Risk of cancer following primary care presentation with fatigue: a population-based cohort study of a quarter of a million patients

Identifying relevant factors influencing cancer-related fatigue in patients with diffuse large B-cell lymphoma during chemotherapy

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is a rapidly growing malignant tumor, and chemotherapy is one of the treatments used to combat it. Although advancements of science and technology have resulted in more and more patients being able to receive effective treatment, they still face side effects such as fatigue and weakness. It is important to thoroughly investigate the factors that contribute to cancer-related fatigue (CRF) during chemotherapy.

AIM

To explore the factors related to CRF, anxiety, depression, and mindfulness levels in patients with DLBCL during chemotherapy.

METHODS

General information was collected from the electronic medical records of eligible patients. Sleep quality and mindfulness level scores in patients with DLBCL during chemotherapy were evaluated by the Pittsburgh Sleep Quality Index and Five Facet Mindfulness Questionnaire-Short Form. The Piper Fatigue Scale was used to evaluate the CRF status. The Self-Rating Anxiety Scale and Self-Rating Depression Scale were used to evaluate anxiety and depression status. Univariate analysis and multivariate regression analysis were used to investigate the factors related to CRF.

RESULTS

The overall average CRF level in 62 patients with DLBCL during chemotherapy was 5.74 ± 2.51. In 25 patients, the highest rate of mild fatigue was in the cognitive dimension (40.32%), and in 35 patients the highest moderate fatigue rate in the behavioral dimension (56.45%). In the emotional dimension, severe fatigue had the highest rate of occurrence, 34 cases or 29.03%. The CRF score was positively correlated with cancer experience (all P < 0.01) and negatively correlated with cancer treatment efficacy (all P < 0.01). Tumor staging, chemotherapy cycle, self-efficacy level, and anxiety and depression level were related to CRF in patients with DLBCL during chemotherapy.

CONCLUSION

There was a significant correlation between CRF and perceptual control level in patients. Tumor staging, chemotherapy cycle, self-efficacy level, and anxiety and depression level influenced CRF in patients with DLBCL during chemotherapy.

Predictive value of abnormal blood tests for detecting cancer in primary care patients with nonspecific abdominal symptoms: A population-based cohort study of 477,870 patients in England

BACKGROUND

Identifying patients presenting with nonspecific abdominal symptoms who have underlying cancer is a challenge. Common blood tests are widely used to investigate these symptoms in primary care, but their predictive value for detecting cancer in this context is unknown. We quantify the predictive value of 19 abnormal blood test results for detecting underlying cancer in patients presenting with 2 nonspecific abdominal symptoms.

METHODS AND FINDINGS

Using data from the UK Clinical Practice Research Datalink (CPRD) linked to the National Cancer Registry, Hospital Episode Statistics and Index of Multiple Deprivation, we conducted a population-based cohort study of patients aged ≥30 presenting to English general practice with abdominal pain or bloating between January 2007 and October 2016. Positive and negative predictive values (PPV and NPV), sensitivity, and specificity for cancer diagnosis (overall and by cancer site) were calculated for 19 abnormal blood test results co-occurring in primary care within 3 months of abdominal pain or bloating presentations. A total of 9,427/425,549 (2.2%) patients with abdominal pain and 1,148/52,321 (2.2%) with abdominal bloating were diagnosed with cancer within 12 months post-presentation. For both symptoms, in both males and females aged ≥60, the PPV for cancer exceeded the 3% risk threshold used by the UK National Institute for Health and Care Excellence for recommending urgent specialist cancer referral. Concurrent blood tests were performed in two thirds of all patients (64% with abdominal pain and 70% with bloating). In patients aged 30 to 59, several blood abnormalities updated a patient's cancer risk to above the 3% threshold: For example, in females aged 50 to 59 with abdominal bloating, pre-blood test cancer risk of 1.6% increased to: 10% with raised ferritin, 9% with low albumin, 8% with raised platelets, 6% with raised inflammatory markers, and 4% with anaemia. Compared to risk assessment solely based on presenting symptom, age and sex, for every 1,000 patients with abdominal bloating, assessment incorporating information from blood test results would result in 63 additional urgent suspected cancer referrals and would identify 3 extra cancer patients through this route (a 16% relative increase in cancer diagnosis yield). Study limitations include reliance on completeness of coding of symptoms in primary care records and possible variation in PPVs if extrapolated to healthcare settings with higher or lower rates of blood test use.

