Cancer incidence and competing mortality risk following 15 presenting symptoms in primary care: a population-based cohort study using electronic healthcare records

Objectives: Comprehensive assessment of risk of cancer diagnosis and non-cancer mortality following primary care consultation for 15 new-onset symptoms. Design: Cohort study. Setting: UK primary care (CPRD Gold), 2007 – 2017. Participants: Patients aged 18-99, comprising a randomly-selected reference group and a symptomatic cohort of patients presenting with one of 15 new onset symptoms (abdominal pain, abdominal bloating, rectal bleed, change in bowel habit, dyspepsia, dysphagia, dyspnoea, haemoptysis, haematuria, fatigue, night sweats, weight loss, jaundice, breast lump, post-menopausal bleed). Main outcome measures: Risk of cancer diagnosis and risk of death in the 12 months following index consultation. Time-to-event models were used to estimate outcome-specific hazards for site-specific cancer diagnosis and non-cancer mortality; results were combined using the latent failure time approach to estimate cumulative incidence.

Data were analysed on 1,622,419 patients, of whom 36,802 had a cancer diagnosis and 28,857 died without a cancer diagnosis within 12 months of first consultation. Absolute non-cancer mortality risk exceeded cancer diagnosis risk in the reference group and in symptomatic patients with dyspnoea, dysphagia, weight loss, fatigue, or jaundice; absolute cancer risk exceeded mortality risk for patients with breast lump or post- menopausal bleed; for other symptoms the risk of a cancer diagnosis and non-cancer mortality were similar. Ever-smoking was associated with raised cause-specific hazard for lung cancer (e.g., in women HR 4.8, 95%CI 4.2 to 5.6), and slightly raised hazards for upper GI and urological cancers. For patients with red-flag symptoms, the risk of specific cancers exceeded the UK urgent referral risk threshold of 3% from a relatively young age (e.g., for male smokers with haemoptysis the risk of lung cancer exceeded 3% from age 55). For non-organ-specific symptoms (such as loss of weight, or fatigue), while the risk of any cancer often exceeded . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 4 3%, the risk of any individual cancer type either did not reach this threshold at any age, or reached it only in older patients.

In patients with new-onset symptoms in primary care the risk of cancer diagnosis and of non-cancer mortality are often comparable. Smoking-status is highly informative for cancer risk in patients with respiratory or non-organ-specific symptoms. A holistic approach to risk assessment that includes the risk of multiple different cancer types alongside the risk of mortality due to consequential illnesses other than cancer, especially among older patients, is needed to inform management of symptomatic patients in primary care, particularly for patients with non-organ-specific symptoms. . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 5

Most patients with cancer are diagnosed after symptomatic presentation [1], and, given the paucity of effective tests to enable population-based cancer screening, this is likely to be the case for the coming decade. Appropriately suspecting the diagnosis of cancer in symptomatic patients is difficult, as symptoms may be caused by many other diseases. Even so-termed ‘alarm’ or ‘red-flag’ symptoms typically have positive predictive values for cancer that do not exceed 5% in women of any age or in men younger than 70 [2]. In the UK, many patients with cancer experience diagnostic delays in the form of multiple pre-referral consultations and prolonged intervals to diagnosis, despite practice guidelines issued by the National Institute for Health and Social Care Excellence (NICE) that aimed to enable prompt diagnosis of cancer in primary care [7,8]. Such delays are associated with adverse patient experience and worse clinical outcomes [3–6], Currently, most evidence supporting practice guidelines comes from case-control studies, examining symptom-related risk of specific cancer sites. This study design ignores that presenting symptoms are often shared between different cancers and diseases other than cancer; there has been no comprehensive examination of the risk of the full spectrum of possible cancer types for most relevant presenting symptoms. Further, guideline recommendations handle major cancer risk factors sub-optimally, as smoking status is typically ignored as a risk stratifier, and age typically not considered as a continuous variable, leading to information loss. Competing risk of death is also ignored, meaning that management decisions centred on cancer risk ignore risks related to other diseases. This study is motivated by the need for evidence to support the updating of clinical practice guidelines for the primary care management of patients who present with symptoms of possible underlying cancer. Such evidence is needed both in terms of quantifying the absolute risk of different cancer types and also the probability of patients dying without a cancer diagnosis. We also aim to aid the development of and complement the use of risk prediction tools by describing in detail the associations between symptoms and cancer risk [9,10]. We therefore provide a comprehensive assessment of risk of cancer diagnosis and non-cancer mortality following consultation for 15 new-onset symptoms. . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 6

Study population We used medical records from English National Health Service general practices that contributed anonymized primary-care electronic health records to the Clinical Practice Research Datalink Gold (CPRD), covering approximately 6.9% of the UK population [11]. Patients in CPRD are broadly representative of the UK general population with respect to age, sex, and ethnicity [11]. CPRD was linked to cancer diagnosis information from the English national cancer registry [12]. We first extracted a random sample of patients from CPRD for use as a reference group, choosing index dates randomly from ‘valid’ follow-up during 2007-01-01 to 2017-12-31. We then created a symptomatic cohort of all patients in CPRD Gold who had consulted for any of 15 presenting symptoms and who were not in the reference group, choosing the index date as the date of their first ‘valid’ consultation for a symptom during 2007-01-01 to 2017- 12-31. For an individual patient, follow-up was judged to be ‘valid’ if: they had been registered at their practice for at least one year; their practice was judged by CPRD to be providing data of a suitable standard for use in research (i.e., after the practice’s “up-to-standard” date); it was before the last data transfer to CPRD (i.e., the “last collection” date); the patient was registered at a CPRD practice (i.e., before the patient’s “transfer out” date, and before their death); the patient was aged 30-99; and the patient had not yet had a recorded cancer diagnosis in the cancer registry (excluding non-melanoma skin cancer). A study flowchart is given in Appendix 1 Table 1. Outcomes Both mortality and cancer diagnoses were considered. Mortality was identified from the primary care record; such information is highly concordant with the ‘gold standard’ official death registration records and is correct within one month 98% of the time [13]. Cancers were split into seven groups for men and eight groups for women, summarised below and with a full ICD10 codelist in Appendix 1 Table 2, guided by underlying body systems and corresponding major clinical specialities receiving urgent referrals for suspected cancer in England [14]. Cancer diagnoses were sourced from linkages with the national cancer . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 7 registry and only the first cancer diagnosis was considered; available cancer data covered diagnoses up until 2018-12-31. As non-melanoma skin cancer is imperfectly registered and primarily managed in primary care, diagnoses of non-melanoma skin cancer were not considered in this study. The cancer groups considered were: • Breast cancer (women only), including invasive breast and in-situ breast cancers • Gynaecological cancer (women only), including invasive cervical, in-situ cervical, ovarian, uterine, and vulvar cancers • Lung, including lung cancer and mesothelioma • Upper gastrointestinal (GI), including liver, oesophageal, pancreatic and stomach cancers • Lower GI, including colon and rectal cancers • Urological, including bladder, in-situ bladder, kidney and other urinary tract cancers • Prostate cancer (men only) • Haematological, including Hodgkin lymphoma, non-Hodgkin lymphoma, acute myeloid leukaemia, chronic lymphocytic leukaemia, other leukaemias, myeloma, and other haematological cancers • Other, including all other sites, specifically including melanoma, unknown primary, thyroid, and meningeal cancers, also including testicular cancer and male breast cancer The first outcome (of cancer diagnosis or non-cancer death) experienced by each patient was considered in the analysis. This means, for example, that in the analyses of cumulative incidence a patient who died shortly following a cancer diagnosis would only be considered to have had a cancer diagnosis, and their death would not contribute to the estimation of mortality risk irrespective of cause of death. Patients with a cancer diagnosis on the same day as their death (including, for example, death certificate only registrations of cancer) were treated as having had a cancer diagnosis rather than having died, noting that death certificate only registrations remained <0.4% through the study period [15]. Symptoms We considered a subset of symptoms known to have an association with risk of specific types of cancer and that are already included in referral guidelines for symptomatic cancer [7,16]. The included symptoms form part of the presentation in 40% of all patients with . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 8 cancer England [1]. We identified symptoms from coded primary care data using existing Read v2 phenotyping algorithms [16]. The symptoms we considered were: • Abdominal symptoms o Abdominal pain o Abdominal bloating o Rectal bleeding o Change in bowel habit o Dyspepsia o Dysphagia o Jaundice • Respiratory symptoms o Dyspnoea o Haemoptysis • Urological symptoms o Haematuria • Non-specific symptoms o Fatigue o Night sweats o Weight loss • Breast and reproductive organ symptoms o Breast lump (including in men) o Post-menopausal bleeding Only the first presenting symptom for each patient was included, and each patient was included at most once in the analysis. For example, if a patient had a consultation for breast lump in 2007 that did not result in a cancer diagnosis and a consultation for abdominal pain in 2010 that did result in a cancer diagnosis, only the risk after the 2007 consultation for breast lump would be included in analysis. If two or more of the examined symptoms presented on the same day, all were included as index symptoms (such occurrences were rare, see end of Results). Smoking status, sex, and age Patients were categorised as ever-smokers or never-smokers. Ever-smokers included all patients with a record of being a current or ex-smokers in their entire primary care record, including periods after cancer diagnosis or before their record became eligible for use in this . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 9 study; never-smokers included all other patients. Patients were classed as male or female based on the recorded gender in their primary care record. Patients’ age was estimated as the number of years between the mid-point of their year of birth and their index date. Statistical methods Initial analysis described the distribution of patients in the sample and counts of cancer diagnoses and deaths within 12 months of any index symptom. Hazards for specific cancers and non-cancer mortality were estimated using semi-parametric (Royston-Parmar) time-to-event models [17]. Follow-up for these analyses was censored at the earliest of 18 months after the index symptom, at first event (i.e., cancer diagnosis or death), or at the end of the available cancer registry follow-up on 2018-12-31. Models were stratified by sex and included the following covariates: • Age (restricted cubic spline with six knots) • Smoking status (binary, ever record of smoking in primary care data vs never) • Index symptom (15 binary variables indicating the symptom(s) each patient had on their index date (all zero for patients in the reference group)) • An interaction with follow-up time in months for each index symptom, allowing the association between symptom and cause-specific risk to decay over time. This was motivated by the fact that following many possible symptoms of cancer, excess risk is highest in the first months following presentation (e.g., [18]) Cumulative incidence of cancer group and non-cancer mortality was estimated by combining each of the cause-specific models using the latent failure time approach [19]. We report cumulative incidence for combinations of age-sex-smoking-symptom up to 12 months follow- up, with results focusing on estimated cumulative incidence at 12 months and age considered in five-year intervals. To sense-check these model-based estimates, we additionally examined the crude cumulative incidence for each cancer group and non-cancer mortality within 12 months of each symptom by sex and smoking status using Aalen- Johansen non-parametric cumulative incidence curves [20,21]. Concordant with the methods and evidence that informed the development of NICE guidelines, we have considered the modelled cumulative incidence at 12 months to represent the positive predictive value for the outcome for the symptom [7]. Further, we calculated the (sex/smoking/symptom-specific) age at which cancer risk exceeded the 3% . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 10 risk threshold for referrals used in the UK. We additionally present similar estimates for each individual cancer group. Statistical modelling used Stata 17 MP. Simulation of failure times was performed on a high- performance cluster using Stata 16 MP. Survival models were fit using the merlin package [22], and multistate modelling was facilitated by the multistate package [23]. Data extraction and analysis code are available at https://github.com/MattEBarclay/cprd_symptom_cancer_1. Patient and public involvement The study forms part of a programme of work examining the predictive value of symptoms for cancer diagnosis using electronic health records data. To support this programme, we ran three focus groups in August and September 2023 including a total of 15 patient and public involvement volunteers. Study reporting was informed by PPI input, but no specific changes were made. . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 11

