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Prognostic laboratory score to predict 14-day mortality in terminally ill patients with respiratory malignancy

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Abstract

Background

Some studies have developed a scoring system to determine the short-term survival of patients with respiratory malignancy.

Methods

A total of 649 terminally ill patients with respiratory malignancy admitted to our palliative care unit were included in this study. They were randomly divided into the investigation (n = 390) and validation (n = 259) groups. Nineteen blood parameters were analyzed in the laboratory. Receiver-operating characteristic analysis was performed for each blood factor and the area under the curve was calculated to determine the predictive value for 14-day survival after the blood test. Multivariable logistic regression analysis was performed to identify the significant independent prognostic factors for 14-day mortality. To develop a scoring system, the laboratory prognostic score for respiratory malignancy (R-LPS) was calculated using the sum of the indices of the independent prognostic factors.

Results

Multivariable analysis showed that 8 out of 19 indices, namely, C-reactive protein ≥ 6.8 mg/dL, aspartate aminotransferase ≥ 43 U/L, blood urea nitrogen ≥ 22 mg/dL, white blood cell count ≥ 10.9 × 103/μL, eosinophil percentage ≤ 0.4%, neutrophil-to-lymphocyte ratio ≥ 12.0, red cell distribution width ≥ 16.8, and platelet count ≤ 168 × 103/μL were significant independent factors for 14-day survival in patients with respiratory malignancy. The R-LPS 3 showed acceptable accuracy for 14-day mortality in both the investigation and validation groups and predicted death within 14 days with 75–82% sensitivity and 59–62% specificity.

Conclusions

The R-LPS developed from eight laboratory indices showed acceptable prognostic ability for terminally ill patients with respiratory malignancy.

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References

  1. Ferlay J, Soerjomataram I, Dikshit R et al (2015) Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 136:E359–E386. https://doi.org/10.1002/ijc.29210

    Article  CAS  PubMed  Google Scholar 

  2. Pirovano M, Maltoni M, Nanni O et al (1999) A new palliative prognostic score: a first step for the staging of terminally ill cancer patients. Italian Multicenter and study Group on Palliative Care. J Pain Symptom Manag 17:231–239. https://doi.org/10.1016/s0885-3924(98)00145-6

    Article  CAS  Google Scholar 

  3. Maltoni M, Nanni O, Pirovano M et al (1999) Successful validation of the palliative prognostic score in terminally ill cancer patients. Italian Multicenter study Group on pa11iative care. J Pain Symptom Manag 17:240–247. https://doi.org/10.1016/s0885-3924(98)00146-8

    Article  CAS  Google Scholar 

  4. Scarpi E, Maltoni M, Miceli R et al (2011) Survival prediction for terminally ill cancer patients: revision of the palliative prognostic score with incorporation of delirium. Oncologist 16:1793–1799. https://doi.org/10.1634/theoncologist.2011-0130

    Article  PubMed  PubMed Central  Google Scholar 

  5. Morita T, Tsunoda J, Inoue S et al (1999) The Palliative Prognostic Index: a scoring system for survival prediction of terminally ill cancer patients. Support Care Cancer 7:128–133. https://doi.org/10.1007/s005200050242

    Article  CAS  PubMed  Google Scholar 

  6. Suh SY, Choi YS, Shim JY et al (2010) Construction of a new, objective prognostic score for terminally ill cancer patients: a multicenter study. Support Care Cancer 18:151–157. https://doi.org/10.1007/s00520-009-0639-x

    Article  PubMed  Google Scholar 

  7. Hyodo I, Morita T, Adachi I et al (2010) Development of a predicting tool for survival of terminally ill cancer patients. Jpn J Clin Oncol 40:442–448. https://doi.org/10.1093/jjco/hyp182

    Article  PubMed  Google Scholar 

  8. Gwilliam B, Keeley V, Todd C et al (2011) Development of prognosis in palliative care study (PiPS) predictor models to improve prognostication in advanced cancer: prospective cohort study. BMJ 343:d4920. https://doi.org/10.1136/bmj.d4920

    Article  PubMed  PubMed Central  Google Scholar 

  9. Omichi M, Konoike S, Yamada Y et al (2017) Development of biological prognostic score versions 2 and 3 for advanced cancer patients and a prospective study on the prediction accuracy. Comparison with the palliative prognostic index. Palliat Care Res 12:140–148. https://doi.org/10.2512/jspm.12.140 (in Japanese with English abstract)

    Article  Google Scholar 

  10. Minami S, Ihara S, Komuta K (2020) Gustave Roussy immune score and Royal Marsden Hospital prognostic score are prognostic markers for extensive disease of small cell lung cancer. World J Oncol 11:98–105. https://doi.org/10.14740/wjon1275

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Pruksakorn D, Phanphaisarn A, Settakorn J et al (2018) Prognostic score for life expectancy evaluation of lung cancer patients after bone metastasis. J Bone Oncol 3(10):1–5. https://doi.org/10.1016/j.jbo.2017.10.001.eCollection

