Annals of Thoracic Medicine Official publication of the Saudi Thoracic Society, affiliated to King Saud University
 
Search Ahead of print Current Issue Archives Instructions Subscribe e-Alerts Login 
Home Email this article link Print this article Bookmark this page Decrease font size Default font size Increase font size


 
Table of Contents   
ORIGINAL ARTICLE
Year : 2021  |  Volume : 16  |  Issue : 1  |  Page : 81-101
The effect of diagnostic assessment programs on the diagnosis and treatment of patients with lung cancer in Ontario, Canada


1 Clinical Programs and Quality Initiatives, Ontario Health (Cancer Care Ontario), Toronto, Ontario, Canada
2 Clinical Programs and Quality Initiatives, Ontario Health (Cancer Care Ontario); Department of Surgery, University of Toronto, Toronto, Ontario, Canada
3 Clinical Programs and Quality Initiatives, Ontario Health (Cancer Care Ontario); Department of Surgery, University of Toronto; Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada

Date of Submission21-May-2020
Date of Acceptance26-Aug-2020
Date of Web Publication14-Jan-2021

Correspondence Address:
Dr. Steven Habbous
Cancer Care Ontario, 525 University Ave, Toronto, Ontario
Canada
Login to access the Email id


DOI: 10.4103/atm.ATM_283_20

Rights and Permissions

   Abstract 


INTRODUCTION: Diagnostic assessment programs (DAPs) were implemented in Ontario, Canada, to improve the efficiency of the lung cancer care continuum. We compared the efficiency and effectiveness of care provided to patients in DAPs relative to usual care (non-DAPs).
METHODS: Lung cancer patients diagnosed between 2014 and 2016 were identified from the Ontario Cancer Registry. Using administrative databases, we identified various health-care encounters 6 months before diagnosis until the start of treatment and compared utilization patterns, timing, and overall survival between DAP and non-DAP patients.
RESULTS: DAP patients were younger (P < 0.0001), had fewer comorbidities (P = 0.0006), and were more likely to have early-stage disease (36% vs. 25%) than non-DAP patients. Although DAP patients had a similar time until diagnosis as non-DAP patients, the time until treatment was 8.5 days shorter for DAP patients. DAP patients were more likely to receive diagnostic tests and specialist consultations and less likely to have duplicate chest imaging. DAP patients were more likely to receive brain imaging. Among early-stage lung cancers, brain imaging was high (74% for DAP and 67% for non-DAP), exceeding guideline recommendations. After adjustment for clinical and demographic factors, DAP patients had better overall survival than non-DAP patients (hazard ratio [HR]: 0.79 [0.76–0.82]), but this benefit was lost after adjusting for emergency presentation (HR: 0.96 [0.92–1.00]). A longer time until treatment was associated with better overall survival.
Conclusion: DAPs provided earlier treatment and better access to care, potentially improving survival. Quality improvement opportunities include reducing unnecessary or duplicate testing and characterizing patients who are diagnosed emergently.


Keywords: Diagnostic assessment program, efficiency, guideline concordance, imaging, lung cancer, wait times


How to cite this article:
Habbous S, Khan Y, Langer DL, Kaan M, Green B, Forster K, Darling G, Holloway CM. The effect of diagnostic assessment programs on the diagnosis and treatment of patients with lung cancer in Ontario, Canada. Ann Thorac Med 2021;16:81-101

How to cite this URL:
Habbous S, Khan Y, Langer DL, Kaan M, Green B, Forster K, Darling G, Holloway CM. The effect of diagnostic assessment programs on the diagnosis and treatment of patients with lung cancer in Ontario, Canada. Ann Thorac Med [serial online] 2021 [cited 2021 Mar 3];16:81-101. Available from: https://www.thoracicmedicine.org/text.asp?2021/16/1/81/307048




For patients with lung cancer, prolonged diagnostic work-up or treatment planning can delay the start of treatment, rendering some patients inoperable and adversely affecting prognosis.[1],[2],[3] In light of this, some guidelines recommend a time from suspicion of lung cancer until diagnosis of 28 days and a time from diagnosis until treatment of 4–6 weeks.[4],[5],[6]

