Myeloma: Patient accounts of their pathways to diagnosis

Prevalence and Consequences of a Delayed Diagnosis in Multiple Myeloma: A Single Institution Experience

BACKGROUND

Multiple myeloma (MM) is a disease with unspecific initial symptoms which may lead into a delay in the diagnosis, seemingly increasing the risk of complications and in turn reducing the overall survival (OS).

OBJECTIVE

To analyze the consequences of a delayed diagnosis of MM in both the OS and the progression-free survival (PFS) of the patients in a single center in México.

METHODS

The study included patients with MM who were diagnosed at Clínica Ruiz, Puebla, México, between 1983 and 2022. According to the time elapsed between the onset of symptoms to the establishment of the definite diagnosis of MM, 4 groups were constructed: 1) Less than 3 months, 2) 3-6 months, 3) 6-12 months, and 4) More than 12 months.

RESULTS

About 136 patients had a complete clinical record and at least a 3-month follow up period. A delay in the diagnosis of MM (more than 3 months from the onset of symptoms) was recorded in 92/136 persons (68%). The median follow-up for the whole group was 24.7 months, median OS was 131.4 months, whereas median PFS was 85.4 months. There was a significant trend for being in earlier stages of the disease and being diagnosed within 3 months from the onset of symptoms (P = .049). Both OS and PFS were similar in the patients diagnosed before or after 3 months from the symptoms onset (P = .772). The 6-12 months group was the group with the better median both OS (197.4 months) and DFS (197.4) from the diagnosis. The median OS for the other groups were similar among them.

CONCLUSION

A delay in the diagnosis of MM is very frequent in México (68% of cases); despite the fact that there was a significant trend for being in earlier stages of the disease and being diagnosed within 3 months from the onset of symptoms, we did not find a relationship between a delay on the diagnosis of the disease and a higher risk of complications and/or poor prognosis. Possible explanations to these findings are discussed.

Determining diagnostic delays in Romanian multiple myeloma patients using the Aarhus statement

Introduction

Multiple Myeloma (MM) is classified as one of the most challenging cancers to diagnose, and the hematological malignancy is associated with prolonged diagnostic delays. Although major steps have been made in the improvement of MM patient diagnosis and care, Romanian patients still face long diagnostic delays. Thus far, there have been no studies evaluating the factors associated with diagnostic errors in Romanian MM patients.

Methods

Using the Aarhus statement, we prospectively determined the diagnostic intervals for 103 patients diagnosed with MM at Fundeni Clinical Institute, between January 2022 and March 2023.

Results

Our data revealed that the main diagnostic delays are experienced during the "patient interval." Patients spend a median of 162 days from the first symptom onset until the first doctor appointment. Bone pain is the most frequently reported symptom by patients (78.64%), but it leads to a medical-seeking behavior in only half of the reporting patients and results in a median delay of 191 days. The changes in routine lab tests are considered most worrisome for patients, leading to a medical appointment after a median of only 25 days. The median primary care interval was 70 days, with patients having an average of 3.7 medical visits until MM suspicion was first raised. The secondary care interval did not contribute to the diagnostic delays.

Discussion

Overall, the median diagnostic path for MM patients in Romania was more than 6 months, leading to a higher number of emergency presentations and myeloma-related end-organ damage.

Symptomatic presentation of cancer in primary care: a scoping review of patients' experiences and needs during the cancer diagnostic pathway

OBJECTIVES

The objective was to map the experiences and needs of patients presenting with symptoms of suspected cancer in the primary care interval (from when they first present to primary care to their first appointment or referral to a secondary or tertiary level healthcare facility).

DESIGN

This was a scoping review.

INCLUSION CRITERIA

Studies or reports written in English which included primary data on the primary care interval experiences and/or needs of adult patients presenting with new symptoms of suspected cancer were eligible. Studies which only included patients with secondary or recurring cancer, conference abstracts and reviews were excluded. No date limits were applied.

METHODS

The Joanna Briggs Institute method for Scoping Reviews guided screening, report selection and data extraction. At least two independent reviewers contributed to each stage. Medline, CINAHL, PsychInfo, Embase and Web of Science were searched and several grey literature resources. Relevant quantitative findings were qualitised and integrated with qualitative findings. A thematic analysis was carried out.

RESULTS

Of the 4855 records identified in the database search, 18 were included in the review, along with 13 identified from other sources. The 31 included studies were published between 2002 and 2023 and most (n=17) were conducted in the UK. Twenty subthemes across four themes (patient experience, interpersonal, healthcare professional (HCP) skills, organisational) were identified. No studies included patient-reported outcome measures. Patients wanted (1) to feel heard and understood by HCPs, (2) a plan to establish what was causing their symptoms, and (3) information about the next stages of the diagnostic process.