CONCLUSIONS

In patients consulting with nonspecific abdominal symptoms, the assessment of cancer risk based on symptoms, age and sex alone can be substantially enhanced by considering additional information from common blood test results. Male and female patients aged ≥60 presenting to primary care with abdominal pain or bloating warrant consideration for urgent cancer referral or investigation. Further cancer assessment should also be considered in patients aged 30 to 59 with concurrent blood test abnormalities. This approach can detect additional patients with underlying cancer through expedited referral routes and can guide decisions on specialist referrals and investigation strategies for different cancer sites.

The presenting symptom signatures of incident cancer: evidence from the English 2018 National Cancer Diagnosis Audit

BACKGROUND

Understanding relationships between presenting symptoms and subsequently diagnosed cancers can inform symptom awareness campaigns and investigation strategies.

METHODS

We used English National Cancer Diagnosis Audit 2018 data for 55,122 newly diagnosed patients, and examined the relative frequency of presenting symptoms by cancer site, and of cancer sites by presenting symptom.

RESULTS

Among 38 cancer sites (16 cancer groups), three classes were apparent: cancers with a dominant single presenting symptom (e.g. melanoma); cancers with diverse presenting symptoms (e.g. pancreatic); and cancers that are often asymptomatically detected (e.g. chronic lymphocytic leukaemia). Among 83 symptoms (13 symptom groups), two classes were apparent: symptoms chiefly relating to cancers of the same body system (e.g. certain respiratory symptoms mostly relating to respiratory cancers); and symptoms with a diverse cancer site case-mix (e.g. fatigue). The cancer site case-mix of certain symptoms varied by sex.

CONCLUSION

We detailed associations between presenting symptoms and cancer sites in a large, representative population-based sample of cancer patients. The findings can guide choice of symptoms for inclusion in awareness campaigns, and diagnostic investigation strategies post-presentation when cancer is suspected. They can inform the updating of clinical practice recommendations for specialist referral encompassing a broader range of cancer sites per symptom.

Phenotypes and rates of cancer-relevant symptoms and tests in the year before cancer diagnosis in UK Biobank and CPRD Gold

Early diagnosis of cancer relies on accurate assessment of cancer risk in patients presenting with symptoms, when screening is not appropriate. But recorded symptoms in cancer patients pre-diagnosis may vary between different sources of electronic health records (EHRs), either genuinely or due to differential completeness of symptom recording. To assess possible differences, we analysed primary care EHRs in the year pre-diagnosis of cancer in UK Biobank and Clinical Practice Research Datalink (CPRD) populations linked to cancer registry data. We developed harmonised phenotypes in Read v2 and CTV3 coding systems for 21 symptoms and eight blood tests relevant to cancer diagnosis. Among 22,601 CPRD and 11,594 UK Biobank cancer patients, 54% and 36%, respectively, had at least one consultation for possible cancer symptoms recorded in the year before their diagnosis. Adjusted comparisons between datasets were made using multivariable Poisson models, comparing rates of symptoms/tests in CPRD against expected rates if cancer site-age-sex-deprivation associations were the same as in UK Biobank. UK Biobank cancer patients compared with those in CPRD had lower rates of consultation for possible cancer symptoms [RR: 0.61 (0.59-0.63)], and lower rates for any primary care consultation [RR: 0.86 (95%CI 0.85-0.87)]. Differences were larger for 'non-alarm' symptoms [RR: 0.54 (0.52-0.56)], and smaller for 'alarm' symptoms [RR: 0.80 (0.76-0.84)] and blood tests [RR: 0.93 (0.90-0.95)]. In the CPRD cohort, approximately representative of the UK population, half of cancer patients had recorded symptoms in the year before diagnosis. The frequency of non-specific presenting symptoms recorded in the year pre-diagnosis of cancer was substantially lower among UK Biobank participants. The degree to which results based on highly selected biobank cohorts are generalisable needs to be examined in disease-specific contexts.

