Lung Cancer Diagnostic Pathway Analysis

Transcription

1 Lung Cancer Diagnostic Pathway Analysis Final report Accelerate, Coordinate, Evaluate (ACE) Programme An early diagnosis of cancer initiative supported by: NHS England, Cancer Research UK and Macmillan Cancer Support ACE Lung Cancer Pathway August 217

2 Authors Clare Pearson Senior Cancer Information Analyst (ACE Programme), CRUK-PHE Partnership Veronique Poirier ACE Programme Data Manager Michael D Peake Clinical Lead for Early Diagnosis, National Cancer Registration and Analysis Service (NCRAS), Public Health England The ACE Programme would like to thank the following for their contributions to this report: This report has also been produced as part of the CRUK - PHE partnership. The CRUK - PHE partnership seeks to strengthen the work of both organisations through collaborating on projects that align to our common goals. The CRUK - PHE partnership specifically seeks to develop cancer intelligence by using the strength of the partnership to enable access to new datasets and produce new analyses that improves cancer prevention, earlier diagnosis and treatment. The authors would also like to thank the following for their expertise and assistance with this report: Gemma Luck (who initiated some of the pathway work) Isabella Carneiro, Anna Fry, David Kennedy, Becky White, Jon Shelton (CRUK-PHE partnership) This work uses data provided by patients and collected by the NHS as part of their care and support. About the ACE Programme The Accelerate, Coordinate, Evaluate (ACE) Programme is an early diagnosis of cancer initiative focused on testing innovations that either identify individuals at high risk of cancer earlier or streamline diagnostic pathways. It was set-up to accelerate the pace of change in this area by adding to the knowledge base and is delivered with support from: NHS England, Cancer Research UK and Macmillan Cancer Support; with support on evaluation provided by the Department of Health s Policy Research Units (PRUs). The first phase of the programme consisted of 6 projects split into various topic-based clusters to facilitate evidence generation and learning. The second phase (pilots live from January 217) comprises five projects exploring Multidisciplinary Diagnostic Centre (MDC) based pathways. The learning from ACE is intended to provide ideas and evidence to those seeking to improve local cancer services. The evaluations and findings are produced independently, and are therefore, not necessarily endorsed by the three supporting organisations. i

3 Executive Summary Introduction & aim Improving the outcomes for cancer patients via earlier diagnosis and treatment of lung cancer is one of the key aims of the ACE programme. Lung cancer survival rates remain low in England compared with other similar countries (1). Lung cancer outcomes are improved for patients who are diagnosed earlier. An important component of trying to achieve earlier diagnosis is to examine and address possible delays during the diagnostic pathway. The aim of this project was to assess potential delay points in lung cancer patients receiving their diagnosis, using linked national datasets, to provide a large scale understanding of common pathways and interval timings. Methods Lung cancer diagnoses in were linked with data from the Diagnostic Imaging Dataset (DID) and Cancer Waiting Times (CWT). Events in the diagnostic pathway were identified, including imaging (Chest X-Ray and CT scans) and dates seen in secondary care. These were used to build pre-diagnostic scenarios of a particular sequence of diagnostic events. Intervals between imaging, being seen in secondary care, and diagnosis date were investigated. The timings of events were also used to assess how many patients had a pathway which adhered to the new National Optimal Lung Cancer Pathway (NOLCP) timings (2). Variation in different scenarios and NOLCP adherence were explored by CCG. Results There were a total of 11,51 lung cancer patients in the cohort. There were a number of different sets of events in the diagnostic period, ranging from relatively simple to complex series. The two most common complex scenarios were the use of Chest X-ray (CXR) and Computed Tomography (CT) before diagnosis and their timings compared with first being seen in secondary care (Seen). These two scenarios differed between when a CT occurred in relation to being first seen (Seen) in secondary care. CXR Seen CT Diagnosis (n = 11,567) CXR CT Seen Diagnosis (n = 12,235) The second of these is clinically preferable and has a shorter diagnostic period (median of 29 days compared with 35 in the first). The proportion of patients in the second scenario has increased over the three years of analysis (from 1% to 12%). There was wide variation by CCG in the proportions of patients in these two scenarios, with a reduction (44% to 35%) in patients in the first scenario between 213 and 215. Time intervals in the diagnostic pathway differed by the referral source of a CXR or CT scan. Overall GP direct access (CXR and CT) and outpatient had longer time intervals compared with A&E and inpatients. Overall the median time from CXR to diagnosis was 35 days; compared with 19 days from CT to diagnosis. The current data were used as a benchmark against the NOLCP. Less than a quarter of patients (23%) met the optimal testing timings, with less than 6% meeting all timings in the ii

4 diagnostic pathway suggested by the optimal pathway. There is also significant variation by CCG of patients meeting optimal pathway timings ( 32%). Discussion/Conclusion Despite some limitations, the use of DID allowed the analysis of diagnostic pathways for lung cancer patients in England, demonstrating an increase over time in proportions of patients have a CT scan prior to first being seen in secondary care. There was variation in time intervals by image referral source. Large CCG variation in benchmarking to the forthcoming NOLCP shows that a move towards a more clinical appropriate pathway could improve the care of the patient and speed up the diagnosis. However, there is still wide variation at national level and it is hoped that the implementation of the NOLCP will help towards improving this. iii