Given the importance of starting treatment as early as possible, a recent scoping review was conducted to better understand the variation in wait times across the lung cancer care continuum.[7] The authors identified 27 studies reporting median wait times from symptom onset until diagnosis ranging from 41 to 143 days and from diagnosis until the start of treatment ranging from 6 to 45 days. Another scoping review examined the effect of various interventions aimed at reducing these wait times, but most of the studies found focused on the time period leading up to diagnosis and many patients did not meet the recommended timeliness targets.[8],[9],[10],[11]

Since 2010, lung diagnostic assessment programs (DAPs) were established across Ontario, Canada, to provide efficient and accessible diagnostic evaluation and treatment planning for patients with suspected lung cancer.[12],[13] Services provided by DAPs include patient navigation, specialist consultations, and psychosocial support according to the standards outlined in Cancer Care Ontario's Lung Cancer Diagnostic Pathway Map.[12] In the current study, we report the effect of lung DAPs on health-care utilization, wait times, and overall survival.


   Methods Top


Cohort selection

Patients with primary lung carcinomas were identified from the Ontario Cancer Registry (OCR) using the ICD-O-3 codes C340–349 restricted to the AJCC version 7 ICD-O-3 histology codes 8000–8576, 8940–8950, and 8980–8981. Patients were categorized as having small-cell lung cancer (histology codes 8041–8045) or non-small-cell lung cancer (all remaining histologies). Only malignant cases (ICD-O-3 behavior code 3) diagnosed between 2014 and 2016 were included.

Patients were excluded if they were <18 or >105 years of age at diagnosis, were diagnosed at the time of death or at autopsy, had an invalid health insurance number (a number unique to each Ontario resident used to access health-care services), were missing age or sex, or had multiple cancer diagnoses in their lifetime. To enable accurate capture of diagnostic and treatment interventions, we excluded patients who had a missing or non-Ontario postal code of residence at the time of diagnosis or had no record in the Ontario Health Insurance Program (OHIP) database within 1 year plus/minus diagnosis.

Data sources

Patients' death dates were obtained from the OCR and supplemented with the Registered Persons Database (RPDB), which contains information on vital statistics for all Ontarians. We obtained sex from the RPDB and neighborhood-level income quintile, immigrant density, urban/rural status, and region of residence from the 2006 Canadian Census using postal codes of residence at the time of diagnosis (linked using the Postal Code Conversion File Plus version 6a). Staging data were obtained from the Collaborative Staging database maintained by Ontario Health . The weighted Charlson Comorbidity Index was calculated (excluding cancer) based on hospital data up to 3 years before the OCR diagnosis date.[14]

Health-care encounters were identified using physician billing codes from OHIP or procedural codes from the Discharge Abstract Database (DAD; inpatient procedures) or the National Ambulatory Care Reporting System (NACRS; outpatient procedures) [Appendix 1]. The date of resective lung surgery was identified using OHIP, DAD, or NACRS [Appendix 2]. Systemic therapy information was obtained from the Activity Level Reporting database, Ontario Drug Benefit Program, New Drug Funding Program, DAD, and NACRS. We included any agent with antineoplastic activity, including chemotherapy, immunotherapy, hormonal therapy, or targeted therapy. Information about radiation was obtained from the Activity Level Reporting database, restricted to radiation applied to the chest.



We classified patients as having had an emergency visit if they had any record in NACRS with an emergency department indicator = 1 or a hospital admission record from DAD with entry code “E” within 7 days before the OCR diagnosis date (inclusive). We also classified patients as having been an inpatient on the diagnosis date if the OCR diagnosis date occurred between DAD admission and discharge dates (inclusive).

During the study period, each DAP in Ontario submitted data using the Diagnostic Data Upload Tool (DDUT). Patient-level data from lung DAPs in the DDUT database were used to identify whether a patient was diagnosed through a DAP.

Definitions

The date of diagnosis was obtained from the OCR, which preferentially uses the specimen retrieval date from the pathology report where evidence of cancer was confirmed. A patient was considered a “DAP patient” if they had a diagnosis date in the DDUT database ± 30 of the OCR diagnosis date. This 60-day window allowed for differences in how diagnosis is ascertained from the two data sources. All other patients were considered “non-DAP patients.”

