Diagnostic interval for non-screening patients undergoing mammography during the COVID-19 pandemic

e23553 Background: Sarcoma patients are hypothesised to experience long intervals to cancer diagnosis, due to the rarity of the disease, heterogeneous presentation, and complexity of histological diagnosis. Comprehensive assessment of patient interval (time between first symptom and presentation to a doctor), diagnostic interval (time between presentation to a doctor and diagnosis ), and their associated factors is needed to improve referral pathways and outcomes for sarcoma patients. We investigated patient and diagnostic intervals and identified factors associated with prolonged intervals in order to identify risk groups. Methods: We conducted a cross-sectional cohort study among adult sarcoma survivors, diagnosed between 2008-2016. After informed consent, patient-reported interval length was collected via (online) questionnaires. This data was linked to the Netherlands Cancer Registry, which collects clinical data from patients at diagnosis. Descriptive statistics were used to describe interval lengths. Logistic regression analyses were conducted to investigate associations of patient and tumour characteristics (gender, age at diagnosis, time since diagnosis, histology, presence of metastases, grade, and localisation), with patient and diagnostic interval length. Results: 1099 survivors completed questionnaires (response rate 58%); 53% had a patient interval ≥1 month. Risk factors for a patient interval ≥1 month were bone or breast sarcoma, and being young(18-39 years). An interval ≥3 months (33%) was associated with dermatofibrosarcoma protuberans, bone sarcoma, low grade, and location in skin or pelvis. Diagnostic interval length was ≥1 month in 53%; risk groups were bone sarcomas, females, and young (18-39) or middle-aged (40-69) patients. In 27% the diagnostic interval lasted ≥3 months; risk groups were synovial sarcoma, chordoma, bone sarcomas in general, females, and being young (18-39) or middle aged (40-69). Conclusions: Over half of sarcoma survivors had a patient interval ≥1 month; a third ≥3 months. More than half of them reported a diagnostic interval ≥1 month; a quarter ≥3 months. Females and young patients were more at risk for a long diagnostic interval. Although limited by its retrospective nature, this study is the largest that studied the interval in a sarcoma survivorship group. Our current QUEST study (NTR-7253) will study the diagnostic trajectory prospectively to confirm these findings and find reasons for these results.

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Duration of the pre-diagnostic interval in medulloblastoma is subgroup dependent

Pediatric Blood & Cancer ◽

10.1002/pbc.25002 ◽

2014 ◽

Vol 61 (7) ◽

pp. 1190-1194 ◽

Cited By ~ 29

Author(s):

Vijay Ramaswamy ◽

Marc Remke ◽

David Shih ◽

Xin Wang ◽

Paul A. Northcott ◽

...

Keyword(s):

Diagnostic Interval

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A longer diagnostic interval is a risk for depression in amyotrophic lateral sclerosis

Palliative & Supportive Care ◽

10.1017/s1478951514000881 ◽

2014 ◽

Vol 13 (4) ◽

pp. 1019-1024 ◽

Cited By ~ 14

Author(s):

Jashelle Caga ◽

Eleanor Ramsey ◽

Anne Hogden ◽

Eneida Mioshi ◽

Matthew C. Kiernan

Keyword(s):