5 Contents Executive Summary... ii Introduction & aim... ii Methods... ii Results... ii Discussion/Conclusion... iii Contents... iv 1 Introduction... 1 Aim and objectives: Methods... 2 Datasets... 2 Data linkage... 2 Data analysis Results Cohort details Sex and age group Deprivation Geographical variation Stage at diagnosis Common pathways/scenarios... 1 Scenario variation by CCG all patients Scenario variation by CCG complete records CXR and CT tests Interval times Benchmarking to optimal pathway timeframes Optimal testing pathway Optimal diagnostic testing pathway timeframes: variation by CCG... 2 Optimal diagnostic pathway Optimal pathway timeframes: variation by CCG Discussion References Abbreviations Appendix iv

6 1 Introduction Improving the outcomes for cancer patients via earlier diagnosis and treatment of lung cancer is one of the key aims of the ACE Programme. The ACE Programme lung cancer report identifies some key areas to streamline the diagnostic pathway, and recommends steps to ensure that in primary and secondary care the pathway runs efficiently (3). Lung cancer survival rates remain low in England when compared with other European and developed countries (1), although there is good evidence that they have been improving in recent years (4,5). It is also clear that outcomes for lung cancer patients are improved when patients are diagnosed earlier (6,7). Identification of possible delays during the diagnostic pathway is important to consider, as they may hamper earlier diagnosis. Reducing such delays, therefore, may lead to a higher proportion of patients being able to receive effective treatment. Linking the cancer registration dataset with data from both the Diagnostic Imaging Dataset (DID) and Cancer Waiting Times (CWT) offers an opportunity to examine diagnostic pathways of lung cancer patients on a large scale. Imaging events and dates of when patients were seen, combined with a diagnosis date, can build up a picture of the diagnostic pathway. This can allow identification of possible delays in the diagnostic pathway for lung cancer patients and identify areas for targeted work to address delays. Aim and objectives: The aim of this report is to investigate diagnostic pathways for lung cancer patients diagnosed in 213, 214 and 215. This will be fulfilled using the following three objectives: 1. Describe different pre-diagnostic scenarios in the six-month pre-diagnostic period and examine variation; 2. Understand intervals in the diagnostic pathway within a six month pre-diagnostic period, investigating points of contact and time intervals; 3. Describe variations in the proportions of patients following the timings of the proposed national lung cancer optimal pathway (NOLCP). 1

7 2 Methods Datasets The Diagnostic Imaging Dataset (DID) has collected diagnostic imaging information undertaken on NHS patients in secondary care facilities since April 212 and is owned by NHS England. Local Radiological Information Systems (RIS) submit imaging data to NHS Digital. Among data recorded, the DID contains the date of imaging test (plus request and report dates), demographic information, the imaging code, and the source of the imaging request. The Cancer Waiting Times (CWT) dataset is owned by NHS England and monitors waiting times for cancer services. It collects information about when a patient was referred to treatment, along with multi-disciplinary team meeting (MDT) date and when the patient was first seen in secondary care. It is important to note that the CWT does not contain data for all registered cancers. For example, older patients diagnosed at a later stage are less likely to have a CWT record, because they are less often referred from primary care. Cancer registration data is available for all diagnosed cancers in England and is held at Public Health England (PHE) within the National Cancer Registration and Analysis Service (NCRAS). Data of interest for this work included date of diagnosis, stage at diagnosis and socio-demographic characteristics of lung cancer patients. Data linkage Imaging records from DID and data from the CWT dataset were linked to cancer registrations using the NHS number and date of birth. The linkage process did not result in 1% of lung cancer patients having a DID and/or CWT record, and as a result a number of cancer registrations had no DID and/or CWT data. Initial analysis presented in this report will use all lung cancer patients (including those with or without DID and/or CWT), with further analyses only included those with complete records (lung cancer patients with both a DID and a CWT record). These patients will be referred to as complete records cases. Data analysis All lung cancers (ICD1 codes: C33 and C34) diagnosed in 213, 214 and 215 were included in this analysis, including small cell and non-small cell lung cancers. Those with multiple lung cancer tumours (n=967) registered in this time frame were excluded, as were lung cancer patients younger than 15 (n=6). Patients diagnosed via their death certificates (n=1,67) were retained for analysis, as they may have undergone some imaging prior to their death. With clinical input, the procedures determined relevant to lung cancer diagnosis were identified using both the National Interim Clinical Imaging Procedure (NICIP) and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) variables in the DID. All Chest X-rays (CXR) and relevant Computerised Tomography (CT) scans in the six months prior to diagnosis were included (Table 1). 2

