
==== Front
JCO Precis Oncol
JCO Precis Oncol
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PO
JCO Precision Oncology
2473-4284
Wolters Kluwer Health

39208373
PO.24.00039
10.1200/PO.24.00039
00202
Original Reports
Biomarkers
Real-World Biomarker Test Ordering Practices in Non–Small Cell Lung Cancer: Interphysician Variation and Association With Clinical Outcomes
https://orcid.org/0000-0002-4182-6349
Baron Jason M. MD, DABP 1
Widatalla Sarrah PhD 1
Gubens Matthew A. MD, MS, FASCO 2
Khalil Farah MD, MMM, FCAP 3 4
1 Roche Diagnostics, Medical and Scientific Affairs, Indianapolis, IN
2 Division of Hematology and Oncology, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA
3 Department of Anatomic Pathology, Moffitt Cancer Center, Tampa, FL
4 University of South Florida, Tampa, FL
Jason M. Baron, MD, DABP; e-mail: jason.baron@roche.com.
2024
29 8 2024
29 8 2024
8 e240003917 1 2024
14 6 2024
31 7 2024
© 2024 by American Society of Clinical Oncology
2024
American Society of Clinical Oncology
https://creativecommons.org/licenses/by-nc-nd/4.0/ Creative Commons Attribution Non-Commercial No Derivatives 4.0 License: http://creativecommons.org/licenses/by-nc-nd/4.0/

PURPOSE

Patients with metastatic or advanced non–small cell lung cancer (NSCLC) need biomarker testing, including, in most cases, anaplastic lymphoma kinase (ALK), epidermal growth factor receptor (EGFR), and PD-L1, to identify options for targeted therapies and to optimally incorporate immune checkpoint inhibitors into therapeutic regimens. We sought to examine real-world patterns of biomarker testing, quantify interphysician practice variation, and correlate testing with clinical outcomes.

METHODS

We extracted real-world data from a nationwide electronic health record–derived deidentified database from 17,165 patients diagnosed with advanced NSCLC between 2018 and 2021 and receiving care in the community setting. We analyzed data using descriptive analyses, fixed- and mixed-effects logistic regression models, and proportional hazard models.

RESULTS

Only 67% of all 17,165 patients and 77% of patients with nonsquamous, metastatic NSCLC had ALK, EGFR, and PD-L1 testing within 90 days of diagnosis. Later diagnosis year (2019-2021 compared with 2018) was associated with higher rates of ALK, EGFR, and PD-L1 testing; stage IIIB/C disease (compared with stage IV), squamous histology, and Black or African American race were associated with lower rates. Interphysician variation was substantial with a median odds ratio between physicians (adjusted for patient factors) of 1.78 for ALK, EGFR, and PD-L1 testing. Patients with nonsquamous, metastatic NSCLC had significantly prolonged survival if tested with all three biomarkers (median, 364 days for all three v 180 for none of the three; hazard ratio, 0.67; P < .001).

CONCLUSION

Rates of biomarker testing appear suboptimal with substantial interphysician variation. Testing correlates with improved survival, although causality cannot be proven from this study. Additional work is needed to address the underlying causes of suboptimal test ordering.

OPEN-ACCESSTRUE
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pmcINTRODUCTION

With approximately 240,000 new cases per year in the United States, lung cancer is the second most commonly diagnosed cancer in both men and women.1 The majority of lung cancers are classified as non–small cell lung cancer (NSCLC), which includes squamous cell, adenocarcinoma, and large cell histologies. Most lung cancers are diagnosed at advanced stages, and 5-year survival is unfortunately poor (estimated at approximately 10% for patients diagnosed with distant metastasis).2

CONTEXT

Key Objective

To examine real-world patterns of testing for anaplastic lymphoma kinase (ALK), epidermal growth factor receptor (EGFR), and PD-L1 in patients with advanced non–small cell lung cancer (NSCLC) treated in a community practice setting and associations between testing and patient survival.

Knowledge Generated

Patients with advanced NSCLC treated in a community setting commonly fail to receive guideline-based testing, with more than one in five patients with metastatic, nonsquamous NSCLC not tested for ALK, EGFR, and/or PD-L1. The analysis identified evidence of substantial variation in testing patterns across different oncologists and demonstrated that in real-world practice, guideline-based testing correlates with improved clinically adjusted survival.

Relevance

These findings highlight an important gap in real-world practice and provide a foundation for efforts to improve testing and reduce unwarranted interphysician variation. The association between testing and survival, although not necessarily fully causal, highlights the importance of optimal testing and of addressing this practice gap.

