Development of a core outcome set based on Case Report Form (CRF) to assess laboratory biomarkers and clinical parameters in Onco-Hematology area
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.
Method: 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) and one set of biomarker laboratory data (Hgb level, HCT, total WBC, RBC and platelet counts) identified in several variables.
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) 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]). Multivariate analysis determined that comorbidity was highly associated with cancer type, stage and ECOG PS (p=0.01). 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. 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).
Conclusion: The current study confirmed that cancer staging, comorbidity and poor performance status were a significant predictive factor. 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.
Development CRF database using Delphi process
Other details about the population within the health area: Comorbidity and cancer staging