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EPMA Journal

, Volume 10, Issue 2, pp 173–183 | Cite as

Development and validation of nomogram estimating post-surgery hospital stay of lung cancer patients: relevance for predictive, preventive, and personalized healthcare strategies

  • Xiang-Lin Hu
  • Song-Tao Xu
  • Xiao-Cen Wang
  • Jin-Long Luo
  • Dong-Ni Hou
  • Xiao-Min Zhang
  • Chen Bao
  • Dong YangEmail author
  • Yuan-Lin Song
  • Chun-Xue Bai
Research
  • 31 Downloads

Abstract

Objective

In the era of fast track surgery, early and accurately estimating whether postoperative length of stay (p-LOS) will be prolonged after lung cancer surgery is very important, both for patient’s discharge planning and hospital bed management. Pulmonary function tests (PFTs) are very valuable routine examinations which should not be underutilized before lung cancer surgery. Thus, this study aimed to establish an accurate but simple prediction tool, based on PFTs, for achieving a personalized prediction of prolonged p-LOS in patients following lung resection.

Methods

The medical information of 1257 patients undergoing lung cancer surgery were retrospectively reviewed and served as the training set. p-LOS exceeding the third quartile value was considered prolonged. Using logistic regression analyses, potential predictors of prolonged p-LOS were identified among various preoperative factors containing PFTs and intraoperative factors. A nomogram was constructed and subjected to internal and external validation.

Results

Five independent risk factors for prolonged p-LOS were identified, including older age, being male, and ratio of residual volume to total lung capacity (RV/TLC) ≥ 45.0% which is the only modifiable risk factor, more invasive surgical approach, and surgical type. The nomogram comprised of these five predictors exhibited sufficient predictive accuracy, with the area under the receiver operating characteristic curve (AUC) of 0.76 [95% confidence interval (CI) 0.73–0.79] in the internal validation. Also its predictive performance remained fine in the external validation, with the AUC of 0.70 (95% CI 0.60–0.79). The calibration curves showed satisfactory agreements between the model predicted probability and the actually observed probability.

Conclusions

Preoperative amelioration of RV/TLC may prevent lung cancer patients from unnecessary prolonged p-LOS. The integrated nomogram we developed could provide personalized risk prediction of prolonged p-LOS. This prediction tool may help patients perceive expected hospital stays and enable clinicians to achieve better bed management after lung cancer surgery.

Keywords

Length of stay Lung cancer Surgery Pulmonary function tests Prediction model Nomogram Advanced healthcare Individualized patient profile Hospitalization Economic burden Risk assessment Predictive preventive personalized medicine 

Notes

Abbreviations

EPMA European Association for Predictive, Preventive and Personalised Medicine

PPPM predictive, preventive and personalized medicine

p-LOS postoperative length of stay

PFTs pulmonary function tests

IC inspiratory capacity

FEV1 forced expiratory volume in 1 s

DLCO diffusion capacity for carbon monoxide

FVC forced vital capacity

COPD chronic obstructive pulmonary disease

RV/TLC ratio of residual volume to total lung capacity

VATS video-assisted thoracic surgery

SD standard deviation

IQR interquartile range

ROC receiver operating characteristic

AUC area under the receiver operating characteristic curve

OR odds ratio

CI confidence interval

Authors’ contributions

Hu Xiang-Lin contributed to the study conception and design, data analysis, interpretation of the data, and drafting the manuscript. Yang Dong, Xu Song-Tao, Song Yuan-Lin, and Bai Chun-Xue contributed to the interpretation of the data and critical revision of the manuscript. Hu Xiang-Lin, Xu Song-Tao, Luo Jin-Long, Wang Xiao-Cen, Hou Dong-Ni, Zhang Xiao-Min, and Bao Chen contributed to the collection of the data. All authors read and approved the final manuscript.

Funding

This work was supported by State’s Key Project of Research and Development Plan in China (2017YFC1310602, 2017YFC1310600).