We defined the diagnostic interval as the time until the lung cancer diagnosis. To identify the starting point of the diagnostic interval, we searched for the first health-care encounter occurring within 6 months before diagnosis, restricting to a general practitioner visit, chest X-ray, chest computed tomography (CT) scan, abdominal CT scan, bronchoscopy, endobronchial ultrasound, chest fluoroscopy, or consultation with a respirologist, general surgeon, general thoracic surgeon, internal medicine specialist, or cardiologist. In sensitivity analysis, we omitted the visit to the general practitioner to provide estimates of the diagnostic interval that were more comparable to published studies that also excluded this date.

We defined the pretreatment interval as the time from diagnosis until the start of treatment within 6 months after diagnosis. We also report the time from the first health-care encounter until treatment initiation as a measure of the duration of the entire diagnostic and treatment planning interval.

We reported the number of health-care encounters for each patient as the number of unique dates on which a patient had one or more health-care encounters.

Statistical methods

We used logistic regression to compare DAP and non-DAP patients' characteristics, linear regression to explore factors associated with continuous outcomes (e.g., wait times), and Cox proportional hazards regression for time-to-event analysis (e.g., overall survival). We also presented unadjusted overall survival analyses using Kaplan–Meier plots. We adjusted analyses for all covariates considered clinically relevant. Unless otherwise indicated, covariates included age, sex, urban/rural residence, neighborhood income quintile, neighborhood immigrant density, region of residence at the level of Local Health Integration Network (LHIN), Euclidean distance to the nearest DAP, Charlson Comorbidity Index, stage, histology, emergency visit within 7 days of diagnosis, and hospital admission on the diagnosis date. We reported odds ratios (OR), beta coefficients, and hazard ratios (HR) with 95% confidence intervals (CI), where appropriate. We used SAS v9.4 for all analyses (Cary, North Carolina: SAS Institute Inc.).

Privacy

All analyses were conducted at Ontario Health for system monitoring and identifying areas for quality improvement. Cells with counts <6 were suppressed.


   Results Top


Cohort characteristics

A total of 22,049 incident lung cancer patients were identified. The mean age at diagnosis was 71 (standard deviation [SD]: 10.4) years, and most patients lived in an urban area (84%) [Table 1]. After adjustment, patients were more likely to be diagnosed in a DAP if they were younger (OR: 0.89 [0.86–0.92] per 10 years), lived in a rural neighborhood (OR: 1.21 [1.08–1.35]), lived in a less immigrant-dense neighborhood (OR: 0.50 [0.43–0.58] for the most versus the least dense), and lived closer to a DAP (OR: 0.88 [0.84–0.93] per 50 km). There was significant regional variability (P < 0.0001). DAP patients had fewer comorbidities (68% vs. 63% had no comorbidities, P = 0.0008), were less likely to have stage IV disease or unknown stage (P < 0.0001), and were 60% less likely to have had an emergency visit (OR: 0.41 [0.36–0.45]) or hospital admission (OR: 0.38 [0.34–0.42]) at the time of diagnosis.
Table 1: Patient characteristics by diagnosis in a diagnostic assessment program

Click here to view


Health-care utilization for diagnosis and treatment

DAP patients had three fewer health-care encounters than non-DAP patients (median: 23 (18, 31) unique dates for DAP patients versus median: 26 (19, 36) unique for non-DAP patients, P < 0.0001), but there was no difference after restricting encounter types to diagnostic tests and consultations specific to diagnosing lung cancer (median: 8 for both DAP and non-DAP patients) [Appendix 3].