8 Table 1: Relevant imaging codes for the diagnosis of lung cancer, identified by lung cancer clinicians Type of image NICIP code SNOMED-CT code Chest X-Ray (CXR) X-ray of Chest and abdomen XCHAB X-ray of Chest XCHES X-ray of Thoracic inlet XTHIN Computed Tomography (CT) CT Angiography of pulmonary, abdominal and pelvic arteries CAPAP CT Angiography of pulmonary artery CAPUG CT Brain, neck, thorax, abdomen and pelvis CBNTA CT Chest and abdomen CCABD CT Chest and abdomen with contrast CCABDC CT Thorax, abdomen and pelvis CCHAP CT Thorax, abdomen and pelvis with contrast CCHAPC CT Chest CCHES CT Thorax with contrast CCHESC CT Head, neck, thorax and abdomen CHNTA CT Head, neck, thorax and abdomen with contrast CHNTAC CT Head, neck, thorax, abdomen and pelvis CHNTAP CT Head, neck, thorax, abdomen and pelvis with contrast CHNTPC CT Chest high resolution CHRC CT Chest high resolution with contrast CHRCHC CT Head, thorax and abdomen CHTA CT Head, thorax, abdomen and pelvis CHTAP CT Head, thorax, abdomen and pelvis with contrast CHTAPC CT Head and thorax CHTH CT Head, thorax and abdomen with contrast CHTHAC CT Head and thorax with contrast CHTHC CT Low dose thorax CLDTH CT Neck, thorax and abdomen with contrast CNCAC CT Neck, thorax, abdomen and pelvis CNCAP CT Neck, thorax, abdomen and pelvis with contrast CNCAPC CT Neck, thorax and abdomen CNCHA CT Neck and thorax CNECH CT Neck and thorax with contrast CNECHC The dataset was analysed at different levels: image, patient and local (CCG) and regional (CCG) level. Pre-diagnostic scenarios were built using the dates available in the DID and CWT datasets, starting by assigning patients to a simple pathway using the DID variables (CXR only, CT only); then moving to increasingly more complex scenarios including dates derived from the CWT data. The scenarios were including dates in the six months prior to diagnosis for all patients in the cohort and analysed also for those with complete records. The CWT variables included in the scenarios were treatment start date (TreatP), date first seen in secondary 3

9 care (Seen), consultant upgrade (Consup) and cancer referral to treatment period (CRTP), which is the start of the CWT period. Variation in the proportions of patients in the most common scenarios was explored (by CCG) for all patients, and those with complete records. To calculate time intervals, the first (for those with multiple imaging records) or only CXR or CT within the six month pre-diagnostic period was selected and the difference between the image date and other variable calculated. Time intervals and pre-diagnostic scenarios were analysed for all patients in the cohort, for those with complete records, by year of diagnosis and also by the most common sources of imaging referral. These sources of imaging were categorised as follows: inpatient, GP Direct access, outpatient, A&E and other. To assess timings of events in relation to benchmarking to the National (Standard) Clinical Pathway for Lung Cancer and a National Optimal Lung Cancer Pathway (NOLCP), the three months pre-diagnostic period was considered, as according to the timeframes in the optimal pathway diagnosis should occur within 31 days of CXR/CT (2). The NOLCP was published in early 216 by the Lung Clinical Reference Group (2)(Figure 1). It has been adopted by NHS England and will be implemented as part of the quality assurance indicators. The first day of the pathway was defined as the request date of the first image within the three months prior to diagnosis. The source of imaging request determined which route (GP or hospital) of the optimal pathway the patient was under (Figure 1), defined as follows: - GP GP direct access, other - Hospital Admission inpatient, outpatient, A&E The timings of the optimal pathway were derived by adapting the NOLCP to make the start of the pathway day. The pathway is divided into two parts for analysis purposes: the testing pathway, and the entire diagnostic pathway. It is important to note that where a patient has a missing imaging request date, they were not able to meet the timings of the optimal pathway. This affected 26,13 patients. 4

10 Figure 1: Optimal pathway and timings, adapted from National (Standard) Clinical Pathway for Lung Cancer and a National Optimal Lung Cancer Pathway (NOLCP) Source: Adapted from Lung Cancer Clinical Expert Group, NHS England %2BMarch%2B216%252FStandard%2Band%2Boptimal%2Blung%2Bcancer%2Bpathways%2Bfinal.pdf [Accessed 24 January 217] 5

11 Percentage 3 Results 3.1 Cohort details There were 11,51 cases of lung cancer in 213, 214 and 215 included in the analysis: 36,695 in 213, 36,839 in 214 and 36,976 in 215. From these, 18,271 (98%) patients had a DID record and 87,316 (79%) had a CWT record. There were 86,493 patients with both a DID and CWT record (78%). Figure 2: Cohort details all patients and complete records by diagnosis year All patients DID records CWT records DID & CWT records Data completeness increased gradually from 213 to 215, with 79% of all patients in 215 having both a DID and a CWT record (Figure 2) Sex and age group The male to female ratio in this cohort was 1:.86, reflecting the higher incidence of lung cancer in men. The age groups from 65 to 84 represented the higher percentages of cases in both sexes. Proportions of females in the oldest age group (85 years and older) were higher than males, whereas there were high proportions of males in the age groups (Figure 3). 6

