Abstract
Introduction
Compared to conventional chemotherapy schedules, use of dose-dense chemotherapy, which refers to administration of chemotherapy at standard doses with reduced cycle lengths, is known to improve survival, although may confer greater toxicity risk. We evaluated the patient factors associated with use of conventional vs dose-dense chemotherapy administration schedules.
Methods
Analyses include 4,685 women treated with adjuvant chemotherapy between 2005-2019 for Stage I-IIIA breast cancer at Kaiser Permanente Northern California and Kaiser Permanente Washington. Among women treated with drug combinations for which dose-dense administration schedules were observed, we used generalized linear models of the Poisson family with a log-link function to calculate prevalence ratios (PRatios) for the associations between patient factors and use of conventional vs dose-dense administration schedules.
Results
Several factors were associated with receipt of conventional administration schedule, including older age (PRatio75+vs18-39: 2.97; 95% CI: 2.35-3.75; p-trend<0.001), renal impairment (PRatio:1.55; 95% CI: 1.11-2.17), and HER2+ status (PRatio: 1.50; 95% CI: 1.38-1.62), among others. Factors associated with a lower likelihood of receipt of a conventional regimen schedule include: higher median household income (PRatioQ4 vs Q1 0.73; 95% CI: 0.67-0.80; p-trend<0.001), diagnosis in later years (PRatio:2012-19 vs 2005-11 0.44; 95% CI: 0.41-0.48), and higher stage (PRatiostage IIIA vs stage I: 0.51; 95% CI: 0.46-0.58; p-trend<0.001).
Conclusion
Patients receiving conventional schedule vs dose-dense chemotherapy represent those typically most vulnerable to toxicity or with lower risk disease, but may also represent groups vulnerable to disparities. Further research is needed to establish how to improve the uptake of dose-dense chemotherapy where appropriate.
Keywords: breast cancer, adjuvant chemotherapy, dose dense chemotherapy
Introduction
Chemotherapy has been shown to be highly effective in the treatment of early-stage breast cancer. Variations in drug, dose and dosing schedule have evolved based on the results of randomized controlled trials. One such variation is dose-dense treatment, which involves the administration of chemotherapy at standard doses with reduced cycle lengths.1,2 The rationale for higher dose density of treatment was derived from Gompertzian models of tumor behavior and the Norton-Simon hypothesis, which proposes that the smaller the tumor, the higher the percentage of dividing cells and therefore the more sensitive to chemotherapy.3 Increasing dose density would repeatedly expose tumors to chemotherapy with little time for proliferation between doses.
Dose-dense regimens have been studied across a range of active agents, including anthracyclines and taxanes.4 For several agents used in dose-dense chemotherapy, myeloid toxicity can be dose-limiting; however, the introduction of granulocyte colony-stimulating factor (G-CSF) has helped to ameliorate unacceptable myelotoxicity and allow for the wider usage of dose-dense regimens.5,6 Toxicities including anemia, transaminitis, and myalgias7 are experienced at higher rates by patients receiving dose-dense treatment compared to conventional schedule (i.e. non-dose-dense) treatment.
Overall, dose-dense regimens show improved disease-free and overall survival across multiple clinical trials and are recommended across multiple guidelines. They have become widely used in the management of early-stage breast cancer.2,8–10 For some patients, including those at risk of severe toxicity or adverse effects impacting treatment completion, providers may opt for conventional dosing schedules. As with all aspects of chemotherapy prescribing, dose density is an aspect of the regimen where differences in use may occur according to non-clinical factors. As dose-dense regimens increasingly become used as the standard of care, it is of interest to understand which patients are still receiving conventional schedule regimens and which patient factors are associated with their use. To our knowledge, no such studies have examined this. We have conducted this analysis to evaluate demographic, socioeconomic and cancer characteristics associated with the use of conventional schedule regimens and inform understanding of which patients are more likely to receive this treatment.
Methods
Overview
This analysis was conducted as part of the Optimal Breast Cancer Chemotherapy Dosing (OBCD) Study, which examines chemotherapy use in stage I-IIIA breast cancer patients. As part of this study, detailed data on intended chemotherapy have been collected alongside a range of patient factors.
Study Population
The study population has been described in detail elsewhere, but briefly, includes those aged 18+ years at diagnosis with stages I–IIIA breast cancer at Kaiser Permanente Northern California (KPNC) and Kaiser Permanente Washington (KPWA) from 2004–2019 (n = 34,109). Eligibility criteria for the OBCD study included the following: aged ≥18 years diagnosed with primary stage I-IIIA breast cancer with no prior history of cancer (except non-melanoma skin cancer) or same day diagnosis of any cancer, enrolled at either KPWA or KPNC at time of diagnosis, had available medical records, and did not opt out of research studies. KPWA also included a subsample of women diagnosed with stage I-II breast cancer that were part of a previous study (COMBO, Commonly used Medications and Breast Cancer Outcomes) which included abstracted treatment data.11
Of the 11,854 women who received adjuvant chemotherapy in this cohort, we limited this analysis to 5,208 women receiving chemotherapy drug combinations where we observed patients intended to receive dose-dense administration schedules. These drug combinations included doxorubicin and cyclophosphamide (AC); doxorubicin, cyclophosphamide and paclitaxel (ACT); doxorubicin, cyclophosphamide and docetaxel (AC+DOC); doxorubicin, cyclophosphamide, paclitaxel and trastuzumab (ACTH); doxorubicin, cyclophosphamide, docetaxel and trastuzumab (ACT+DOC+TRAS); doxorubicin, cyclophosphamide, paclitaxel, trastuzumab and pertuzumab (ACTHP); doxorubicin, cyclophosphamide, docetaxel, trastuzumab and pertuzumab (AC+DOC+TRAS+PERT); 5-fluorouracil, doxorubicin, cyclophosphamide, and paclitaxel (FAC+PAC). We further excluded 236 women with an unknown intended administration schedule and 287 women with incomplete data, leaving a final sample size of 4,685.