Diagnostic tests

DAP patients were more likely to have received a positron emission tomography (PET)-CT scan (70% vs. 36%), a bronchoscopy (48% vs. 37%), an endobronchial ultrasound (18% vs. 9%), and a biopsy (91% vs. 80%) but less likely to have had an abdominal CT scan (55% vs. 68%) [Figure 1] and [Appendix 3]. Regardless of stage, DAP patients were more likely to have received a brain magnetic resonance imaging or CT scan (86% vs. 77% for stage IV and 69% vs. 64% for stage I). DAP patients were less likely to have received a second or a third chest CT than non-DAP patients (16% vs. 24% received >1 chest CT scans), even though the initial scan was frequently a non-contrast scan (74% for DAP and 75% of non-DAP). If second chest CTs did occur, they were performed a median of 3–4 weeks after the first scan for DAP patients and after a median of 4–5 weeks for non-DAP patients [Appendix 4].
Figure 1: Receipt of diagnostic tests or consultations from 6 months before diagnosis until either the date of first treatment or 2 months after diagnosis (if no treatment). Absolute difference in frequency of testing between DAP and non-DAP patients is shown on the x-axis, which was calculated as % DAP-% non-DAP so that positive values indicate higher utilization in DAPs. Corresponding percentages are reported in [Appendix 3]. The dot corresponds to the mean difference in proportions, and the horizontal lines represent the 95% confidence interval. DAP = Diagnostic assessment program, CT = Computed tomography, PET = Positron emission tomography, MRI = Magnetic resonance imaging

Click here to view



Consultations

Among stage III/IV patients, DAP patients were more likely to have a consultation with a radiation oncologist and a medical oncologist. DAP patients were more likely to have a consultation with a general surgeon or general thoracic surgeon, regardless of stage.

Treatment

DAP patients were also more likely to receive treatment [Figure 1] and [Appendix 3]: 67% versus 57% of stage I and 64% versus 42% of stage II patients received surgery; 66% versus 56% of stage III and 43% versus 30% of stage IV patients received radiation; and 58% versus 45% of stage III and 49% versus 34% of stage IV patients received systemic therapy. Overall, 1,329 (15%) of DAP patients and 4,130 (32%) of non-DAP patients had no evidence of surgery, radiation, or chemotherapy within 6 months of diagnosis.

Duration of intervals between investigations or consultations and diagnosis

For both DAP and non-DAP patients, the chest X-ray was typically the earliest imaging procedure, occurring a median of 18 (0, 68) days before diagnosis for DAP patients and a median of 39 (15, 74) days before diagnosis for non-DAP patients [Appendix 4]. The time from diagnosis until PET scan was 3 weeks for non-DAP patients (median: 22 (−5, 38) days) but 5 days for DAP patients (median: 5 [−8, 20]). Both DAP and non-DAP patients waited 3 weeks after diagnosis to receive a consultation with a medical oncologist or a radiation oncologist. DAP patients received a consultation with a general thoracic surgeon a median of 8 days before diagnosis, yet non-DAP patients received these consultations a median of 2 days after diagnosis. The median wait time for a general thoracic surgeon consultation between DAP and non-DAP patients was similar for stage I patients (12–14 days) but shorter for DAP patients among stage II patients (median: −1 day vs. +10 days).

Wait time – diagnostic interval

The time from first health-care encounter until diagnosis was a median 61 (13, 130) days (mean: 73 [SD: 62] days) for non-DAP patients and a median 64 (33, 123) days (mean: 78 [SD: 54] days) for DAP patients. After adjustment, DAP patients had a similar time until diagnosis as non-DAP patients (beta: −0.8 [−2.7, 1.1] days) [Table 2]. The diagnostic interval was longer for patients with more comorbidities (beta: 11.5 [9.3, 13.7] days longer for patients with Charlson score 1 vs. 0); shorter for patients with more advanced disease (beta: −16.8 [−19.5, −14.1] days for patients with stage III disease vs. stage I); 1 month shorter for patients who visited the emergency department within 1 week before diagnosis (beta: −28.5 [−31.1, −26.0] days); and 10 days longer for patients who were admitted at the time of diagnosis (beta: 9.8 [7.5, 12.1] days). Geographically, the maximum difference was <10 days between the regions with the longest and shortest diagnostic intervals.
Table 2: Factors associated with wait times

Click here to view


Wait time – pretreatment interval

The time from diagnosis until the start of treatment was similar between DAP (median: 41 (19, 69) days) and non-DAP patients (median: 39 (22, 58) days) [wait times by stage in [Appendix 5]. After adjustment, DAP patients had a significantly shorter pretreatment interval (beta: −8.5 [−9.7, −7.3] days) [Table 2]. The pretreatment interval was longer for patients with non-small-cell lung cancer (beta: 20.5 [18.9, 22.2] days) and for patients who had an emergency department visit within 1 week of diagnosis (beta: 19.4 [17.8, 21.1] days) but shorter for patients who were admitted at the time of diagnosis (beta: −30.4 [−31.9, −28.9] days).