Mental Health ◽

Risk Factors ◽

Amyotrophic Lateral Sclerosis ◽

Depressive Symptoms ◽

Psychiatric History ◽

Related Risk ◽

Health And Disease ◽

Diagnostic Interval ◽

Als Patients ◽

Lateral Sclerosis

AbstractObjective:Recognizing depressive symptoms in patients with amyotrophic lateral sclerosis (ALS) remains problematic given the potential overlap with the normal psychological responses to a terminal illness. Understanding mental health and disease-related risk factors for depression is key to identifying psychological morbidity. The present study aimed to determine the prevalence of depressive symptoms in ALS and to explore mental health and disease-related risk factors for depression.Method:Structured medical and psychiatric history questionnaires and a validated depression scale (Depression, Anxiety, Stress Scale–21) were completed by 27 ALS patients (60% female; 59% limb onset; age 65.11 ± SE 2.21) prior to their initial review at a multidisciplinary clinic. Physical function was assessed with the Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS–R).Results:At the time of initial assessment, 44% of patients had a previous psychiatric history, although the majority (62%) reported no symptoms of depression. The mean ALSFRS–R score was 37.78 ± SE 1.22, with an average diagnostic interval of 16.04 ± SE 2.39 months. Logistic regression analysis revealed that the length of the diagnostic interval alone predicted depressive symptoms (χ2(3, n = 26) = 9.21, Odds Ratio (OR) = 1.12, p < 0.05.Significance of Results:The illness experiences of ALS patients rather than established mental health risk factors influence the manifestation of depressive symptoms in the early stages of the disease, with clinical implications for the assessment and treatment of psychological morbidity. Patients with lengthy diagnostic intervals may be prime targets for psychological assessment and intervention, especially in the absence of ALS-specific tests and biomarkers.

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National Cancer Diagnosis Audit: avoidable delays to diagnosis

British Journal of General Practice ◽

10.3399/bjgp19x703073 ◽

2019 ◽

Vol 69 (suppl 1) ◽

pp. bjgp19X703073

Author(s):

Ruth Swann ◽

Georgios Lyratzopoulos ◽

Greg Rubin ◽

Elizabeth Pickworth ◽

Sean McPhail

Keyword(s):

Cancer Diagnosis ◽

Cancer Type ◽

Logistic Regression Models ◽

Patient Demographics ◽

Cancer Types ◽

General Practices ◽

Diagnostic Interval ◽

Reported Data ◽

The Impact

BackgroundA prolonged time taken to diagnose cancer can lead to poorer survival and reduced quality of life. Characterising avoidable delays to a patient’s diagnosis could help to direct quality improvement initiatives aimed at enhancing patient safety and ultimately patient outcomes.AimTo assess the validity of data on avoidable delays collected as part of the English National Cancer Diagnosis Audit (NCDA) and to estimate the predictors of avoidable delays to diagnosis by patient demographics and cancer type.MethodParticipating general practices (n = 439; 5% practices) submitted primary care data on patients (n = 17 042) diagnosed with cancer in 2014 in England. GPs reported delays to the diagnosis that they considered avoidable. Quantile regression was used to understand the impact of an avoidable delay on the diagnostic interval. Logistic regression models were used to investigate the factors associated with avoidable delay.ResultsThe GP recorded an avoidable delay to cancer diagnosis in 24% of cases (n = 3372). The median diagnostic interval was 57 days longer in patients where an avoidable delay was recorded. Results have shown significant associations between avoidable delay and certain cancer types (odds ratio [OR] 1.73 for stomach versus lung cancer) and an increasing number of comorbidities (OR 1.43 for patients with ≥3 versus 0).ConclusionGP-reported data showed a longer diagnostic interval in patients thought to have had an avoidable delay to their diagnosis, indicating construct validity of the data collected. Data from the NCDA is being used to better understand avoidable delays to diagnosis, and identify possible solutions for improving the diagnostic pathway in some cases.

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Time to diagnosis of symptomatic gastric and oesophageal cancer in the Netherlands: Where is the room for improvement?

United European Gastroenterology Journal ◽

10.1177/2050640620917804 ◽

2020 ◽

Vol 8 (5) ◽

pp. 607-620

Author(s):

NF van Erp ◽

CW Helsper ◽

P Slottje ◽

D Brandenbarg ◽

FL Büchner ◽

...

Keyword(s):

