Outcomes at rapid diagnostic centres and the association between non-specific symptoms and cancer: A systematic review and meta-analyses of up to 21,392 patients.
INTRODUCTION Cancer remains a leading cause of mortality and poses a significant public health challenge. Several non-specific symptoms (NSSs) often indicate non-serious disease but can also accompany malignancy even in the absence of organ-specific signs. Therefore, the aim of the study was to comprehensively delineate the association between the most common NSSs (weight loss, fatigue, pain and nausea/appetite loss) and cancer or non-cancer diagnoses. METHODS Database searches of PubMed and Embase were conducted applying search criteria to identify studies that investigated common NSSs in cancer patients diagnosed through rapid diagnostic centres (RDCs). The quality of the included studies was assessed using a modified Newcastle-Ottawa Scale (NOS). For each symptom, pooled relative risks (RRs) with 95% confidence intervals were derived using random-effects meta-analysis. RESULTS Eleven studies met the inclusion criteria. All studies were considered to be of high methodological quality. The most frequent disease locations for cancer entities included hematologic, lung and lower gastrointestinal. Together with miscellaneous, rheumatic, and musculoskeletal conditions, these were the most common for non-cancer diagnoses. Nausea/appetite loss showed a statistically significant association with cancer (RR=1.20, 95%-CI 1.07-1.35). Pain showed a non-significant association (RR=1.07, 95%-CI 0.75-1.53) with substantial between-study heterogeneity, and weight loss showed a non-significant inverse trend (RR=0.92, 95%-CI 0.84-1.02). Fatigue showed no association with cancer (RR=1.00, 95%-CI 0.85-1.18). DISCUSSION/CONCLUSION NSSs may be valuable for cancer risk assessment, but the associations remain modest. The complexity of patients' clinical presentations suggests that additional factors likely influence the cancer risk. Future research should examine symptom combinations and, where data allow, perform subgroup analyses.