Overall survival

In unadjusted analysis, DAP patients had significantly better overall survival than non-DAP patients (HR: 0.69 [0.66–0.71]). After adjustment for age, sex, rurality, neighborhood residence, comorbidity, stage, and histology, this effect was attenuated but still statistically significant (HR: 0.79 [0.76–0.82], P < 0.0001) [Table 3]. After additionally adjusting for emergency department visit within 7 days of diagnosis and hospital admission at the time of diagnosis, the prognostic effect of DAPs was further reduced (HR: 0.96 [0.92–1.00], P = 0.05).
Table 3: Factors associated with overall survival

Click here to view


We explored the relationship between wait times and overall survival [Table 3], bottom]. In the unadjusted analysis, a longer time until diagnosis was associated with better overall survival, exhibiting a linear trend (P < 0.0001) that was lost after adjustment (P = 0.18). A longer pretreatment interval was also associated with better overall survival, except for patients receiving treatment on the day of diagnosis [Figure 2]. This relationship persisted after adjustment (P < 0.0001). A similar trend was observed across stages [Appendix 6], but patients who received treatment on the diagnosis date had qualitatively different survival patterns according to stage. Treatment on the diagnosis date was associated with better overall survival for stage I patients (HR: 0.35 [0.24–0.50]) but worse survival for stage IV patients (HR: 2.29 [1.94–2.69]) [Appendix 6].
Figure 2: Kaplan–Meier plot for overall survival stratified by the time from diagnosis until treatment. Log-rank P < 0.0001

Click here to view




   Discussion Top


Our study demonstrates that DAP patients receive more treatment and have better overall survival than non-DAP patients, despite comparable wait times for diagnosis. This is consistent with data published by the International Cancer Benchmarking Partnership showing that among Canadian provinces, Ontario had the highest survival rates but the longest wait-times.[17] Taken together, these results imply that organized diagnostic assessment and treatment for lung cancer offers benefits that are clinically important beyond shorter wait times. Furthermore, we have previously reported that patient navigation associated with DAPs successfully mitigates the negative effects of longer wait times on patient experience.[16] Compared with non-DAP patients, DAP patients were more likely to receive diagnostic tests and consultations with specialists. By providing more streamlined access to specialist assessment, DAP patients had increased opportunity for treatment. DAP patients had a shorter pre-treatment interval, but there was no evidence that the reduced interval improved survival.

Although overall survival for DAP patients was better than non-DAP patients, the mechanism is unknown. We found that the prognosis for DAP patients was better than for non-DAP patients after adjusting for most clinical and demographic characteristics. However, this effect was largely explained by patients who presented to emergency or required hospital admission. One explanation is that urgent presentation is usually a reflection of symptoms which in turn are often related to advanced disease and thus is a strong confounder for the effect of DAPs on survival. Another explanation is that DAPs reduce the likelihood of such urgent cases from arising through early referrals and fast-tracking, thereby serving as a mediator. Since patients diagnosed emergently comprise almost half of all lung cancers, these patients should be further characterized in future work.[15],[17],[18]

In international comparisons including nine jurisdictions, wait times for lung cancer diagnosis in Ontario were longer than Wales, Denmark Sweden, England, and Scotland.[17] To improve the efficiency of lung cancer diagnosis and treatment, many cancer programs in Canada implemented programmatic changes that focused on reducing the duration of the diagnostic interval. The “Time to Treat” program launched at a single hospital in Toronto in 2005 used a clinical pathway that included checklists, patient navigators, and dedicated booking times for CT scanning or bronchoscopy.[9] Program implementation was associated with a reduction in the median time from suspicious chest radiograph until diagnosis from 128 to 20 days, but referral patterns were markedly different pre- and postimplementation, making comparisons difficult.[9] One program in Newfoundland, Canada, hired additional CT technologists and extended CT operating hours, which reduced the time from initial imaging to confirmatory CT from 19 days to 7.5 days and first abnormal image until biopsy from 81 days until 48 days.[1] We observed a similar reduction from the time of the first chest X-ray until the first chest CT scan (12 days for non-DAP patients and 7 days for DAP patients), but an earlier chest CT did not reduce the diagnostic interval in Ontario. In many lung DAPs, patients' diagnostic and staging evaluations are directed by the same thoracic surgeon who ultimately assumes responsibility for treating that patient. This eliminates the need for surgical referral following diagnosis, which may explain the shorter pretreatment interval for DAP patients who had general thoracic surgeon consultations sooner than non-DAP patients. DAPs may also enable better access to health-care services for patients who do not have a general practitioner.[15]

