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Risk factors and the utility of three different kinds of prediction models for postoperative fatigue after gastrointestinal tumor surgery

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Abstract

Background

Postoperative fatigue (POF) is a common complication after gastrointestinal tumor surgery, and it also brings negative effect on prognosis and life quality. However, there are no prediction models for POF, and studies of risk factors are not comprehensive. Therefore, the aim of this study is to investigate the risk factors and pick out the best prediction model for POF and to validate it.

Methods

A prospective study was conducted for patients undergoing elective gastrointestinal tumor surgery. Physiological, psychological, and socioeconomic factors were collected. Logistic regression, back-propagation artificial neural networks (BP-ANNs), and classification and regression tree (CART) were constructed and compared.

Results

A total of 598 patients consisting of 463 derivation sample and 135 validation sample were included. The incidence of POF in derivation sample, validation sample, and total were 58.3%, 57.0%, and 58.7%, respectively. Logistic regression results showed age, higher degree of education, lower personal monthly income, advanced cancer, hypoproteinemia, preoperative anxiety or depression, and limited social support were risk factors for POF. Receiver operating characteristic curve (ROC) and performance indices were used to test three models. BP-ANN was the best by the comparison of models, and its strong predictive performance was also validated.

Conclusions

More attention should be paid on specific patients after gastrointestinal tumor surgery. BP-ANN is a powerful mathematical tool that could predict POF exactly. It may be used as a noninvasive screening tool to guide clinicians for early identification of high-risk patients and grading interventions.

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References

  1. Siegel RL, Miller KD, Jemal A (2018) Cancer statistics. CA Cancer J Clin 68(1):7–30. https://doi.org/10.3322/caac.21442

    Article  Google Scholar 

  2. Jankowski M, Las-Jankowska M, Sousak M, Zegarski W (2018) Contemporary enteral and parenteral nutrition before surgery for gastrointestinal cancers: a literature review. World J Surg Oncol 16(1):94. https://doi.org/10.1186/s12957-018-1393-7

    Article  PubMed  PubMed Central  Google Scholar 

  3. Rose EA, King TC (1978) Understanding postoperative fatigue. Surg Gynecol Obstet 147(1):97–102

    CAS  PubMed  Google Scholar 

  4. Oliveira M, Oliveira G, Souza-Talarico J, Mota D (2016) Surgical oncology: evolution of postoperative fatigue and factors related to its severity. Clin J Oncol Nurs 20(1):E3–E8. https://doi.org/10.1188/16.CJON.E3-E8

    Article  PubMed  Google Scholar 

  5. Yu J, Zhuang CL, Shao SJ, Liu S, Chen WZ, Chen BC, Shen X, Yu Z (2015) Risk factors for postoperative fatigue after gastrointestinal surgery. J Surg Res 194(1):114–119. https://doi.org/10.1016/j.jss.2014.09.041

    Article  PubMed  Google Scholar 

  6. Thibault R, Chikhi M, Clerc A, Darmon P, Chopard P, Genton L, Kossovsky MP, Pichard C (2011) Assessment of food intake in hospitalised patients: a 10-year comparative study of a prospective hospital survey. Clin Nutr 30(3):289–296. https://doi.org/10.1016/j.clnu.2010.10.002

    Article  PubMed  Google Scholar 

  7. Paddison JS, Booth RJ, Cameron LD, Robinson E, Frizelle FA, Hill AG (2009) Fatigue after colorectal surgery and its relationship to patient expectations. J Surg Res 151(1):145–152. https://doi.org/10.1016/j.jss.2008.01.030

    Article  PubMed  Google Scholar 

  8. Lin Z, Kahrilas PJ, Roman S, Boris L, Carlson D, Pandolfino JE (2012) Refining the criterion for an abnormal integrated relaxation pressure in esophageal pressure topography based on the pattern of esophageal contractility using a classification and regression tree model. Neurogastroenterol Motil 24(8):e356–e363. https://doi.org/10.1111/j.1365-2982.2012.01952.x

    Article  PubMed  PubMed Central  Google Scholar 

  9. Pergialiotis V, Pouliakis A, Parthenis C, Damaskou V, Chrelias C, Papantoniou N, Panayiotides I (2018) The utility of artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women. Public Health 164:1–6. https://doi.org/10.1016/j.puhe.2018.07.012

    Article  CAS  PubMed  Google Scholar 

  10. Ejaz A, Schmidt C, Johnston FM, Frank SM, Pawlik TM (2017) Risk factors and prediction model for inpatient surgical site infection after major abdominal surgery. J Surg Res 217:153–159. https://doi.org/10.1016/j.jss.2017.05.018

    Article  PubMed  Google Scholar 

  11. Paddison JS, Booth RJ, Hill AG, Cameron LD (2006) Comprehensive assessment of peri-operative fatigue: development of the identity-consequence fatigue scale. J Psychosom Res 60(6):615–622. https://doi.org/10.1016/j.jpsychores.2005.08.008

    Article  PubMed  Google Scholar 

  12. Nostdahl T, Bernklev T, Raeder J, Sandvik L, Fredheim O (2016) Postoperative fatigue; translation and validation of a revised 10-item short form of the identity-consequence fatigue scale (ICFS). J Psychosom Res 84:1–7. https://doi.org/10.1016/j.jpsychores.2016.03.002

    Article  PubMed  Google Scholar 

  13. Zigmond AS, Snaith RP (1983) The hospital anxiety and depression scale. Acta Psychiatr Scand 67(6):361–370. https://doi.org/10.1111/j.1600-0447.1983tb09716.x