CONCLUSIONS

Scoping review findings can contribute to service planning as the cancer diagnostic pathway for symptomatic presentation of cancer evolves. The effectiveness of this pathway should be evaluated not only in terms of clinical outcomes, but also patient-reported outcomes and experience, along with the perspectives of primary care HCPs.

Patient-reported symptoms and diagnostic journey in Multiple Myeloma

Introduction

Late presentation of multiple myeloma (MM) heightens the risk of complication risks, including end-organ damage. This study aimed to: 1) detail the diagnostic journey of MM patients, encompassing symptoms, initial diagnoses, and healthcare professionals met; 2) establish the median duration from symptom onset to MM diagnosis; and 3) examine factors linked to timely MM diagnosis within 12 weeks.

Methods

A total of 300 adults self-reporting MM were analysed from the Rare and Undiagnosed Diseases cohort Study (RUDY). The RUDY study is a web-based platform, where participants provide dynamic consent and self-report their MM diagnosis and information about their diagnostic journey. This includes the estimated date of initial potential first symptoms, descriptions of these symptoms, the healthcare professionals they consulted, and other diagnoses received before the MM diagnosis. Descriptive statistics, combinatorial analyses and logistic regression analyses were used to describe and examine the diagnostic journey of individuals with MM.

Results

Overall, 52% of the participants reported other diagnoses before MM diagnosis, with musculoskeletal disorders (47.8%), such as osteoporosis, costochondritis, or muscle strains, being the most common. The most prevalent initial reported symptom was back pain/vertebral fractures (47%), followed by chest/shoulder pain, including rib pain and fractures (20%), and fatigue/tiredness (19.7%). 40% of participants were diagnosed by direct referral from primary care to haematology without seeing other healthcare professionals whilst 60% consulted additional specialists before diagnosis. The median time from symptom onset to MM diagnosis was 4 months (IQR 2-10 months, range 0-172). Seeing an Allied Healthcare Professional such as a physiotherapist, chiropractor or an osteopath (OR = 0.25, 95% CI [0.12, 0.47], p <0.001), experiencing infection symptoms (OR = 0.32, 95% CI [0.13, 0.76], p = 0.013), and having chest or shoulder pain (OR = 0.45, 95% CI [0.23, 0.86], p = 0.020) were associated with a lower likelihood of being diagnosed with MM within 12 weeks. Older age (OR = 1.04, 95% CI [1.02, 1.07], p = 0.001) was associated with a higher likelihood of diagnosis within 12 weeks.

Discussion

Developing resources for allied health professionals may improve early recognition of MM.

Presenting Symptoms in Newly Diagnosed Myeloma, Relation to Organ Damage, and Implications for Symptom-Directed Screening: A Secondary Analysis from the Tackling Early Morbidity and Mortality in Myeloma (TEAMM) Trial

Multiple myeloma (MM) patients risk diagnostic delays and irreversible organ damage. In those with newly diagnosed myeloma, we explored the presenting symptoms to identify early signals of MM and their relationships to organ damage. The symptoms were recorded in patients' own words at diagnosis and included diagnostic time intervals. Those seen by a haematologist >6 months prior to MM diagnosis were classified as precursor disease (PD). Most (962/977) patients provided data. Back pain (38%), other pain (31%) and systemic symptoms (28%) predominated. Patients rarely complain of 'bone pain', simply 'pain'. Vertebral fractures are under-recognised as pathological and are the predominant irreversible organ damage (27% of patients), impacting the performance status (PS) and associated with back pain (odds ratio (OR) 6.14 [CI 4.47-8.44]), bone disease (OR 3.71 [CI 1.88-7.32]) and age >65 years (OR 1.58 [CI 1.15-2.17]). Renal failure is less frequent and associated with gastrointestinal symptoms (OR 2.23 [CI1.28-3.91]), age >65 years (OR 2.14 [CI1.28-3.91]) and absence of back pain (OR 0.44 [CI 0.29-0.67]). Patients with known PD (n = 149) had fewer vertebral fractures (p = 0.001), fewer adverse features (p = 0.001), less decline in PS (p = 0.001) and a lower stage (p = 0.04) than 813 with de novo MM. Our data suggest subgroups suitable for trials of 'symptom-directed' screening: those with back pain, unexplained pain, a general decline in health or low-impact vertebral compression fractures.

An Economic Model to Establish the Costs Associated With Routes to Presentation for Patients With Multiple Myeloma in the United Kingdom

OBJECTIVES

Patients with myeloma often face significant diagnostic delay, with up to one-third of UK patients diagnosed after an emergency presentation (EP). Compared with other routes, patients presenting as an emergency have more advanced disease, increased complications, and poorer prognosis.