In addition to wait times, the effectiveness of a DAP can be explored by assessing the alignment of care with various guidelines. First, the timely use of PET among DAP patients (median: 5 days after diagnosis) is consistent with evidence, suggesting that PET should be performed quickly following biopsy.[19] DAPs likely accomplished this by requesting PET scanning earlier in the process of determining disease extent than non-DAPs, potentially even before a biopsy was performed. Second, the shorter time until treatment observed among DAP patients may partly be explained by fewer repeated CTs, suggesting better access to original images and better coordination of care as more tests are performed in the same place and within the same medical record system.[20] The earlier use of chest CT in DAPs (25 versus 3 days before diagnosis) could reduce the use of less sensitive diagnostic imaging (e.g., repeat chest X-ray or sputum cytology) that has been linked to duplicate testing and delay.[21] However, repeated CTs were still frequently observed. Third, although DAP patients were less likely to receive an abdominal CT scan, more than half of all patients received this scan. Abdominal CTs are not broadly recommended for lung cancer patients because chest CTs include the liver and PET scans are more accurate for the diagnosis of intra-abdominal metastases.[22],[23] Fourth, the use of brain imaging was higher among DAP patients, but utilization was high even among stage I–II patients. This finding is consistent with prior reports, where the perceived risk of brain metastasis and subsequent impact to patient management is felt to be high enough to justify imaging despite guidelines.[24],[25],[26],[27]

Despite longer wait times for lung cancer diagnosis in Ontario, 1- and 5-year survival rates were higher in international comparisons.[17],[28] There is little evidence that shorter diagnostic or pretreatment intervals improve survival.[7] Wait times for lung cancer diagnosis and treatment in Ontario are similar to those reported elsewhere in Canada and internationally, but 1 and 5 year survival rates were higher in Ontario.[19],[28] Confounding of the relationship between wait times and survival may persist even after adjustment for the best-known and available prognostic factors (e.g., stage, comorbidity, histology, and age), as demonstrated by the often inverse relationship between survival and wait times (e.g., due to appropriate triaging).[29] Thus, quality improvement initiatives should strive to improve outcomes such as efficiency, quality of life, concordance with evidence-based care, patient experience, and value-for-money rather than the more readily measured wait times.

Although this is a large population-based study, there are some limitations. First, delayed referral to a DAP may result in misclassification of DAP status, as some patients may have had some of their diagnostic assessment performed in usual care. This will underestimate the effect of DAPs. Second, administrative data do not include indications for tests, so we cannot speak to the appropriateness of duplicate imaging (e.g., for progression of symptoms). Third, we did not estimate the effect of DAPs on patients who are ultimately determined to be cancer free. We anticipate that they would have had a similar experience in the diagnostic interval to those with a cancer diagnosis. Fourth, implementation of evidence-based pathways in DAPs may have also influenced care pathways outside of DAPs. This blending of exposure may result in an underestimation of the true effect of DAPs. Finally, we did not examine the impact of DAPs on patient experience and quality of life, but prior studies have reported better patient experience associated with DAPs.


   Conclusion Top


Lung cancer patients diagnosed through a DAP were more likely to receive testing and consultation with specialists during the diagnostic and pretreatment intervals and subsequently, to receive treatment. Although DAPs reduced the time from diagnosis until treatment, this duration still exceeds recommended targets and the frequency of duplicate imaging was higher than expected. To optimize health care utilization and outcomes, further work is required to assess apparent inefficiencies such as repeated chest CT scans, abdominal CT scans despite PET-CT, and brain imaging for stage I patients.