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethics approval and consent for participation

This study was approved by the Ethics Committee of Zhongshan Hospital, Fudan University, Shanghai, 200032, China. Informed consent for participation was obtained in this study. All procedures performed in the study involving human participants were in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Supplementary material

13167_2019_168_MOESM1_ESM.docx (15 kb)
ESM 1 (DOCX 15 kb)

References

  1. 1.
    Malhotra J, Malvezzi M, Negri E, La Vecchia C, Boffetta P. Risk factors for lung cancer worldwide. Eur Respir J. 2016;48(3):889–902.  https://doi.org/10.1183/13993003.00359-2016.CrossRefGoogle Scholar
  2. 2.
    Aggarwal A, Lewison G, Idir S, Peters M, Aldige C, Boerckel W, et al. The state of lung cancer research: a global analysis. J Thorac Oncol. 2016;11(7):1040–50.  https://doi.org/10.1016/j.jtho.2016.03.010.CrossRefGoogle Scholar
  3. 3.
    Howington JA, Blum MG, Chang AC, Balekian AA, Murthy SC. Treatment of stage I and II non-small cell lung cancer: diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013;143(5 Suppl):e278S–313S.  https://doi.org/10.1378/chest.12-2359.CrossRefGoogle Scholar
  4. 4.
    Edwards JP, Datta I, Hunt JD, Stefan K, Ball CG, Dixon E, et al. The impact of computed tomographic screening for lung cancer on the thoracic surgery workforce. Ann Thorac Surg. 2014;98(2):447–52.  https://doi.org/10.1016/j.athoracsur.2014.04.076.CrossRefGoogle Scholar
  5. 5.
    Farjah F, Lou F, Rusch VW, Rizk NP. The quality metric prolonged length of stay misses clinically important adverse events. Ann Thorac Surg. 2012;94(3):881–7; 887-8.  https://doi.org/10.1016/j.athoracsur.2012.04.082.CrossRefGoogle Scholar
  6. 6.
    Giambrone GP, Smith MC, Wu X, Gaber-Baylis LK, Bhat AU, Zabih R, et al. Variability in length of stay after uncomplicated pulmonary lobectomy: is length of stay a quality metric or a patient metric? Eur J Cardiothorac Surg. 2016;49(4):e65–7.  https://doi.org/10.1093/ejcts/ezv476.CrossRefGoogle Scholar
  7. 7.
    Khullar OV, Fernandez FG, Perez S, Knechtle W, Pickens A, Sancheti MS, et al. Time is money: hospital costs associated with video-assisted thoracoscopic surgery lobectomies. Ann Thorac Surg. 2016;102(3):940–7.  https://doi.org/10.1016/j.athoracsur.2016.03.024.CrossRefGoogle Scholar
  8. 8.
    Choi H, Mazzone P. Preoperative evaluation of the patient with lung cancer being considered for lung resection. Curr Opin Anaesthesiol. 2015;28(1):18–25.  https://doi.org/10.1097/ACO.0000000000000149.CrossRefGoogle Scholar
  9. 9.
    Brunelli A, Kim AW, Berger KI, Addrizzo-Harris DJ. Physiologic evaluation of the patient with lung cancer being considered for resectional surgery: diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013;143(5 Suppl):e166S–90S.  https://doi.org/10.1378/chest.12-2395.CrossRefGoogle Scholar
  10. 10.
    Matsuo M, Hashimoto N, Usami N, Imaizumi K, Wakai K, Kawabe T, et al. Inspiratory capacity as a preoperative assessment of patients undergoing thoracic surgery. Interact Cardiovasc Thorac Surg. 2012;14(5):560–4.  https://doi.org/10.1093/icvts/ivr090.CrossRefGoogle Scholar
  11. 11.
    Almquist D, Khanal N, Smith L, Ganti AK. Preoperative pulmonary function tests (PFTs) and outcomes from resected early stage non-small cell lung cancer (NSCLC). Anticancer Res. 2018;38(5):2903–7.  https://doi.org/10.21873/anticanres.12537.Google Scholar
  12. 12.
    Jawitz OK, Wang Z, Boffa DJ, Detterbeck FC, Blasberg JD, Kim AW. The differential impact of preoperative comorbidity on perioperative outcomes following thoracoscopic and open lobectomies. Eur J Cardiothorac Surg. 2017;51(1):169–74.  https://doi.org/10.1093/ejcts/ezw239.CrossRefGoogle Scholar
  13. 13.
