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Supportive Care in Cancer
Published in: Supportive Care in Cancer 6/2016

01-06-2016 | Original Article

Predicting early post-chemotherapy adverse events in patients with hematological malignancies: a retrospective study

Authors: Xiaoming Fei, Fang Lei, Haifeng Zhang, Hua Lu, Yan Zhu, Yu Tang

Published in: Supportive Care in Cancer | Issue 6/2016

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Abstract

Purpose

The purpose of this study was to develop a mathematical model that predicts the definite adverse events following chemotherapy in patients with hematological malignancies (HMs).

Methods

This is a retrospective cohort study including 1157 cases with HMs. Firstly, we screened and verified the independent risk factors associated with post-chemotherapy adverse events by both univariate and multivariate logistic regression analysis using 70 % of randomly selected cases (training set). Secondly, we proposed a mathematical model based on those selected factors. The calibration and discrimination of the model were assessed by Hosmer-Lemeshow (H-L) test and area under the receiver operating characteristic (ROC) curve, respectively. Lastly, the predicative power of this model was further tested in the remaining 30 % of cases (validation set).

Results

Our statistical analysis indicated that liver dysfunction (OR = 2.164), active infection (OR = 3.619), coagulation abnormalities (OR = 4.614), intensity of chemotherapy (OR = 10.001), acute leukemia (OR = 2.185), and obesity (OR = 1.604) were independent risk factors for post-chemotherapy adverse events in HM patients (all P < 0.05). Based on the verified risk factors, a predictive model was proposed. This model had good discrimination and calibration. When 0.648 was selected as the cutoff point, the sensitivity and specificity of this predictive model in validation sets was 72.7 and 87.4 %, respectively. Furthermore, this proposed model’s positive predictive value, negative predictive value, and consistency rate were 87.3, 73.0 and 80.0 %, respectively.