    Article  Google Scholar 

  12. Katz MH (2008) Multivariate analysis: a practical guide for clinicians. Cambridge University Press, Cambridge

    Google Scholar 

  13. Lim RBL (2016) End-of-life care in patients with advanced lung cancer. Ther Adv Respir Dis 10:455–467. https://doi.org/10.1177/1753465816660925

    Article  PubMed  PubMed Central  Google Scholar 

  14. Hiratsuka Y, Suh SY, Maeda I et al (2021) Factors influencing spiritual well-being in terminally ill cancer inpatients in Japan. Support Care Cancer 29:2795–2802. https://doi.org/10.1007/s00520-020-05802-5

    Article  PubMed  Google Scholar 

  15. Tomita M, Ayabe T, Maeda R et al (2019) The prognostic values of a novel preoperative inflammation-based score in Japanese patients with non-small cell lung cancer. World J Oncol 10:176–180. https://doi.org/10.14740/wjon1222

    Article  PubMed  PubMed Central  Google Scholar 

  16. Takamori S, Takada K, Shimokawa M et al (2021) Clinical utility of pretreatment Glasgow prognostic score in non-small-cell lung cancer patients treated with immune checkpoint inhibitors. Lung Cancer 152(27–33):17. https://doi.org/10.1016/j.lungcan.2020.11.026

    Article  Google Scholar 

  17. Deng T, Zhang J, Meng Y et al (2018) Higher pretreatment lactate dehydrogenase concentration predicts worse overall survival in patients with lung cancer. Medicine (Baltimore) 97(38):e12524. https://doi.org/10.1097/MD.0000000000012524

    Article  CAS  Google Scholar 

  18. Zhang H, Gao L, Zhang B et al (2016) Prognostic value of platelet to lymphocyte ratio in non-small cell lung cancer: a systematic review and meta-analysis. Sci Rep 6:22618. https://doi.org/10.1038/srep22618

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Wang Y, Huang D, Xu WY et al (2019) Prognostic value of pretreatment lymphocyte-to-monocyte ratio in non-small cell lung cancer: a meta-analysis. Oncol Res Treat 42:523–531. https://doi.org/10.1159/000501726

    Article  PubMed  Google Scholar 

  20. Simmons CP, Koinis F, Fallon MT et al (2015) Prognosis in advanced lung cancer—a prospective study examining key clinicopathological factors. Lung Cancer 88:304–309. https://doi.org/10.1016/j.lungcan.2015.03.020

    Article  PubMed  Google Scholar 

  21. Yılmaz A, Tekin SB, Bilici M et al (2020) The significance of controlling nutritional status (CONUT) score as a novel prognostic parameter in small cell lung cancer. Lung 198:695–704. https://doi.org/10.1007/s00408-020-00361-2

    Article  CAS  PubMed  Google Scholar 

  22. Li D, Yuan X, Liu J et al (2018) Prognostic value of prognostic nutritional index in lung cancer: a meta-analysis. J Thorac Dis 10:5298–5307. https://doi.org/10.21037/jtd.2018.08.51

    Article  PubMed  PubMed Central  Google Scholar 

  23. Li S, Wang H, Yang Z et al (2021) Naples Prognostic Score as a novel prognostic prediction tool in video-assisted thoracoscopic surgery for early-stage lung cancer: a propensity score matching study. Surg Endosc 35:3679–3697. https://doi.org/10.1007/s00464-020-07851-7

    Article  PubMed  Google Scholar 

  24. Glare P, Virik K, Jones M et al (2003) A systematic review of physicians’ survival predictions in terminally ill cancer patients. BMJ 327:195–198. https://doi.org/10.1136/bmj.327.7408.195

    Article  PubMed  PubMed Central  Google Scholar 

  25. Hui D, Park M, Liu D et al (2016) Clinician prediction of survival versus the palliative prognostic score: which approach is more accurate? Eur J Cancer 64:89–95. https://doi.org/10.1016/j.ejca.2016.05.009

    Article  PubMed  PubMed Central  Google Scholar 

  26. Tavares T, Oliveira M, Gonçalves J et al (2018) Predicting prognosis in patients with advanced cancer: a prospective study. Palliat Med 32:413–416. https://doi.org/10.1177/0269216317705788

    Article  PubMed  Google Scholar 

  27. Ermacora P, Mazzer M, Isola M et al (2019) Prognostic evaluation in palliative care: final results from a prospective cohort study. Support Care Cancer 27:2095–2102. https://doi.org/10.1007/s00520-018-4463-z

    Article  PubMed  Google Scholar 

  28. Yamada T, Morita T, Maeda I et al (2017) A prospective, multicenter cohort study to validate a simple performance status—based survival prediction system for oncologists. Cancer 123:1442–1452. https://doi.org/10.1002/cncr.30484

    Article  CAS  PubMed  Google Scholar 

  29. Ozyurek BA, Ozdemirel TS, Ozden SB et al (2017) Prognostic value of the neutrophil to lymphocyte ratio (NLR) in lung cancer cases. Asian Pac J Cancer Prev 18:1417–1421. https://doi.org/10.22034/APJCP.2017.18.5.1417

