Visualizing clinical predictors of febrile neutropenia in Asian cancer patients receiving myelosuppressive chemotherapy

Abstract

Purpose:

Febrile neutropenia is a serious complication among cancer patients receiving myelosuppressive chemotherapy. Patient-specific risk factors, chemotherapy-related and disease-related characteristics can affect the clinical outcome and management of febrile neutropenia. Although many factors have been identified, they vary among different patient populations. We identified clinically-relevant febrile neutropenia predictors in Asian cancer patients through visualization of these factors.

Methods:

A single-centered, retrospective study was conducted from May to July 2011 at a local cancer center. Demographics and risk factor data were collated from electronic health records and four cancer registries. Data were summarized using descriptive statistics. Additionally, potential febrile neutropenia predictors were identified using categorical principal component and multiple correspondence analyses.

Results:

A total of 583 patients were analyzed. Majority was females (79%), Chinese (75%) and diagnosed with breast cancers (60%). Six risk factors were identified as potential predictors: types of cancer (16.9–19.8% of variance), chemotherapy regimen (anthracycline-based 11.8–12.9%, taxane-based 8.1%), liver function tests (alanine transaminase 8.6%, alkaline phosphatase 4.0%), renal function tests (serum creatinine 3.1%), prior granulocyte colony stimulating factor use (5.6%) and diabetes mellitus (6.6–6.9%). In terms of cancer types, lymphomas were more predictive than breast cancers.

Conclusion:

From our knowledge, this is the first study that has identified clinically-relevant febrile neutropenia predictors in Asian cancer patients through visualization of their risk factors. The use of these predictors to identify patients at risk for adverse reactions, such as FN, can allow clinicians to optimize prophylactic granulocyte colony stimulating factor usage in these patients.

Keywords

Clinical predictors data reduction techniques febrile neutropenia multiple correspondence analysis principal component analysis

Get full access to this article

View all access options for this article.

References

National Comprehensive Cancer Network. NCCN clinical practice guidelines in oncology – myeloid growth factors version 1.2012. Available from: http://www.nccn.org/professionals/physician_gls/pdf/myeloid_growth.pdf (accessed 17 March 2012)..

Heuser

Ganser

. Colony-stimulating factors in the management of neutropenia and its complications. Ann Hematol 2005; 84: 697–708.

Chan A, Chen C, Chiang J, et al. Incidence of febrile neutropenia among early-stage breast cancer patients receiving anthracycline-based chemotherapy. Support Care Cancer 2012; 20(7): 1525–1532.

Chan

Leng

Chiang

. Comparison of daily filgrastim and pegfilgrastim to prevent febrile neutropenia in Asian lymphoma patients. Asia Pacific J Clin Oncol 2011; 7: 75–81.

Ang

Tan

. Breakthrough febrile neutropenia and associated complications in non-Hodgkin’s lymphoma patients receiving pegfilgrastim. Acta Haematol 2011; 125: 107–114.

Chan

Shih

. Impact of colony-stimulating factors to reduce febrile neutropenic events in breast cancer patients receiving docetaxel plus cyclophosphamide chemotherapy. Support Care Cancer 2011; 19: 497–504.

Kuderer

Dale

Crawford

. Mortality, morbidity, and cost associated with febrile neutropenia in adult cancer patients. Cancer 2006; 106: 2258–2266.

Kuderer

Dale

Crawford

. Impact of primary prophylaxis with granulocyte colony-stimulating factor on febrile neutropenia and mortality in adult cancer patients receiving chemotherapy: a systematic review. J Clin Oncol 2007; 25: 3158–3167.

Smith

Khatcheressian

Lyman

. 2006 update of recommendations for the use of white blood cell growth factors: an evidence-based clinical practice guideline. J Clin Oncol 2006; 24: 3187–3205.

10.

Aapro

Cameron

Pettengell

. EORTC guidelines for the use of granulocyte-colony stimulating factor to reduce the incidence of chemotherapy-induced febrile neutropenia in adult patients with lymphomas and solid tumours. Eur J Cancer 2006; 42: 2433–2453.

11.

Cengiz

Kuruoğlu

. Interpretation of the repetitive nerve stimulation test results using principal component analysis. Clin Neurophysiol 2006; 117: 2073–2078.

12.

Chow

Fan

Hadi

. Symptom clusters in cancer patients with brain metastases. Clin Oncol 2008; 20: 76–82.

13.

Hadi

Fan

Hird

. Symptom clusters in patients with cancer with metastatic bone pain. J Palliat Med 2008; 11: 591–600.

14.