12 Percentage Percentage Figure 3: Age and sex distributions of lung cancer patients ( ) Male Female Age group Source: NCRAS, PHE Deprivation There was a gradual increase in the percentage of cases from the least deprived quintile to most deprived. The distribution was similar in all years (Figure 4). Figure 4: Deprivation quintiles (income domain of IMD) of lung cancer patients by diagnosis year (least deprived) (most deprived) Deprivation quintile Source: NCRAS, PHE 7

13 Cases per 1, over 65 Cases per 1, of population Geographical variation Figure 5: Crude incidence rate of lung cancers per 1, people by region and diagnosis year East Midlands East of England London North East North West South East South West West Midlands Region Yorkshire & The Humber Source: NCRAS, PHE and ONS Figure 6: Crude incidence rate of lung cancers per 1, people over 65 years by region and diagnosis year East Midlands East of England London North East North West South East South West West Midlands Region Yorkshire & The Humber Source: NCRAS, PHE and ONS There was clear geographical variation across England (Figure 5) in the cases per 1, people, with highest incidence in the North East and North West and lower incidence in London and the South East. There were some regions with small increases in incidence from 213 to 215 (East and South East), whereas others had a small decrease (West Midlands and Yorkshire & the Humber) and other regions show no discernible pattern. Figure 6 shows an increased incidence in the over 65s (linked with Figure 3 above) Most regions (with the 8

14 Percentage exceptions of the East and the South East) had a decreased incidence in lung cancer rates in those over 65 in 215 compared with Stage at diagnosis Figure 7: Distribution of stage at diagnosis of lung cancer patients by diagnosis year Unknown Stage at diagnosis Source: NCRAS, PHE For the overall cohort, 49% of patients were diagnosed at stage 4, with only 14% and 7% being diagnosed at stages 1 and 2 respectively. For those diagnosed in 214 and 215, there was a slight increase in the proportion of stages 1, 2, 3 and 4 and a reduction in the proportion of those with an unknown stage, likely to be due to better capture of cancer stage data in England (Figure 7). 9

15 3.2 Common pathways/scenarios Several alternative scenarios were investigated and categories are presented in table 2 for the entire cohort and also for those with complete records. Please refer to methods section for definitions. The full list of scenarios is listed in table 8 in the Appendix. Table 2: Different scenarios 6 months prior to diagnosis All lung cancer patients (n = 11,51) Patients with DID & CWT record (n = 86,493) All years All years N % N % N % N % N % N % N % N % A: CXR only 5, , , , B: CT only 1, C: CXR CT only 14, , , , , D: CT CXR only 1, E: CXR first 6, , , , , , , , F: CT first 3, , , , , , , , G: CXR CT first 16, , , , , , , , H: CT CXR first 1, , J: CRTP TreatP no imaging 2, , , K: Consultant upgrade 4, , , , , , , , L: CXR CRTP Seen 3, , ,9 3. 3, , ,9 3.7 M: CXR CRTP CT 1, , N: CXR CRTP Seen CT 12, , , , , , , , P: CXR CRTP CT Seen 12, , , , , , , , Q: CXR CT CRTP Seen 5, , , , , , , , R: CRTP first/same day 3, , , , , , , , S: Other CWT 8, , , , , , , , T: No DID/CWT 3, , , , Total 11, , , , , , , ,27 1 1

16 Percentage In all patients, those without CWT data (21%) would only be able to feature in scenarios A - D or have no DID/CWT data (scenario T). For patients with complete records, proportions in the simpler scenarios (A D) were reduced with corresponding increases in the more complex scenarios. Of the more complex scenarios (J S), the most common were N (CXR CRTP Seen CT) and P (CXR CRTP CT Seen). Scenario N often occurs clinically, although (Scenario P), where a patient has a CT prior to first being seen in secondary care, is preferable. There was an increase by year of diagnosis in the proportions of patients in scenario P and a corresponding decrease in proportions in scenario N (Figure 8). Figure 8 Proportions of patients in scenarios N and P by diagnosis year N: CXR CRTP Seen CT P: CXR CRTP CT Seen Diagnosis Year For patients in scenarios N and P, their pathway length (from CXR test to diagnosis) was also investigated. The median days between CXR and diagnosis was 35 days for Scenario N patients, and for those in scenario P, the median was 29 days. This indicates that, on average, scenario P resulted in a shorter diagnostic pathway by 6 days. Overall, for patients in complex scenarios, 38% had a CT after they were first seen in secondary care. This proportion decreased from 213 (4%) to 215 (35%). 11

17 Percentage Percentage Scenario variation by CCG all patients For all lung cancer patients, the variation in proportions of patients by CCG who fall into scenarios N and P are presented in Figures 9 and 1. CCG proportions were ranked from highest to lowest, with the median represented by the green line. The median (range) proportion in scenario N by CCG was 9.7% (.1% to 39%) and 1.1% (.2% and 33% for scenario P). The CCG median proportion in scenario N decreased from 9.4% in 213 to 8.3% in 215. The median proportion for scenario P increased from 7.8% in 213 to 11% in 215. Although the overall distribution of proportions for scenario N and P across the CCGs offered a similar pattern it should be noted that 1 (48%) of CCGs achieved a higher proportions of patients in scenario P than scenario N. Figure 9: Proportion of all lung cancer patients in scenario N by CCG ( ) N: CXR CRTP Seen CT CCG Figure 1: Proportion of all lung cancer patients in scenario P by CCG ( ) P: CXR CRTP CT Seen CCG 12