    Article  PubMed Central  Google Scholar 

  30. Cedrés S, Torrejon D, Martínez A et al (2012) Neutrophil to lymphocyte ratio (NLR) as an indicator of poor prognosis in stage IV non-small cell lung cancer. Clin Transl Oncol 14:864–869. https://doi.org/10.1007/s12094-012-0872-5

    Article  PubMed  Google Scholar 

  31. Kawai N, Yuasa N (2018) Laboratory prognostic score for predicting 30-day mortality in terminally ill cancer patients. Nagoya J Med Sci 80:571–582. https://doi.org/10.18999/nagjms.80.4.571

    Article  PubMed  PubMed Central  Google Scholar 

  32. Valent P, Gleich GJ, Reiter A et al (2012) Pathogenesis and classification of eosinophil disorders: a review of recent developments in the field. Expert Rev Hematol 5:157–176. https://doi.org/10.1586/ehm.11.81

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Abidi K, Belayachi J, Derras Y et al (2011) Eosinopenia, an early marker of increased mortality in critically ill medical patients. Intensive Care Med 37:1136–1142. https://doi.org/10.1007/s00134-011-2170-z

    Article  PubMed  Google Scholar 

  34. Cikrikcioglu MA, Soysal P, Dikerdem D et al (2012) Absolute blood eosinophil count and 1-year mortality risk following hospitalization with acute heart failure. Eur J Emerg Med 19:257–263. https://doi.org/10.1097/MEJ.0b013e32834c67eb

    Article  PubMed  Google Scholar 

  35. Holland M, Alkhalil M, Chandromouli S et al (2010) Eosinopenia as a marker of mortality and length of stay in patients admitted with exacerbations of chronic obstructive pulmonary disease. Respirology 15:165–167. https://doi.org/10.1111/j.1440-1843.2009.01651.x

    Article  PubMed  Google Scholar 

  36. Tefferi A, Hanson CA, Inwards DJ (2005) How to interpret and pursue an abnormal complete blood cell count in adults. Mayo Clin Proc 80:923–936. https://doi.org/10.4065/80.7.923

    Article  PubMed  Google Scholar 

  37. Lappé JM, Horne BD, Shah SH et al (2011) Red cell distribution width, C-reactive protein, the complete blood count, and mortality in patients with coronary disease and a normal comparison population. Clin Chim Acta 412:2094–2099. https://doi.org/10.1016/j.cca.2011.07.018

    Article  CAS  PubMed  Google Scholar 

  38. Kim J, Kim YD, Song TJ et al (2012) Red blood cell distribution width is associated with poor clinical outcome in acute cerebral infarction. Thromb Haemost 108:349–356. https://doi.org/10.1160/TH12-03-0165

    Article  CAS  PubMed  Google Scholar 

  39. Sadaka F, O’Brien J, Prakash S (2013) Red cell distribution width and outcome in patients with septic shock. J Intensive Care Med 28:307–313. https://doi.org/10.1177/0885066612452838

    Article  PubMed  Google Scholar 

  40. Förhécz Z, Gombos T, Borgulya G et al (2009) Red cell distribution width in heart failure: prediction of clinical events and relationship with markers of ineffective erythropoiesis, inflammation, renal function, and nutritional state. Am Heart J 158:659–666. https://doi.org/10.1016/j.ahj.2009.07.024

    Article  PubMed  Google Scholar 

  41. Koma Y, Onishi A, Matsuoka H et al (2013) Increased red blood cell distribution width associates with cancer stage and prognosis in patients with lung cancer. PLoS ONE 8:e80240. https://doi.org/10.1371/journal.pone.0080240 (eCollection)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Wang Y, Zhou Y, Zhou K et al (2020) Prognostic value of pre-treatment red blood cell distribution width in lung cancer: a meta-analysis. Biomarkers 25:241–247. https://doi.org/10.1080/1354750X.2020.1731763

    Article  CAS  PubMed  Google Scholar 

  43. Ma Y, Li G, Yu M et al (2020) Prognostic significance of thrombocytosis in lung cancer: a systematic review and meta-analysis. Platelets. https://doi.org/10.1080/09537104.2020.1810653

    Article  PubMed  Google Scholar 

  44. Onishi K, Kawai N, Mizuno K et al (2021) Laboratory prognostic score for predicting 14-day mortality in terminally ill patients with gynecologic malignancy. Int J Clin Oncol 26:1345–1352. https://doi.org/10.1007/s10147-021-01923-x

    Article  CAS  PubMed  Google Scholar 

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Funding

This work is supported by the Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital Research Grant (NFRCH21-001). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Correspondence to Norihiro Yuasa.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee (approval reference number: 2016-130) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study protocol was approved by the Institutional Review Board of our hospital, which waived the requirement for informed consent owing to the retrospective nature of the study.

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Tanaka, M., Kawai, N. & Yuasa, N. Prognostic laboratory score to predict 14-day mortality in terminally ill patients with respiratory malignancy. Int J Clin Oncol 27, 655–664 (2022). https://doi.org/10.1007/s10147-021-02105-5

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  • DOI: https://doi.org/10.1007/s10147-021-02105-5

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