Skip to main content
SpringerLink
Log in

Pleural effusion-based nomogram to predict outcomes in unselected patients with multiple myeloma: a large single center experience

  • Original Article
  • Published:
Annals of Hematology Aims and scope Submit manuscript

Abstract

Pleural effusion (PE) is prevalent in unselected “real-life” populations of multiple myeloma (MM). However, its prognostic value on MM is currently elusive. This study aimed to explore the role of PE on MM prognosis and to develop a novel prognostic nomogram for a cohort of Chinese patients with MM. Patients diagnosed with MM form 2000 through 2017 were retrospectively enrolled. PE was evaluated by chest computed tomography (CT) scans. Independent predictors of overall survival (OS) were identified using a multivariable Cox regression model performed on variables selected by the least absolute shrinkage and selection operator (LASSO) algorithm. A nomogram was constructed based on these variables. The concordance index (C-index) and the calibration curve were used to evaluate the predictive performance of the nomogram. Among 861 patients analyzed, 368 patients developed PE. Multivariate cox regression and restricted mean survival time (RMST) analyses revealed that patients with PE experienced worse OS vs. patients without PE. A nomogram predictive of OS was constructed using PE, plasma cell proportion, international staging system (ISS) stage, Charlson comorbidity index (CCI), 1q21 gain, and autologous hematopoietic stem cell transplantation (HSCT). The nomogram showed satisfactory discrimination in the derivation cohort (C-index=0.729) and the validation cohort (C-index=0.684), outperforming the Durie-Salmon (DS) and ISS staging systems. Moreover, the nomogram accurately classified patients into two distinct high- and low-risk groups. PE is frequently encountered in the disease course for MM patients. We derivated and validated a novel nomogram for MM based on PE, outperforming the DS/ISS staging systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data Availability

Data and material are available on reasonable request by contacting the first author Zi-Liang Hou (bright120@126.com).

References

  1. Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin 69(1):7–34. https://doi.org/10.3322/caac.21551

    Article  PubMed  Google Scholar 

  2. Boyd KD, Ross FM, Chiecchio L, Dagrada GP, Konn ZJ, Tapper WJ, Walker BA, Wardell CP, Gregory WM, Szubert AJ, Bell SE, Child JA, Jackson GH, Davies FE, Morgan GJ, NCRI Haematology Oncology Studies Group (2012) A novel prognostic model in myeloma based on co-segregating adverse FISH lesions and the ISS: analysis of patients treated in the MRC Myeloma IX trial. Leukemia 26(2):349–355. https://doi.org/10.1038/leu.2011.204

    Article  CAS  PubMed  Google Scholar 

  3. Palumbo A, Avet-Loiseau H, Oliva S, Lokhorst HM, Goldschmidt H, Rosinol L, Richardson P, Caltagirone S, Lahuerta JJ, Facon T, Bringhen S, Gay F, Attal M, Passera R, Spencer A, Offidani M, Kumar S, Musto P, Lonial S, Petrucci MT, Orlowski RZ, Zamagni E, Morgan G, Dimopoulos MA, Durie BG, Anderson KC, Sonneveld P, San Miguel J, Cavo M, Rajkumar SV, Moreau P (2015) Revised international staging system for multiple myeloma: a report from International Myeloma Working Group. J Clin Oncol 33(26):2863–2869. https://doi.org/10.1200/JCO.2015.61.2267

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Kastritis E, Terpos E, Roussou M, Gavriatopoulou M, Migkou M, Eleutherakis-Papaiakovou E, Fotiou D, Ziogas D, Panagiotidis I, Kafantari E, Giannouli S, Zomas A, Konstantopoulos K, Dimopoulos MA (2017) Evaluation of the revised international staging system in an independent cohort of unselected patients with multiple myeloma. Haematologica 102(3):593–599. https://doi.org/10.3324/haematol.2016.145078