METHODS

An economic model was developed using a decision-tree framework and lifetime time horizon to estimate costs related to different presentation routes (EP, general practitioner [GP] 2-week wait, GP urgent, GP routine, and consultant to consultant) for UK patients diagnosed as having myeloma. After diagnosis, patients received one of 3 first-line management options (observation, active treatment, or end-of-life care). Inputs were derived from UK health technology assessments and targeted literature reviews, or based on authors' clinical experience where data were unavailable. Active treatment, complication, and end-of-life care costs were included.

RESULTS

The average per-patient cost of treating myeloma (across all routes) was estimated at £146 261. The average per-patient cost associated with EP (£152 677) was the highest; differences were minimal compared with GP 2-week wait (£149 631) and consultant to consultant (£147 237). GP urgent (£140 025) and GP routine (£130 212) were associated with marginally lower costs. Complication (£42 252) and end-of-life care (£11 273) costs were numerically higher for EP than other routes (£25 021-£38 170 and £9772-£10 458, respectively).

CONCLUSIONS

An economic benefit may be associated with earlier diagnosis, gained via reduced complication and end-of-life care costs. Strategies to expedite myeloma diagnosis and minimize EPs have the potential to improve patient outcomes and may result in long-term savings that could offset any upfront costs associated with their implementation.

What causes delays in diagnosing blood cancers? A rapid review of the evidence

OBJECTIVE

We undertook a rapid review of literature relating to the diagnosis of blood cancers, to find out what factors contribute to delays in diagnosis, including symptom recognition, appraisal and help-seeking behaviours.

METHODS

We used rapid review methodology following Tricco et al. to synthesise current literature from two electronic databases. We searched for studies about symptom appraisal help-seeking for all blood cancers published between 2001 and 2021, written in English.

RESULTS

Fifteen studies were included in the review, of which 10 were published in the United Kingdom. We found a number of factors associated with delays in blood cancer diagnosis. These included patient factors such as gender, age and ethnicity, as well as health system factors such as poor communication and seeing a locum clinician in primary care. A narrative synthesis of the evidence produced four types of symptom interpretation by patients: (1) symptoms compatible with normal state of health, (2) event-linked problems, (3) mild or chronic illness and (4) non-specific unwell state. These four interpretations were linked to different help-seeking behaviours. After seeking help, patients often experienced delays due to healthcare professionals' (HCPs') non-serious interpretation of symptoms, misleading blood tests, discontinuity of care and other barriers in the diagnostic pathway.

CONCLUSION

Blood cancers are difficult to diagnose due to non-specific heterogeneous symptoms, and this is reflected in how those symptoms are interpreted by patients and managed by HCPs. It is important to understand how different interpretations affect delays in help-seeking, and what HCPs can do to support timely follow-up for patients.

Delayed diagnosis resulting in increased disease burden in multiple myeloma: the legacy of the COVID-19 pandemic

The COVID-19 pandemic has had global healthcare impacts, including high mortality from SARS-CoV-2 infection in cancer patients; individuals with multiple myeloma (MM) are especially susceptible to poor outcomes. However, even for MM patients who avoided severe infection, the ramifications of the pandemic have been considerable. The consequences of necessary socio-geographical behavior adaptation, including prolonged shielding and interruptions in delivery of non-pandemic medical services are yet to be fully understood. Using a real-world dataset of 323 consecutive newly diagnosed MM patients in England, we investigated the impact of the COVID-19 pandemic on routes to myeloma diagnosis, disease stage at presentation and relevant clinical outcomes. We demonstrate increasing MM presentations via emergency services and increased rates of bony and extra-medullary disease. Differences were seen in choice of induction therapy and the proportion of eligible patients undertaking autologous stem cell transplantation. Whilst survival was statistically inferior for emergency presentations, significant survival differences have yet to be demonstrated for the entire cohort diagnosed during the pandemic, making extended follow-up critical in this group. This dataset highlights wide-ranging issues facing MM patients consequent of the COVID-19 pandemic, with full impacts for clinicians and policy-makers yet to be elucidated.

Pathways to ovarian cancer diagnosis: a qualitative study

Background

Ovarian cancer is often diagnosed at a late stage, when survival is poor. Qualitative narratives of patients’ pathways to ovarian cancer diagnoses may identify opportunities for earlier cancer detection and, consequently, earlier stage at diagnosis.