18 Percentage Figure 11 demonstrates the relationship between proportions in scenarios N and P by CCG. The rankings high to low (in red) are for scenario P with the corresponding proportion of N in that CCG also plotted (in blue). Where there are high proportions in scenario P, there are generally low proportions in scenario N, but there is not a significant inverse relationship between the two scenarios. Where there were low proportions for CCGs in both of these two scenarios, there were likely to be patients without CWT data but with imaging records that were only able to be in a simpler scenario (A-D only) in addition to other complex scenarios (e.g. involving consultant upgrades). Figure 11: Proportion of all lung cancer patients in scenario P and N by CCG ( ) N: CXR CRTP Seen CT P: CXR CRTP CT Seen CCG 13

19 Percentage Percentage Scenario variation by CCG complete records For patients with complete records, variation by CCG who follow events as per scenario N and P are presented in Figures 12 and 13. The median CCG proportion (and range) of patients in scenario N was 13% (.2% - 45%) and was 13% (.3% - 37%) for scenario P. These patterns mirrored those in Figures 8 and 9, with similar distributions but definitively higher proportions of patients in scenarios N and P in those with complete records due to the reduction in patients in simpler scenarios. Figure 12: Proportion of lung cancer patients with DID and CWT records in scenario N by CCG N: CXR CRTP Seen CT CCG Figure 13: Proportion of lung cancer patients with DID and CWT records in scenario P by CCG P: CXR CRTP CT Seen CCG 14

20 Percentage Figure 14 demonstrates the relationship between proportions in N and P by CCG for those with complete records. As with all lung cancer patients, there are low proportions in scenario N where there are high proportions in P, but no significant inverse relationship. The overall increase in proportions in these scenarios is partly explained by all complete records patients having CWT variables and therefore the potential to be in one of these more complex scenarios. Figure 14: Proportion of lung cancer patients with DID and CWT records in scenario P and N by CCG N: CXR CRTP Seen CT P: CXR CRTP CT Seen CCG 15

21 3.3 CXR and CT tests Interval times Time intervals for those patients with a CXR or CT in the six months before diagnosis are presented in tables 3 and 4, which show the median days (and the 95% range) between the CXR or CT and other events in the diagnostic pathway. For patients with multiple images recorded in the timeframe of interest (six months before diagnosis date), the image for these interval calculations is the first recorded CXR or CT in that time period. This is presented for the entire cohort, by year of diagnosis and by image referral source. A negative value indicates that the second event took place prior to the first (e.g. the patient was seen in secondary care before their CT or CXR took place). For each of the time intervals, only patients with DID and CWT records for the seen interval are included in these calculations, and only those with a relevant image will be included in the interval calculations (not all patients will have a recorded CXR or CT before they are diagnosed). The CT test to report and test to seen intervals were shorter than the corresponding CXR time intervals. There was variation in CXR and CT median time intervals by source of image request. Images requested from an inpatient setting had the shortest time intervals for both CT and CXR. The longest time intervals occurred for those requested via GP direct access or the outpatient setting. For time intervals from the first CXR, there were no real differences by year of diagnosis but the median CT test to diagnosis interval increased by one day per year from 213 to 215. The 95% ranges for CT test to report, CT test to date of first seen in secondary care and CT test to diagnosis increased between 213 and 215. When restricting the analysis to only those with complete records, the observations were similar, except there was an increase in median days from CT test to diagnosis, usually adding one day to the median. (Full results for complete records cases are shown in Tables 6 and 7 in the Appendix). 16

22 Table 3: CXR Interval times by year and by source of imaging request for lung cancer patients Number of patients with CXR Days All (n=93,652) 213 (n=3,813) By year of diagnosis 214 (n=31,468) 215 (n=31,171) Inpatient (n=14,27) Outpatient (n=8,761) By source of CXR request GP Direct access (n=44,26) A&E (n=24,371) Other (n=2,467) CXR test to report 1 (-14) 1 (-14) 1 (-14) 1 (-13) 1 (-22) 2 (-28) 1 (-8) 2 (-18) 1 (-12) CXR test to CRTP 5 ( ) 5 ( ) 5 ( ) 5 ( ) ( ) 1 ( ) 7 (-11) 4 (-1-133) 11 ( ) CXR test to date first seen in secondary care 14 ( ) 14 ( ) 14 ( ) 14 ( ) (-76-13) 5 ( ) 16 (4-19) 8 (-138) 11 ( ) CXR test to diagnosis 29 (-155) 28 (-154) 29 (-154) 29 (-155) 1 (-155) 39 (-167) 35 (7-147) 2 (-158) 22 (-152) Table 4: CT Interval times by year and by source of imaging request for lung cancer patients Number of patients with CT Days All (n=85,77) 213 (n=26,432) By year of diagnosis 214 (n=29,554) 215 (n=29,721) Inpatient (n=27,152) Outpatient (n=41,325) By source of CT request GP Direct access (n=12,952) A&E (n=2,459) Other (n=1,819) CT test to report (-8) (-7) (-9) (-9) (-1) 1 (-11) 1 (-1) (-1) (-7) CT test to CRTP -5 (-28-27) -5 (-28-24) -5 (-28-28) -4 (-28-28) -1 (-18-2) -8 (-36-35) (-15-16) (-13-15) (-2-39) CT test to date first seen in secondary care (-17-34) (-17-3) (-17-35) (-17-37) -1 (-13-3) (-22-41) 6 (-7-27) (-7-28) (-2-39) CT test to diagnosis 13 (-17) 12 (-14) 13 (-18) 14 (-18) 5 (-83) 19 (-122) 19 (-88) 6 (-82) 15 (-13) 17