    Article  PubMed  PubMed Central  Google Scholar 

  5. Durie BG, Salmon SE (1975) A clinical staging system for multiple myeloma correlation of measured myeloma cell mass with presenting clinical features, response to treatment, and survival. Cancer 36(3):842–854. https://doi.org/10.1002/1097-0142(197509)36:3<842::aid-cncr2820360303>3.0.co;2-u

    Article  CAS  PubMed  Google Scholar 

  6. Greipp PR, San Miguel J, Durie BG, Crowley JJ, Barlogie B, Blade J, Boccadoro M, Child JA, Avet-Loiseau H, Kyle RA, Lahuerta JJ, Ludwig H, Morgan G, Powles R, Shimizu K, Shustik C, Sonneveld P, Tosi P, Turesson I, Westin J (2005) International staging system for multiple myeloma. J Clin Oncol 23(15):3412–3420. https://doi.org/10.1200/JCO.2005.04.242

    Article  PubMed  Google Scholar 

  7. Terpos E, Katodritou E, Roussou M, Pouli A, Michalis E, Delimpasi S, Parcharidou A, Kartasis Z, Zomas A, Symeonidis A, Viniou NA, Anagnostopoulos N, Economopoulos T, Zervas K, Dimopoulos MA, Greek Myeloma Study Group G (2010) High serum lactate dehydrogenase adds prognostic value to the international myeloma staging system even in the era of novel agents. Eur J Haematol 85(2):114–119. https://doi.org/10.1111/j.1600-0609.2010.01466.x

    Article  PubMed  Google Scholar 

  8. Gkotzamanidou M, Kastritis E, Gavriatopoulou MRMMM, Nikitas N, Gika D, Mparmparousi D, Matsouka C, Terpos E, Dimopoulos MA (2011) Increased serum lactate dehydrongenase should be included among the variables that define very-high-risk multiple myeloma. Clin Lymphoma Myeloma Leuk 11(5):409–413. https://doi.org/10.1016/j.clml.2011.07.001

    Article  CAS  PubMed  Google Scholar 

  9. Inamoto Y, Kurahashi S, Imahashi N, Fukushima N, Adachi T, Kinoshita T, Tsushita K, Miyamura K, Naoe T, Sugiura I (2009) Combinations of cytogenetics and international scoring system can predict poor prognosis in multiple myeloma after high-dose chemotherapy and autologous stem cell transplantation. Am J Hematol 84(5):283–286. https://doi.org/10.1002/ajh.21390

    Article  PubMed  Google Scholar 

  10. Moreau P, Cavo M, Sonneveld P, Rosinol L, Attal M, Pezzi A, Goldschmidt H, Lahuerta JJ, Marit G, Palumbo A, van der Holt B, Blade J, Petrucci MT, Neben K, San Miguel J, Patriarca F, Lokhorst H, Zamagni E, Hulin C, Gutierrez N, Facon T, Caillot D, Benboubker L, Harousseau JL, Leleu X, Avet-Loiseau H, Mary JY (2014) Combination of international scoring system 3, high lactate dehydrogenase, and t(4;14) and/or del(17p) identifies patients with multiple myeloma (MM) treated with front-line autologous stem-cell transplantation at high risk of early MM progression-related death. J Clin Oncol 32(20):2173–2180. https://doi.org/10.1200/JCO.2013.53.0329

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Shah JJ, Abonour R, Gasparetto C, Hardin JW, Toomey K, Narang M, Srinivasan S, Kitali A, Zafar F, Flick ED, Rifkin RM (2017) Analysis of common eligibility criteria of randomized controlled trials in newly diagnosed multiple myeloma patients and extrapolating outcomes. Clin Lymphoma Myeloma Leuk 17(9):575–583.e2. https://doi.org/10.1016/j.clml.2017.06.013