However, recent advancements in the diagnosis and treatment of lung cancer have led to improved survival.3 For example, anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitor (TKI) therapies, including alectinib, brigatinib, and lorlatinib, target lung cancers driven by rearrangements in the ALK gene and may significantly improve survival and other patient outcomes in comparison with traditional chemotherapy.4 Likewise, NSCLC with epidermal growth factor receptor (EGFR) mutations can benefit from EGFR inhibitor therapies.5,6 Compared with platinum-based chemotherapy, patients with EGFR-mutant cancer have better response rates and progression-free survival when treated with EGFR TKIs, including erlotinib, gefitinib, and afatinib.5 In addition, immune checkpoint inhibitors (ICIs), such as pembrolizumab, nivolumab, or atezolizumab, target PD-1, PD-L1, or CTLA-4, and have been linked to improved response rates, alone or in combination with chemotherapy compared with chemotherapy alone, offering significant benefits in treating patients with NSCLC.7

Biomarker testing is crucial in advanced NSCLC. Mutational profiling (eg, EGFR and ALK) will determine if targeted therapies will be beneficial, and if so, the optimal therapeutic options. In addition, the level of PD-L1 expression will guide the incorporation of ICIs into the therapeutic regimen (and finding of EGFR or ALK alterations will generally lead to their omission because of a lack of benefit). National Comprehensive Cancer Network guidelines6 recommend biomarker testing in advanced NSCLC for EGFR mutations, ALK rearrangements, ROS1 rearrangements, BRAF mutations, NTRK1/2/3, MET exon 14 skipping mutations, RET mutations, HER2 overexpression, and PD-L1 expression. Mutational profiling is most strongly recommended in patients with metastatic disease and nonsquamous histology.6 Although broad mutational profiling is important and recommended by guidelines (at least for advanced or metastatic disease of nonsquamous histology), the evidence for testing is particularly strong in the case of ALK, EGFR, and PD-L1,6 and thus we focus on these three markers for this paper.

Although compliance with guideline recommendations for biomarker testing in NSCLC is essential to provide patients with the most effective and personalized treatment options, unfortunately, patients often fail to receive guideline-based testing. For example, Waterhouse et al8 find that only 79%, 80%, and 72% of patients with NSCLC treated in a community data set had ALK, EGFR, and PD-L1 testing, respectively. Guideline nonconformance and interphysician variation in care have been well documented9-12 across a wide range of areas of laboratory testing.

Here, using real-world electronic health record (EHR) data, we examined clinical and practice factors that predict whether patients will receive guideline-based testing. Moreover, we correlated testing with clinical outcomes to see if patients who receive guideline-based testing have improved survival.

METHODS

We extracted and assembled a data set including 17,165 patients with advanced NSCLC receiving care in the community setting. We then used these data to assess patterns of ALK, EGFR, and PD-L1 testing, variation in care, and associations between biomarker testing and survival. An overview of the data flow and methods is shown in Figure 1. Key methods are described below with additional detail provided in the Data Supplement (Part A).

FIG 1. Summary of methods used in this study. ALK, anaplastic lymphoma kinase; EGFR, epidermal growth factor receptor; NSCLC, non–small cell lung cancer. aRandom intercepts for physician and practice.

Data Set and Ethics

This study used the nationwide Flatiron Health EHR-derived deidentified database, a longitudinal database, comprising deidentified patient-level structured and unstructured data, curated via technology-enabled abstraction.13,14 The data are subject to obligations to prevent reidentification and protect patient confidentiality. During the study period, the deidentified data in the overall database originated from approximately 280 US cancer clinics (approximately 800 sites of care); about half of these 280 clinics contributed to the advanced NSCLC extended datamart used in this study as specifically quantified below. The majority of patients in the database originate from community oncology settings; relative community/academic proportions may vary depending on study cohort. One hundred and forty-one community practices and 1,250 known physicians were included in our final cohort. Because all data used were deidentified, this study was not considered human subject research and institutional review board approval was not needed.

Inclusion Criteria and General Patient Attributes

We initially included all patients within the database who were determined to have advanced-stage NSCLC (stage IIIB or higher) at the time of initial diagnosis, who were diagnosed between 2018 and 2021 and received care in the community setting. We extracted data delivered as of June 2022. We excluded 3.1% of patients meeting the above criteria who had testing for ALK, EGFR, and/or PD-L1 but with uncertain timing.

