Meena Rafiq

Patients with suspected cancer frequently present with non-specific symptoms (NSS) that do not meet site-specific referral criteria. Rapid Diagnostic Centres (RDCs) in the UK aim to streamline assessment of these patients, but their evaluation has been limited by the lack of linked longitudinal primary-care data. We conducted a retrospective validation study of 2,021 patients referred to a London RDC between November 2021 and December 2024, linked to primary-care records within the Whole Systems Integrated Care (WSIC) platform. We assessed the feasibility of reconstructing NSS pathways using WSIC and evaluated data completeness and outcome concordance against RDC records.WSIC substantially improved data completeness for frailty, comorbidities, lifestyle factors, healthcare utilisation and diagnostic outcomes compared with RDC records alone. The linked dataset enabled reconstruction of symptom histories, laboratory investigations, prescribing patterns, healthcare contacts and diagnostic outcomes over time, capabilities unavailable from RDC records alone. Cancer ascertainment showed near-complete agreement between datasets, with 74 cancers identified in RDC records and 75 in WSIC. Longitudinal follow-up within WSIC identified an additional 34 cancer diagnoses after RDC discharge.These findings validate WSIC as a reliable platform for reconstructing NSS diagnostic pathways and demonstrate the value of linked primary-secondary care data for early cancer signal detection.

Background: In patients with cancer, diagnosis through or soon after the use of emergency hospital care (‘emergency diagnosis’) is associated with advanced disease and poor prognosis. The varied and complex reasons for emergency diagnoses—including rapid disease progression, patient factors, and system delays—are unlikely to be unique to cancer. However, little is known about the frequency and prognostic outcomes of emergency diagnosis in other conditions. We aimed to document the frequency of emergency diagnoses across various non-neoplastic conditions and examine the association of emergency diagnosis with clinical outcomes. Methods and findings: We analysed records from linked primary care, secondary care, and death data (Clinical Practice Research Datalink (CPRD), Hospital Episode Statistics, Office for National Statistics) for 1,701,154 patients with one of 13 exemplar conditions (axial spondyloarthritis, coeliac disease, coronary/ischaemic heart disease, chronic obstructive pulmonary disease (COPD), inflammatory bowel disease (IBD), Lyme disease, multiple sclerosis (MS), Parkinson’s disease, polycystic ovary syndrome, rheumatoid arthritis, schizophrenia, subacute bacterial endocarditis, and tuberculosis) in England between 1999 and 2019. We examined the percentage of patients diagnosed as an emergency and compared their mortality and time in hospital in the year post-diagnosis with patients who were not diagnosed as an emergency. Emergency diagnosis occurred in at least 20% of patients for 9 out of 13 conditions in our sample, including more than 30% of patients with Parkinson’s disease and 35% of patients with COPD. For 9 out of 13 conditions, there was a substantial increase (at least +10% difference) in 1-year mortality in patients diagnosed as an emergency versus patients who were not diagnosed as an emergency. After adjusting for age and year of diagnosis, deprivation, comorbidity burden, and the healthcare setting in which the diagnosis was made, patients diagnosed as an emergency were typically much more likely to die within one year. This association held even in conditions with generally good prognosis, such as coeliac disease (adjusted odds ratio 7.53, 95% CI [5.64, 10.1], women) and IBD (adjusted odds ratio 5.99, 95% CI [5.30, 6.78], men). Similarly large differences were observed for time in hospital in the year post-diagnosis by emergency diagnosis status. For example, adjusted rate ratios of 8.76 (95% CI [6.52, 11.8]) for men with coeliac disease, and 5.59 (95%CI [2.57, 12.2]) for women diagnosed with Lyme disease. The findings were consistent across two subsets of CPRD data (Aurum, GOLD). Analyses of patients with five supplementary cancer sites (brain, colon, lung, pancreas, and ovary) produced findings concordant with prior literature, supporting the validity of the study methods. The main study limitation is the assumption of accurate recording of diagnoses in health records. Conclusions: Emergency diagnosis is common and associated with worse clinical outcomes in a wide range of conditions with diverse aetiology. Further research and investment to reduce the number of emergency diagnoses is needed, particularly for diseases where diagnosis in community settings should be expected (e.g., rheumatoid arthritis and COPD), and conditions such as coeliac disease, IBD, and MS, where emergency diagnosis is associated with particularly poor outcomes.