    Article  PubMed  Google Scholar 

  12. Kleber M, Ihorst G, Terhorst M, Koch B, Deschler B, Wasch R, Engelhardt M (2011) Comorbidity as a prognostic variable in multiple myeloma: comparative evaluation of common comorbidity scores and use of a novel MM-comorbidity score. Blood Cancer J 1(9):e35. https://doi.org/10.1038/bcj.2011.34

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Shariat SF, Karakiewicz PI, Suardi N, Kattan MW (2008) Comparison of nomograms with other methods for predicting outcomes in prostate cancer: a critical analysis of the literature. Clin Cancer Res 14(14):4400–4407. https://doi.org/10.1158/1078-0432.Ccr-07-4713

    Article  CAS  PubMed  Google Scholar 

  14. Roberts ME, Neville E, Berrisford RG, Antunes G, Ali NJ, Group BTSPDG (2010) Management of a malignant pleural effusion: British Thoracic Society Pleural Disease Guideline 2010. Thorax 65(Suppl 2):ii32–ii40. https://doi.org/10.1136/thx.2010.136994

    Article  PubMed  Google Scholar 

  15. Kintzer JS, Rosenow EC, Kyle RA (1978) Thoracic and pulmonary abnormalities in multiple myeloma: a review of 958 cases. Arch Intern Med 138(5):727–730. https://doi.org/10.1001/archinte.1978.03630290039015

    Article  PubMed  Google Scholar 

  16. Rajkumar SV, Dimopoulos MA, Palumbo A, Blade J, Merlini G, Mateos MV, Kumar S, Hillengass J, Kastritis E, Richardson P, Landgren O, Paiva B, Dispenzieri A, Weiss B, LeLeu X, Zweegman S, Lonial S, Rosinol L, Zamagni E, Jagannath S, Sezer O, Kristinsson SY, Caers J, Usmani SZ, Lahuerta JJ, Johnsen HE, Beksac M, Cavo M, Goldschmidt H, Terpos E, Kyle RA, Anderson KC, Durie BG, Miguel JF (2014) International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol 15(12):e538–e548. https://doi.org/10.1016/S1470-2045(14)70442-5

    Article  PubMed  Google Scholar 

  17. Moy MP, Levsky JM, Berko NS, Godelman A, Jain VR, Haramati LB (2013) A new, simple method for estimating pleural effusion size on CT scans. Chest 143(4):1054–1059. https://doi.org/10.1378/chest.12-1292

    Article  PubMed  Google Scholar 

  18. Byun JM, Kim KH, Choi IS, Park JH, Kim JS, Shin DY, Koh Y, Kim I, Yoon SS, Lim HJ (2017) Pleural effusion in multiple myeloma: characteristics and practice patterns. Acta Haematol 138(2):69–76. https://doi.org/10.1159/000477793

    Article  PubMed  Google Scholar 

  19. Charlson MEPP, Ales KL, MacKenzie CR (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40(5):373–383. https://doi.org/10.1016/0021-9681(87)90171-8

    Article  CAS  PubMed  Google Scholar 

  20. Charlson M, Szatrowski TP, Peterson J, Gold J (1994) Validation of a combined comorbidity index. J Clin Epidemiol 47(11):1245–1251. https://doi.org/10.1016/0895-4356(94)90129-5

    Article  CAS  PubMed  Google Scholar 

  21. Harrell FE, Lee KL, Pollock BG (1988) Regression models in clinical studies: determining relationships between predictors and response. J Natl Cancer Inst 80(15):1198–1202. https://doi.org/10.1093/jnci/80.15.1198

    Article  PubMed  Google Scholar 

  22. Camp RL, Dolled-Filhart M, Rimm DL (2004) X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res 10(21):7252–7259. https://doi.org/10.1158/1078-0432.CCR-04-0713

    Article  CAS  PubMed  Google Scholar 

  23. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc B Stat Methodol 58(1):267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x