For each remaining patient, we extracted attributes including survival, age, biologic sex, race, ethnicity, smoking status, tumor stage, tumor histology, physician (deidentified code), and practice (deidentified code). We also captured key comorbid diagnoses and translated these into a modified Charlson Comorbidity Index15,16 (Data Supplement). Fifteen percent and 33%, respectively, of patients had unknown race and unknown ethnicity; these patients were assigned to a category of unknown (see the Data Supplement for additional analysis of missing data and bias considerations). Although our analysis focuses on patients seen in the community, we performed a supplemental look at certain aspects of care in four academic practices included within the database (Data Supplement). Except as otherwise noted, all results herein refer to the community practices.

Survival

We captured survival as the number of days between the date of diagnosis and the date of death. To improve accuracy of mortality data, the database pulls dates of death from not only the EHR systems, but also the social security master death file and an obituary database. Survival for patients not listed as deceased as of the date of data extract (mid-2022) was censored to the date of the patient's last listed visit.

Biomarker Testing

For our primary analyses, we evaluated ALK, EGFR, and PD-L1 testing within 90 days of diagnosis and as a secondary outcome within 28 days of diagnosis. Because we had approximately 6 months of data after the final patient was diagnosed, there should have been sufficient longitudinal follow-up to determine whether all patients had the noted biomarkers within 90 days.

Statistical Approach and Plot Generation

Statistical analysis was performed in R.17 Univariate analysis was used to assess rates of biomarker testing by patient subgroup; CIs were calculated using the Wilson method (R binom package). Mixed-effects logistic regression was used to assess patient factors that affect biomarker test ordering with physician and practice included as random effects. Mixed-effects logistic regression was performed in R using the lme4 package.18 Random intercepts were summarized in terms of median odds ratios (MORs) and variance partition coefficients19,20; bootstrap CIs were generated with the bootMER package.21

Survival analysis was used to assess the impact of testing on survival and was performed using the R survival package22 and the survminer package.23 Proportional hazard models were used to calculate hazard ratios and P values; Kaplan-Meier (KM) survival curve functionality was used to generate the survival times and CIs used in plotting KM curves and in calculating median and IQRs for survival.

RESULTS

The study included 17,165 patients diagnosed with advanced NSCLC from January 1, 2018, through December 31, 2021. The patients ultimately included in the study were under the care of 1,250 unique clinicians and 141 unique practices. Patient characteristics are summarized in Table 1. The Data Supplement (Table S1) compares patient characteristics for patients with known race/ethnicity to those with unknown race/ethnicity.

TABLE 1. Patient Attributes

Attribute	No. (%)	
Demographic		
 Age, years		
  ≤60	3,421 (20)	
  61-65	2,675 (16)	
  66-70	2,992 (17)	
  71-75	3,103 (18)	
  >75	4,974 (29)	
 Biologic sex		
  Male	9,062 (53)	
  Female	8,103 (47)	
 Race		
  Asian	501 (3)	
  Black or African American	1,540 (9)	
  Other races	2,028 (12)	
  Unknown	2,603 (15)	
  White	10,493 (61)	
 Hispanic/Latinx		
  Yes	533 (3)	
  No	10,911 (64)	
  Unknown	5,721 (33)	
Tumor		
 Stage		
  IIIB	2,735 (16)	
  IIIC	444 (3)	
  IV	13,986 (82)	
 Histology		
  Nonsquamous cell carcinoma	11,931 (70)	
  NSCLC histology NOS	824 (5)	
  Squamous cell carcinoma	4,410 (26)	
 Survival		
  Deceased during study period	10,472 (61)	
Abbreviations: NOS, not otherwise specified; NSCLC, non–small cell lung cancer.

Rates of Testing

Overall, 67% of patients had ALK, EGFR, and PD-L1 testing within 90 days. Eight percent had both ALK and EGFR but not PD-L1, and 12% had PD-L1 but not both ALK and EGFR. Fourteen percent of patients had no biomarker testing within 90 days (this includes rare cases of patients with EGFR or ALK, but not both). Because testing guidelines most strongly recommend testing for ALK and EGFR, among other biomarkers in the setting of metastatic disease and nonsquamous histology,6 we further analyzed testing among this group. In patients with stage IV tumors and nonsquamous histology, testing rates were 77% for ALK, EGFR, and PD-L1, with 9% having both ALK and EGFR but not PD-L1, and 7% having PD-L1 but not both ALK and EGFR. Seven percent of patients with stage IV tumors and nonsquamous histology were tested for neither PD-L1 nor ALK and EGFR. Table 2 provides testing rates across a range of other subgroups. A supplemental look at testing in the academic setting is shown in the Data Supplement (Table S2). Given the limited number of academic practices (four), we do not directly compare these results with the community analysis found herein.