    Sancheti MS, Chihara RK, Perez SD, Khullar OV, Fernandez FG, Pickens A, et al. Hospitalization costs after surgery in high-risk patients with early stage lung cancer. Ann Thorac Surg. 2018;105(1):263–70.  https://doi.org/10.1016/j.athoracsur.2017.08.038.CrossRefGoogle Scholar
  14. 14.
    Farjah F, Backhus LM, Varghese TK, Mulligan MS, Cheng AM, Alfonso-Cristancho R, et al. Ninety-day costs of video-assisted thoracic surgery versus open lobectomy for lung cancer. Ann Thorac Surg. 2014;98(1):191–6.  https://doi.org/10.1016/j.athoracsur.2014.03.024.CrossRefGoogle Scholar
  15. 15.
    Shariat SF, Capitanio U, Jeldres C, Karakiewicz PI. Can nomograms be superior to other prediction tools? BJU Int. 2009;103(4):492–5.  https://doi.org/10.1111/j.1464-410X.2008.08073.x.CrossRefGoogle Scholar
  16. 16.
    Balachandran VP, Gonen M, Smith JJ, DeMatteo RP. Nomograms in oncology: more than meets the eye. Lancet Oncol. 2015;16(4):e173–80.  https://doi.org/10.1016/S1470-2045(14)71116-7.CrossRefGoogle Scholar
  17. 17.
    McDevitt J, Kelly M, Comber H, Kelleher T, Dwane F, Sharp L. A population-based study of hospital length of stay and emergency readmission following surgery for non-small-cell lung cancer. Eur J Cardiothorac Surg. 2013;44(4):e253–9.  https://doi.org/10.1093/ejcts/ezt389.CrossRefGoogle Scholar
  18. 18.
    Vogelmeier CF, Criner GJ, Martinez FJ, Anzueto A, Barnes PJ, Bourbeau J, et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 report: GOLD executive summary. Am J Respir Crit Care Med. 2017;195:557–82.  https://doi.org/10.1164/rccm.201701-0218PP.CrossRefGoogle Scholar
  19. 19.
    Iasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2008;26(8):1364–70.  https://doi.org/10.1200/JCO.2007.12.9791.CrossRefGoogle Scholar
  20. 20.
    Zhang Z, Kattan MW. Drawing nomograms with R: applications to categorical outcome and survival data. Ann Transl Med. 2017;5(10):211.  https://doi.org/10.21037/atm.2017.04.01.CrossRefGoogle Scholar
  21. 21.
    DeLuzio MR, Keshava HB, Wang Z, Boffa DJ, Detterbeck FC, Kim AW. A model for predicting prolonged length of stay in patients undergoing anatomical lung resection: a National Surgical Quality Improvement Program (NSQIP) database study. Interact Cardiovasc Thorac Surg. 2016;23(2):208–15.  https://doi.org/10.1093/icvts/ivw090.CrossRefGoogle Scholar
  22. 22.
    Rosen JE, Hancock JG, Kim AW, Detterbeck FC, Boffa DJ. Predictors of mortality after surgical management of lung cancer in the National Cancer Database. Ann Thorac Surg. 2014;98(6):1953–60.  https://doi.org/10.1016/j.athoracsur.2014.07.007.CrossRefGoogle Scholar
  23. 23.
    Hu XL, Xu ST, Wang XC, Hou DN, Chen CC, Song YL, et al. Prevalence of and risk factors for presenting initial respiratory symptoms in patients undergoing surgery for lung cancer. J Cancer. 2018;9(19):3515–21.  https://doi.org/10.7150/jca.26209.CrossRefGoogle Scholar
  24. 24.
    Raviv S, Hawkins KA, DeCamp MM Jr, Kalhan R. Lung cancer in chronic obstructive pulmonary disease: enhancing surgical options and outcomes. Am J Respir Crit Care Med. 2011;183(9):1138–46.  https://doi.org/10.1164/rccm.201008-1274CI.CrossRefGoogle Scholar
  25. 25.
    Rodriguez M, Gómez-Hernandez MT, Novoa N, Jiménez MF, Aranda JL, Varela G. Refraining from smoking shortly before lobectomy has no influence on the risk of pulmonary complications: a case-control study on a matched population. Eur J Cardiothorac Surg. 2017;51(3):498–503.  https://doi.org/10.1093/ejcts/ezw359.Google Scholar
  26. 26.
    Zhou Y, Zhong NS, Li X, Chen S, Zheng J, Zhao D, et al. Tiotropium in early-stage chronic obstructive pulmonary disease. N Engl J Med. 2017;377:923–35.  https://doi.org/10.1056/NEJMoa1700228.CrossRefGoogle Scholar
  27. 27.
    Hu XL, Xu ST, Wang XC, Hou DN, Chen CC, Yang D, et al. Status of coexisting chronic obstructive pulmonary disease and its clinicopathological features in patients undergoing lung cancer surgery: a cross-sectional study of 3,006 cases. J Thorac Dis. 2018;10(4):2403–11.  https://doi.org/10.21037/jtd.2018.03.165.CrossRefGoogle Scholar