Background: Early detection in primary care could improve pancreatic cancer survival, but diagnosis is often delayed due to the low prevalence of the disease, the nonspecific nature of early symptoms, and the broad range of conditions and volume of consultations managed by general practitioners (GPs). In Australia, improving pancreatic cancer outcomes, including via earlier diagnosis, is a priority being progressed under the National Pancreatic Cancer Roadmap developed by Cancer Australia. Computerized clinical decision support systems (CDSSs) have shown promise in aiding timely cancer diagnosis; however, barriers to adopting CDSS such as mistrust of the recommendations or not being embedded in the clinical workflow remain. Simulation techniques, which offer flexible and cost-effective ways to evaluate digital health interventions, can be used to test CDSS before real-world implementation. Objective: This study aims to assess the acceptability and feasibility of identifying patients with symptoms associated with pancreatic cancer through a CDSS within a simulated environment. Methods: We developed a CDSS that interacted with an electronic health record used in general practice to identify patients with symptoms, which may indicate pancreatic cancer (unintended weight loss or new-onset diabetes), in a simulation laboratory for digital interventions. We tested it by inviting GPs (n=11) to use the CDSS, with patient actors simulating specific clinical scenarios. We then interviewed GPs about the interaction to assess the acceptability and feasibility of the CDSS in their clinical practice. We used thematic analysis and 2 relevant frameworks to analyze the data. Results: GPs found the CDSS easy to use, unobstructive, and effective as a prompt to consider investigations for people with risk factors for pancreatic cancer. However, they expressed concerns about possible overtesting, financial costs, and the potential for anxiety in patients with a very low probability of having cancer. Conclusions: While GPs found the tool useful and compatible with their workflow, concerns about overtesting, lack of evidence, and cost-effectiveness were identified as barriers. GPs favored a stepwise approach to investigations rather than immediate imaging. Despite the overall acceptability of the tool, additional evidence to underpin clinical recommendations is necessary before implementing a CDSS with these specific recommendations for pancreatic cancer in primary care.

Background: Pancreatic cancer is often diagnosed at an advanced stage with poor survival. Risk assessment tools have been developed to aid early diagnosis of pancreatic cancer in primary care settings (QCancer ^® , electronic Risk Assessment Tool [eRAT], and the Queensland Institute of Medical Research [QIMR] Berghofer Pancreatic Cancer Decision Support Tool [QPaC Tool]) but have not been validated in the Australian setting. Aim: To estimate and compare the performance of these tools for identifying patients with undiagnosed pancreatic cancer using Australian primary care data. Design and setting: A cohort study was conducted using linked primary care and cancer registry data from Victoria, Australia. Method: Patients presenting to primary care with signs and/or symptoms included in the tools (recorded in the primary care ‘reason for encounter’) were included. Diagnostic accuracy statistics for each tool (and their individual signs and symptoms) were compared. Results: Patients with pancreatic cancer were more likely ( P ® was 0.8% (95% CI = 0.54 to 1.15). Conclusion: When applied to Australian primary care data, none of the tools were strongly predictive of pancreatic cancer. New diagnostic models incorporating additional data could potentially improve their predictive performance.

Timely diagnosis and treatment are important in lymphoma care. This systematic review examined articles published up to April 2025 which reported intervals from symptom onset to treatment initiation and a subset examining associations with health-related outcomes. Of 11,606 articles screened, 67 were included (23 reported associations). Significant heterogeneity was noted, with 27 intervals reported across various lymphoma subtypes. Methodological issues included poor reporting of interval variability, small sample sizes, and arbitrary interval categorization. Commonly reported intervals included symptom onset to diagnosis (articles; median range), (27; 26-217 days), symptom onset to first presentation (23; 9-91 days), first presentation to diagnosis (17; 15-126 days), and diagnosis to treatment start (25; 1-42 days). Most association studies considered treatment interval and survival, finding inconsistencies. Only few examined the length of diagnostic or patient interval impacts on health outcomes. Future research should apply the Aarhus Checklist - a tool designed to enhance precision and transparency in early cancer diagnosis research to improve the consistency and quality of interval reporting. Further, non-linear interval-survival associations should be explored to capture paradoxical effects.

Background: Emergency diagnosis of lung cancer is common and associated with worse prognosis. Aim: To compare prediagnostic healthcare use between emergency-diagnosed patients and patients referred routinely or urgently. Design and setting: Population-based linked English primary care, hospital admission, imaging, and cancer registration data were analysed for patients with lung cancer (2007–2018). Method: Monthly prediagnosis rates of consultations (for any clinical reason and selected symptoms) and chest imaging by diagnostic route (emergency, routine referral, and urgent referral) were measured. Multivariable Poisson regression estimated route-specific event rates and inflection points. Results: The study examined 4473 patients with lung cancer with features representative of the nationwide incident cohort, of whom 33% ( n = 1491) were emergency diagnosed. Most (emergency diagnosis, n = 1473/1491; routine referral, n = 1023/1026; urgent referral, n = 1252/1259; and other, n = 684/697) had consulted in primary care in the year prediagnosis, independent of diagnostic route. Consultation and imaging rates increased from 5- and 4-months prediagnosis, respectively, with shorter diagnostic windows in emergency-diagnosed than referred route patients. Compared with emergency-diagnosed patients, referred route patients had higher prediagnostic consultations rates for cough (adjusted incidence rate ratio [aIRR] compared with emergency-diagnosed 1.90, 95% confidence interval [CI] = 1.58 to 2.30 for routinely and 1.94, 95% CI = 1.61 to 2.33 for urgently referred) and chest X-ray use (aIRR 1.91, 95% CI = 1.53 to 2.38 for routinely and 1.77, 95% CI = 1.42 to 2.21 for urgently referred). Conclusion: Similar or shorter diagnostic windows suggest similar potential for earlier diagnosis among emergency-diagnosed and referred route patients alike. Earlier detection may be supported through improved management of non-specific symptoms, timely follow-up of imaging, and greater access to chest computed tomography. Future research should measure missed diagnostic opportunities to identify clinical actions to further reduce emergency lung cancer diagnoses.