TABLE 2. Proportion of Patients Tested With Biomarkers Within 28 and 90 Days of Diagnosis

Subgroup (or overall)	ALK, EGFR, and PD-L1	PD-L1	
Metastatic Disease and Not Squamous	All Patients	Metastatic Disease and Not Squamous	All Patients	
28 Days	90 Days	28 Days	90 Days	28 Days	90 Days	28 Days	90 Days	
All patients	68.2% (67.3%-69.1%)	76.8% (76%-77.6%)	58.5% (57.7%-59.2%)	66.8% (66%-67.5%)	78.5% (77.7%-79.2%)	84.0% (83.3%-84.6%)	72.6% (71.9%-73.3%)	78.3% (77.7%-78.9%)	
Age, years									
 ≤60	68.7% (66.8%-70.6%)	77.6% (75.8%-79.2%)	59.3% (57.6%-60.9%)	67.5% (65.9%-69%)	78% (76.3%-79.6%)	83.8% (82.2%-85.2%)	72.1% (70.6%-73.6%)	77.9% (76.5%-79.3%)	
 61-65	68.8% (66.6%-71%)	78.2% (76.2%-80.1%)	58.8% (56.9%-60.7%)	67.9% (66.1%-69.6%)	79.6% (77.6%-81.4%)	85.8% (84.1%-87.4%)	73.1% (71.4%-74.7%)	79.3% (77.8%-80.8%)	
 66-70	69.8% (67.7%-71.8%)	77.1% (75.2%-79%)	59.1% (57.3%-60.8%)	66.5% (64.8%-68.2%)	79.9% (78%-81.6%)	84.6% (82.9%-86.1%)	72.7% (71.1%-74.3%)	78.2% (76.7%-79.7%)	
 71-75	67.3% (65.2%-69.3%)	75.8% (73.8%-77.7%)	56.7% (54.9%-58.4%)	65.2% (63.5%-66.9%)	78.2% (76.3%-80%)	83.2% (81.4%-84.8%)	71.6% (70%-73.1%)	77.1% (75.5%-78.5%)	
 >75	67.1% (65.5%-68.8%)	75.9% (74.4%-77.4%)	58.5% (57.1%-59.8%)	66.8% (65.4%-68.1%)	77.5% (76%-78.9%)	83.2% (81.9%-84.5%)	73.3% (72.1%-74.5%)	78.7% (77.6%-79.9%)	
Dx year									
 2018	66.6% (64.9%-68.2%)	74.1% (72.5%-75.6%)	56% (54.6%-57.4%)	63.1% (61.7%-64.4%)	77.8% (76.3%-79.3%)	82.5% (81.2%-83.9%)	72% (70.7%-73.3%)	76.9% (75.7%-78.1%)	
 2019	68% (66.3%-69.7%)	76.9% (75.4%-78.4%)	57.6% (56.1%-59%)	65.7% (64.3%-67.1%)	79.1% (77.6%-80.6%)	84.5% (83.2%-85.8%)	72.5% (71.2%-73.8%)	77.8% (76.6%-79%)	
 2020	69.5% (67.7%-71.3%)	78.1% (76.5%-79.7%)	59.6% (58.1%-61.1%)	68.2% (66.7%-69.6%)	78.6% (77%-80.2%)	84.8% (83.4%-86.2%)	73.3% (72%-74.7%)	79.5% (78.2%-80.7%)	
 2021	69.1% (67.3%-70.9%)	78.6% (77%-80.1%)	61.1% (59.6%-62.6%)	70.7% (69.3%-72.1%)	78.3% (76.6%-79.8%)	84.1% (82.6%-85.5%)	72.7% (71.3%-74.1%)	79.1% (77.8%-80.3%)	
Biologic sex									
 Female	68.6% (67.4%-69.8%)	77.4% (76.3%-78.5%)	60.6% (59.5%-61.7%)	69.2% (68.1%-70.2%)	79% (77.9%-80%)	84.6% (83.6%-85.5%)	73.9% (72.9%-74.8%)	79.7% (78.8%-80.6%)	
 Male	67.8% (66.5%-69%)	76.2% (75.1%-77.3%)	56.5% (55.5%-57.6%)	64.6% (63.6%-65.6%)	77.9% (76.8%-79%)	83.3% (82.3%-84.3%)	71.5% (70.5%-72.4%)	77% (76.1%-77.8%)	