23 Percentage 3.4 Benchmarking to optimal pathway timeframes Optimal testing pathway 25,311 patients (23% of the cohort) met the optimal testing pathway timings of having relevant imaging within 3 days of the image request. Figure 15 shows proportions of patients meeting the timeframes of the optimal diagnostic testing pathway for all patients, and also for those with complete records by year of diagnosis. In both cohorts, proportions meeting the testing timeframes increased by year of diagnosis, with a slightly higher proportion of all patients meeting timings than those with complete records. Figure 15: Proportions of all lung cancer patients and those with complete records meeting diagnostic testing optimal pathway timeframes All patients Complete records All years Table 5 shows percentages of each demographic category meeting both the testing and entire diagnostic pathway timeframes. Statistically significant differences between patients meeting the timeframes, and patients not, are displayed in bold (Chi 2 test results of p<.5). The percentages were calculated by row within the table and only the percentages of those who did meet the pathway timings were included, so the rows/columns do not add up to 1%. There were significant differences in compliance with the testing and entire diagnostic pathway by age group, region and stage. In the testing pathway there was additional variation by deprivation quintile. Significantly higher proportions of patients diagnosed at stage 4 complied with the timings of the optimal pathways, compared to all other stages. 18

24 Table 5: Percentages of all lung cancer patients and those with complete records following the optimal pathway timeframes ( ) Percentage of cases meeting timeframes Diagnostic testing pathway Entire diagnostic pathway All patients Complete records All patients Complete records Age group Under to to Above Sex Male Female Deprivation quintile 1 (least deprived) (most deprived) Region East Midlands East of England London North East North West South East South West West Midlands Yorkshire & The Humber Stage Unknown Entire cohort Totals

25 Percentage Percentage Optimal diagnostic testing pathway timeframes: variation by CCG Variation in the proportion of all patients in CCGs who met the NOLCP testing timeframes are presented in Figure 16 (for all years combined) and for those with complete records in Figure 17. The proportions ranged from % to 73% (median 24%) for all patients and % to 75% (median: 23%) for those with complete records. Figure 16: Proportion of all lung cancer patients by CCG meeting diagnostic testing optimal pathway timeframes ( ) CCG Figure 17: Proportion of lung cancer patients with DID and CWT records meeting diagnostic testing optimal pathway timeframes ( ) CCG 2

26 Percentage Optimal diagnostic pathway 6,56 patients (5.5% of the cohort) had timings which complied with the NOLCP. Figure 18 demonstrates a different pattern by year than the testing pathway adherence (Figure 15). Those with complete records have higher proportions of meeting the timings and whilst there is an increase between 213 and 214, there is a decrease in 215, to below levels of 213. Variation in the proportion of all patients in CCGs who met the entire diagnostic optimal pathway timeframes are presented in Figure 18 (for all years combined) and Figure 19 for those with complete records. The proportion by CCG meeting the entire pathway ranged from % to 27% (median 5.1%) for all patients. Six CCGs had no patients who met all of the pre-diagnostic timings (Figure 19). For those with complete records the median CCG proportion ranged from to 33% (median 6.4%) (Figure 2). Figure 18: Proportions of all lung cancer patients and those with complete records meeting diagnostic optimal pathway timeframes ( ) All patients Complete records All years

27 Percentage Percentage Optimal pathway timeframes: variation by CCG Figure 19: Proportion of all lung cancer patients meeting diagnostic optimal pathway timeframes ( ) CCG Figure 2: Proportion of lung cancer patients with DID and CWT records meeting diagnostic optimal pathway timeframes ( ) CCG 22