Methods

We conducted semi-structured interviews of ovarian cancer patients and survivors (n = 14) and healthcare providers (n = 11) between 10/2019 and 10/2021. Interviews focused on the time leading up to an ovarian cancer diagnosis. Thematic analysis was conducted by two independent reviewers using a two-phase deductive and inductive coding approach. Deductive coding used a priori time intervals from the validated Model of Pathways to Treatment (MPT), including self-appraisal and management of symptoms, medical help-seeking, diagnosis, and pre-treatment. Inductive coding identified common themes within each stage of the MPT across patient and provider interviews.

Results

The median age at ovarian cancer diagnosis was 61.5 years (range, 29–78 years), and the majority of participants (11/14) were diagnosed with advanced-stage disease. The median time from first symptom to initiation of treatment was 2.8 months (range, 19 days to 4.7 years). The appraisal and help-seeking intervals contributed the greatest delays in time-to-diagnosis for ovarian cancer. Nonspecific symptoms, perceptions of health and aging, avoidant coping strategies, symptom embarrassment, and concerns about potential judgment from providers prolonged the appraisal and help-seeking intervals. Patients and providers also emphasized access to care, including financial access, as critical to a timely diagnosis.

Conclusion

Interventions are urgently needed to reduce ovarian cancer morbidity and mortality. Population-level screening remains unlikely to improve ovarian cancer survival, but findings from our study suggest that developing interventions to improve self-appraisal of symptoms and reduce barriers to help-seeking could reduce time-to-diagnosis for ovarian cancer. Affordability of care and insurance may be particularly important for ovarian cancer patients diagnosed in the United States.

Optimising the management of patients with multiple myeloma in Spain: A measurement of the social return on investment

OBJECTIVE

The aim of this study was to reach a consensus on a set of proposals to optimise the disease management of Multiple myeloma (MM) within the Spanish National Health System (SNHS) and to apply the Social Return on Investment (SROI) method to estimate their social impact.

METHODS

A Multidisciplinary Working Team (MWT) including MM main stakeholders was organised. A survey was administered to gather information from patients regarding the impact of MM on different life domains. A forecast-type SROI analysis, with a 1-year timeframe, was applied.

RESULTS

Fifteen proposals were selected, to optimise MM management, including actions for early diagnosis, psychological support, improvement of information for patients and quick access to palliative care, among others. The implementation of these proposals would benefit patients, their informal caregivers and the SNHS. The investment required would amount to 10.32 million euros with a social return of 43.31 million euros: 4.2 euros for each euro invested. According to the sensitivity analysis, this ratio could range from 3.38 to 5.20 euros from the worst to the best-case scenario.

CONCLUSIONS

The current management of MM could be optimised by implementing a set of proposals that would most likely result in an overall positive social return.

Factors influencing symptom appraisal and help-seeking of older adults with possible cancer: a mixed-methods systematic review

BACKGROUND

The cancer burden falls predominantly on older (≥65 years) adults. Prompt presentation to primary care with cancer symptoms could result in earlier diagnosis. However, patient symptom appraisal and help-seeking decisions involving cancer symptoms are complex and may be further complicated in older adults.

AIM

To explore the effect of older age on patients' appraisal of possible cancer symptoms and their decision to seek help for these symptoms.

DESIGN AND SETTING

Mixed-methods systematic review.

METHOD

MEDLINE, EMBASE, CINAHL, PsycINFO, Cochrane Library, Web of Science Core Collection, ASSIA, the ISRCTN registry, and the National Institute for Health and Care Excellence were searched for studies on symptom appraisal and help-seeking decisions for cancer symptoms by adults aged ≥65 years. Studies were analysed using thematic synthesis and according to the Synthesis Without Meta-Analysis guidelines.

RESULTS

Eighty studies were included with a total of 32 995 participants. Studies suggested a possible association between increasing age and prolonged symptom appraisal interval. Reduced knowledge of cancer symptoms and differences in symptom interpretation may contribute to this prolonged interval. In contrast, in the current study a possible association was found between increasing age and prompt help-seeking. Themes affecting help-seeking in older adults included the influence of family and carers, competing priorities, fear, embarrassment, fatalism, comorbidities, a desire to avoid doctors, a perceived need to not waste doctors' time, and patient self-management of symptoms.

CONCLUSION

This review suggests that increasing age is associated with delayed cancer symptom appraisal. When symptoms are recognised as potentially serious, increasing age was associated with prompt help-seeking although other factors could prolong this. Policymakers, charities, and GPs should aim to ensure older adults are able to recognise potential symptoms of cancer and seek help promptly.

Incurable but treatable: Understanding, uncertainty and impact in chronic blood cancers-A qualitative study from the UK's Haematological Malignancy Research Network

Objective

Most blood cancers are incurable and typically follow unpredictable remitting-relapsing pathways associated with varying need for treatment, which may be distressing for patients. Our objective was to conduct a qualitative study to explore understanding among patients with such malignancies, including the explanations given by HCPs and the impact of uncertain trajectories, to generate evidence that could guide improvements in clinical practice.