Histology									
 Nonsquamous	69% (68.1%-69.9%)	77.6% (76.8%-78.4%)	66.2% (65.4%-67.1%)	74.8% (74%-75.6%)	79.3% (78.5%-80%)	84.7% (84%-85.4%)	76.6% (75.8%-77.3%)	82.2% (81.5%-82.8%)	
 NOS	55.6% (51.8%-59.3%)	64.9% (61.2%-68.4%)	52.2% (48.8%-55.6%)	61.4% (58%-64.7%)	65.8% (62.1%-69.3%)	72.7% (69.2%-75.9%)	62.4% (59%-65.6%)	69.1% (65.8%-72.1%)	
 Squamous cell	NA	NA	38.6% (37.2%-40%)	45.9% (44.4%-47.4%)	NA	NA	63.9% (62.4%-65.3%)	69.5% (68.1%-70.8%)	
Latinx									
 No	68.8% (67.7%-69.8%)	77.4% (76.4%-78.3%)	58.7% (57.8%-59.6%)	67.1% (66.2%-67.9%)	79.5% (78.5%-80.4%)	84.8% (83.9%-85.6%)	73.2% (72.4%-74%)	78.8% (78%-79.5%)	
 Unknown	67.8% (66.3%-69.3%)	76.2% (74.8%-77.5%)	58.2% (56.9%-59.5%)	66.3% (65.1%-67.5%)	77% (75.6%-78.3%)	82.7% (81.5%-83.9%)	71.6% (70.4%-72.7%)	77.3% (76.2%-78.4%)	
 Yes	62.3% (57.3%-67.1%)	72.9% (68.1%-77.2%)	55.5% (51.3%-59.7%)	65.3% (61.2%-69.2%)	73.4% (68.7%-77.7%)	80.8% (76.4%-84.5%)	71.3% (67.3%-75%)	78.4% (74.7%-81.7%)	
Race									
 Asian	70.5% (65.7%-74.8%)	81.7% (77.5%-85.3%)	63.5% (59.2%-67.6%)	74.5% (70.5%-78.1%)	78.3% (73.9%-82.2%)	86.2% (82.3%-89.3%)	73.5% (69.4%-77.1%)	81.6% (78%-84.8%)	
 Black or African American	63.4% (60.3%-66.3%)	73.1% (70.3%-75.7%)	55.6% (53.1%-58%)	64.4% (61.9%-66.7%)	76.7% (74%-79.2%)	82.4% (80%-84.6%)	71.4% (69.1%-73.6%)	77.3% (75.2%-79.4%)	
 Other races	68.1% (65.5%-70.5%)	78% (75.7%-80.2%)	57.7% (55.6%-59.9%)	67% (64.9%-69%)	76.9% (74.6%-79.1%)	83.5% (81.4%-85.4%)	71% (69%-72.9%)	77.6% (75.7%-79.4%)	
 Unknown	67.6% (65.3%-69.8%)	75% (72.9%-77%)	58.2% (56.3%-60%)	65.6% (63.7%-67.4%)	76.3% (74.2%-78.3%)	82% (80.1%-83.7%)	71.5% (69.7%-73.2%)	77.1% (75.4%-78.7%)	
 White	69% (67.9%-70.1%)	77.3% (76.3%-78.3%)	58.8% (57.9%-59.8%)	67% (66.1%-67.9%)	79.6% (78.6%-80.5%)	84.7% (83.8%-85.5%)	73.4% (72.5%-74.2%)	78.7% (77.9%-79.4%)	
Stage									
 IIIB	NA	NA	35.6% (33.8%-37.4%)	43.2% (41.4%-45.1%)	NA	NA	53.1% (51.2%-55%)	58.9% (57%-60.7%)	
 IIIC	NA	NA	40.5% (36.1%-45.2%)	47.1% (42.5%-51.7%)	NA	NA	56.5% (51.9%-61.1%)	61.9% (57.3%-66.3%)	
 IV	68.2% (67.3%-69.1%)	76.8% (76%-77.6%)	63.5% (62.7%-64.3%)	72% (71.2%-72.7%)	78.5% (77.7%-79.2%)	84% (83.3%-84.6%)	76.9% (76.2%-77.6%)	82.6% (81.9%-83.2%)	
Abbreviations: ALK, anaplastic lymphoma kinase; EGFR, epidermal growth factor receptor; NA, not applicable; NOS, not otherwise specified.

