflammatory response in the dependent and nondependent lung in patients undergoing lung resection surgery: a prospective observational study. Eur J Anaesthesiol. 2015;32:872–80.Google Scholar
  40. 40.
    Casanova J, Garutti I, Simon C, et al. The effects of anesthetic preconditioning with sevoflurane in an experimental lung autotransplant model in pigs. Anesth Analg. 2011;113:742–8.CrossRefGoogle Scholar
  41. 41.
    Fligiel SE, Standiford T, Fligiel HM, et al. Matrix metalloproteinases and matrix metalloproteinase inhibitors in acute lung injury. Hum Pathol. 2006;37:422–30.CrossRefGoogle Scholar
  42. 42.
    Greenlee KJ, Werb Z, Kheradmand F. Matrix metalloproteinases in lung: multiple, multifarious, and multifaceted. Physiol Rev. 2007;87:69–98.CrossRefGoogle Scholar
  43. 43.
    Martin TR. Cytokines and the acute respiratory distress syndrome (ARDS): a question of balance. Nat Med. 1997;3:272–3.CrossRefGoogle Scholar
  44. 44.
    Hotchkiss RS, Monneret G, Payen D. Immunosuppression in sepsis: a novel understanding of the disorder and a new therapeutic approach. Lancet Infect Dis. 2013;13:260–8.CrossRefGoogle Scholar
  45. 45.
    Dimopoulou I, Armaganidis A, Douka E, et al. Tumour necrosis factor-alpha (TNFα) and interleukin-10 are crucial mediators in post-operative systemic inflammatory response and determine the occurrence of complications after major abdominal surgery. Cytokine. 2007;37:55–61.CrossRefGoogle Scholar
  46. 46.
    Aelony Y, Finegold S. Serious infectious complications after flexible fibreoptic bronchoscopy. West J Med. 1979;131:327–33.Google Scholar
  47. 47.
    Rubinstein-Aguñín. P Infectious complications following bronchoscopy: does sedation play a role? J Lung Pulm Respir Res. 2018;5(4):112–8.Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Ignacio Garutti
    • 1
    • 6
    Email author
  • Francisco De la Gala
    • 1
  • Patricia Piñeiro
    • 1
  • Lisa Rancan
    • 2
  • Elena Vara
    • 2
  • Almudena Reyes
    • 1
  • Luis Puente-Maestu
    • 3
  • Jose María Bellón
    • 5
  • Carlos Simón
    • 4
  1. 1.Department AnaesthesiaHospital General Marañón (HGUGM)MadridSpain
  2. 2.Department Biochemistry, School of MedicineUniversidad Complutense de Madrid (UCM)MadridSpain
  3. 3.Department PneumologyHGUGMMadridSpain
  4. 4.Department Thoracic SurgeryHGUGMMadridSpain
  5. 5.Department StatisticsHGUGM Biomedical Research FoundationMadridSpain
  6. 6.Department Pharmacology, School of MedicineUCMMadridSpain

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