Methods

The study is set within a population-based patient cohort (the Haematological Malignancy Research Network), in which care is delivered across 14 hospitals according to national guidelines. In-depth interviews were conducted with 35 patients with chronic lymphocytic leukaemia, follicular lymphoma, marginal zone lymphoma or myeloma; and 10 accompanying relatives. Purposive sampling ensured selection of information-rich participants and the data were interrogated using reflective thematic analysis.

Results

Rich data were collected and four themes (11 sub-themes) were identified: 1) Knowledge and understanding of chronic haematological malignancies; 2) Incurable but treatable; 3) Uncertainty about the future; and 4) Treatable (but still incurable): Impact on patients. Patients had rarely heard of blood cancer and many expressed difficulty understanding how an incurable malignancy that could not be removed, was treatable, often for long periods. While some were reassured that their cancer did not pose an immediate survival threat, others were particularly traumatised by the uncertain future it entailed, suffering ongoing emotional distress as a result, which could be more burdensome than any physical symptoms. Nonetheless, most interviewees understood that uncertain pathways were caused by the unpredictability of their disease trajectory, and not information being withheld.

Conclusions

Many participants lacked knowledge about chronic haematological malignancies. HCPs acted to reassure patients about their diagnosis, and while this was appropriate and effective for some, it was less so for others, as the cancer-impact involved struggling to cope with ongoing uncertainty, distress and a shortened life-span.

Routine blood biomarkers for the detection of multiple myeloma using machine learning

INTRODUCTION

Primary laboratory tests performed in the diagnosis of multiple myeloma (MM) include bone marrow examination and free light chain assay; however, these may only be ordered after clinical suspicion of disease. In contrast, routine blood test results are readily available.

METHODS

Machine learning algorithms (ML) combined with routine blood tests were used to detect MM. Feature selection was performed to achieve improved classification performance. The robustness of the classification models was assessed in an internal and external validation data set. To minimize the divergence, the training and validation data sets were combined and used to assess the performance of the ML algorithms.

RESULTS

The AdaBoost-DecisionTable produced the best performance (accuracy =94.75%, sensitivity =87.70%, positive predictive value (PPV) =92.50%, F-measure =90.00%, and areas under the receiver operating characteristic curves (AUC) =97.50%) in the training data set using a 10-fold cross-validation. Performance in the validation data sets was affected by the divergence of the data sets, with accuracy greater than 85% and AUC greater than 90% in the validation data sets. The ML algorithm achieved a high accuracy of 92.61%, high AUC (96.80%), a sensitivity value of 85.20%, a PPV value of 88.50%, and an F-measure of 86.80% in a test set that was randomly selected from the combined data set.

CONCLUSIONS

Combining ML and routine serum biomarkers hold a potential benefit in MM diagnosis.

What Can Patient Narratives Reveal to Us About the Experience of a Diagnosis of Myeloma? A Qualitative Scoping Review

In recent years, there have been major advances in treatment options for myeloma and an improved prognosis as a result. There is a wealth of literature that explores the experience of specific treatments from a clinical perspective but there is comparatively little known about the reality of living with myeloma. This research aims to explore the experience of a myeloma diagnosis, to map out key patient experience literature, and examine common themes to support both medical practice and the planning of further research. Following a scoping review methodology, PubMed, StarPlus (the University of Sheffield online database), and Scopus were searched and 15 studies identified for analysis and qualitative synthesis. The literature indicated that myeloma was increasingly being experienced as a chronic condition rather than an acute diagnosis and that there are challenges meeting the needs of patients, understanding the overall symptom burden and the role of the family. The paper identifies emotional and psychological adjustment and coping as a potential area requiring further exploration in the context of a whole team approach to care.

Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection

Simple Summary

Multiple myeloma is a malignant neoplasm of plasma cells with complex pathogenesis. With major progresses in multiple myeloma research, it is essential that we reconsider our methods for diagnosing and monitoring multiple myeloma disease. This fact needs the integration of serology, histology, radiology, and genetic data; therefore, multiple myeloma study has generated massive quantities of granular high-dimensional data exceeding human understanding. With improved computational techniques, artificial intelligence tools for data processing and analysis are becoming more and more relevant. Artificial intelligence represents a wide set of algorithms for which machine learning and deep learning are presently among the most impactful. This review focuses on artificial intelligence applications in multiple myeloma research, first illustrating machine learning and deep learning procedures and workflow, followed by how these algorithms are used for multiple myeloma diagnosis, prognosis, bone lesions identification, and evaluation of response to the treatment.