Gift

Jablonski

Stommel

. Symptom clusters in elderly patients with lung cancer. Oncol Nurs Forum 2004; 31: 202–212.

15.

Wang

Tsai

Chen

. Symptom clusters and relationships to symptom interference with daily life in Taiwanese lung cancer patients. J Pain Sympt Manage 2008; 35: 258–266.

16.

Greenacre

Hastie

. The geometric interpretation of correspondence analysis. J Am Statist Assoc 1987; 82: 437–447.

17.

Greenacre

. Interpreting multiple correspondence analysis. Appl Stoch Model Data Anal 1991; 7: 195–210.

18.

Dunteman

. Principal components analysis Newbury Park, CA: Sage Publications, 1989.

19.

Yap

KY-L

Yak

Shih

. Application of unsupervised learning in clinical oncology practice – exploring anxiety characteristics in chemotherapy-induced nausea and vomiting through principal variables. J Comput 2010; 2: 163–171.

20.

Yap

Chan

Low

. Clinical predictors of chemotherapy-induced nausea and vomiting through identification of principal variables in patients undergoing AC and XELOX regimens. Support Care Cancer 2010; 18: S142–S142.

21.

Yap

Chan

Low

. Identification of anxiety symptoms in patients with chemotherapy-induced nausea and vomiting undergoing moderately-emetogenic chemotherapy. Support Care Cancer 2010; 18: S189–S189.

22.

Yap

Low

Chui

. Computational prediction of state anxiety in Asian patients with cancer susceptible to chemotherapy-induced nausea and vomiting. J Clin Psychopharmacol 2012; 32: 207–217.

23.

Joel

Shah

Clark

. Predicting etoposide toxicity: relationship to organ function and protein binding. J Clin Oncol 1996; 14: 257–267.

24.

Eklund

Trifilio

Mulcahy

. Chemotherapy dosing in the setting of liver dysfunction. Oncology (Williston Park) 2005; 19: 1057–1063. discussion 1063–1054, 1069.

25.

Donelli

Zucchetti

Munzone

. Pharmacokinetics of anticancer agents in patients with impaired liver function. Eur J Cancer 1998; 34: 33–46.

26.

Dranitsaris

Rayson

Vincent

. Identifying patients at high risk for neutropenic complications during chemotherapy for metastatic breast cancer with doxorubicin or pegylated liposomal doxorubicin: the development of a prediction model. Am J Clin Oncol 2008; 31: 369–374.

27.

Pettengell

Bosly

Szucs

. Multivariate analysis of febrile neutropenia occurrence in patients with non-Hodgkin lymphoma: data from the INC-EU Prospective Observational European Neutropenia Study. Br J Haematol 2009; 144: 677–685.

28.

Ziepert

Schmits

Trumper

. Prognostic factors for hematotoxicity of chemotherapy in aggressive non-Hodgkin’s lymphoma. Ann Oncol 2008; 19: 752–762.

29.

Pettengell

Schwenkglenks

Leonard

. Neutropenia occurrence and predictors of reduced chemotherapy delivery: results from the INC-EU prospective observational European Neutropenia Study. Support Care Cancer 2008; 16: 1299–1309.

30.

Srokowski

Fang

Hortobagyi

. Impact of diabetes mellitus on complications and outcomes of adjuvant chemotherapy in older patients with breast cancer. J Clin Oncol 2009; 27: 2170–2176.

31.

Markos

Vozalis

Margaritis

. An optimal scaling approach to collaborative filtering using categorical principal component analysis and neighborhood formation. Int Federat Informat Process Adv Informat Commun Technol 2010; 339: 22–29.

32.

Guadagnoli

Velicer

. Relation of sample size to the stability of component patterns. Psychol Bull 1988; 103: 265–275.

33.

Osborne JW and Costello AB. Sample size and subject to item ratio in principal components analysis. Practical Assessment, Research and Evaluation 2004; 9(11): http://pareonline.net/getvn.asp?v=9&n=11 (accessed 17 March 2012).

34.

Ford

MacCallum

Tait

. The application of exploratory factor analysis in applied psychology: a critical review and analysis. Personnel Psychol 1986; 39: 291–314.

35.

Costello AB and Osborne JW. Exploring best practices in factor analysis: four mistakes applied researchers make. In: The annual meeting of the American Educational Research Association, Chicago, Illinois, 21–25 April 2003.

36.

Costello AB and Osborne JW. Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Practical Assessment, Research and Evaluation 2005; 10: 1–9, http://pareonline.net/pdf/v10n7.pdf (accessed 17 March 2012).