    Article  Google Scholar 

  24. Tibshirani R (1997) The lasso method for variable selection in the Cox model. Stat Med 16(4):385–395. https://doi.org/10.1002/(sici)1097-0258(19970228)16:4<385::aid-sim380>3.0.co;2-3

    Article  CAS  PubMed  Google Scholar 

  25. Ghorbel IB, Feki NB, Lamloum M, Hamzaoui A, Khanfir M, Salem TB, Said F, Romdhane NB, Houman MH (2015) Pleural myelomatous involvement in multiple myeloma: five cases. Ann Saudi Med 35(4):327–330. https://doi.org/10.5144/0256-4947.2015.327

    Article  PubMed  PubMed Central  Google Scholar 

  26. Kamble R, Wilson CS, Fassas A, Desikan R, Siegel DS, Tricot G, Anderson P, Sawyer J, Anaissie E, Barlogie B (2005) Malignant pleural effusion of multiple myeloma: prognostic factors and outcome. Leuk Lymphoma 46(8):1137–1142. https://doi.org/10.1080/10428190500102845

    Article  CAS  PubMed  Google Scholar 

  27. Ryu JS, Ryu HJ, Lee SN, Memon A, Lee SK, Nam HS, Kim HJ, Lee KH, Cho JH, Hwang SS (2014) Prognostic impact of minimal pleural effusion in non-small-cell lung cancer. J Clin Oncol 32(9):960–967. https://doi.org/10.1200/JCO.2013.50.5453

    Article  PubMed  Google Scholar 

  28. Cook G, Royle K-L, Pawlyn C, Hockaday A, Shah V, Kaiser MF, Brown SR, Gregory WM, Child JA, Davies FE, Morgan GJ, Cairns DA, Jackson GH (2019) A clinical prediction model for outcome and therapy delivery in transplant-ineligible patients with myeloma (UK Myeloma Research Alliance Risk Profile): a development and validation study. Lancet Haematol 6(3):e154–e166. https://doi.org/10.1016/s2352-3026(18)30220-5

    Article  PubMed  PubMed Central  Google Scholar 

  29. Perrot A, Lauwers-Cances V, Tournay E, Hulin C, Chretien ML, Royer B, Dib M, Decaux O, Jaccard A, Belhadj K, Brechignac S, Fontan J, Voillat L, Demarquette H, Collet P, Rodon P, Sohn C, Lifermann F, Orsini-Piocelle F, Richez V, Mohty M, Macro M, Minvielle S, Moreau P, Leleu X, Facon T, Attal M, Avet-Loiseau H, Corre J (2019) Development and validation of a cytogenetic prognostic index predicting survival in multiple myeloma. J Clin Oncol 37(19):1657–1665. https://doi.org/10.1200/JCO.18.00776

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Iriuchishima H, Saitoh T, Handa H, Isoda A, Matsumoto M, Sawamura M, Iwasaki A, Ushie C, Hattori H, Sasaki Y, Mitsui T, Yokohama A, Tsukamoto N, Murakami H, Nojima Y (2015) A new staging system to predict prognosis of patients with multiple myeloma in an era of novel therapeutic agents. Eur J Haematol 94(2):145–151. https://doi.org/10.1111/ejh.12407

    Article  CAS  PubMed  Google Scholar 

  31. Terebelo HR, Abonour R, Gasparetto CJ, Toomey K, Durie BGM, Hardin JW, Jagannath S, Wagner L, Narang M, Flick ED, Srinivasan S, Yue L, Kitali A, Agarwal A, Rifkin RM, Investigators CMR (2019) Development of a prognostic model for overall survival in multiple myeloma using the Connect((R)) MM Patient Registry. Br J Haematol 187(5):602–614. https://doi.org/10.1111/bjh.16139

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Shariat SF, Capitanio U, Jeldres C, Karakiewicz PI (2009) Can nomograms be superior to other prediction tools? BJU Int 103(4):492–495. https://doi.org/10.1111/j.1464-410X.2008.08073.x

