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Development of a core outcome set based on Case Report Form (CRF) to assess laboratory biomarkers and clinical parameters in Onco-Hematology area

Trials201516(Suppl 1):P23

https://doi.org/10.1186/1745-6215-16-S1-P23

Published: 29 May 2015

Keywords

Case Report FormShort Survival TimeCancer Incidence RateCore OutcomeDelphi Process

Background

The number of cases, the crude and age-standardized incidence, mortality rates and the prevalence proportions estimated by the Italian Association of Cancer Registries (AIRTUM) presently providing the epidemiological indicators for the major cancers used in ICD-O-3.1 [13]. By 2012, the breast cancer incidence in women (age 25±over 85 years) was about 29%; trends for stomach and colorectal cancer were about 5% and 14% for both genders (age 35/45±over 85 years); the lung cancer incidence rates was about 15% in men (age 45±over 85 years) and 6% in women (age 40±over 85) in 2009 [4, 5]. From 2011 onwards the tendency changed: the female rates (20 per 100,000) increased much more rapidly than the male rates [6].

Aim of this study is to examine the relationships among the incidence of genera-cancer-associated risk factors and routine laboratory in cancer patients through CRF.

Materials and Methods

The CRF database has been developed by a dedicated working group using Delphi process. It contain anonymous records on patient characteristics (gender, age, alcohol and smoking history, height, body weight, performance status measured using the Eastern Cooperative Oncology Group-ECOG PS, chronic comorbidities weighted by the Charlson Comorbidity Index-CCI, type and stage of tumor) (Figure 1) [79] and one set of biomarker laboratory data identified in several variables (Table 1) [10, 11].
Fig 1

Charlson Comorbidity Index Acces.

Table 1

Multivariate Analyses of cancer type, comorbidity score and biomarkers laboratory

Comorbidity

Breast

p

Colon-rectum

p

Stomach

p

Lung

p

HCT_cod

0.105

0.708

0.387

0.078

Hb_cod

0.035

0.775

0.466

0.351

RBC_cod

0.564

0.343

0.194

0.448

WBC_cod

0.292

0.172

0.930

0.583

PLT_cod

0.167

0.535

0.401

0.332

CCI_SCORE ≥4

0.495

0.029

0.092

0.381

cancer type: breast, colon-rectum, stomach and lung; biomarkers laboratory: HCT, Hb, RBC, WBC, PLT;

comorbidity: CCI_SCORE ≥4.

Results

Between 2012 and 2014, 1373 cancer patients were enrolled at three Italian Oncological Institutions after informed consent. Among these patients, 36% were men and 64% were women (mean age 71±45 years) (Figure 2) and breast was the most frequent type cancer (43%) followed by lung (29%), colon-rectum (18%) and stomach (9%). 72% (n=85) of the lung, 67% (n=24) of the stomach, 33% (n=25) of the colon-rectum, 4% (n=7) of the breast cancer patients had comorbidities weighted with 3 point and above (Age Unadjusted Charlson-Comorbidity-Index≥4; HR=6.38; 99% CI [3.07,13.24]) [12, 13] (Figure 3). Multivariate analysis determined that comorbidity was highly associated with cancer type, stage and ECOG PS (p=0.01) (Figure 4). Evaluation between cardiovascular disease, risk of bleeding, deep-vein thrombosis and colon-rectum cancer stage (p=0.01), breast (p=0.03), lung (p=0.01) compared into comorbidities (Figure 5). The other tested variables: Hgb level, neutrophil and platelet counthad had the strongest relationship with breast, lung cancer stage (p=0.02), stomach (p=0.002) and colon-rectum (p=0.1) [14, 15].
Fig 2

CCI and their respective point scores.

Fig 3

CCI and their respective point scores.

Fig 4

Multivariate Analysis and the comorbidities of CCI with IBM SPSS Italian version 21 statistical software.

Fig 5

Multivariate Analysis and the comorbidities of CCI with IBM SPSS Italian version 21 statistical software.

Conclusions

The appropriateness of results could be useful to better describe the role of CRF and biomarkers recorded in patient charts as well as the other variables could allow nurses to identify patients at risk for shorter survival time following hospitalization [16, 17].

Declarations

Acknowledgments

This study was conducted with three Italian Oncological Institutions funded by Cancer Research in Bergamo, Milan and Rome. The author would like to thank General Directors and Nurse Directors of these Hospitals for authorizing the study; patients, nursing coordinators, nurses working in Onco-Hematology area for their hospitality and support in data collection process and for their assistance in patient recruitment.

Authors’ Affiliations

(1)
Department of Immunohematology and Transfusion Medicine, Hospital Papa Giovanni XXIII, Bergamo, Italy
(2)
University of Milan Bicocca, Milan, Italy

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Copyright

© Vanalli and Rio; licensee BioMed Central Ltd. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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