Abstract

Artificial intelligence has recently modified the panorama of oncology investigation thanks to the use of machine learning algorithms and deep learning strategies. Machine learning is a branch of artificial intelligence that involves algorithms that analyse information, learn from that information, and then employ their discoveries to make abreast choice, while deep learning is a field of machine learning basically represented by algorithms inspired by the organization and function of the brain, named artificial neural networks. In this review, we examine the possibility of the artificial intelligence applications in multiple myeloma evaluation, and we report the most significant experimentations with respect to the machine and deep learning procedures in the relevant field. Multiple myeloma is one of the most common haematological malignancies in the world, and among them, it is one of the most difficult ones to cure due to the high occurrence of relapse and chemoresistance. Machine learning- and deep learning-based studies are expected to be among the future strategies to challenge this negative-prognosis tumour via the detection of new markers for their prompt discovery and therapy selection and by a better evaluation of its relapse and survival.

Factors affecting the decision to investigate older adults with potential cancer symptoms: a systematic review

BACKGROUND

Older age and frailty increase the risk of morbidity and mortality from cancer surgery and intolerance of chemotherapy and radiotherapy. The effect of old age on diagnostic intervals is unknown; however, older adults need a balanced approach to the diagnosis and management of cancer symptoms, considering the benefits of early diagnosis, patient preferences, and the likely prognosis of a cancer.

AIM

To examine the association between older age and diagnostic processes for cancer, and the specific factors that affect diagnosis.

DESIGN AND SETTING

A systematic literature review.

METHOD

Electronic databases were searched for studies of patients aged >65 years presenting with cancer symptoms to primary care considering diagnostic decisions. Studies were analysed using thematic synthesis and according to the Synthesis Without Meta-analysis guidelines.

RESULTS

Data from 54 studies with 230 729 participants were included. The majority of studies suggested an association between increasing age and prolonged diagnostic interval or deferral of a decision to investigate cancer symptoms. Thematic synthesis highlighted three important factors that resulted in uncertainty in decisions involving older adults: presence of frailty, comorbidities, and cognitive impairment. Data suggested patients wished to be involved in decision making, but the presence of cognitive impairment and the need for additional time within a consultation were significant barriers.

CONCLUSION

This systematic review has highlighted uncertainty in the management of older adults with cancer symptoms. Patients and their family wished to be involved in these decisions. Given the uncertainty regarding optimum management of this group of patients, a shared decision-making approach is important.

Clinical features and diagnosis of multiple myeloma: a population-based cohort study in primary care

OBJECTIVES

Patients with multiple myeloma (MM) experience significant delays in diagnosis due to non-specific symptomatology. The aim of this study was to characterise the frequency and timing of clinical features in the primary care setting prior to MM diagnosis.

DESIGN

Population-based cohort study.

SETTING

Electronic health records data of approximately 17 million patients (2006-2016) within the UK Clinical Practice Research Datalink.

PARTICIPANTS

Patients aged ≥18 years with newly diagnosed MM (NDMM), no history of solid tumours and ≥2 years registration in a primary care practice prior to MM diagnosis.

MAIN OUTCOME MEASURES

Clinical features and symptoms including bone pain, skeletal-related events (SREs), investigation and confirmation of MM diagnostic CRAB criteria (hyperCalcaemia, Renal impairment, Anaemia, Bone lesions) during the 2 years prior to MM diagnosis; time between symptom manifestation and/or relevant investigation and diagnosis of MM.

RESULTS

Among 2646 patients with NDMM, 47.5% had a bone pain record during the 2-year period prior to MM diagnosis, mainly affecting the back. Regardless of baseline bone pain, investigations for serum calcium level were used in 36.4% of patients prior to MM diagnosis, followed by haemoglobin (65.6%) or renal function (74.1%). Median (Q1, Q3) time from first-recorded bone pain to MM diagnosis was 220 (80, 476) days. Median (Q1, Q3) time from first-recorded hypercalcaemia, renal impairment or anaemia to MM diagnosis was 23 (12, 46), 58 (17, 254) and 73 days (28, 232), respectively. An imaging investigation or referral for imaging was recorded for 60.0% of patients with bone pain/SRE and 32% without.

CONCLUSIONS

Nearly half of patients diagnosed with NDMM presented with bone pain approximately 7 months prior to MM diagnosis. Investigations to evaluate all CRAB criteria, including targeted imaging, were underused. Early recognition of myeloma clinical features and optimised use of investigations in primary care may potentially expedite MM diagnosis.

Employment of Artificial Intelligence Based on Routine Laboratory Results for the Early Diagnosis of Multiple Myeloma

Objective

In order to enhance the detection rate of multiple myeloma and execute an early and more precise disease management, an artificial intelligence assistant diagnosis system is developed.