The analysis cohort included 1,622,419 patients, 835,995 with an eligible first symptom recorded between 2007 and 2017 (Table 1). More than half of the cohort (64%, 1,040,762) were aged under 60 at index, with 24,731 (1.5%) patients aged 90 or older. The distribution of symptoms was uneven, with 14.4% of the cohort having abdominal pain as index symptom, followed by dyspnoea (8.7%), fatigue (8.1%), dyspepsia (6.7%), rectal bleeding (3.0%), breast lump (2.4%), haematuria (1.6%), abdominal bloating (1.4%), weight loss (1.2%), change in bowel habit (1.1%), dysphagia (0.9%), post-menopausal bleeding (0.5%), night sweats (0.5%), haemoptysis (0.4%), and jaundice (0.1%). The majority of patients (64%) had at least one smoking-related Read code in their records and were identified as ever-smokers. Within 12 months of their first recorded symptom, 36,802 patients had a cancer diagnosis and 28,867 patients died without a cancer diagnosis (a further 9,288 died following a cancer diagnosis); both cancer and mortality risk were higher in older patients. Ever-smokers had slightly higher cancer risk than patients without any smoking-related codes. Age-adjusted cancer-specific hazard ratios for smoking and each index symptom Both male and female ever-smokers had far higher cancer-specific hazard of lung cancer than non-smokers (Figure 1 and Appendix 4, HR 4.8, 95%CI 4.2-5.6, for women and HR 4.0, 95%CI 3.5-4.6, for men), and elevated hazards of urological (e.g., for men: HR 1.4, 95%CI 1.2-1.5, Appendix 4 Table 4) and upper GI cancers (e.g., for men: HR 1.4, 95%CI 1.2-1.5, Appendix 4 Table 1). Patients consulting for symptoms of possible cancer had similar or greater cause-specific hazards for almost every cancer site than the reference population (Figure 1 and Appendix 4). Yet for ten of the fifteen studied symptoms, the symptom was associated with lower cause-specific hazards for death than the reference group (the exceptions being dysphagia, jaundice, dyspnoea, haemoptysis, and weight loss). Further, for many symptoms associated with very high initial hazard of a specific cancer, while the hazard typically remained elevated at least to 12 months after the index consultation, it tended to reduce over time (Figure 1). Abdominal symptoms (abdominal pain, abdominal bloating, rectal bleeding, change in bowel habit, dyspepsia, dysphagia, jaundice) For both men and women presentations with abdominal symptoms were associated with increased hazard of multiple types of cancer. At the same time, abdominal symptoms were . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 12 associated with decreased hazard of death without a cancer diagnosis when compared with the reference group, except for dysphagia and jaundice (Figure 1, and Appendix 4 Tables 2- 3 and 12-13). Cause-specific hazard ratios at one month after presentation were highest regarding lower GI cancer for rectal bleeding and change in bowel habit (e.g., CIBH for men: HR 17.4, 95% CI 15.7-19.4) and highest regarding upper GI cancer for jaundice and dysphagia (e.g., dysphagia in women: HR 16.4, 95%CI 14.0-19.2); hazard ratios decreased substantially over follow-up for these symptoms. Abdominal pain and abdominal bloating were associated with hazard ratios at consultation of around 4 for both upper and lower GI cancers (e.g., abdominal bloating in women with HR for lower GI cancer of 3.0, 95%CI 2.3- 4.0), with abdominal bloating having a similar association for gynaecological cancers in women (HR 4.8, 95%CI 4.0-5.6), while dyspepsia was associated with a hazard ratio of around 4 for upper GI cancer. Patients with abdominal symptoms also appeared at elevated risk for urological and haematological cancers, and for prostate and gynaecological cancers. Respiratory symptoms (dyspnoea, haemoptysis) Respiratory symptoms were primarily associated with lung cancer, but the strength of the association varied (Figure 1 and Appendix 4 Tables 1 and 11). Patients with haemoptysis had a cause-specific hazard ratio of around 16 at consultation compared with the reference group (e.g., for men, HR 17.1, 95%CI 14.8-19.8), while the association with dyspnoea was weaker but still notable (e.g., for men, HR 2.6, 95%CI 2.4-2.9). Other types of cancer, notably haematological cancers, also had elevated cause-specific hazards; (e.g., for men, the HR for haematological cancer being 2.8, 95%CI 1.7-4.6, Appendix 4 Tables 6 and 15). Urological symptoms (Haematuria) Haematuria in women was primarily associated with urological cancers (HR 57, 95%CI 48- 67) and with gynaecological cancers (HR 4.6, 95%CI 3.7-5.6) (Figure 1 and Appendix 4 Tables 10 and 14). In men, it was associated with urological cancers (HR 45, 95%CI 40-50) and prostate cancer (HR 5.3, 95%CI 4.8-5.8) (Appendix 4, Tables 4 and 5). Non-specific symptoms (Fatigue, night sweats, weight loss) Non-specific symptoms were typically associated with elevated cause-specific hazard ratios for all cancer groups considered (Figure 1 and Appendix 4), and generally HRs appeared relatively similar in strength for each of the three non-specific symptoms. Weight loss had the strongest associations overall (cancer-specific HRs general between 2 and 5), followed by night sweats (HRs generally between 1 and 4, though imprecisely estimated), followed by fatigue (HRs between 1 and 2). It often appeared that the strongest cause-specific . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 13 associations were for haematological cancers, though confidence intervals tended to overlap with those of other cancer groups. Breast and reproductive organ symptoms (breast lump, post-menopausal bleeding) Post-menopausal bleeding was associated with large cause-specific hazard ratios for gynaecological cancer (HR 43, 95%CI 39-47) and substantial cause-specific HRs for urological cancer (HR 4.1, 95%CI 2.6-6.4) (Figure 1 and Appendix 4 Tables 10 and 14). Breast lump in women was associated principally with breast cancer (HR 65, 95%CI 61-69) and to a lesser extent with haematological cancer (HR 2.6, 95%CI 1.80-3.6) (Appendix 4 Tables 9 and 15). A small number of men present with breast lump, and these men had cause-specific hazard ratios for the ‘other cancers’ group, which included male breast cancer, of 7.1 (95%CI 5.0-10.0) (Appendix 4 Table 7). Risk of specific cancer sites by age, sex, and smoking status After symptom presentation for patients with single index symptoms, and based on simulations combining the cause-specific models, we present simulated cumulative incidence of each cancer site and of death without cancer at 3 months (Appendix 2), 6 months (Appendix 3), and 12 months (Figures 2-5, Appendix 5). Hereafter in this section, we discuss cumulative incidence at 12 months after symptom consultation. Unlike the hazard ratios presented above, estimates of cumulative incidence varied substantially by sex, as women have lower baseline cancer risk. 3% any cancer risk thresholds at 12 months Patients reaching a 3% risk of any cancer may not reach such a risk level for any specific cancer group, especially for symptoms associated with multiple types of cancer. For example, female smokers presenting with weight loss had a 3% risk of cancer from age 60, but did not reach the 3% risk threshold at any age when any of the individual cancer groups were considered on their own (Table 2). For male non-smokers, risk of any cancer reached the 3% threshold from the following ages and onwards: 45 for jaundice; 55 for dysphagia, weight loss, haematuria, and change in bowel habit; 60 for haemoptysis and rectal bleeding; 65 for abdominal pain and bloating, night sweats and breast lump; and 70 for dyspepsia, dyspnoea, and fatigue (Table 2). For smokers, this threshold was often reached up to five years younger. Conversely, compared with male patients presenting with the same symptom, female patients reached the 3% threshold at an older age on average, with the main exception being breast lump for which the 3% threshold (in women) was reached from age 40. . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 14 Notably, male smokers in the reference group had a 3% risk of any cancer from age 75, and male non-smokers from age 90; women in the reference group did not reach a 3% risk of cancer at any age. A summary of risk of individual cancers is given in Appendix 6, plus additional graphical and tabular results in Appendices 3 and 5. Risk of non-cancer mortality For most of the studied symptoms, symptomatic patients were less likely to die (without a cancer diagnosis) than similar patients in the reference group (Figures 2-5). The three principal exceptions were jaundice, dysphagia and weight loss, for which post-presentation mortality exceeded that in the reference group, and also older patients with less-specific symptoms for whom the risk of non-cancer mortality was often higher than the risk of any cancer. For example, for male smokers presenting with dyspnoea, around 6% who presented at age 80 would develop cancer within 12 months while 9% would die (Figure 3, Appendix 5 Table 1). Presentation with multiple symptoms Among symptomatic patients, 1.2% (10,360 of 835,995) consulted for more than one of the fifteen studied symptoms on their index date, and a further 2.5% (21,167) consulted for an additional studied symptom within 30 days of an index symptom but before a cancer diagnosis. The proportion of patients with multiple index symptoms subsequently diagnosed with cancer within 12 months of index (4.6%, 95% CI 4.2% to 5.1%) was higher than for patients with a single index symptom (3.5%, 95% CI 3.5% to 3.5%). This higher risk of cancer in patients with multiple index symptoms appeared applicable to many of the symptoms considered, but sample size limitations meant proportions developing cancer could often not be estimated precisely. The cause-specific time-to-event models accommodated multiple index symptoms that were consulted for on the same day, so for example the cause-specific hazard ratio for upper GI cancer for abdominal pain is already adjusted for the presence of dysphagia, for the infrequent occasions (see above) where both were recorded – although possible interaction effects were not considered. Symptoms that were not consulted for on the same day as index were not considered. In principle, estimates of cancer risk for any combination of . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 15 symptoms can be estimated from the cause-specific models, but these have not been produced due to computational limitations and the very large number of potential combinations. . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 16

Using a cohort design, we comprehensively estimated the risk of different cancer diagnoses and non-cancer mortality following presentation in primary care with one of 15 index symptoms, and in a reference group that was not selected based on symptom status and so should approximate the risk in the general population. There was considerable variation in risk by age and by sex. Smoking-status was highly informative for cancer risk for patients with respiratory or non-organ-specific symptoms. Smokers typically reached the 3% threshold warranting referral for cancer investigations up to five years younger than non- smokers. The findings highlight the importance of including smoking status in clinical guidelines and referral decisions in patients with a new onset symptom. Even symptoms with strong, well-established associations (e.g., dyspnoea and lung cancer) have notable associations with other types of cancer (e.g., haematological cancers). We also provide estimates of cancer risk while considering the potential for non-cancer mortality. For the oldest patients – and for those with symptoms such as dysphagia or jaundice – risk of death without a cancer diagnosis reached or exceeded the risk of cancer. Referral decisions based on a universally applied 3% cancer risk threshold, as currently set out in UK clinical guidelines, may not be appropriate for these patients. Strengths and weaknesses Key strengths of the study are (a) the large representative dataset – allowing examination of a range of both common and rare symptoms and outcomes – (b) the joint estimation of the risks of the different outcomes, including of non-cancer mortality and risk of different types of cancer, and (c) the use of cancer registry data to ascertain presence of cancer, as cancer may be under- or over-recorded in non-registry sources [24]. While this study represents the most comprehensive and detailed description of risk of cancer in symptomatic patients to date, there are various areas where future work could make further improvements. Considering limitations, the study only considers deaths in patients without cancer, but it may be important to understand if patients die quickly after a cancer diagnosis. Our measure of smoking status does not allow for a refined appreciation of smoking history and dose- response relationships. Additionally, our analytical approach only allowed each patient to be included once, not making full use of the longitudinal nature of EHR datasets [25]. We did not consider interactions between symptoms and simulated outcomes for patients with a single symptom only, in part due to only few patients having multiple symptoms. We did not have access to free-text data, despite evidence that coded data does not capture all . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 17 symptoms [26,27]. Finally, we only examined 15 symptoms, ignoring the many other symptoms and important health conditions that may be associated with risk of cancer [1,16,28]. A more detailed examination of potential limitations is given in Appendix 7. Comparison with literature A large and growing literature describes risk of cancer following symptom presentations in primary care; Moore and colleagues summarised the literature pre-2020 [16], and there are several recent papers [18,31–33]. Existing literature (a) rarely considers competing non- cancer mortality risk, (b) rarely considers smoking status, and (c) frequently provides no or only limited information on the age-dependent and sex-specific nature of the risk of different cancers. Much of the previous evidence additionally considers either the risk of all cancers combined or focuses on specific cancer sites judged to be of relevance to the specific examined symptoms a priori. We improve on previous descriptive studies by presenting a broad range of possible cancer diagnoses following presentation with wider spectrum of index symptoms. Further research is needed to extend analyses similar to those reported here to a wider collection of symptoms. Some existing evidence on so-called red flag symptoms such as rectal bleeding and haemoptysis suggests the risk of cancer exceeds 3% for all ages, but did not examine the risk in different age groups [16]; our findings indicate that risk of cancer following these symptoms only exceeds 3% beyond certain age cut-offs. Furthermore, we show that for non- specific symptoms, the risk of any cancer exceeds 3% at a considerably earlier age than the risk of a specific cancer type, underscoring the need for studies that comprehensively examine all major cancer types. Weight loss provides a cardinal example, where risk of any cancer exceeded 3% in male non-smokers from age 55 but risk of any individual site only reached 3% at age 85. Other studies have aimed to develop risk prediction tools for cancer intended for use in a primary care setting (see for example, [34–36]), and in particular the QCancer risk prediction tool [9,10] already considers a range of symptoms and risk of diagnosis of different types of cancer. For decisions about the management of an individual patient, a risk prediction tool including multiple potential predictors may be more suitable than the results presented in this paper. We view our results as complementary; by describing what is effectively the average risk in patients presenting with these symptoms (by age, sex, and smoking status), we can inform high-level policy decisions around symptomatic diagnosis of cancer such as clinical guideline recommendations, and help developers of more detailed risk prediction models by . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 18 highlighting symptoms they may wish to consider. Further, our consideration of mortality risk provides relevant information that is frequently missing from current risk prediction tools (including QCancer) and that is especially important in frail and elderly populations. Implications Symptoms recorded in primary care data can be highly informative about both cancer risk and short-term mortality risk. In some cases, for example lung cancer, smoking-status is very strongly associated with the risk of cancer following a certain symptom. Risk of cancer and non-cancer mortality varies considerably by age; describing “overall” risk of cancer following a symptom may be misleading if non-cancer mortality is not considered. Some (non-cancer) deaths will relate to as-yet undiagnosed disease which, like cancer diagnosis, necessitates specialist assessment in secondary care, though this should be the subject of future enquiries. For researchers, our results underline the methodological importance of accounting for the fact that symptoms may be associated with multiple different disease outcomes. Advanced statistical modelling strategies are helpful in assessing diagnostic outcomes using EHR data, and current statistical packages allow for relatively straightforward handling of competing risks either by directly modelling cumulative incidence (e.g., the Fine-Gray model [37]) or, as here, by combining several cause-specific models [38]. Diagnostic research should adopt strategies that allow consideration of risk of several potentially related diseases (e.g., multiple types of cancer, as in this study), which can be done even with simple analytical approaches such as appropriate use of logistic regression [32]. For clinicians and policy makers, our systematic assessment of risk of cancer (and of non- cancer mortality) in symptomatic patients in primary care raises two key questions. First, whether all age-sex-smoking status groups presenting with each of the studied symptoms and with an estimated any-cancer risk of above 3% should explicitly be added to NICE referral guidelines. This may indeed be justified, though given the high mortality rates in the oldest patients, there might also be a risk of over-testing in older men in particular. However, the degree to which risk of over-testing is a concern relates to the exact causes of non-cancer mortality and the extent to which it relates to pre-diagnosed or new non- neoplastic diseases which could benefit from specialist diagnostic assessment and earlier diagnosis. As the components of non-cancer mortality due to pre-existing or new conditions is unclear, this should be addressed by future research. The current approach to cancer . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 19 referral uses a normative threshold applicable to patients of any age and with any symptoms, and the results highlight the importance of considering whether patients are likely to benefit from prompt diagnosis. Second, whether current referral pathways are necessarily ideal. For example, many abdominal symptoms were strongly associated with lower GI, upper GI and gynaecological cancers, and some form of referral pathway offering combined multi-specialty assessment may be justified for patients with these symptoms. Further, symptoms were often strongly associated with less common cancers such as haematological neoplasms but, due to the low incidence of these conditions, absolute risk rarely or never reached 3%; optimal diagnostic management of these patients is clearly challenging. Our findings may be helpful in clarifying referral criteria for new non-specific cancer pathways.

The risk of cancer diagnosis and non-cancer mortality after symptomatic presentation can be comparable and both should be considered in referral and investigation decisions – alongside age, sex, and smoking status. A holistic and stratified assessment of risk in symptomatic patients, which considers the risk of a cancer diagnosis, the risk of a diagnosis of individual types of cancer, and the risk of non-cancer mortality is needed particularly for patients presenting with which are vague or non-specific symptoms associated with multiple cancer types and appreciable non-cancer mortality risk. Our results can support the updating of referral and management guidelines for symptomatic patients presenting in primary care. . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 20 Ethics statements Ethical approval This study was approved by the UK Medicines and Healthcare products Regulatory Agency Independent Scientific Advisory Committee (ISAC Protocol number 18_299), under Section 251 (NHS Social Care Act 2006). This study is based on data from the Clinical Practice Research Datalink obtained under license from the UK Medicines and Healthcare products Regulatory Agency. The data is provided by patients and collected by the UK National Health Service (NHS) as part of their care and support. Data availability statement Potential concerns around patient confidentiality prevent open sharing of the underlying data for this study. CPRD Gold data can be obtained from CPRD, subject to protocol approval via CPRD’s Research Data Governance Process. Further details can be found at https://cprd.com/data-access. Data extraction and analysis code are available at https://github.com/MattEBarclay/cprd_symptom_cancer_1.