Gastric Cancer ◽

Primary Care ◽

Confidence Interval ◽

Secondary Care ◽

Interval Duration ◽

Diagnostic Pathway ◽

Long Duration ◽

Time To Diagnosis ◽

Diagnostic Interval ◽

Upper Gastrointestinal

Background An efficient diagnostic pathway and early stage diagnosis for cancer patients is widely pursued. This study aims to chart the duration of the diagnostic pathway for patients with symptomatic oesophageal and gastric cancer, to identify factors associated with long duration and to assess the association of duration with tumour stage at diagnosis. Methods This was a retrospective cohort study, using electronic health records of six routine primary care databases covering about 640,000 patients, partly linked to the Netherlands Cancer Registry. Symptomatic patients with oesophageal and gastric cancer (2010–2015) that presented in primary care were included. Duration of four diagnostic intervals was determined: patient interval; first symptoms to primary care consultation, primary care interval; consultation to referral, secondary care interval; referral to diagnosis, and the diagnostic interval; consultation to diagnosis. Characteristics associated with ‘long duration’ (≥P75 duration) were assessed using log-binomial regression. Median durations were stratified for tumour stages. Results Among 312 symptomatic patients with upper gastrointestinal cancer, median durations were: patient interval: 29 days (interquartile interval 15–73), primary care interval: 12 days (interquartile interval 1–43), secondary care interval: 13 days (interquartile interval 6–29) and diagnostic interval: 31 days (11–74). Patient interval duration was comparable for patients with and without alarm symptoms. Absence of cancer-specific alarm symptoms was associated with ‘long duration’ of primary care interval and secondary care interval: relative risk 5.0 (95% confidence interval 2.7–9.1) and 2.1 (95% confidence interval 1.3–3.7), respectively. Median diagnostic interval duration for local stage disease was 51 days (interquartile interval 13–135) versus 27 days (interquartile interval 11–71) for advanced stage ( p = 0.07). Conclusion In the diagnostic pathway of upper gastrointestinal cancers, the longest interval is the patient interval. Reducing time to diagnosis may be achieved by improving patients’ awareness of alarm symptoms and by diagnostic strategies which better identify cancer patients despite low suspicion.

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Determining the Cancer Diagnostic Interval Using Administrative Health Care Data in a Breast Cancer Cohort

JCO Clinical Cancer Informatics ◽

10.1200/cci.18.00131 ◽

2019 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Patti A. Groome ◽

Colleen Webber ◽

Marlo Whitehead ◽

Rahim Moineddin ◽

Eva Grunfeld ◽

...

Keyword(s):

Breast Cancer ◽

Health Care ◽

Control Charts ◽

Population Level ◽

Population Based ◽

Statistical Control ◽

Health Care Data ◽

Cancer Diagnostic ◽

Breast Cancer Cohort ◽

Diagnostic Interval

PURPOSE Population-based administrative health care data could be a valuable resource with which to study the cancer diagnostic interval. The objective of the current study was to determine the first encounter in the diagnostic interval and compute that interval in a cohort of patients with breast cancer using an empirical approach. METHODS This is a retrospective cohort study of patients with breast cancer diagnosed in Ontario, Canada, between 2007 and 2015. We used cancer registry, physician claims, hospital discharge, and emergency department visit data to identify and categorize cancer-related encounters that were more common in the three months before diagnosis. We used statistical control charts to define lookback periods for each encounter category. We identified the earliest cancer-related encounter that marked the start of the diagnostic interval. The end of the interval was the cancer diagnosis date. RESULTS The final cohort included 69,717 patients with breast cancer. We identified an initial encounter in 97.8% of patients. Median diagnostic interval was 36 days (interquartile range [IQR], 19 to 71 days). Median interval decreased with increasing stage at diagnosis and varied across initial encounter categories, from 9 days (IQR, 1 to 35 days) for encounters with other cancer as the diagnosis to 231 days (IQR 77 to 311 days) for encounters with cyst aspiration or drainage as the procedure. CONCLUSION Diagnostic interval research can inform early detection guidelines and assess the success of diagnostic assessment programs. Use of administrative data for this purpose is a powerful tool for improving diagnostic processes at the population level.

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Determining the cancer diagnostic interval using administrative data in a cohort of patients with pancreatic ductal adenocarcinoma (PDAC).

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e13551 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e13551-e13551

Author(s):

Bailey Paterson ◽

Shiying Kong ◽

Alyson Mahar ◽

Colleen Webber ◽

Richard M. Lee-Ying ◽

...