    Article  PubMed  Google Scholar 

  33. Greipp PRLT, Bennett JM, Gaillard JP, Klein B, Stewart JA, Oken MM, Kay NE, Van Ness B, Kyle RA (1998) Plasmablastic morphology-an independent prognostic factor with clinical and laboratory correlates: Eastern Cooperative Oncology Group (ECOG) myeloma trial E9486 report by the ECOG Myeloma Laboratory Group. Blood 91(7):2501–2507. https://doi.org/10.1182/blood.V91.7.2501

    Article  CAS  PubMed  Google Scholar 

  34. Group FL (1999) Long-term survival in multiple myeloma: a Finnish Leukaemia Group study. Br J Haematol 105(4):942–947. https://doi.org/10.1046/j.1365-2141.1999.01462.x

    Article  Google Scholar 

  35. Rajkumar SV, Fonseca R, Dispenzieri A, Lacy MQ, Lust JA, Witzig TE, Therneau TM, Kyle RA, Greipp PR, Gertz MA (2001) Methods for estimation of bone marrow plasma cell involvement in myeloma: predictive value for response and survival in patients undergoing autologous stem cell transplantation. Am J Hematol 68(4):269–275. https://doi.org/10.1002/ajh.10003

    Article  CAS  PubMed  Google Scholar 

  36. Offidani M, Corvatta L, Polloni C, Centurioni R, Visani G, Brunori M, Gentili S, Catarini M, Samori A, Blasi N, Alesiani F, Caraffa P, Burattini M, Galieni P, Fraticelli P, Ferranti M, Giuliodori L, Leoni P (2012) Assessment of vulnerability measures and their effect on survival in a real-life population of multiple myeloma patients registered at Marche Region Multiple Myeloma Registry. Clin Lymphoma Myeloma Leuk 12(6):423–432. https://doi.org/10.1016/j.clml.2012.06.008

    Article  PubMed  Google Scholar 

  37. Bila J, Jelicic J, Djurasinovic V, Vukovic V, Sretenovic A, Andjelic B, Antic D, Todorovic M, Mihaljevic B (2015) Prognostic effect of comorbidity indices in elderly patients with multiple myeloma. Clin Lymphoma Myeloma Leuk 15(7):416–419. https://doi.org/10.1016/j.clml.2015.03.004

    Article  PubMed  Google Scholar 

  38. Nemec P, Zemanova Z, Greslikova H, Michalova K, Filkova H, Tajtlova J, Kralova D, Kupska R, Smetana J, Krejci M, Pour L, Zahradova L, Sandecka V, Adam Z, Buchler T, Spicka I, Gregora E, Kuglik P, Hajek R (2010) Gain of 1q21 is an unfavorable genetic prognostic factor for multiple myeloma patients treated with high-dose chemotherapy. Biol Blood Marrow Transplant 16(4):548–554. https://doi.org/10.1016/j.bbmt.2009.11.025

    Article  CAS  PubMed  Google Scholar 

  39. An G, Xu Y, Shi L, Shizhen Z, Deng S, Xie Z, Sui W, Zhan F, Qiu L (2014) Chromosome 1q21 gains confer inferior outcomes in multiple myeloma treated with bortezomib but copy number variation and percentage of plasma cells involved have no additional prognostic value. Haematologica 99(2):353–359. https://doi.org/10.3324/haematol.2013.088211

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Palumbo A, Cavallo F, Gay F, Di Raimondo F, Ben Yehuda D, Petrucci MT, Pezzatti S, Caravita T, Cerrato C, Ribakovsky E, Genuardi M, Cafro A, Marcatti M, Catalano L, Offidani M, Carella AM, Zamagni E, Patriarca F, Musto P, Evangelista A, Ciccone G, Omede P, Crippa C, Corradini P, Nagler A, Boccadoro M, Cavo M (2014) Autologous transplantation and maintenance therapy in multiple myeloma. N Engl J Med 371(10):895–905. https://doi.org/10.1056/NEJMoa1402888