A taxonomy of early diagnosis research to guide study design and funding prioritisation

Researchers and research funders aiming to improve diagnosis seek to identify if, when, where, and how earlier diagnosis is possible. This has led to the propagation of research studies using a wide range of methodologies and data sources to explore diagnostic processes. Many such studies use electronic health record data and focus on cancer diagnosis. Based on this literature, we propose a taxonomy to guide the design and support the synthesis of early diagnosis research, focusing on five key questions: Do healthcare use patterns suggest earlier diagnosis could be possible? How does the diagnostic process begin? How do patients progress from presentation to diagnosis? How long does the diagnostic process take? Could anything have been done differently to reach the correct diagnosis sooner? We define families of diagnostic research study designs addressing each of these questions and appraise their unique or complementary contributions and limitations. We identify three further questions on relationships between the families and their relevance for examining patient group inequalities, supported with examples from the cancer literature. Although exemplified through cancer as a disease model, we recognise the framework is also applicable to non-neoplastic disease. The proposed framework can guide future study design and research funding prioritisation.

Pre-diagnostic prescribing patterns in dyspnoea patients with as-yet-undiagnosed lung cancer: A longitudinal study of linked primary care and cancer registry data

INTRODUCTION

Patients with as-yet undiagnosed lung cancer (LC) can present to primary care with non-specific symptoms such as dyspnoea, often in the context of pre-existing chronic obstructive pulmonary disease (COPD). Related medication prescriptions pre-diagnosis might represent opportunities for earlier diagnosis, but UK evidence is limited. Consequently, we explored prescribing patterns of relevant medications in patients who presented with dyspnoea in primary care and were subsequently diagnosed with LC.

METHOD

Linked primary care (Clinical Practice Research Datalink) and National Cancer Registry data were used to identify 5434 patients with incident LC within a year of a dyspnoea presentation in primary care between 2006 and 2016. Primary care prescriptions relevant to dyspnoea management were examined: antibiotics, inhaled medications, oral steroids, and opioid analgesics. Poisson regression models estimated monthly prescribing rates during the year pre-diagnosis. Variation by COPD status (52 % pre-existing, 36 % COPD-free, 12 % new-onset) was examined. Inflection points were identified indicating when prescribing rates changed from the background rate.

RESULTS

63 % of patients received 1 or more relevant prescriptions 1-12 months pre-diagnosis. Pre-existing COPD patients were most prescribed inhaled medications. COPD-free and new-onset COPD patients were most prescribed antibiotics. Most patients received 2 or more relevant prescriptions. Monthly prescribing rates of all medications increased towards time of diagnosis in all patient groups and were highest in pre-existing COPD patients. Increases in prescribing activity were observed earliest in pre-existing COPD patients 5 months pre-diagnosis for inhaled medications, antibiotics, and steroids, CONCLUSION: Results indicate that a diagnostic window of appreciable length exists for potential earlier LC diagnosis in some patients. Lung cancer diagnosis may be delayed if early symptoms are misattributed to COPD or other benign conditions.

Multi-cancer early detection test in symptomatic patients referred for cancer investigation in England and Wales (SYMPLIFY): a large-scale, observational cohort study

BACKGROUND

Analysis of circulating tumour DNA could stratify cancer risk in symptomatic patients. We aimed to evaluate the performance of a methylation-based multicancer early detection (MCED) diagnostic test in symptomatic patients referred from primary care.

METHODS

We did a multicentre, prospective, observational study at National Health Service (NHS) hospital sites in England and Wales. Participants aged 18 or older referred with non-specific symptoms or symptoms potentially due to gynaecological, lung, or upper or lower gastrointestinal cancers were included and gave a blood sample when they attended for urgent investigation. Participants were excluded if they had a history of or had received treatment for an invasive or haematological malignancy diagnosed within the preceding 3 years, were taking cytotoxic or demethylating agents that might interfere with the test, or had participated in another study of a GRAIL MCED test. Patients were followed until diagnostic resolution or up to 9 months. Cell-free DNA was isolated and the MCED test performed blinded to the clinical outcome. MCED predictions were compared with the diagnosis obtained by standard care to establish the primary outcomes of overall positive and negative predictive value, sensitivity, and specificity. Outcomes were assessed in participants with a valid MCED test result and diagnostic resolution. SYMPLIFY is registered with ISRCTN (ISRCTN10226380) and has completed follow-up at all sites.