Keyword(s):

Administrative Data ◽

Control Charts ◽

Population Based ◽

Administrative Databases ◽

Interval Length ◽

Ductal Adenocarcinoma ◽

Statistical Control ◽

Wide Range ◽

Age Of Diagnosis ◽

Diagnostic Interval

e13551 Background: PDAC is a leading cause of cancer death that is often diagnosed at an advanced stage. Population-based administrative data can be a valuable resource for studying the diagnostic interval, defined as the time from the first related healthcare encounter to cancer diagnosis. The objective of this study was to determine the diagnostic interval in a cohort of patients with PDAC using an empirical approach. Methods: This is a retrospective, cohort study of patients diagnosed with PDAC from 2007 – 2015 in Alberta, Canada. We used the Alberta Cancer Registry, physician billing claims, hospital discharge and emergency room visits to identify and categorize cancer-related healthcare encounters before diagnosis. We used statistical control charts to define the lookback period for each encounter category and used these lookback periods to identify the earliest encounter that represented the start of the diagnostic interval (index contact date). The end of the interval was the diagnosis date. Quantile regression was used to determine factors associated with the diagnostic interval. Results: We identified 3,142 patients with PDAC. Median age of diagnosis was 71 (IQR 61-80). We identified an index contact date and thus a diagnostic interval in 96.5% of patients. The median diagnostic interval length was 76 days (IQR 21-191; 90th percentile 276 days). A higher Elixhauser comorbidity score (+18.57 days/ 1 point increase, 95% CI 16.07-21.07, p<0.001) and stage 3 disease compared to stage 2 disease (+22.55 days, 95% CI 5.02-40.08, p=0.01) were associated with a longer diagnostic interval. Conclusions: In this cohort of patients with PDAC, there was a wide range in the diagnostic interval with 10% of patients having a diagnostic interval of approximately 9 months. Diagnostic interval research using administrative databases can understand variations in diagnosis times and can inform early detection efforts by identifying where and in whom delays may occur.

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Time intervals experienced between first symptom recognition and pathologic diagnosis of breast cancer in Addis Ababa, Ethiopia: a cross-sectional study

BMJ Open ◽

10.1136/bmjopen-2019-032228 ◽

2019 ◽

Vol 9 (11) ◽

pp. e032228 ◽

Cited By ~ 3

Author(s):

Alem Gebremariam ◽

Adamu Addissie ◽

Alemayehu Worku ◽

Mathewos Assefa ◽

Lydia E Pace ◽

...

Keyword(s):

Breast Cancer ◽

Associated Factors ◽

Addis Ababa ◽

Diagnostic Delay ◽

Cross Sectional Study ◽

Sectional Study ◽

Cross Sectional ◽

Interval Time ◽

Diagnostic Interval ◽

Diagnostic Intervals

ObjectivesThis study aimed to estimate the magnitude of patient and diagnostic delays and associated factors among women with breast cancer in Addis Ababa.DesignThis is a cross-sectional study.Settings and participantsAll women newly diagnosed with breast cancer in seven major healthcare facilities in Addis Ababa (n=441) were included in the study.Main outcomes and measuresPatient interval (time from recognition of first symptom to medical consultation) and diagnostic interval (time from first consultation to diagnosis). Patient intervals >90 days and diagnostic intervals >30 days were considered delays, and associated factors were determined using multivariable Poisson regressions with robust variance.ResultsThirty-six percent (95% CI [31.1%, 40.3%]) of the patients had patient intervals of >90 days, and 69% (95% CI [64.6%, 73.3%]) of the patients had diagnostic intervals of >30 days. Diagnostic interval exceeded 1 year for 18% of patients. Ninety-five percent of the patients detected the first symptoms of breast cancer by themselves, with breast lump (78.0%) as the most common first symptom. Only 8.0% were concerned about cancer initially, with most attributing their symptoms to other factors. In the multivariable analysis, using traditional medicine before consultation was significantly associated with increased prevalence of patient delay (adjusted prevalence ratio (PR) = 2.13, 95% CI [1.68, 2.71]). First consultation at health centres (adjusted PR = 1.19, 95% CI [1.02, 1.39]) and visiting ≥4 facilities (adjusted PR = 1.24, 95% CI [1.10, 1.40]) were associated with higher prevalence of diagnostic delay. However, progression of symptoms before consultation (adjusted PR = 0.73, 95% CI [0.60, 0.90]) was associated with decreased prevalence of diagnostic delay.ConclusionsPatients with breast cancer in Addis Ababa have prolonged patient and diagnostic intervals. These underscore the need for public health programme to increase knowledge about breast cancer symptoms and the importance of early presentation and early diagnosis among the general public and healthcare providers.

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