Conclusions

Our study indicated that this six risk factor-based mathematical model is accurate and sufficient enough to predict definite post-chemotherapy adverse events in a HM patient and it may aid clinicians to optimize treatment for a HM patient.
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Literature
1.
2.
Kobayashi Y (2011) Molecular target therapy in hematological malignancy: front-runners and prototypes of small molecule and antibody therapy. Jpn J Clin Oncol 41:157–164CrossRefPubMed
3.
Chen CY, Sheng WH, Cheng A, Tsay W, Huang SY, Tang JL, Chen YC, Wang JY, Tien HF, Chang SC (2011) Clinical characteristics and outcomes of Mycobacterium tuberculosis disease in adult patients with hematological malignancies. BMC Infect Dis 11:324CrossRefPubMedPubMedCentral
4.
5.
Charlson ME, Pompei P, Ales KL, Mackenzie CR (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40:373–383CrossRefPubMed
6.
Extermann M (2000) Measurement and impact of co-morbidity in older cancer patients. Crit Rev Oncol Hematol 35:181–200CrossRefPubMed
7.
8.
Sorror ML, Maris MB, Storb R, Baron F, Sandmaier BM, Maloney DG, Storer B (2005) Hematopoietic cell transplantation (HCT)-specific comorbidity index: a new tool for risk assessment before allogeneic HCT. Blood 106:2912–2919CrossRefPubMedPubMedCentral
9.
Verplancke T, Looy SV, Benoit D, Vansteelandt S, Depuydt P, Turck FD, Decruyenaere J (2008) Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies. BMC Med Inform Decis Mak 8:56CrossRefPubMedPubMedCentral
10.
Terwey TH, Hemmati PG, Martus P, Dietz E, Vuong LG, Massenkeil G, Dörken B, Arnold R (2010) A modified EBMT risk score and the hematopoietic cell transplantation-specific comorbidity index for pre-transplant risk assessment in adult acute lymphoblastic leukemia. Haematologica 95:810–818CrossRefPubMedPubMedCentral
11.
DeFor TE, Majhail NS, Weisdorf DJ, Brunstein CG, McAvoy S, Arora M, Le CT (2010) A modified comorbidity index for hematopoietic cell transplantation. Bone Marrow Transplant 45:933–938CrossRefPubMed
12.
Patnaik JL, Byers T, DiGuiseppi C, Denberg TD, Dabelea D (2011) The influence of comorbidities on overall survival among older women diagnosed with breast cancer. J Natl Cancer Inst 103:1101–1111CrossRefPubMedPubMedCentral
13.
Tammemagi CM, Pinsky PF, Caporaso NE, Kvale PA, Hocking WG, Church TR, Riley TL, Commins J, Oken MM, Berg CD, Prorok PC (2011) Lung cancer risk prediction: prostate, lung, colorectal and ovarian cancer screening trial models and validation. J Natl Cancer Inst 103:1058–1068CrossRefPubMedPubMedCentral
14.
Albertsen PC, Moore DF, Shih W, Lin Y, Li H, Grace L, Yao L (2011) Impact of comorbidity on survival among men with localized prostate cancer. J Clin Oncol 29:1335–1341CrossRefPubMedPubMedCentral
15.
Wood W, Deal A, Whitley J, Sharf A, Serody J, Gabriel D, Shea T (2011) Usefulness of the hematopoietic cell transplantation-specific comorbidity index(HCT-CI) in predicting outcomes for adolescents and young adults with hematologic malignancies undergoing allogeneic stem cell transplant. Pediatr Blood Cancer 57:499–505CrossRefPubMed
16.
Hill BT, Sweetenham J (2012) Clinical implications of the molecular subtypes of diffuse large B-cell lymphoma. Leuk Lymphoma 53:763–769CrossRefPubMed
17.
Hallek M (2013) Chronic lymphocytic leukemia: 2013 update on diagnosis, risk stratification and treatment. Am J Hematol 88:803–816CrossRefPubMed
18.
Mishra A, Corrales-Yepez M, Ali NA, Kharfan-Dabaja M, Padron E, Zhang L, Epling-Burnette PK, Pinilla-Ibarz J, Lancet JE, List AF, Komrokji RS (2013) Validation of the revised International Prognostic Scoring System in treated patients with myelodysplastic syndromes. Am J Hematol 88:566–570CrossRefPubMedPubMedCentral
19.
Tumbarello M, Trecarichi EM, Caira M, Candoni A, Pastore D, Cattaneo C, Fanci R, Nosari A, Spadea A, Busca A, Vianelli N, Spanu T, Pagano L (2012) Derivation and validation of a scoring system to identify patients with bacteremia and hematological malignancies at higher risk for mortality. PLoS One 7, e51612CrossRefPubMedPubMedCentral
20.
21.
Marignani M, Gigante E, Begini P, Marzano A, di Fonzo M, Deli I, Gallina S, Cox MC, Delle Fave G (2012) Patients with hematological malignancies and serological signs of prior resolved hepatitis B. World J Gastrointest Oncol 4:37–45CrossRefPubMedPubMedCentral
22.
Kawatani T, Suou T, Tajima F, Ishiga K, Omura H, Endo A, Ohmura H, Ikuta Y, Idobe Y, Kawasaki H (2001) Incidence of hepatitis virus infection and severe liver dysfunction in patients receiving chemotherapy for hematologic malignancies. Eur J Haematol 67:45–50CrossRefPubMed
23.
Takai S, Tsurumi H, Ando K, Kasahara S, Sawada M, Yamada T, Hara T, Fukuno K, Takahashi T, Oyama M, Onishi H, Tomita E, Takami T, Imawari M, Moriwaki H (2005) Prevalence of hepatitis B and C virus infection in haematological malignancies and liver injury following chemotherapy. Eur J Haematol 74:158–165CrossRefPubMed
24.
Mahale P, Kontoyiannis DP, Chemaly RF, Jiang Y, Hwang JP, Davila M, Torres HA (2012) Acute exacerbation and reactivation of chronic hepatitis C virus infection in cancer patients. J Hepatol 57:1177–1185CrossRefPubMed
25.
Wisplinghoff H, Seifert H, Wenzel RP, Edmond MB (2003) Current trends in the epidemiology of nosocomial bloodstream infections in patients with hematological malignancies and solid neoplasms in hospitals in the United States. Clin Infect Dis 36:1103–1110CrossRefPubMed
26.
Pagano L, Caira M, Rossi G, Tumbarello M, Fanci R, Garzia MG, Vianelli N, Filardi N, De Fabritiis P, Beltrame A, Musso M, Piccin A, Cuneo A, Cattaneo C, Aloisi T, Riva M, Rossi G, Salvadori U, Brugiatelli M, Sannicolò S, Morselli M, Bonini A, Viale P, Nosari A, Aversa F (2012) A prospective survey of febrile events in hematological malignancies. Ann Hematol 91:767–774CrossRefPubMed
27.
Cornely OA, Ullmann AJ, Karthaus M (2003) Evidence-based assessment of primary antifungal prophylaxis in patients with hematologic malignancies. Blood 101:3365–3372CrossRefPubMed
28.
Hahn-Ast C, Glasmacher A, Mückter S, Schmitz A, Kraemer A, Marklein G, Brossart P, von Lilienfeld-Toal M (2010) Overall survival and fungal infection-related mortality in patients with invasive fungal infection and neutropenia after myelosuppressive chemotherapy in a tertiary care centre from 1995 to 2006. J Antimicrob Chemother 65:761–768CrossRefPubMedPubMedCentral
29.
Worth LJ, Slavin MA (2009) Bloodstream infections in hematology: risks and new challenges for prevention. Blood Rev 23:113–122CrossRefPubMed
30.
Pagano L, Caira M, Candoni A, Offidani M, Fianchi L, Martino B, Pastore D, Picardi M, Bonini A, Chierichini A, Fanci R, Caramatti C, Invernizzi R, Mattei D, Mitra ME, Melillo L, Aversa F, Van Lint MT, Falcucci P, Valentini CG, Girmenia C, Nosari A (2006) The epidemiology of fungal infections in patients with hematologic malignancies: the SEIFEM-2004 study. Haematologica 91:1068–1075PubMed
31.
Falanga A, Marchetti M, Russo L (2012) Venous thromboembolism in the hematologic malignancies. Curr Opin Oncol 24:702–710CrossRefPubMed
32.
Falanga A, Russo L, Tartari CJ (2011) Pathogenesis and treatment of thrombohemorrhagic diathesis in acute promyelocytic leukemia. Mediterr J Hematol Infect Dis 3:e2011068CrossRefPubMedPubMedCentral
33.
Hurria A1, Togawa K, Mohile SG, Owusu C, Klepin HD, Gross CP, Lichtman SM, Gajra A, Bhatia S, Katheria V, Klapper S, Hansen K, Ramani R, Lachs M, Wong FL, Tew WP (2011) Predicting chemotherapy toxicity in older adults with cancer: a prospective multicenter study. J Clin Oncol 29:3457–3465CrossRefPubMedPubMedCentral
Metadata
Title
Predicting early post-chemotherapy adverse events in patients with hematological malignancies: a retrospective study
Authors
Xiaoming Fei
Fang Lei
Haifeng Zhang
Hua Lu
Yan Zhu
Yu Tang
Publication date
01-06-2016
Publisher
Springer Berlin Heidelberg
Published in
Supportive Care in Cancer / Issue 6/2016
Print ISSN: 0941-4355
Electronic ISSN: 1433-7339
DOI
https://doi.org/10.1007/s00520-016-3085-6

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