The work was supported by the International Alliance for Cancer Early Detection, a partnership between Cancer Research UK (C18081/A31373), Canary Center at Stanford University, the University of Cambridge, OHSU Knight Cancer Institute, University College London, and the University of Manchester. SI is additionally supported by Cancer Research UK (EDDPMA-May22\100062) and HH and MB by CRUK International Alliance for Cancer Early Detection (ACED) Pathway Awards (EDDAPA-2022/100001 and EDDAPA- 2022/100002, respectively). GL was supported by a Cancer Research UK (C18081/A18180) Advanced Clinician Scientist Fellowship. CR acknowledges funding from Cancer Research UK Early Detection and Diagnosis Committee (grant number EDDCPJT\100018). JUS is supported by a National Institute of Health Research Advanced Fellowship (NIHR300861). ACA is support by Cancer Research UK grant: PPRPGM-Nov20\100002. SI, AW and ACA are supported by the National Institute for Health and Care Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312) [*]. AW is part of the BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement No 116074. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. All authors had access to statistical reports, tables, and analysis code. MB, CR, BW, SI and GL had full access to all of the data. . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 21 Transparency declaration The lead author affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained. Competing interests All authors have completed the ICMJE uniform disclosure form at http://www.icmje.org/disclosure-of-interest/ and declare: no support from any organisation for the submitted work; MB has received personal fees from Grail Inc for membership of an Independent Data Monitoring Committee; no other relationships or activities that could appear to have influenced the submitted work. Contributors MB designed the statistical analysis, wrote analytical code, cleaned and analysed the data, and drafted and revised the paper. He is the guarantor. CR, HH and GL contributed to drafting the paper. CR, JU-S, NP and GL provided clinical interpretation. HH, AT, BW, SI and SD contributed to data management and phenotyping. JL, AW and ACA contributed to the design and interpretation of the analysis. All authors provided revisions to the paper and gave final approval to the submitted manuscript. . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 22 Table 1. Cohort summary.   Cohort   Cancers within 12 months   Deaths within 12 months, no preceding cancer diagnosis   Deaths within 12 months, following a cancer diagnosis     N (col %) N (row %) N (row %) N (row %) Total 1,622,419 36,802 (2.3%) 28,867 (1.8%) 9,288 (0.6%) Age at index (grouped)   30 to 39 395,313 (24.4%) 1,571 (0.4%) 426 (0.1%) 62 (0.0%) 40 to 49 350,133 (21.6%) 3,063 (0.9%) 792 (0.2%) 235 (0.1%) 50 to 59 295,316 (18.2%) 5,080 (1.7%) 1,343 (0.5%) 762 (0.3%) 60 to 69 259,039 (16.0%) 9,014 (3.5%) 2,829 (1.1%) 1,970 (0.8%) 70 to 79 185,854 (11.5%) 10,142 (5.5%) 6,007 (3.2%) 2,960 (1.6%) 80 to 89 111,933 (6.9%) 6,818 (6.1%) 11,453 (10.2%) 2,720 (2.4%) 90 to 99 24,731 (1.5%) 1,114 (4.5%) 6,017 (24.3%) 579 (2.3%) Sex   Women 880,888 (54.3%) 19,808 (2.2%) 15,671 (1.8%) 4,259 (0.5%) Men 741,531 (45.7%) 16,994 (2.3%) 13,196 (1.8%) 5,029 (0.7%) IMD group   Least deprived 377,575 (23.3%) 8,934 (2.4%) 5,661 (1.5%) 2,001 (0.5%) 2 356,859 (22.0%) 8,347 (2.3%) 6,177 (1.7%) 2,031 (0.6%) 3 342,184 (21.1%) 7,755 (2.3%) 6,355 (1.9%) 1,889 (0.6%) 4 294,638 (18.2%) 6,483 (2.2%) 5,559 (1.9%) 1,805 (0.6%) Most deprived 251,163 (15.5%) 5,283 (2.1%) 5,115 (2.0%) 1,562 (0.6%) Any record of smoking   Never smoker 586,639 (36.2%) 10,390 (1.8%) 10,043 (1.7%) 2,259 (0.4%) Ever smoker 1,035,780 (63.8%) 26,412 (2.5%) 18,824 (1.8%) 7,029 (0.7%) Index symptom

group 786,424 (48.5%) 7,536 (1.0%) 12,520 (1.6%) 2,034 (0.3%) Abdominal pain 233,933 (14.4%) 5,605 (2.4%) 2,163 (0.9%) 1,640 (0.7%) Abdominal bloating 22,629 (1.4%) 628 (2.8%) 261 (1.2%) 169 (0.7%) Rectal bleeding 48,515 (3.0%) 1,868 (3.9%) 860 (1.8%) 220 (0.5%) Change in bowel habit 17,212 (1.1%) 1,067 (6.2%) 163 (0.9%) 197 (1.1%) Dyspepsia 108,488 (6.7%) 2,120 (2.0%) 959 (0.9%) 609 (0.6%) Dysphagia 14,992 (0.9%) 1,036 (6.9%) 1,167 (7.8%) 451 (3.0%) Jaundice 1,817 (0.1%) 456 (25.1%) 217 (11.9%) 280 (15.4%) Dyspnoea 141,094 (8.7%) 3,945 (2.8%) 6,268 (4.4%) 1,490 (1.1%) Haemoptysis 5,859 (0.4%) 412 (7.0%) 146 (2.5%) 183 (3.1%) Haematuria 25,753 (1.6%) 2,770 (10.8%) 591 (2.3%) 378 (1.5%) Fatigue 141,932 (8.7%) 2,405 (1.7%) 2,212 (1.6%) 739 (0.5%) Night sweats 7,675 (0.5%) 133 (1.7%) 30 (0.4%) 35 (0.5%) Weight loss 19,617 (1.2%) 1,238 (6.3%) 1,173 (6.0%) 623 (3.2%) Breast lump 38,307 (2.4%) 4,789 (12.5%) 88 (0.2%) 185 (0.5%) Post-menopausal bleed 8,172 (0.5%) 794 (9.7%) 49 (0.6%) 55 (0.7%) . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 23 Table 2. Modelled age at which the 3% referral threshold is crossed for any cancer and for each cancer site. Cohort Symptom Any Breast Gynae. Lung Upper GI Lower GI Urological Prostate Haem. Other Male non-smokers Reference group 90 n/a n/a Abdominal pain 60 n/a n/a Abdominal bloating 65 n/a n/a Rectal bleeding 60 n/a n/a 65 Change in bowel habit 55 n/a n/a 60 Dyspepsia 65 n/a n/a Dysphagia 55 n/a n/a 60 Jaundice 45 n/a n/a 50 55 Dyspnoea 70 n/a n/a Haemoptysis 60 n/a n/a 70 Haematuria 55 n/a n/a 55 65 Fatigue 65 n/a n/a Night sweats 65 n/a n/a Weight loss 60 n/a n/a 80 Breast lump 65 n/a n/a 75 Male smokers Reference group 75 n/a n/a Abdominal pain 60 n/a n/a Abdominal bloating 60 n/a n/a Rectal bleeding 60 n/a n/a 60 Change in bowel habit 55 n/a n/a 60 Dyspepsia 65 n/a n/a Dysphagia 55 n/a n/a 55 Jaundice 45 n/a n/a 50 55 Dyspnoea 65 n/a n/a Haemoptysis 55 n/a n/a 55 Haematuria 50 n/a n/a 55 70 Fatigue 65 n/a n/a Night sweats 60 n/a n/a Weight loss 55 n/a n/a 70 75 Breast lump 60 n/a n/a 70 Female non-smokers Reference group (n/a) n/a Abdominal pain 65 n/a Abdominal bloating 65 n/a Rectal bleeding 60 70 n/a Change in bowel habit 60 70 n/a Dyspepsia 75 n/a Dysphagia 65 70 n/a Jaundice 45 50 n/a 60 Dyspnoea (n/a) n/a Haemoptysis 65 n/a Haematuria 60 65 n/a . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 24 Cohort Symptom Any Breast Gynae. Lung Upper GI Lower GI Urological Prostate Haem. Other Fatigue 75 n/a Night sweats 75 n/a Weight loss 65 n/a Breast lump 35 40 n/a Post-menopausal bleeding 30 30 n/a Female smokers Reference group (n/a) n/a Abdominal pain 65 n/a Abdominal bloating 65 n/a Rectal bleeding 60 70 n/a Change in bowel habit 60 70 n/a Dyspepsia 70 n/a Dysphagia 60 70 n/a Jaundice 40 45 n/a 55 Dyspnoea 70 n/a Haemoptysis 55 60 n/a Haematuria 55 60 n/a Fatigue 70 n/a Night sweats 70 n/a Weight loss 60 n/a Breast lump 35 35 n/a Post-menopausal bleeding 30 30 n/a . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 25 Figure 1. Modelled cancer and mortality risk at 12 months by index symptom, male non-smokers. . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 26 Figure 2. Modelled cancer and mortality risk at 12 months by index symptom, male smokers. . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 27 Figure 3. Modelled cancer and mortality risk at 12 months by index symptom, female non-smokers. . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 28 Figure 4. Modelled cancer and mortality risk at 12 months by index symptom, female smokers. . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 29 Table 3. Summary of cancer outcomes for patients with multiple different recorded symptoms at index presentation, and within 30 days of index symptom. Index symptom Any other symptoms at index Patients Cancers within 12 months of index N % (95% CI) Any No 825,635 28,834 3.5% (3.5%, 3.5%) Yes 10,360 480 4.6% (4.2%, 5.1%) Within 30 days* 21,167 1429 6.8% (6.4%, 7.1%) Abdominal pain No 231,598 5,510 2.4% (2.3%, 2.4%) Yes 2,335 101 4.3% (3.6%, 5.2%) Within 30 days* 6,122 379 6.2% (5.6%, 6.8%) Abdominal bloating No 825,635 28,834 3.5% (3.5%, 3.5%) Yes 10,360 480 4.6% (4.2%, 5.1%) Within 30 days* 21,167 1429 6.8% (6.4%, 7.1%) Rectal bleeding No 47,774 1,831 3.8% (3.7%, 4.0%) Yes 741 38 5.1% (3.8%, 7.0%) Within 30 days* 1,116 61 5.5% (4.3%, 7.0%) Change in bowel habit No 16,857 1,042 6.2% (5.8%, 6.6%) Yes 355 25 7.0% (4.8%, 10.2%) Within 30 days* 520 77 14.8% (12.0%, 18.1%) Dyspepsia No 106,843 2,090 2.0% (1.9%, 2.0%) Yes 1,645 35 2.1% (1.5%, 2.9%) Within 30 days* 3,282 219 6.7% (5.9%, 7.6%) Dysphagia No 14,760 1,021 6.9% (6.5%, 7.3%) Yes 232 17 7.3% (4.6%, 11.4%) Within 30 days* 1,054 56 5.3% (4.1%, 6.8%) Jaundice No 1,759 450 25.6% (23.6%, 27.7%) Yes 58 9 15.5% (8.4%, 26.9%) Within 30 days* 81 17 21.0% (13.5%, 31.1%) Dyspnoea No 139,758 3,899 2.8% (2.7%, 2.9%) Yes 1,336 61 4.6% (3.6%, 5.8%) Within 30 days* 2,655 173 6.5% (5.6%, 7.5%) Haemoptysis No 5,750 406 7.1% (6.4%, 7.8%) Yes 109 6 5.5% (2.5%, 11.5%) Within 30 days* 198 20 10.1% (6.6%, 15.1%) Haematuria No 25,438 2,749 10.8% (10.4%, 11.2%) Yes 315 22 7.0% (4.7%, 10.3%) Within 30 days* 636 76 12.0% (9.7%, 14.7%) Fatigue No 140,212 2,353 1.7% (1.6%, 1.7%) Yes 1,720 58 3.4% (2.6%, 4.3%) Within 30 days* 3,132 157 5.0% (4.3%, 5.8%) Night sweats No 7,527 128 1.7% (1.4%, 2.0%) Yes 148 5 3.4% (1.5%, 7.7%) Within 30 days* 162 6 3.7% (1.7%, 7.8%) Weight loss No 19,168 1,193 6.2% (5.9%, 6.6%) Yes 449 52 11.6% (8.9%, 14.9%) Within 30 days* 725 90 12.4% (10.2%, 15.0%) Breast lump No 38,045 4,765 12.5% (12.2%, 12.9%) Yes 262 25 9.5% (6.5%, 13.7%) Within 30 days* 345 18 5.2% (3.3%, 8.1%) Post-menopausal bleed No 8,092 784 9.7% (9.1%, 10.4%) Yes 80 10 12.5% (6.9%, 21.5%) Within 30 days* 145 14 9.7% (5.8%, 15.6%) *subset of patients with no other symptoms at index . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 30

1 Zakkak N, Barclay ME, Swann R, et al. The presenting symptom signatures of incident cancer: evidence from the English 2018 National Cancer Diagnosis Audit. Br J Cancer. 2023;1–11. 2 Jones R, Latinovic R, Charlton J, et al. Alarm symptoms in early diagnosis of cancer in primary care: cohort study using General Practice Research Database. BMJ. 2007;334:1040–4. 3 Neal RD, Tharmanathan P, France B, et al. Is increased time to diagnosis and treatment in symptomatic cancer associated with poorer outcomes? Systematic review. British Journal of Cancer. 2015;112:S92–107. 4 Lyratzopoulos G, Neal RD, Barbiere JM, et al. Variation in number of general practitioner consultations before hospital referral for cancer: findings from the 2010 National Cancer Patient Experience Survey in England. The Lancet Oncology. 2012;13:353–65. 5 Mendonca SC, Abel GA, Saunders CL, et al. Pre-referral general practitioner consultations and subsequent experience of cancer care: evidence from the English Cancer Patient Experience Survey. European journal of cancer care. 2015;25:478–90. 6 Swann R, McPhail S, Witt J, et al. Diagnosing cancer in primary care: results from the National Cancer Diagnosis Audit. British Journal of General Practice. 2018;68:e63–72. 7 National Institute for Health and Care Excellence. Suspected cancer: recognition and referral. NICE guidelines. 2015;NG12. 8 Hamilton W, Hajioff S, Graham J, et al. Suspected cancer (part 2--adults): reference tables from updated NICE guidance. Bmj. 2015;350:h3044. 9 Hippisley-Cox J, Coupland C. Symptoms and risk factors to identify men with suspected cancer in primary care: derivation and validation of an algorithm. British Journal of General Practice. 2013;63:e1– 10. 