Acknowledgments

We acknowledge Grace Bannerman who helped edit the manuscript as a medical writer.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
   References Top

1.
Byrne SC, Barrett B, Bhatia R. The impact of diagnostic imaging wait times on the prognosis of lung cancer. Can Assoc Radiol J 2015;66:53-7.  Back to cited text no. 1
    
2.
Kuroda H, Sugita Y, Ohya Y, Yoshida T, Arimura T, Sakakura N, et al. Importance of avoiding surgery delays after initial discovery of suspected non-small-cell lung cancer in clinical stage IA patients. Cancer Manag Res 2019;11:107-15.  Back to cited text no. 2
    
3.
Frelinghuysen M, Fest J, Van der Voort Van Zyp NC, Van der Holt B, Hoogeman M, Nuyttens J. Consequences of referral time and volume doubling time in inoperable Patients With Early Stage Lung Cancer. Clin Lung Cancer 2017;18:e403-9.  Back to cited text no. 3
    
4.
Kasymjanova G, Small D, Cohen V, Jagoe RT, Batist G, Sateren W, et al. Lung cancer care trajectory at a Canadian centre: An evaluation of how wait times affect clinical outcomes. Curr Oncol 2017;24:302-9.  Back to cited text no. 4
    
5.
Olsson JK, Schultz EM, Gould MK. Timeliness of care in patients with lung cancer: A systematic review. Thorax 2009;64:749-56.  Back to cited text no. 5
    
6.
Di Girolamo C, Walters S, Gildea C, Benitez Majano S, Rachet B, Morris M. Can we assess Cancer Waiting Time targets with cancer survival? A population-based study of individually linked data from the National Cancer Waiting Times monitoring dataset in England, 2009-2013. PLoS One 2018;13:e0201288.  Back to cited text no. 6
    
7.
Jacobsen MM, Silverstein SC, Quinn M, Waterston LB, Thomas CA, Benneyan JC, et al. Timeliness of access to lung cancer diagnosis and treatment: A scoping literature review. Lung Cancer 2017;112:156-64.  Back to cited text no. 7
    
8.
Malalasekera A, Nahm S, Blinman PL, Kao SC, Dhillon HM, Vardy JL. How long is too long? A scoping review of health system delays in lung cancer. Eur Respir Rev 2018;27:180045.  Back to cited text no. 8
    
9.
Lo DS, Zeldin RA, Skrastins R, Fraser IM, Newman H, Monavvari A, et al. Time to treat: A system redesign focusing on decreasing the time from suspicion of lung cancer to diagnosis. J Thorac Oncol 2007;2:1001-6.  Back to cited text no. 9
    
10.
Malmström M, Rasmussen BH, Bernhardson BM, Hajdarevic S, Eriksson LE, Andersen RS, et al. It is important that the process goes quickly, isn't it?&quot; A qualitative multi-country study of colorectal or lung cancer patients' narratives of the timeliness of diagnosis and quality of care. Eur J Oncol Nurs 2018;34:82-8.  Back to cited text no. 10
    
11.
Largey G, Ristevski E, Chambers H, Davis H, Briggs P. Lung cancer interval times from point of referral to the acute health sector to the start of first treatment. Aust Health Rev 2016;40:649-54.  Back to cited text no. 11
    
12.
Evans WK, Ung YC, Assouad N, Chyjek A, Sawka C. Improving the quality of lung cancer care in ontario: The lung cancer disease pathway initiative. J Thorac Oncol 2013;8:876-82.  Back to cited text no. 12
    
13.
Honein-AbouHaidar GN, Stuart-McEwan T, Waddell T, Salvarrey A, Smylie J, Dobrow MJ, et al. How do organisational characteristics influence teamwork and service delivery in lung cancer diagnostic assessment programmes? A mixed-methods study. BMJ Open 2017;7:e013965. doi: 10.1136/bmjopen-2016-013965.  Back to cited text no. 13
    
14.
Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 2005;43:1130-9.  Back to cited text no. 14
    
15.
Elliss-Brookes L, McPhail S, Ives A, Greenslade M, Shelton J, Hiom S, et al. Routes to diagnosis for cancer Determining the patient journey using multiple routine data sets. Br J Cancer 2012;107:1220-6.  Back to cited text no. 15
    