28 Discussion This is the first time that DID-CWT-Cancer registry linkage has been utilised to examine different diagnostic scenarios for lung cancer patients. There was variation in both diagnostic pathway events and in their order. There was also wide range of difference between timings for some specific patients, with some experiencing lengthy time intervals (over 3 days) between diagnostic events. The ordering of the CT imaging for lung cancer patients in relation to when first seen in secondary care is important in addressing potential delays in diagnosis. According to the National Optimal Lung Cancer Pathway NOLCP, the preferable order is that the CT scan should take place before someone is first seen in secondary care, because diagnosis and treatment plans are heavily reliant on the CT scan results. Having a CT scan after first being seen adds a possible delay to the diagnosis. The difference in the CT scan timing compared with being first seen is demonstrated in scenarios N and P. It is reassuring to see the increase in the potentially quicker scenario P and the comparable decrease in longer scenario N from 213 to 215; the more preferable scenario was more frequently occurring and also had a shorter diagnostic pathway. However, there was still wide variation in these scenarios by CCG and improvements can be made to streamline pathways. Alongside the ordering of events in the diagnostic pathway, having short time periods between diagnostic events is also important in receiving a more rapid diagnosis. There has been a slight increase in average timings from first CT scan to diagnosis from 213 to 215. There was variation in days between events, depending on the source of the image request. A small proportion of patients diagnosed with lung cancer in met the timeframes of the optimal pathway. Proportions meeting the testing timeframes gradually increased from , but there has not been a corresponding increase in proportions meeting the entire diagnostic pathway timeframes. The proportion of those complying with the timeframes diagnosed in 215 reduced after a small increase in 214. This may be due to data quality improvements, because there may be more breaches due to more patients having complete data records. There are other possible reasons, linked to the national reduction in those meeting the CWT 62 day wait targets. Another reason could be that patients were more likely to undergo different and additional diagnostic tests that were not recorded in this analysis (e.g. EBUS Endobronchial Ultrasound; PET-CT scans Positron Emission Tomography-Computed Tomography) in more recent years. There was also significant variation in proportions meeting the optimal pathway timings by CCG. There is clearly work to be done to ensure that this variation is reduced and that the timings of the NOLCP are achieved, so that more patients receive a timely diagnosis of lung cancer to improve survival chances. However, this first attempt at benchmarking only describes certain elements of the pathway and does not reflect the complexity of the entire pathway, nor has it taken account of differences in case mix between CCGs. There are, for example, likely to be differences in compliance to optimal timings by stage: those diagnosed at a later stage being generally more likely to meet the optimal pathway timings. This may be due to the fact that at earlier stages, where curative treatment is more likely to occur, 23

29 more work-up may be required on these patients, which could lead to longer diagnostic pathways. Other factors could include that in patients diagnosed at stage 1, a number are tumours are discovered as incidental findings and that many patients diagnosed at stage 4 are often diagnosed as part of an emergency presentation, where investigation and decision-making is are likely to be achieved more rapidly. Restricting the cohort to only those patients with DID and CWT records did not change time intervals, but increased the proportions of patients with more complex scenarios. The optimal pathway proportions did not change significantly. There were some limitations to this analysis. Firstly, the DID is a relatively new dataset, collecting imaging data since April 212. There are some known data quality issues which could affect these results, and the DID does not record imaging funded by private healthcare. There are some providers within certain CCGs that were poor at recording imaging data in 213, though this has been gradually improving. This could result in underestimating the numbers of patients meeting the optimal pathway timings or being assigned a simpler or other scenario. The DID records all NHS imaging, but it is not possible to be certain that the images recorded for these lung cancer patients were undertaken because of a suspicion of cancer. The first image within the first six (or three for the optimal pathway) months was used, it is not possible to say if this is the definite diagnostic CXR/CT. 2% of lung cancer patients also have no CWT data, and will therefore be missing from the more complex scenarios and will not meet the entire optimal diagnostic pathway timings. There may also be missing data of one (or more) of the CWT variables (seen, MDT date), which could also contribute to the small numbers of patients complying. The optimal pathway timings are not yet implemented in England and indeed, this analysis focuses on a time period when this pathway was not yet even developed, although some trusts were already trying to implement such pathways. However, it gives a useful indication of where services would need to improve in order to reduce variation and meet the timeframes laid out in the optimal pathway. The value of measuring compliance with the NOLCP it is a relatively simple metric for assessing variation and progress over time. 24

30 References 1. Coleman MP, Forman D, Bryant H, Butler J, Rachet B, Maringe C, et al. Cancer survival in Australia, Canada, Denmark, Norway, Sweden, and the UK, (the International Cancer Benchmarking Partnership): an analysis of population-based cancer registry data. Lancet [Internet]. 211;377. Available from: 2. Lung Cancer Clinical Expert Group, NHS England. National (Standard) Clinical Pathway for Lung Cancer and a National Optimal Lung Cancer Pathway [Internet]. [viewed 24 Jan 217]. Available from: 3. ACE Programme. Improving diagnostic pathways for patients with suspected lung cancer. Final report. [Internet]. Cancer Research UK; 217 [viewed 15 Jun 217]. Available from: 4. Walters S, Benitez-Majano S, Muller P, Coleman MP, Allemani C, Butler J, et al. Is England closing the international gap in cancer survival. Br J Cancer. 215 Sep 1;113(5): McPhail S, Johnson S, Greenberg D, Peake M, Rous B. Stage at diagnosis and early mortality from cancer in England. Br J Cancer. 215 Mar 31;112(s1):S Chansky K, Detterbeck FC, Nicholson AG, Rusch VW, Vallières E, Groome P, et al. The IASLC Lung Cancer Staging Project: External Validation of the Revision of the TNM Stage Groupings in the Eighth Edition of the TNM Classification of Lung Cancer. J Thorac Oncol [Internet]. Available from: 7. Cancer Research UK. Lung cancer survival statistics. 215 [Internet]. [viewed 27 April 217]. Available from: Contact ACE If you have any queries about ACE, please contact the team at: ACEteam@cancer.org.uk In addition, you can visit our webpage: where we will publish news and reports. The ACE Programme Accelerate, Coordinate, Evaluate 25