10 Hippisley-Cox J, Coupland C. Symptoms and risk factors to identify women with suspected cancer in primary care: derivation and validation of an algorithm. The British Journal of General Practice. 2013;63:e11. 11 Herrett E, Gallagher AM, Bhaskaran K, et al. Data Resource Profile: Clinical Practice Research Datalink (CPRD). International Journal of Epidemiology. 2015;44. 12 Henson KKE, Elliss-Brookes L, Coupland VH, et al. Data Resource Profile: National Cancer Registration Dataset in England. International Journal of Epidemiology. 2020;49:16–16h. 13 Harshfield A, Abel GA, Barclay S, et al. Do GPs accurately record date of death? A UK observational analysis. BMJ Supportive & Palliative Care. 2020;10:e24–e24. 14 NHS England. NHS Data Model and Dictionary. Two Week Wait Cancer or Symptomatic Breast Referral Type. 2023. https://www.datadictionary.nhs.uk/data_elements/two_week_wait_cancer_or_symptomatic_breast_refe rral_type.html (accessed 5 October 2023) 15 NHS England,. Routes to Diagnosis, 2018. 2022. https://digital.nhs.uk/data-and- information/publications/statistical/routes-to-diagnosis/2018 (accessed 21 November 2023) 16 Moore SF, Price SJ, Chowienczyk S, et al. The impact of changing risk thresholds on the number of people in England eligible for urgent investigation for possible cancer: an observational cross-sectional study. British Journal of Cancer. 2021;125:1593–7. . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 31 17 Royston P, Parmar MKB. Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Statistics in Medicine. 2002;21:2175–97. 18 White B, Rafiq M, Gonzalez-Izquierdo A, et al. Risk of cancer following primary care presentation with fatigue: a population-based cohort study of a quarter of a million patients. British Journal of Cancer. 2022;126:1627–36. 19 Prentice R, Kalbfleisch J, Peterson Jr A, et al. The Analysis of Failure Times in the Presence of Competing Risks. Biometrics. 1978;34:541–54. 20 Aalen O, Johansen S. An Empirical Transition Matrix for Non-Homogeneous Markov Chains Based on Censored Observations. Scandinavian Journal of Statistics. 1978;5:141–50. 21 Wolkewitz M, Cooper BS, Bonten MJM, et al. Interpreting and comparing risks in the presence of competing events. BMJ. 2014;349:g5060–g5060. 22 Crowther MJ. merlin—A unified modeling framework for data analysis and methods development in Stata. The Stata Journal: Promoting communications on statistics and Stata. 2020;20:763–84. 23 Crowther MJ, Lambert PC. Parametric multistate survival models: Flexible modelling allowing transition- specific distributions with application to estimating clinically useful measures of effect differences. Statistics in Medicine. 2017;36:n/a-n/a. 24 Arhi CS, Bottle A, Burns EM, et al. Comparison of cancer diagnosis recording between the Clinical Practice Research Datalink, Cancer Registry and Hospital Episodes Statistics. Cancer Epidemiology. 2018;57:148–57. 25 Keogh RH, Seaman SR, Barrett JK, et al. Dynamic Prediction of Survival in Cystic Fibrosis: A Landmarking Analysis Using UK Patient Registry Data. Epidemiology. 2019;30:29–37. 26 Kostopoulou O, Tracey C, Delaney BC. Can decision support combat incompleteness and bias in routine primary care data? Journal of the American Medical Informatics Association. 2021;28:1461–7. 27 Price SJ, Stapley SA, Shephard E, et al. Is omission of free text records a possible source of data loss and bias in Clinical Practice Research Datalink studies? A case-control study. BMJ open. 2016;6. doi: 10.1136/BMJOPEN-2016-011664 28 Skillrud D, Offord K, Miller R. Higher Risk of Lung Cancer in Chronic Obstructive Pulmonary Disease. Annals of Internal Medicine. 1986;105:503. 29 Barclay M, Renzi C, Antoniou A, et al. Phenotypes and rates of cancer-relevant symptoms and tests in the year before cancer diagnosis in UK Biobank and CPRD Gold. PLOS Digital Health. 2023;2:e0000383. 30 Majano SB, Lyratzopoulos G, Rachet B, et al. Do presenting symptoms, use of pre-diagnostic endoscopy and risk of emergency cancer diagnosis vary by comorbidity burden and type in patients with colorectal cancer? British Journal of Cancer 2021 126:4. 2021;126:652–63. 31 White B, Renzi C, Barclay M, et al. Underlying cancer risk among patients with fatigue and other vague symptoms: a population-based cohort study in primary care. British Journal of General Practice. 2023;73:e75–87. 32 Herbert A, Rafiq M, Pham TM, et al. Predictive values for different cancers and inflammatory bowel disease of 6 common abdominal symptoms among more than 1.9 million primary care patients in the UK: A cohort study. PLOS Medicine. 2021;18:e1003708. 33 Price SJ, Gibson N, Hamilton WT, et al. Intra-abdominal cancer risk with abdominal pain: a prospective cohort primary care study. British Journal of General Practice. 2022;72:e361–8. . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint 32 34 Williams TGS, Cubiella J, Griffin SJ, et al. Risk prediction models for colorectal cancer in people with symptoms: a systematic review. BMC Gastroenterology. 2016;16:63. 35 Harrison H, Usher-Smith JA, Li L, et al. Risk prediction models for symptomatic patients with bladder and kidney cancer: a systematic review. British Journal of General Practice. 2022;72:e11–8. 36 Funston G, Hardy V, Abel G, et al. Identifying Ovarian Cancer in Symptomatic Women: A Systematic Review of Clinical Tools. Cancers. 2020;12:3686. 37 Fine JP, Gray RJ. A Proportional Hazards Model for the Subdistribution of a Competing Risk. Journal of the American Statistical Association. 1999;94:496–509. 38 Putter H, Fiocco M, Geskus RB. Tutorial in biostatistics: competing risks and multi-state models. Statistics in Medicine. 2007;26:2389–430. . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 21, 2024. ; https://doi.org/10.1101/2024.05.21.24307662doi: medRxiv preprint