16.
Wheeler S, Gilbert J, Kaan M, Klonikowski E, Holloway C. The patient: The importance of knowing your navigator. Patient Exp J 2015;2;Article 12 . [Doi: 10.35680/2372-0247.1058].  Back to cited text no. 16
    
17.
Menon U, Vedsted P, Zalounina Falborg A, Jensen H, Harrison S, Reguilon I, et al. Time intervals and routes to diagnosis for lung cancer in 10 jurisdictions: Cross-sectional study findings from the International Cancer Benchmarking Partnership (ICBP). BMJ Open 2019;9:e025895.  Back to cited text no. 17
    
18.
Suhail A, Crocker CE, Das B, Payne JI, Manos D. Initial presentation of lung cancer in the emergency department: A descriptive analysis. CMAJ Open 2019;7:E117-23.  Back to cited text no. 18
    
19.
Yeung CS, Musaddaq B, Hare S, Wagner T. 18F-FDG-PET/CT study after lung biopsy in suspected lung cancer patients: Time is of the essence. Nucl Med Commun 2017;38:99-100.  Back to cited text no. 19
    
20.
Moore HB, Loomis SB, Destigter KK, Mann-Gow T, Dorf L, Streeter MH, et al. Airway, breathing, computed tomographic scanning: Duplicate computed tomographic imaging after transfer to trauma center. J Trauma Acute Care Surg 2013;74:813-7.  Back to cited text no. 20
    
21.
Verma A, Lim AY, Tai DY, Goh SK, Kor AC, Dokeu AA, et al. Timeliness of diagnosing lung cancer: Number of procedures and time needed to establish diagnosis: Being right the first time. Medicine (Baltimore) 2015;94:e1216.  Back to cited text no. 21
    
22.
Verschakelen JA, Bogaert J, De Wever W. Computed tomography in staging for lung cancer. Eur Respir J Suppl 2002;35:40s-8s.  Back to cited text no. 22
    
23.
Maziak DE, Darling GE, Inculet RI, Gulenchyn KY, Driedger AA, Ung YC, et al. Positron emission tomography in staging early lung cancer: A randomized trial. Ann Intern Med 2009;151:221-8, W-48.  Back to cited text no. 23
    
24.
Schoenmaekers JJ, Dingemans AC, Hendriks LE. Brain imaging in early stage non-small cell lung cancer: Still a controversial topic? J Thorac Dis 2018;10:S2168-71.  Back to cited text no. 24
    
25.
Diaz ME, Debowski M, Hukins C, Fielding D, Fong KM, Bettington CS. Non-small cell lung cancer brain metastasis screening in the era of positron emission tomography-CT staging: Current practice and outcomes. J Med Imaging Radiat Oncol 2018;62:383-8.  Back to cited text no. 25
    
26.
Ando T, Kage H, Saito M, Amano Y, Goto Y, Nakajima J, et al. Early stage non-small cell lung cancer patients need brain imaging regardless of symptoms. Int J Clin Oncol 2018;23:641-6.  Back to cited text no. 26
    
27.
Balekian AA, Fisher JM, Gould MK. Brain imaging for staging of patients with clinical stage IA non-small cell lung cancer in the national lung screening trial: Adherence with recommendations from the choosing wisely campaign. Chest 2016;149:943-50.  Back to cited text no. 27
    
28.
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, 1995-2007 (the International Cancer Benchmarking Partnership): An analysis of population-based cancer registry data. Lancet 2011;377:127-38.  Back to cited text no. 28
    
29.
Brookhart MA, Stürmer T, Glynn RJ, Rassen J, Schneeweiss S. Confounding control in healthcare database research: Challenges and potential approaches. Med Care 2010;48:S114-20.  Back to cited text no. 29
    


    Figures

  [Figure 1], [Figure 2]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]



 

Top
Print this article  Email this article
 
  Search
 
  
    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Article in PDF (1,496 KB)
    Citation Manager
    Access Statistics
    Reader Comments
    Email Alert *
    Add to My List *
* Registration required (free)  


    Abstract
   Methods
   Results
   Discussion
   Conclusion
    References
    Article Figures
    Article Tables

 Article Access Statistics
    Viewed672    
    Printed12    
    Emailed0    
    PDF Downloaded78    
    Comments [Add]    

Recommend this journal