31 Abbreviations ACE CCG CRTP CT CWT CXR DID IMD MDT NCRAS NICIP NOLCP ONS PET-CT PHE RIS SNOMED-CT Accelerate, Coordinate and Evaluate Clinical Commissioning Group Cancer Referral to Treatment Period Computed Tomography Cancer Waiting Times dataset Chest X-Ray Diagnostic Imaging Dataset Index of Multiple Deprivation Multi-Disciplinary Team National Cancer Registration and Analysis Service National Interim Clinical Imaging Procedure National Optimal Lung Cancer Pathway Office for National Statistics Positron-Emission Tomography-Computed Tomography Public Health England Radiological Information Systems Systematized Nomenclature of Medicine - Clinical Terms 26

32 Appendix Table 6: CXR Interval times by year and by source of imaging request for lung cancer patients with complete DID & CWT records Number of patients with CXR Days All (n=75,191) By year of diagnosis 213 (n=24,489) 214 (n=25,171) 215 (n=25,359) inpatient (n=1,641) outpatient (n=7,48) By source of CXR request GP Direct access (n=37,918) A&E (n=16,974) Other (n=2,6) CXR test to report 1 (-13) 1 (-13) 1 (-14) 1 (-13) 1 (-22) 2 (-27) 1 (-8) 2 (-18) 1 (-13) CXR test to CRTP 5 ( ) 5 ( ) 5 ( ) 5 ( ) ( ) 1 ( ) 7 (-11) 4 (-1-133) 3 (-22-93) CXR test to date first seen in secondary care 14 ( ) 14 ( ) 14 ( ) 14 ( ) (-76-13) 5 (-7-137) 16 (4-19) 8 (-138) 11 ( ) CXR test to diagnosis 3 (-154) 29 (-153) 3 (-153) 31 (-155) 1 (-154) 36 (-166) 35 (8-146) 23 (1-159) 23 (-153) Table 7: CT Interval times by year and by source of imaging request for lung cancer patients with complete DID & CWT records Number of patients with CT Days All (n=71,2693) 213 (n=21,846) By year of diagnosis 214 (n=24,599) 215 (n=24,824) inpatient (n=19,526) outpatient (n=36,92) By source of CT request GP Direct access (n=11,557) A&E (n=1,792) Other (n=1,492) CT test to report (-9) (-7) (-1) (-1) (-1) 1 (-11) 1 (-1) (-1) (-7) CT test to CRTP -5 (-28-27) -5 (-28-24) -5 (-28-28) -4 (-28-28) -1 (-18-2) -8 (-36-35) (-15-16) (-13-15) (-2-39) CT test to date first seen in secondary care (-17-35) (-17-3) (-17-35) (-17-37) -1 (-13-3) (-22-41) 6 (-7-27) (-7-28) (-2-39) CT test to diagnosis 14 (-16) 13 (-13) 14 (-17) 15 (-19) 6 (-83) 19 (-121) 19 (-87) 7 (-86) 16 (-13) 27

33 Table 8: Comprehensive list of diagnostic scenarios for lung cancer patients ( ) All lung cancer patients (n = 11,51) Patients with DID & CWT record (n = 86,493) All years All years N % N % N % N % N % N % N % N %.1: CXR only 5, , , , : CT only 1, : CXR=CT only : CXR CT only 13, , , , , : CT CXR only 1, : CXR first 6, , , , , , , , : CT first 3, , , , , , , , : CXR=CT first 1, , : CXR CT first 15, 14 4, , , , 17 4, , , : CT CXR first 1, , : CRTP Treat P : CRTP seen Treat P 1, , : cons up Treat P - no imaging : cons up Treat P - imaging : CXR CRTP seen 3, , ,9 3. 3, , , : CXR CRTP CT 1, , : CXR CRTP seen CT 12, , , , , , , , : CXR CRTP CT seen 12, , , , , , , , : CXR CT CRTP seen 5, , , , , , , , : CXR cons up : CXR seen cons up : CXR cons up seen : CXR CT cons up : CXR CT cons up seen 1, , : CXR CT seen cons up : CRTP CXR seen 1, , : CRTP CXR seen CT : CRTP CXR CT seen : CRTP seen CXR CT : CRTP seen CT CXR No DID/CWT 3, , , , Other 8, , , , , , , , Total 11, , , , , , , ,