    Article  CAS  Google Scholar 

  14. Broadbent E, Petrie KJ, Main J, Weinman J (2006) The brief illness perception questionnaire. J Psychosom Res 60(6):631–637. https://doi.org/10.1016/j.jpsychores.2005.10.020

    Article  PubMed  Google Scholar 

  15. Xiao S-Y (1994) Social support assessment scale: the theoretical basis and research application. J Clin Psychiatry 2:98–100 (in Chinese)

    Google Scholar 

  16. Draeger DL, Sievert KD, Hakenberg OW (2018) Cross-sectional patient-reported outcome measuring of health-related quality of life with establishment of cancer- and treatment-specific functional and symptom scales in patients with penile cancer. Clin Genitourin Cancer 16(6):e1215–e1220. https://doi.org/10.1016/j.clgc.2018.07.029

    Article  PubMed  Google Scholar 

  17. Jackaman C, Tomay F, Duong L, Abdol RN, Pixley FJ, Metharom P, Nelson DJ (2017) Aging and cancer: the role of macrophages and neutrophils. Ageing Res Rev 36:105–116. https://doi.org/10.1016/j.arr.2017.03.008

    Article  CAS  PubMed  Google Scholar 

  18. Tait N, Aisner J (1989) Nutritional concerns in cancer patients. Semin Oncol Nurs 5(2 Suppl 1):58–62. https://doi.org/10.1016/0749-2081(89)90082-x

    Article  CAS  PubMed  Google Scholar 

  19. Maccio A, Madeddu C, Gramignano G, Mulas C, Tanca L, Cherchi MC, Floris C, Omoto I, Barracca A, Ganz T (2015) The role of inflammation, iron, and nutritional status in cancer-related anemia: results of a large, prospective, observational study. Haematologica 100(1):124–132. https://doi.org/10.3324/haematol.2014.112813

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Prado CM, Bekaii-Saab T, Doyle LA, Shrestha S, Ghosh S, Baracos VE, Sawyer MB (2012) Skeletal muscle anabolism is a side effect of therapy with the MEK inhibitor: selumetinib in patients with cholangiocarcinoma. Br J Cancer 106(10):1583–1586. https://doi.org/10.1038/bjc.2012.144

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Hussein KM, Ghosn Y, Karam K, Nader AA, El-Mahmoud A, Bou-Ayash N, El-Khoury M, Farhat S (2018) Adenoma detection before and after the age of 50: a retrospective analysis of Lebanese outpatients. BMJ Open Gastroenterol 5(1):e253. https://doi.org/10.1136/bmjgast-2018-000253

    Article  Google Scholar 

  22. Roberts SE, Morrison-Rees S, Samuel DG, Thorne K, Akbari A, Williams JG (2016) Review article: the prevalence of helicobacter pylori and the incidence of gastric cancer across Europe. Aliment Pharmacol Ther 43(3):334–345. https://doi.org/10.1111/apt.13474

    Article  CAS  PubMed  Google Scholar 

  23. Rubin GJ, Hardy R, Hotopf M (2004) A systematic review and meta-analysis of the incidence and severity of postoperative fatigue. J Psychosom Res 57(3):317–326. https://doi.org/10.1016/S0022-3999(03)00615-9

    Article  PubMed  Google Scholar 

  24. Rubin GJ, Cleare A, Hotopf M (2004) Psychological factors in postoperative fatigue. Psychosom Med 66(6):959–964. https://doi.org/10.1097/01.psy.0000143636.09159.f1

    Article  PubMed  Google Scholar 

  25. Li D, Zhang DJ, Shao JJ, Qi XD, Tian L (2014) A meta-analysis of the prevalence of depressive symptoms in Chinese older adults. Arch Gerontol Geriatr 58(1):1–9. https://doi.org/10.1016/j.archger.2013.07.016

    Article  CAS  PubMed  Google Scholar 

  26. Wang TY, Chen VC, Yang YH, Chen CY, Lee CP, Wu SI (2019) The effects of anxiety on the receipt of treatments for esophageal cancer. Psychooncology 28(1):31–38. https://doi.org/10.1002/pon.4903

    Article  PubMed  Google Scholar 

  27. Posluszny DM, Bovbjerg DH, Syrjala KL, Agha M, Dew MA (2019) Correlates of anxiety and depression symptoms among patients and their family caregivers prior to allogeneic hematopoietic cell transplant for hematological malignancies. Support Care Cancer 27(2):591–600. https://doi.org/10.1007/s00520-018-4346-3

    Article  PubMed  Google Scholar 

  28. Woodward M, Tunstall-Pedoe H, Peters SA (2017) Graphics and statistics for cardiology: clinical prediction rules. HEART 103(7):538–545. https://doi.org/10.1136/heartjnl-2016-310210

    Article  PubMed  PubMed Central  Google Scholar 

  29. Scheffold K, Philipp R, Koranyi S, Engelmann D, Schulz-Kindermann F, Harter M, Mehnert A (2018) Insecure attachment predicts depression and death anxiety in advanced cancer patients. Palliat Support Care 16(3):308–316. https://doi.org/10.1017/S1478951517000281

    Article  PubMed  Google Scholar 

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Funding

This work was funded by the Postgraduate Research and Practice Innovation Program of Jiangsu Province.

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Correspondence to Qin Xu.

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Xu, XY., Lu, JL., Xu, Q. et al. Risk factors and the utility of three different kinds of prediction models for postoperative fatigue after gastrointestinal tumor surgery. Support Care Cancer 29, 203–211 (2021). https://doi.org/10.1007/s00520-020-05483-0

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