    Article  CAS  PubMed  Google Scholar 

  41. Gay F, Oliva S, Petrucci MT, Conticello C, Catalano L, Corradini P, Siniscalchi A, Magarotto V, Pour L, Carella A, Malfitano A, Petro D, Evangelista A, Spada S, Pescosta N, Omede P, Campbell P, Liberati AM, Offidani M, Ria R, Pulini S, Patriarca F, Hajek R, Spencer A, Boccadoro M, Palumbo A (2015) Chemotherapy plus lenalidomide versus autologous transplantation, followed by lenalidomide plus prednisone versus lenalidomide maintenance, in patients with multiple myeloma: a randomised, multicentre, phase 3 trial. Lancet Oncol 16(16):1617–1629. https://doi.org/10.1016/S1470-2045(15)00389-7

    Article  CAS  PubMed  Google Scholar 

  42. Smol T, Dufour A, Tricot S, Wemeau M, Stalnikiewicz L, Bernardi F, Terre C, Ducourneau B, Bisiau H, Daudignon A (2017) Combination of t(4;14), del(17p13), del(1p32) and 1q21 gain FISH probes identifies clonal heterogeneity and enhances the detection of adverse cytogenetic profiles in 233 newly diagnosed multiple myeloma. Mol Cytogenet 10:26. https://doi.org/10.1186/s13039-017-0327-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Shah V, Sherborne AL, Walker BA, Johnson DC, Boyle EM, Ellis S, Begum DB, Proszek PZ, Jones JR, Pawlyn C, Savola S, Jenner MW, Drayson MT, Owen RG, Houlston RS, Cairns DA, Gregory WM, Cook G, Davies FE, Jackson GH, Morgan GJ, Kaiser MF (2018) Prediction of outcome in newly diagnosed myeloma: a meta-analysis of the molecular profiles of 1905 trial patients. Leukemia 32(1):102–110. https://doi.org/10.1038/leu.2017.179

    Article  CAS  PubMed  Google Scholar 

  44. Neben K, Lokhorst HM, Jauch A, Bertsch U, Hielscher T, van der Holt B, Salwender H, Blau IW, Weisel K, Pfreundschuh M, Scheid C, Duhrsen U, Lindemann W, Schmidt-Wolf IG, Peter N, Teschendorf C, Martin H, Haenel M, Derigs HG, Raab MS, Ho AD, van de Velde H, Hose D, Sonneveld P, Goldschmidt H (2012) Administration of bortezomib before and after autologous stem cell transplantation improves outcome in multiple myeloma patients with deletion 17p. Blood 119(4):940–948. https://doi.org/10.1182/blood-2011-09-379164

    Article  CAS  PubMed  Google Scholar 

  45. Shaughnessy JD, Zhou Y, Haessler J, van Rhee F, Anaissie E, Nair B, Waheed S, Alsayed Y, Epstein J, Crowley J, Barlogie B (2009) TP53 deletion is not an adverse feature in multiple myeloma treated with total therapy 3. Br J Haematol 147(3):347–351. https://doi.org/10.1111/j.1365-2141.2009.07864.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Biran N, Malhotra J, Bagiella E, Cho HJ, Jagannath S, Chari A (2014) Patients with newly diagnosed multiple myeloma and chromosome 1 amplification have poor outcomes despite the use of novel triplet regimens. Am J Hematol 89(6):616–620. https://doi.org/10.1002/ajh.23705

    Article  PubMed  Google Scholar 

  47. Nahi H, Vatsveen TK, Lund J, Heeg BM, Preiss B, Alici E, Moller MB, Wader KF, Moller HE, Groseth LA, Ostergaard B, Dai HY, Holmberg E, Gahrton G, Waage A, Abildgaard N (2016) Proteasome inhibitors and IMiDs can overcome some high-risk cytogenetics in multiple myeloma but not gain 1q21. Eur J Haematol 96(1):46–54. https://doi.org/10.1111/ejh.12546

