Skip to main content

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

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
figure 1

Charlson Comorbidity Index Acces.

Table 1 Multivariate Analyses of cancer type, comorbidity score and biomarkers laboratory

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
figure 2

CCI and their respective point scores.

Fig 3
figure 3

CCI and their respective point scores.

Fig 4
figure 4

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

Fig 5
figure 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].

References

  1. Abbasi S, Badheeb A: Prognostic factors in advanced non-small-cell lung cancer patients: Patient characteristics and type of chemotherapy. Lung Cancer International. 2011, 0 (0): 1-4.

    Article  Google Scholar 

  2. Albain KS, Crowley JJ, LeBlanc M, Livingston RB: Survival determinants in extensive-stage non-small-cell lung cancer: The Southwest Oncology Group experience. Journal of Clinical Oncology. 1991, 9: 1618-1626.

    CAS  PubMed  Google Scholar 

  3. American Cancer Society: Cancer facts and figures. 2012, Retrieved from [http://www.cancer.org/acs/groups/content/@epidemiologysurveilance/documents/document/acspc-031941.pdf]. Accessed November 21, 2014

    Google Scholar 

  4. Boyd CM, Vollenweider D, Puhan MA: Informing evidence-based decision-making for patients with comorbidity: availability of necessary information in clinical trials for chronic diseases. PLoS ONE. 2012, 7: e41601-10.1371/journal.pone.0041601.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. Charlson ME, Pompei P, Ales KL, MacKenzie CR: A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chronic Dis. 1987, 40 (5): 373-383. 10.1016/0021-9681(87)90171-8.

    CAS  Article  PubMed  Google Scholar 

  6. Corbett J: Laboratory tests and diagnostic procedures with nursing diagnoses. 2008, Upper Saddle River, NJ: Pearson/Prentice Hall, 7

    Google Scholar 

  7. Cox D, Oakes D: Analysis of survival data. 1984, London, England: Chapman and Hall

    Google Scholar 

  8. Foucher ES, O’Callaghan M, Ferlay J, Masuyer E, Rosso S, Forman D, Bray F, Comber H: The European Cancer Observatory: A new data resource. European Journal of Cancer. 2014, 0 (0): 1-13.

    Google Scholar 

  9. Gatta G, Ciampichini R, Bisanti L, Contiero P, Tessandori R, Baili P, Rossi S: Estimates of cancer burden in Lombardy. Tumori. 2013, 99 (3): 277-84.

    PubMed  Google Scholar 

  10. Grande E, Inghelmann R, Francisci S, Verdecchia A, Micheli A, Baili P, Capocaccia R, De Angelis R: Regional estimates of all cancer malignancies in Italy. Tumori. 2007, 93: 345-351.

    PubMed  Google Scholar 

  11. Jemal A, Siegel R, Ward E, Hao Y, Xu J, Thun MJ: Cancer statistics. A Cancer Journal for Clinicians. 2009, 59 (4): 225-249. 10.3322/caac.20006.

    Article  Google Scholar 

  12. International Agency for Research on Cancer: Cancer Incidence in Five Continents Annual Dataset. 2014, Accessed November 21, [http://ci5.iarc.fr/CI5plus/ci5plus.htm]

    Google Scholar 

  13. Luo J, Chen YJ, Narsavage GL, Ducatman A: Predictors of Survival in Patients with Non-Small Cell Lung Cancer. Oncology Nursing Forum. 2012, 39 (6): 609-16. 10.1188/12.ONF.609-616.

    Article  PubMed  Google Scholar 

  14. Oken MM, Creech RH, Tormey DC, Horton J, Davis TE, Mc-fadden ET, Carbone PP: Toxicity and response criteria of the Eastern Cooperative Oncology Group. American Journal of Clinical Oncology. 1982, 5 (6): 649-655. 10.1097/00000421-198212000-00014.

    CAS  Article  PubMed  Google Scholar 

  15. Quan H, Li B, Couris CM, Fushimi K, Graham P, Hider P, Januel JM, Sundararajan V: Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 2011, 173: 676-82. 10.1093/aje/kwq433.

    Article  PubMed  Google Scholar 

  16. Radovanovic D, Seifert B, Urban P, Eberli FR, Rickli H, Bertel O, Puhan MA, Erne P: Validity of Charlson Comorbidity Index in patients hospitalised with acute coronary syndrome. Insights from the nationwide AMIS Plus registry 2002-2012. Heart. 2014, 100 (4): 288-94. 10.1136/heartjnl-2013-304588. [ClinicalTrials.gov Identifier NCT01305785]

    Article  PubMed  Google Scholar 

  17. Yurkovich M, Zubieta JAA, Thomas J, Gorenchtein M, Lacaille D: A systematic review identifies valid comorbidity indices derived from administrative health data. Journal of Clinical Epidemiology. 2015, 68: 3-14. 10.1016/j.jclinepi.2014.09.010.

    Article  PubMed  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mariangela Vanalli.

Rights and permissions

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.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Vanalli, M., Rio, F. Development of a core outcome set based on Case Report Form (CRF) to assess laboratory biomarkers and clinical parameters in Onco-Hematology area. Trials 16, P23 (2015). https://doi.org/10.1186/1745-6215-16-S1-P23

Download citation

  • Published:

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

Keywords

  • Case Report Form
  • Short Survival Time
  • Cancer Incidence Rate
  • Core Outcome
  • Delphi Process