Improving Diagnosis and Care for Patients With Sarcoma: Do Real-World General Practitioners Data and Prospective Data Collections Have a Place Next to Clinical Trials?

There has been growing interest in the use of real-world data (RWD) to address clinically and policy-relevant (research) questions that cannot be answered with data from randomized controlled trials (RCTs) alone. This is, for example, the case in rare malignancies such as sarcomas as limited patient numbers pose challenges in conducting RCTs within feasible timeliness, a manageable number of collaborators, and statistical power. This narrative review explores the potential of RWD to generate real-world evidence (RWE) in sarcoma research, elucidating its application across different phases of the patient journey, from prediagnosis to the follow-up/survivorship phase. For instance, examining electronic health records (EHRs) from general practitioners (GPs) enables the exploration of consultation frequency and presenting symptoms in primary care before a sarcoma diagnosis. In addition, alternative study designs that integrate RWD with well-designed observational RCTs may offer relevant information on the effectiveness of clinical treatments. As, especially in cases of ultrarare sarcomas, it can be an extreme challenge to perform well-powered randomized prospective studies. Therefore, it is crucial to support the adaptation of novel study designs. Regarding the follow-up/survivorship phase, examining EHR from primary and secondary care can provide valuable insights into identifying the short- and long-term effects of treatment over an extended follow-up period. The utilization of RWD also comes with several challenges, including issues related to data quality and privacy, as described in this study. Notwithstanding these challenges, this study underscores the potential of RWD to bridge, at least partially, gaps between evidence and practice and holds promise in contributing to the improvement of sarcoma care.

The prediagnostic general practitioner care of sarcoma patients: A real-world data study.

BACKGROUND: Limited understanding exists regarding early sarcoma symptoms presented during general practitioner (GP) consultations. The study explores GP visit patterns and recorded diagnoses in the 12 months preceding sarcoma diagnosis. METHODS: Sarcoma cases diagnosed from 2010 to 2020 were identified through the Netherlands Cancer Registry alongside general practice data. Sarcoma cases were age and gender matched to cancer-free controls (2:1 or 1:1 ratio). RESULTS: A total of 787 individuals with soft-tissue sarcoma (STS) and 188 individuals with bone sarcoma (BS) were identified. There was a significant difference in monthly GP contacts from 4 months to the last month before STS diagnosis, and 2 months before BS diagnosis between cases and controls. Most prevalent diagnoses recorded by the GP for STS cases included musculoskeletal neoplasm (26.6%), uncomplicated hypertension (15.6%), and cystitis/other urinary infections (12.2%). For BS cases, musculoskeletal neoplasm (42.8%), knee symptoms/complaints (9.7%), and shoulder symptoms/complaints (9.7%) were most frequent. CONCLUSIONS AND DISCUSSION: A significant difference in GP contacts between cases and controls preceding sarcoma diagnosis. STS cases were predominantly diagnosed with nonspecific symptoms, whereas BS cases with diagnoses more suggestive of BS. Better understanding of the prediagnostic trajectory could aid GPs in early identification of sarcoma.

Real-world data of HER2-low metastatic breast cancer: A population based cohort study.

BACKGROUND: With the introduction of investigational human epidermal growth factor receptor 2 (HER2) targeting treatments, thorough understanding of breast cancer with different HER2 expression levels is critical. The aim of this study was to compare clinicopathologic characteristics and survival of patients with metastatic breast cancer according to the level of HER2 expression. METHODS: Women with distant metastatic breast cancer during 2008-2016 were selected from PALGA, the Dutch Pathology Registry, and linked to the PHARMO Database Network. Breast cancer samples were categorised as HER2 immunohistochemistry score 0 (IHC0), HER2-low or HER2+. RESULTS: Among women with hormone receptor (HR) positive metastatic breast cancer (n = 989), 373 (38%) cancers were HER2 IHC0, 472 (48%) were HER2-low and 144 (15%) were HER2+. Among HR negative patients (n = 272), the proportion of HER2 IHC0, HER2-low and HER2+ was 110 (40%), 104 (38%) and 58 (21%) respectively. Within the HR + cohort, patients with HER2 IHC0 or HER2-low cancer were significantly older compared to HER2+ patients. This age difference was not seen in the HR-cohort. The localisation of distant metastases differed significantly between HER2 IHC0 or HER2-low versus HER2+ cases. Survival rates did not differ markedly by subtypes. CONCLUSION: Substantial proportion of patients had a HER2-low breast cancer. No clear differences in survival were found when comparing HER2 and HR status. Getting more granular insights in the level of HER2 expression and addressing HER2-low as a separate category could help to assess the impact of emerging treatment strategies. Therefore, more detailed information on HER2 expression should be routinely reported.

Risk Factor Clusters and Cardiovascular Disease in High-Risk Patients: The UCC-SMART Study.

Background: Clustering of vascular risk factors, i.e., the co-existence of two or more risk factors, has been associated with a higher risk of cardiovascular disease (CVD) in the general population. This study aims to firstly, examine patterns of clustering of major cardiovascular risk factors in high-risk patients and their relation with the risk of recurrent cardiovascular disease and all-cause mortality. Secondly, to assess which combinations are associated with the highest risk of CVD and all-cause mortality and to study population attributable fractions. Methods: A total of 12,616 patients from the Utrecht Cardiovascular Cohort - Second Manifestations of ARTerial diseases (UCC-SMART) study consisting of patients with or a high risk to develop cardiovascular disease were studied. We constructed sixteen clusters based on four individual modifiable risk factors (hypertension, dyslipidemia, current smoking, overweight). Patients were followed from September 1997 to March 2017. Cox proportional hazard models were used to compute adjusted hazard ratios for CVD risk and all-cause mortality and 95% confidence intervals for clusters, with patients without any risk factor as reference group. The population attributable fractions (PAFs) were calculated. Subgroup analyses were conducted by age and sex. Results: During a mean follow-up period of 8.0 years, 1836 CVD events were registered. The prevalence of patients with zero, one, two, three, and four risk factors was 1.4, 11.4, 32.0, 44.8 and 10.4%. The corresponding hazard ratios (HR) for CVD risk and all-cause mortality were 1.65 (95% CI 0.77; 3.54) for one risk factor, 2.61 (1.24; 5.50) for two, 3.25 (1.55; 6.84) for three, and 3.74 (1.77; 7.93) for four risk factors, with patients without any risk factor as reference group. The PAFs were 6.9, 34.0, 50.1 and 22.2%, respectively. The smoking-hypertension-dyslipidemia combination was associated with the highest HR: 4.06 (1.91; 8.63) and the hypertension-dyslipidemia combination with the highest PAF: 37.1%. Conclusion: Clusters including smoking and hypertension contributed to the highest risk of CVD and all-cause mortality. This study confirms that risk factor clustering is common among patients at high-risk for CVD and is associated with an increased risk of CVD and all-cause mortality.