  28. 28.
    Hashimoto N, Matsuzaki A, Okada Y, Imai N, Iwano S, Wakai K, et al. Clinical impact of prevalence and severity of COPD on the decision-making process for therapeutic management of lung cancer patients. BMC Pulm Med. 2014;14(14).  https://doi.org/10.1186/1471-2466-14-14.
  29. 29.
    Golubnitschaja O. Time for new guidelines in advanced healthcare: the mission of The EPMA Journal to promote an integrative view in predictive, preventive and personalized medicine. EPMA J. 2012;3(1):5.  https://doi.org/10.1186/1878-5085-3-5.CrossRefGoogle Scholar
  30. 30.
    Divisi D, Di Francesco C, Di Leonardo G, Crisci R. Preoperative pulmonary rehabilitation in patients with lung cancer and chronic obstructive pulmonary disease. Eur J Cardiothorac Surg. 2013;43(2):293–6.  https://doi.org/10.1093/ejcts/ezs257.CrossRefGoogle Scholar
  31. 31.
    Stefanelli F, Meoli I, Cobuccio R, Curcio C, Amore D, Casazza D, et al. High-intensity training and cardiopulmonary exercise testing in patients with chronic obstructive pulmonary disease and non-small-cell lung cancer undergoing lobectomy. Eur J Cardiothorac Surg. 2013;44(4):e260–5.  https://doi.org/10.1093/ejcts/ezt375.CrossRefGoogle Scholar
  32. 32.
    Gao K, Yu PM, Su JH, He CQ, Liu LX, Zhou YB, et al. Cardiopulmonary exercise testing screening and pre-operative pulmonary rehabilitation reduce postoperative complications and improve fast-track recovery after lung cancer surgery: a study for 342 cases. Thorac Cancer. 2015;6(4):443–9.  https://doi.org/10.1111/1759-7714.12199.CrossRefGoogle Scholar
  33. 33.
    Otake H, Yasunaga H, Horiguchi H, Matsutani N, Matsuda S, Ohe K. Impact of hospital volume on chest tube duration, length of stay, and mortality after lobectomy. Ann Thorac Surg. 2011;92(3):1069–74.  https://doi.org/10.1016/j.athoracsur.2011.04.087.CrossRefGoogle Scholar
  34. 34.
    von Meyenfeldt EM, Marres GMH, van Thiel E, Damhuis RAM. Variation in length of hospital stay after lung cancer surgery in the Netherlands. Eur J Cardiothorac Surg. 2018;54(3):560–4.  https://doi.org/10.1093/ejcts/ezy074.CrossRefGoogle Scholar
  35. 35.
    Golubnitschaja O, Costigliola V, EPMA. General report & recommendations in predictive, preventive and personalised medicine 2012: white paper of the European Association for Predictive, Preventive and Personalised Medicine. EPMA J. 2012;3(1):14.  https://doi.org/10.1186/1878-5085-3-14
  36. 36.
    Golubnitschaja O, Baban B, Boniolo G, Wang W, Bubnov R, Kapalla M, et al. Medicine in the early twenty-first century: paradigm and anticipation - EPMA position paper 2016. EPMA J. 2016 Oct 25;7(23).  https://doi.org/10.1186/s13167-016-0072-4.
  37. 37.
    Golubnitschaja O, Costigliola V, Grech G. EPMA world congress: traditional forum in predictive, preventive and personalised medicine for multi-professional consideration and consolidation. EPMA J. 2017;8(Suppl:1–54.  https://doi.org/10.1007/s13167-017-0108-4.CrossRefGoogle Scholar
  38. 38.
    Grech G, Zhan X, Yoo BC, Bubnov R, Hagan S, Danesi R, et al. EPMA position paper in cancer: current overview and future perspectives. EPMA J. 2015;6(1):9.  https://doi.org/10.1186/s13167-015-0030-6.CrossRefGoogle Scholar
  39. 39.
    Janssens JP, Schuster K, Voss A. Preventive, predictive, and personalized medicine for effective and affordable cancer care. EPMA J. 2018;9(2):113–23.  https://doi.org/10.1007/s13167-018-0130-1.CrossRefGoogle Scholar

Copyright information

© European Association for Predictive, Preventive and Personalised Medicine (EPMA) 2019

Authors and Affiliations

  1. 1.Department of Pulmonary Medicine, Zhongshan HospitalFudan UniversityShanghaiChina
  2. 2.Department of Thoracic Surgery, Zhongshan HospitalFudan UniversityShanghaiChina
  3. 3.Department of Nursing, Zhongshan HospitalFudan UniversityShanghaiChina

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