Factors Affecting Testing and Variation in Care

We performed multivariate, mixed-effects logistic regression (Fig 2) to further interrogate factors that affect testing in the community setting. These models use a hierarchical framework in which physicians and practices are treated as random intercepts.

FIG 2. Factors associated with the likelihood of biomarker testing. Shown is the proportion of patients tested for at least PD-L1 and the proportion tested for ALK, EGFR, and PD-L1 by patient subgroup. This provides a multivariate assessment of the factors that affect odds of testing. ORs for each factor are in comparison with the reference for each category (shown in the gray boxes to the right of the figure). Error bars represent 95% CIs and the dashed vertical line (OR, 1) represents no difference from the reference; ORs <1 (left of dashed line) indicate less likely to be tested. Factors with CIs not overlapping this dashed line would be considered statistically significant. ALK, anaplastic lymphoma kinase; CCI, Charlson Comorbidity Index; EGFR, epidermal growth factor receptor; NOS, not otherwise specified; NSCLC, non–small cell lung cancer; ORs, odds ratios.

The analysis revealed that more advanced stage and nonsquamous histology significantly increased the likelihood of testing for ALK, EGFR, and PD-L1 all together and for PD-L1, regardless of ALK and EGFR status. Patients were more likely to be tested if diagnosed in 2020 or 2021 than if diagnosed in 2018. Patients who were Black or African American were significantly less likely to be tested for ALK, EGFR, and PD-L1 compared with White patients.

In addition to patient factors, our multivariate analysis revealed significant physician and practice variation. We assessed variation on the basis of MOR (Fig 2; Table 3) and the variance partition coefficient (Table 3). For example, the MOR in the community setting for PD-L1 ordering within 90 days across physicians is 1.78 (Fig 2; Table 3); this means that if we randomly selected two practices, we would expect on average (median) that a patient seen at one practice would theoretically have 1.78 times the odds of testing compared with being seen at the other practice.

TABLE 3. Interphysician and Interpractice Variation

Target	Random Effect	Null Model (random intercepts only)	Full Model (random intercepts and patient-level independent variables)	PCV, %	
Variance	VPC (ICC)	MOR	Variance	VPC (ICC)	MOR	
ALK, EGFR, and PD-L1 ordering	Physician	0.225 (0.175-0.278)	0.062 (0.049-0.076)	1.573 (1.49-1.654)	0.251 (0.206-0.313)	0.068 (0.056-0.084)	1.612 (1.541-1.705)	12.8	
Practice	0.108 (0.07-0.16)	0.03 (0.02-0.044)	1.368 (1.287-1.465)	0.125 (0.056-0.189)	0.034 (0.016-0.051)	1.402 (1.252-1.514)	
Physician and practice combined	0.334 (0.273-0.404)	0.092 (0.077-0.11)	1.735 (1.647-1.833)	0.376 (0.322-0.455)	0.103 (0.09-0.122)	1.795 (1.718-1.902)	
PD-L1 ordering regardless of ALK/EGFR	Physician	0.34 (0.285-0.427)	0.091 (0.077-0.112)	1.744 (1.664-1.865)	0.363 (0.31-0.466)	0.096 (0.083-0.121)	1.777 (1.701-1.918)	9.4	
Practice	0.121 (0.068-0.196)	0.032 (0.019-0.052)	1.394 (1.282-1.525)	0.141 (0.089-0.222)	0.037 (0.024-0.058)	1.431 (1.329-1.568)	
Physician and practice combined	0.461 (0.39-0.569)	0.123 (0.107-0.149)	1.911 (1.815-2.053)	0.505 (0.424-0.626)	0.133 (0.115-0.162)	1.969 (1.861-2.126)	
NOTE. Physician and practice are treated as random intercepts in both the null model and the full model. The null model includes no fixed effects. The full model adjusts for the following fixed effects (as plotted in Fig 2): Age category, biologic sex, tumor stage, tumor histology, comorbidity score, race, and ethnicity. MORs also plotted in Figure 2 for comparison with fixed effects. Results for variance, VPC, and MOR include with point estimate with the 95% confidence (on the basis of a bootstrap procedure) in parentheses.

Abbreviations: ALK, anaplastic lymphoma kinase; EGFR, epidermal growth factor receptor; ICC, intraclass correlation coefficient; MOR, median odds ratio; PCV, percent change in variance (between the null model as the reference and full model); VPC, variance partition coefficient.

Testing Correlates With Survival

As shown in Figure 3, patients tested for ALK, EGFR, and PD-L1 had significantly prolonged survival compared with patients not tested for any of these in unadjusted and multivariate analysis. In unadjusted analysis (Fig 3A), patients with metastatic disease and nonsquamous histology survived a median of 364 days (349-384 days, IQR) if tested for ALK, EGFR, and PD-L1, compared with a median of 180 days (149-223) if tested for none of these (hazard ratio, 0.67; P < .001).