Across the state of Victoria, Australia around one-third of residents live in rural areas. There are persisting disparities and poorer prostate cancer outc

Background: Pancreatic cancer causes non-specific symptoms, potentially leading to delays in diagnosis. Decision support tools may help primary care practitioners to triage patients for pancreatic imaging. Aim: To investigate the sensitivity of three different tools for identifying patients who may have pancreatic cancer. Design & setting: An observational study in Australia. Method: We investigated the performance of the Risk Assessment Tool (RAT) for pancreatic cancer, the QCancer ^® tool, and a tool developed through a consensus process led by QIMR Berghofer (the QPaC Tool). We applied these tools to people with pancreatic cancer who were interviewed about their symptoms on first presentation to a clinician. We designated patients as ‘flagged’ by each tool if they met specific criteria, and calculated the percentage flagged (that is, the sensitivity). Participants with jaundice were excluded from analyses of QCancer ^® . Results: We included 190 participants in analyses of the RAT and QPaC Tool (142 in analyses of QCancer ^® ). The sensitivity of the QPaC Tool and the RAT were 54% and 27%, respectively. QCancer ^® had a sensitivity of 14%, at a probability threshold of 1%; in the same 142 participants, QPaC and the RAT flagged 44% and 7%, respectively. Conclusion: The QPaC Tool was the most sensitive, largely owing to its inclusion of severe epigastric pain and emphasis on diabetes, but it has unknown specificity. More research is needed to determine whether any tool could reduce delays in diagnosis; in the interim, the QPaC Tool may support clinicians to consider pancreatic cancer in their differential diagnoses.

BACKGROUND Early detection in primary care could improve pancreatic cancer survival, but diagnosis is often delayed due to the low prevalence of the disease, the nonspecific nature of early symptoms, and the broad range of conditions and volume of consultations managed by general practitioners (GPs). In Australia, improving pancreatic cancer outcomes, including via earlier diagnosis, is a priority being progressed under the National Pancreatic Cancer Roadmap developed by Cancer Australia. Computerized clinical decision support systems (CDSSs) have shown promise in aiding timely cancer diagnosis; however, barriers to adopting CDSS such as mistrust of the recommendations or not being embedded in the clinical workflow remain. Simulation techniques, which offer flexible and cost-effective ways to evaluate digital health interventions, can be used to test CDSS before real-world implementation. OBJECTIVE This study aims to assess the acceptability and feasibility of identifying patients with symptoms associated with pancreatic cancer through a CDSS within a simulated environment. METHODS We developed a CDSS that interacted with an electronic health record used in general practice to identify patients with symptoms, which may indicate pancreatic cancer (unintended weight loss or new-onset diabetes), in a simulation laboratory for digital interventions. We tested it by inviting GPs (n=11) to use the CDSS, with patient actors simulating specific clinical scenarios. We then interviewed GPs about the interaction to assess the acceptability and feasibility of the CDSS in their clinical practice. We used thematic analysis and 2 relevant frameworks to analyze the data. RESULTS GPs found the CDSS easy to use, unobstructive, and effective as a prompt to consider investigations for people with risk factors for pancreatic cancer. However, they expressed concerns about possible overtesting, financial costs, and the potential for anxiety in patients with a very low probability of having cancer. CONCLUSIONS While GPs found the tool useful and compatible with their workflow, concerns about overtesting, lack of evidence, and cost-effectiveness were identified as barriers. GPs favored a stepwise approach to investigations rather than immediate imaging. Despite the overall acceptability of the tool, additional evidence to underpin clinical recommendations is necessary before implementing a CDSS with these specific recommendations for pancreatic cancer in primary care.

Background: Abnormal results in commonly used primary care blood tests could be early markers of cancer in patients presenting with non-specific abdominal symptoms. Methods: Using linked data from the UK Clinical Practice Research Datalink (CPRD) and national cancer registry we compared blood test use and abnormal results from the 24-months pre-diagnosis in 10,575 cancer patients (any site), and 52,875 matched-controls aged ≥30 presenting, with abdominal pain or bloating to primary care. Results: Cancer patients had two-fold increased odds of having a blood test (odds ratio(OR):1.51–2.29) and 2-3-fold increased odds of having an abnormal blood test result (OR:2.42–3.30) in the year pre-diagnosis compared to controls. Raised inflammatory markers were the most common abnormality (74–79% of tested cases). Rates of blood test use and abnormal results progressively increased from 7 months pre-diagnosis in cancer patients, with relatively small corresponding increases in symptomatic controls. In cancer patients, the largest increases from baseline were raised platelets in males with abdominal pain (increased 33-fold), raised white blood cell count in males with abdominal bloating (increased 37-fold) and low albumin in females with either symptom (increased 22–41 fold). Conclusions: Common blood test abnormalities are early signals of cancer in some individuals with non-specific abdominal symptoms and could support expedited cancer diagnosis.