FINDINGS

6238 participants were recruited between July 7 and Nov 30, 2021, across 44 hospital sites. 387 were excluded due to staff being unable to draw blood, sample errors, participant withdrawal, or identification of ineligibility after enrolment. Of 5851 clinically evaluable participants, 376 had no MCED test result and 14 had no information as to final diagnosis, resulting in 5461 included in the final cohort for analysis with an evaluable MCED test result and diagnostic outcome (368 [6·7%] with a cancer diagnosis and 5093 [93·3%] without a cancer diagnosis). The median age of participants was 61·9 years (IQR 53·4-73·0), 3609 (66·1%) were female and 1852 (33·9%) were male. The MCED test detected a cancer signal in 323 cases, in whom 244 cancer was diagnosed, yielding a positive predictive value of 75·5% (95% CI 70·5-80·1), negative predictive value of 97·6% (97·1-98·0), sensitivity of 66·3% (61·2-71·1), and specificity of 98·4% (98·1-98·8). Sensitivity increased with increasing age and cancer stage, from 24·2% (95% CI 16·0-34·1) in stage I to 95·3% (88·5-98·7) in stage IV. For cases in which a cancer signal was detected among patients with cancer, the MCED test's prediction of the site of origin was accurate in 85·2% (95% CI 79·8-89·3) of cases. Sensitivity 80·4% (95% CI 66·1-90·6) and negative predictive value 99·1% (98·2-99·6) were highest for patients with symptoms mandating investigation for upper gastrointestinal cancer.

INTERPRETATION

This first large-scale prospective evaluation of an MCED diagnostic test in a symptomatic population demonstrates the feasibility of using an MCED test to assist clinicians with decisions regarding urgency and route of referral from primary care. Our data provide the basis for a prospective, interventional study in patients presenting to primary care with non-specific signs and symptoms.

FUNDING

GRAIL Bio UK.

Underlying cancer risk among patients with fatigue and other vague symptoms: a population-based cohort study in primary care

BACKGROUND

Presenting to primary care with fatigue is associated with slightly increased cancer risk, although it is unknown how this varies in the presence of other 'vague' symptoms.

AIM

To quantify cancer risk in patients with fatigue who present with other 'vague' symptoms in the absence of 'alarm' symptoms for cancer.

DESIGN AND SETTING

Cohort study of patients presenting in UK primary care with new-onset fatigue during 2007-2015, using Clinical Practice Research Datalink data linked to national cancer registration data.

METHOD

Patients presenting with fatigue without co-occurring alarm symptoms or anaemia were identified, who were further characterised as having co-occurrence of 19 other 'vague' potential cancer symptoms. Sex- and age-specific 9-month cancer risk for each fatigue-vague symptom cohort were calculated.

RESULTS

Of 285 382 patients presenting with new-onset fatigue, 84% (n = 239 846) did not have co-occurring alarm symptoms or anaemia. Of these, 38% (n = 90 828) presented with ≥1 of 19 vague symptoms for cancer. Cancer risk exceeded 3% in older males with fatigue combined with any of the vague symptoms studied. The age at which risk exceeded 3% was 59 years for fatigue-weight loss, 65 years for fatigue-abdominal pain, 67 years for fatigue-constipation, and 67 years for fatigue-other upper gastrointestinal symptoms. For females, risk exceeded 3% only in older patients with fatigue-weight loss (from 65 years), fatigue-abdominal pain (from 79 years), or fatigue-abdominal bloating (from 80 years).

CONCLUSION

In the absence of alarm symptoms or anaemia, fatigue combined with specific vague presenting symptoms, alongside patient age and sex, can guide clinical decisions about referral for suspected cancer.

Development and external validation of a machine learning-based prediction model for the cancer-related fatigue diagnostic screening in adult cancer patients: a cross-sectional study in China

PURPOSE

Cancer-related fatigue (CRF) is the most common symptom in cancer patients and may interfere with patients' daily activities and decrease survival rate. However, the etiology of CRF has not been identified. Diagnosing CRF is challenging. Thus, our study aimed to develop a CRF prediction model in cancer patients, using data that healthcare professionals routinely obtained from electronic health records (EHRs) based on the 3P model and externally validate this model in an independent dataset collected from another hospital.