FIG 3. Correlation between testing and survival. (A) Kaplan-Meier survival curves for patients with nonsquamous histology and metastatic disease, grouped into having ALK, EGFR, and PD-L1 (green), some but not all of these (blue), and none of these (orange). (B) Multivariate survival analysis. The vertical dashed line (HR, 1) represents no effect; HR >1 (right of dashed line) indicates shorter survival, and HR <1 indicates longer survival. Error bars represent 95% CIs, with error bars not crossing the dashed vertical line indicating that the factor is statistically significant. See the Data Supplement (Part D) for discussion of association between comorbidities and mortality. Blank cells in the risk table indicate fewer than 10 patients. ALK, anaplastic lymphoma kinase; CCI, Charlson Comorbidity Index; EGFR, epidermal growth factor receptor; HR, hazard ratio; NOS, not otherwise specified; NSCLC, non–small cell lung cancer; ref, reference.

DISCUSSION

Here, using real-world EHR data, extracted from the Flatiron Health database, we show that nearly one in four patients with nonsquamous, metastatic NSCLC fails to receive testing for ALK, EGFR, and PD-L1, despite clear guidelines recommending this testing. Furthermore, nearly one in three patients overall with stage IIIB or higher NSCLC (including those with squamous histology) fails to receive all three of these tests. We show that testing patterns vary substantially between physicians and between practices; indeed, a patient may have very different odds of testing were the patient to change physicians or practices, even within a similar location. Figure 2 allows visual comparison of the magnitude (eg, distance from the OR, 1 vertical line) of the median physician and practice variation to the impact of patient-level factors; physician and practice are among the most impactful factors predicting biomarker ordering.

Some of the guideline nonconformance may be driven by patient preference and goals of care; for example, some patients, including those in poor health before the lung cancer diagnosis or with particularly poor prognoses may opt for palliative treatment and forgo testing. However, it seems unlikely that this explains the majority of the guideline nonconformance. We suspect that more prevalent reasons for guideline nonconformance may include limitations in practice infrastructure, clinician knowledge gaps, limited tissue availability, and patient financial resources and insurance coverage. We sought to interrogate the impact of insurance coverage, but the rate of missing insurance information in our data set was too high for this analysis to be included. The clinician and practice variation we find would be consistent with some of these clinician- and practice-related barriers. For example, physicians with differing levels of familiarity with the importance of biomarker testing may order these tests at different rates, contributing to the interphysician variation. Interpractice differences in practice infrastructure and the financial resources of the local patient population may explain some of the practice-related variation. Strategies including clinical decision support, improved infrastructure and workflows, improvements in testing access and coverage, and tissue stewardship initiatives may help to mitigate the impact of some of these factors. Because detailed information about specific physicians and practices was not included in the database, we could not empirically assess clear reasons underlying the variation in care; this would be a useful topic for future research with other data sets.

Our results, including the variation in care and the finding that Black or African American patients are less likely to receive ALK, EGFR, and PD-L1 testing (Fig 2), also raise concerns regarding significant health care disparities with the potential for negative impacts on health care outcomes. Health care disparities may manifest in various ways, including differences in access and utilization of biomarker testing.

Moreover, we show that NSCLC patients with metastatic disease and nonsquamous histology have significantly improved survival if they receive guideline-based testing. Admittedly, our analysis does not demonstrate causality and it is likely that confounding factors in part explain this effect. Perhaps, for example, the overall quality of care delivered by the patient's physician along with sociodemographic and unmodeled clinical attributes may correlate both with likelihood of testing and with survival, leading to the observed correlation between biomarker testing and survival. Goals of care and expected survival at presentation may also partially explain these findings; for example, patients opting for palliative care may be less likely to receive testing and may have shorter survival. A similar phenomenon may exist for patients who died shortly after diagnosis and did not live long enough to receive the testing or the test results. Nonetheless, given that we adjusted for clinical and selected demographic covariates, and that patients who are properly tested are more likely to receive targeted or optimized therapy, it likewise seems plausible and likely that some of the association between testing and survival is causal.

The rates of testing we find are broadly consistent with those in the study by Waterhouse et al8 despite using two different data sets, further confirming that community-based NSCLC patients often fail to receive guideline-based testing. In addition, we expand on the findings in the study by Waterhouse et al8 by (1) evaluating the association between clinical factors and testing; (2) showing more recent trends; (3) quantifying interphysician and interpractice variation; and (4) demonstrating correlations between testing and survival.