Methods

4,187 routine blood and biochemical examination records were collected from Shengjing Hospital affiliated to China Medical University from January 2010 to January 2020, which include 1,741 records of multiple myeloma (MM) and 2,446 records of non-myeloma (infectious diseases, rheumatic immune system diseases, hepatic diseases and renal diseases). The data set was split into training and test subsets with the ratio of 4:1 while connecting hemoglobin, serum creatinine, serum calcium, immunoglobulin (A, G and M), albumin, total protein, and the ratio of albumin to globulin data. An early assistant diagnostic model of MM was established by Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), Deep Neural Networks (DNN), and Random Forest (RF). Out team calculated the precision and recall of the system. The performance of the diagnostic model was evaluated by using the receiver operating characteristic (ROC) curve.

Results

By designing the features properly, the typical machine learning algorithms SVM, DNN, RF and GBDT all performed well. GBDT had the highest precision (92.9%), recall (90.0%) and F1 score (0.915) for the myeloma group. The maximized area under the ROC (AUROC) was calculated, and the results of GBDT (AUC: 0.975; 95% confidence interval (CI): 0.963–0.986) outperformed that of SVM, DNN and RF.

Conclusion

The model established by artificial intelligence derived from routine laboratory results can accurately diagnose MM, which can boost the rate of early diagnosis.

Adolescents and young adults' (AYA) views on their cancer knowledge prior to diagnosis: Findings from a qualitative study involving AYA receiving cancer care

Background

Cancer is rare amongst adolescents and young adults (AYA). Previous research has reported (healthy) AYA’s knowledge of risk factors and symptoms as limited, with this potentially leading to delays in help‐seeking and diagnosis.

Objectives

We explored AYA’s views on their cancer knowledge prior to diagnosis and if/how they perceived this as having affected their experiences of diagnosis and care.

Methods

We interviewed 18 AYA diagnosed with cancer (aged 16‐24 years). Interviews were recorded and transcribed verbatim. We undertook qualitative descriptive analysis, exploring both a priori topics and emergent themes, including cancer knowledge prior to diagnosis.

Results

Adolescents and young adults characterized their knowledge of cancer and treatment prior to diagnosis and treatment initiation as limited and superficial. AYA perceived gaps in their knowledge as having profound consequences throughout their cancer journey. These included: hindering recognition of symptoms, thereby delaying help‐seeking; impeding understanding of the significance of tests and referrals; amplifying uncertainty on diagnosis; and affording poor preparation for the harsh realities of treatment.

Conclusions

Adolescents and young adults perceived their limited cancer knowledge prior to diagnosis as affecting experiences of diagnosis and initial/front‐line care. These findings prompt consideration of whether, when and how, AYA’s knowledge of cancer might be improved. Two broad approaches are discussed: universal education on AYA cancer and/or health; and targeted education (enhanced information and counselling) at and after diagnosis.

Patient or Public Contribution

Our work was informed throughout by discussions with an advisory group, whose membership included AYA treated for cancer.

International Framework for Red Flags for Potential Serious Spinal Pathologies

The International Federation of Orthopaedic Manipulative Physical Therapists (IFOMPT) led the development of a framework to help clinicians assess and manage people who may have serious spinal pathology. While rare, serious spinal pathology can have devastating and life-changing or life-limiting consequences, and must be identified early and managed appropriately. Red flags (signs and symptoms that might raise suspicion of serious spinal pathology) have historically been used by clinicians to identify serious spinal pathology. Currently, there is an absence of high-quality evidence for the diagnostic accuracy of most red flags. This framework is intended to provide a clinical-reasoning pathway to clarify the role of red flags. J Orthop Sports Phys Ther 2020;50(7):350-372. Epub 21 May 2020. doi:10.2519/jospt.2020.9971.

'Unpacking' pathways to lymphoma and myeloma diagnosis: Do experiences align with the Model of Pathways to Treatment? Findings from a UK qualitative study with patients and relatives

OBJECTIVES

To explore alignment of experiences before lymphoma and myeloma diagnosis with the appraisal, help seeking and diagnostic intervals in the Model of Pathways to Treatment (MPT).

DESIGN

A qualitative study using in-depth semistructured interviews with patients and relatives. Interviews were transcribed verbatim, anonymised and analysed using qualitative description.

SETTING

A UK population-based haematological malignancy patient cohort.

PARTICIPANTS

Fifty-five patients (35 lymphoma, 20 myeloma: diagnosed 2014-2016) and 28 relatives participated, within around a year of the patient's diagnosis. Patients were selected from those in the cohort who had returned a questionnaire about their symptoms and help seeking, and consented to contact for further research. Sampling was purposive, to achieve maximum variation in age, sex and time to diagnosis.