METHODS

Between April 2022 and September 2022, a cross-sectional study was conducted on adult cancer patients at two first-class tertiary hospitals in China. Data that healthcare professionals routinely obtained from electronic health records (EHRs) based on the 3P model were collected. The outcome measure was according to ICD-10 diagnostic criteria for CRF. Data from one hospital (n = 305) were used for model development and internal validation. An independent data set from another hospital (n = 260) was utilized for external validation. logistic regression, random forest (RF), Naive Bayes (NB), and extreme gradient boosting (XGBoost) were constructed and compared. The model performance was evaluated in terms of both discrimination and calibration.

RESULTS

The prevalence of CRF in the two centers was 57.9% and 56.1%, respectively. The Random Forest model achieved the highest AUC of 0.86 among the four types of classifiers in the internal validation. The AUC of RF and NB were above 0.7 in the external validation, suggesting that the models also have an acceptable generalization ability.

CONCLUSIONS

The incidence of CRF remains high and deserves more attention. The fatigue prediction model based on the 3P theory can accurately predict the risk of CRF. Nonlinear algorithms such as Random Forest and Naive Bayes are more suitable for diagnosing and evaluating symptoms.

The Impact of the COVID Pandemic on the Incidence of Presentations with Cancer-Related Symptoms in Primary Care

Simple Summary

The coronavirus pandemic profoundly affected how patients access health care services, as many individuals attempted to minimise risks of infectious contact and reduce burdens on health systems. This study aims to explore the effects of the coronavirus pandemic on patient presentations for cancer-related symptoms in primary care. It utilises routine clinical data for 1.23 million people in the Netherlands, comparing the first year of the pandemic to the two years prior. These data identify a 34% reduction in the incidence of cancer-related symptoms during the first wave (March to June 2020), with overall incidence returning to pre-corona levels after this period. In the first wave, the incidence of many symptoms was substantially reduced: breast lump (−17%), haematuria (−15%), abdominal mass (−21%), tiredness (−45%), lymphadenopathy (−25%), and naevus (−37%). In the second wave (October 2020 to February 2021), the incidence of breast lump and rectal bleeding was increased (both +14%), and tiredness was decreased (−20%), with the majority of other symptoms being similar to pre-COVID levels. These data describe large-scale primary care avoidance that did not increase until the end of the first COVID year for many cancer-related symptoms, suggestive that substantial numbers of patients delayed presenting to primary care.

Abstract

Introduction: In the Netherlands, the onset of the coronavirus pandemic saw shifts in primary health service provision away from physical consultations, cancer-screening programs were temporarily halted, and government messaging focused on remaining at home. In March and April 2020, weekly cancer diagnoses decreased to 73% of their pre-COVID levels, and 39% for skin cancer. This study aims to explore the effect of the COVID pandemic on patient presentations for cancer-related symptoms in primary care in The Netherlands. Methods: Retrospective cohort study using routine clinical primary care data. Monthly incidences of patient presentations for cancer-related symptoms in five clinical databases in The Netherlands were analysed from March 2018 to February 2021. Results: Data demonstrated reductions in the incidence of cancer-related symptom presentations to primary care during the first COVID wave (March-June 2020) of −34% (95% CI: −43 to −23%) for all symptoms combined. In the second wave (October 2020–February 2021) there was no change in incidence observed (−8%, 95% CI −20% to 6%). Alarm-symptoms demonstrated decreases in incidence in the first wave with subsequent incidences that continued to rise in the second wave, such as: first wave: breast lump −17% (95% CI: −27 to −6%) and haematuria −15% (95% CI −24% to −6%); and second wave: rectal bleeding +14% (95% CI: 0 to 30%) and breast lump +14% (95% CI: 2 to 27%). Presentations of common non-alarm symptom such as tiredness and naevus demonstrated decreased in-cidences in the first wave of 45% (95% CI: −55% to −33%) and 37% (95% CI −47% to −25%). In the second wave, tiredness incidence was reduced by 20% (95% CI: −33% to −3%). Subgroup analy-sis did not demonstrate difference in incidence according to sex, age groups, comorbidity status, or previous history of cancer. Conclusions: These data describe large-scale primary care avoidance that did not increase until the end of the first COVID year for many cancer-related symptoms, suggestive that substantial numbers of patients delayed presenting to primary care. For those patients who had underlying cancer, this may have had impacted the cancer stage at diagnosis, treatment, and mortality.