    Article  CAS  PubMed  Google Scholar 

  48. Ludwig H, Durie BG, Bolejack V, Turesson I, Kyle RA, Blade J, Fonseca R, Dimopoulos M, Shimizu K, San Miguel J, Westin J, Harousseau JL, Beksac M, Boccadoro M, Palumbo A, Barlogie B, Shustik C, Cavo M, Greipp PR, Joshua D, Attal M, Sonneveld P, Crowley J (2008) Myeloma in patients younger than age 50 years presents with more favorable features and shows better survival: an analysis of 10 549 patients from the International Myeloma Working Group. Blood 111(8):4039–4047. https://doi.org/10.1182/blood-2007-03-081018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Chretien ML, Hebraud B, Cances-Lauwers V, Hulin C, Marit G, Leleu X, Karlin L, Roussel M, Stoppa AM, Guilhot F, Lamy T, Garderet L, Pegourie B, Dib M, Sebban C, Lenain P, Brechignac S, Royer B, Wetterwald M, Legros L, Orsini-Piocelle F, Voillat L, Delbrel X, Caillot D, Macro M, Facon T, Attal M, Moreau P, Avet-Loiseau H, Corre J (2014) Age is a prognostic factor even among patients with multiple myeloma younger than 66 years treated with high-dose melphalan: the IFM experience on 2316 patients. Haematologica 99(7):1236–1238. https://doi.org/10.3324/haematol.2013.098608

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Sun G-W, Shook TL, Kay GL (1996) Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. J Clin Epidemiol 49(8):907–916. https://doi.org/10.1016/0895-4356(96)00025-x

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank Ying Tian and Jiao Wang for their assistance with obtaining the data.

Code availability

Software application or custom code is available on reasonable request by contacting the first author Zi-Liang Hou (bright120@126.com).

Funding

This work was supported by a grant from the National Major Science and Technology Projects of China (2018ZX09733003).

Author information

Authors and Affiliations

  1. Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chaoyang Hospital, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, 100020, China

    Zi-Liang Hou, Zhen Wang, Feng Wang, Yan-Xia Yu & Huan-Zhong Shi

  2. Department of Respiratory and Critical Care Medicine, Beijing Luhe Hospital, Capital Medical University, Beijing, 101100, China

    Zi-Liang Hou

  3. Department of Internal Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China

    Yu Kang

  4. Department of Hematology and Multiple Myeloma Research Center of Beijing, Beijing Chaoyang Hospital, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, 100020, China

    Guang-Zhong Yang & Wen-Ming Chen

Authors
  1. Zi-Liang Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guang-Zhong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Feng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yan-Xia Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Wen-Ming Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Huan-Zhong Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Huan-Zhong Shi and Wen-Ming Chen designed and coordinated the study. Zi-Liang Hou wrote the first draft of the manuscript. Zi-Liang Hou and Yu Kang collected and analyzed the data. Guang-Zhong Yang, Zhen Wang, Feng Wang, and Yan-Xia Yu collected the data. All authors reviewed, revised, and approved the final version of the manuscript.

Corresponding authors

Correspondence to Wen-Ming Chen or Huan-Zhong Shi.

Ethics declarations

Ethics approval

The study was carried out ethically in conformity to the World Medical Association Declaration of Helsinki and was approved by the Ethics Committee of Beijing Chaoyang Hospital.

Consent to participate

Informed consent was waived because this was a retrospective study.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hou, ZL., Kang, Y., Yang, GZ. et al. Pleural effusion-based nomogram to predict outcomes in unselected patients with multiple myeloma: a large single center experience. Ann Hematol 100, 1789–1801 (2021). https://doi.org/10.1007/s00277-021-04484-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00277-021-04484-1

Keywords

Search

Navigation