RESULTS

Participants described time from symptom onset to diagnosis as ranging from several weeks to years. Pathways largely aligned with MPT components and help seeking could lead to the rapid investigations and identification of abnormalities. However, symptoms could be vague and/or inadvertently interpreted as other conditions, which if perpetuated, could cause diagnostic delay. The latter was associated with chaotic pathways, with activities rarely occurring only once or in a linear sequence. Rather, intermittent or ongoing processes were described, moving forward and backwards through intervals. This is 'unpacked' within five themes: (1) appraisal and reappraisal; (2) patient-initiated self-management/treatment; (3) initial help seeking; (4) re-presentation; and (5) patient-initiated actions, decisions and emotions during re-presentation. Within these themes, various healthcare professionals were consulted, often many times, as symptoms persisted/progressed. Input from family/friends was described as substantial, as was the extent to which information seeking occurred.

CONCLUSION

Lymphoma and myeloma pathways align with the MPT, but do not fully capture the repetition and complexity described by participants. Time to diagnosis was often prolonged, despite the best efforts of patients, relatives and healthcare professionals. The impact of National Health Service England's Multi-diagnostic Disciplinary Centres on time to haematological cancer diagnosis remains to be seen.

Hodgkin lymphoma detection and survival: findings from the Haematological Malignancy Research Network

BACKGROUND

Hodgkin lymphoma is usually detected in primary care with early signs and symptoms, and is highly treatable with standardised chemotherapy. However, late presentation is associated with poorer outcomes.

AIM

To investigate the relationship between markers of advanced disease, emergency admission, and survival following a diagnosis of classical Hodgkin lymphoma (CHL).

DESIGN & SETTING

The study was set within a sociodemographically representative UK population-based patient cohort of ~4 million, within which all patients were tracked through their care pathways, and linked to national data obtained from Hospital Episode Statistics (HES) and deaths.

METHOD

All 971 patients with CHL newly diagnosed between 1 September 2004-31 August 2015 were followed until 18th December 2018.

RESULTS

The median diagnostic age was 41.5 years (range 0-96 years), 55.2% of the patients were male, 31.2% had stage IV disease, 43.0% had a moderate-high or high risk prognostic score, and 18.7% were admitted via the emergency route prior to diagnosis. The relationship between age and emergency admission was U-shaped: more likely in patients aged <25 years and ≥70 years. Compared to patients admitted via other routes, those presenting as an emergency had more advanced disease and poorer 3-year survival (relative survival 68.4% [95% confidence interval {CI} = 60.3 to 75.2] versus 89.8% [95% CI = 87.0 to 92.0], respectively [P<0.01]). However, after adjusting for clinically important prognostic factors, no difference in survival remained.

CONCLUSION

These findings suggest that CHL survival as a whole could be increased by around 4% if the cancer in patients who presented as an emergency had been detected at the same point as in other patients.

Time from Symptom Onset to Diagnosis and Treatment among Haematological Malignancies: Influencing Factors and Associated Negative Outcomes

Background and objectives: Diagnostic delay causes unfavorable outcomes among cancer patients. It has been widely analyzed in solid tumors. However, data regarding hematological malignancies diagnostic delay are scarce. We aimed to evaluate diagnostic intervals, their influencing factors, and the negative effect on clinical outcomes among multiple myeloma and lymphoma patients. Materials and methods: One hundred patients diagnosed with multiple myeloma (n = 53) or lymphoma (n = 47) (ICD codes-C90, C81-C84) were asked to participate during their scheduled hematology consultations. Interval durations and the majority of influencing factors were assessed based on a face-to-face questionnaire. Data of disease characteristics were collected from medical records. Results: The median interval from symptom onset to registration for medical consultation was 30 (0-730) days, from registration to consultation 2 (0-30) days, from first consultation to diagnosis 73 (6-1779) days, and from diagnosis to treatment 5 (0-97) days. Overall time to diagnosis median was 151 (23-1800) days. Factors significantly prolonging diagnostic intervals in multivariate linear regression were living in big cities (p = 0.008), anxiety and depression (p = 0.002), self-medication (p = 0.019), and more specialists seen before diagnosis (p = 0.022). Longer diagnostic intervals resulted in higher incidences of multiple myeloma complications (p = 0.024) and more advanced Durie-Salmon stage (p = 0.049), but not ISS stage and Ann-Arbor staging systems for lymphomas. Conclusion: Median overall diagnostic delay was nearly 5 months, indicating that there is room for improvement. The most important factors causing delays were living in big cities, anxiety and depression, self-medication, and more specialists seen before diagnosis. Diagnostic delay may have a negative influence on clinical outcomes for multiple myeloma patients.