These findings may support improved patient care in at least two ways. First, they establish that some patients with NSCLC may receive suboptimal care and that significant disparities may exist. As alluded to above, interventions including targeted clinician education, clinical decision support, and improvement in testing access may support better testing practices. Given that our findings identified important racial and other disparities in testing, practice improvement efforts must consider health equity and should aim to ensure clinically optimal and equitable care across all patient groups. Second, our finding of a correlation between optimal testing and patient survival should provide additional evidence of the importance of testing and should encourage efforts to improve testing practice. Future research is needed to further elucidate the specific factors underlying the variation in care and the suboptimal testing practice.

Strengths of this study include the large patient population; use of well-curated, nationally representative data (with mortality information drawn from multiple sources); and the clear findings. Using hierarchical modeling, we can quantify the specific interphysician and interpractice variation in test ordering. Moreover, relatively few studies have examined the real-world impact of tumor biomarker testing on patient survival (as opposed to specific test results or therapeutic decisions based on them), and we believe that this is an important area of investigation.

Nonetheless, this study is subject to several key limitations. Foremost, the data are from community-based practices that contribute data to the real-world database and thus these findings may not generalize to large health systems and large academic medical centers. In addition, as noted above, we cannot clearly delineate the extent to which the relationship between testing and survival is causal. A substantial proportion of individuals had unknown race and ethnicity (15% and 33%, respectively). To the extent that these were missing completely at random, the missing race and ethnicity should not introduce biases; however, if the data are missing not at random, this could lead to biases. Missing race/ethnicity is likely in most cases because of the failure to capture this information in the EHR at the practice level (data transformation and other technical issues could also explain occasional cases). Although we have no reason to believe that this cause of missing data would introduce biases in our analysis, it also does not exclude this possibility. To better evaluate the potential for biases, we compared patient attributes by whether race or ethnicity was known or unknown (Data Supplement, Table S1) and plotted missing categories on relevant figures (Figs 2 and 3). Similarly, if outcomes data (mortality or testing) were missing, this could bias our results; however, as noted, the database draws mortality information from three sources, including the social security death index, so mortality data are less likely to be missing. It is possible that in some cases, testing could have been performed at outside practices and not documented in the medical record in such a way that it did not get captured in the database.

In conclusion, we aim to present these findings to raise awareness of the reality that many patients with NSCLC are not receiving guideline-based testing and that this is likely leading to suboptimal clinical outcomes. We aim to use these findings as a foundation for practice improvement.

ACKNOWLEDGMENT

The authors thank Yahya Rasoully for his contribution to the writing of the manuscript. Editorial support was provided by Liz Southey, The Salve Health Ltd, United Kingdom, and funded by Roche Diagnostics. Descriptions of the Flatiron database included in the methods and the structure of the first sentence of the results were included verbatim or adapted from boilerplate text provided by Flatiron for this purpose (used with permission).

SUPPORT

AUTHOR CONTRIBUTIONS

Conception and design: Jason M. Baron, Sarrah Widatalla, Matthew A. Gubens, Farah Khalil

Administrative support: Sarrah Widatalla

Collection and assembly of data: Jason M. Baron, Sarrah Widatalla

Data analysis and interpretation: Jason M. Baron, Sarrah Widatalla, Matthew A. Gubens, Farah Khalil

Manuscript writing: All authors

Final approval of manuscript: All authors

Accountable for all aspects of the work: All authors

AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/po/author-center.

Open Payments is a public database containing information reported by companies about payments made to US-licensed physicians (Open Payments).

Supported by Roche Diagnostics, Medical and Scientific Affairs, Indianapolis, IN. Roche neither received remuneration for the study nor provided any monetary support to any non-Roche author.

Jason M. Baron

Employment: Roche

Stock and Other Ownership Interests: Roche

Honoraria: Elsevier

Research Funding: Roche

Travel, Accommodations, Expenses: Roche

Sarrah Widatalla

Employment: Roche

Matthew A. Gubens

This author is a member of the JCO Precision Oncology Editorial Board. Journal policy recused the author from having any role in the peer review of this manuscript.

Consulting or Advisory Role: AstraZeneca, Guardant Health, Cardinal Health, Anheart Therapeutics, Gilead Sciences, Summit Therapeutics, InVitae, Atreca, Bristol Myers Squibb, Merus, Johnson & Johnson/Janssen

Research Funding: Merck (Inst), Trizell (Inst), Amgen (Inst), Johnson & Johnson/Janssen (Inst)

Farah Khalil

Honoraria: Roche, AstraZeneca, GlaxoSmithKline, Sanofi

Consulting or Advisory Role: Roche, AstraZeneca, GlaxoSmithKline, Sanofi

Travel, Accommodations, Expenses: Roche

No other potential conflicts of interest were reported.
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