KPNC and KPWA are both members of a consortium of integrated healthcare delivery systems known as the Health Care Systems Research Network (HCSRN). Cases and corresponding tumor data were identified from an internal cancer registry that reports to Surveillance, Epidemiology, and End Results (SEER) Registries (KPNC), or from linkage of health plan members to the local SEER Registry (KPWA). The SEER Program is supported by the National Cancer Institute (NCI).12 Further data were obtained from clinical administrative databases housed within a Virtual Data Warehouse (VDW).13,14
Exposure
Exposures of interest included sociodemographic, cancer, and tumor characteristics. Patient factors included: age at diagnosis (years) (18-35; 36-49; 50-64; 65-74; 75+), race/ethnicity (Non-Hispanic White, Non-Hispanic Black/African-American, Asian, Hispanic, Other), body mass index (kg/m2) (BMI: <18.5; 18.5-<25; 25-<30; 30-<35; ≥35), median neighborhood household income (Q1: <$66,392; Q2: $66,392-<$89,107; Q3: $89,107-<$117,278; Q4: ≥$117,278), and pre-existing comorbidities, which included diabetes, cardiovascular disease, renal impairment, hepatic impairment, neutropenia, and thrombocytopenia (yes; no, for all). Comorbidities were defined using a combination of lab values, ICD codes, and CPT codes. Specifically, diabetes was identified per the KPNC Diabetes Registry;15 cardiovascular disease was identified per the Pathways Heart Study;16,17 other comorbidities were defined in Table 1.
Table 1.
Definitions of comorbidity
| Comorbidity | Definition |
|---|---|
| Renal Impairment | Creatinine Clearancea < 30mL/min |
| Hepatic Impairmentb | Total Bilirubin > 1.5mg/dL OR AST > 3 times ULN OR ALT > 3 times ULN OR ALP > 2.5 times ULN |
| Neutropenia | Absolute Neutrophil Count < 1500μL OR ICD-9: 288.0*, 288.5* OR ICD-10: D70*, D72.81* |
| Thrombocytopenia | Platelets < 100,000μL OR ICD-9: 287.3*, 287.4*, 287.5, 289.84, 99.05 OR ICD-10: D47.3, D69.3, D69.4*, D69.5*, D69.6, D75.82*, 30233R1, 30243R1 |
Creatinine Clearance was calculated by using serum creatinine and weight, with the Cockroft-Gault Equation (Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron. 1976;16(1):31-41. doi:10.1159/000180580).
AST: aspartate transaminase; ALT: alanine transaminase; ALP: alkaline phosphatase; ULN: upper limit of normal
All sub-codes were included
Cancer and tumor characteristics included: year of diagnosis (2005-2011, 2012-2019); American Joint Committee on Cancer (AJCC) cancer stage (I, II, IIIA), Grade (1, 2, 3), hormone receptor status (estrogen receptor− and progesterone receptor−; estrogen receptor+ and/or progesterone receptor+), HER2 status (HER2−; HER2+), surgery (breast conserving surgery, mastectomy).
Outcome
To determine intended chemotherapy regimens, we used a regimen identification process that has been described in detail elsewhere.18 Briefly, we used a combination of algorithms based on drugs and cycle length, and medical chart abstraction to determine intended drug combinations. We classified participants via algorithm into intended regimens based on National Comprehensive Cancer Network (NCCN) guideline regimens. If the intended regimen was unclear, including possible intention to receive non-guideline regimens, chart abstraction was used to help identify intended regimen.
We divided regimens into dose-dense and conventional schedule regimens based on designation within the NCCN guidelines; however, for non-guideline administration schedules, we made this determination based on cycle length, as blueprinted to the NCCN guidelines. Dose dense regimens were those receiving any guideline drug combination in which any drug was administered on a dose-dense schedule (i.e., every 2 weeks instead of every 3 weeks). Regimens with drugs intended to be administered at lower doses on a weekly schedule were considered part of the conventional schedule group, as these were all lower doses relative to the every 2 or 3 weekly administration pattern.
We identified 30 dose-dense regimens and 30 conventional schedule regimens across the 8 drug combinations noted above. Out of 8 drug combinations, 3 (AC; ACT; ACTH) combinations had ≥1 dose dense regimen delineated in guidelines, while other 5 combinations (AC+DOC; AC+DOC+TRAS; AC+DOC+TRAS+PERT; ACTHP; FAC+PAC) only had non-guideline dose-dense administration schedules. It should be noted that the vast majority (95%) of patients included in this study received one of the 3 drug combinations that have dose-dense administration schedules delineated in the guidelines (n=4,685).
Statistical Analysis
Analyses were conducted for each exposure of interest using generalized linear models of the Poisson family with a log-link function and robust standard errors to estimate prevalence ratios (PRatio) and corresponding 95% confidence intervals (95% CIs)19. Patient, tumor, and cancer characteristics were entered as independent variables in separate minimally-adjusted models to identify predictors of receipt of conventional regimens (compared to dose-dense regimens). Minimally-adjusted models were adjusted for age, stage, study site and year of diagnosis. Multivariable models were further adjusted for all statistically significant variables in minimally-adjusted models.
Secondary analyses were conducted stratified by time (2005-2011, 2012-2019) with adjustment for the covariates included in the overall models and interaction between exposures and time assessed using a Wald test.
The dose-dense administration schedules for 8 drug combinations entered the guidelines at different times over the study period. While we know that administration schedules are used prior to their entry in the guidelines (and outside of the guidelines),20 given that that ACT was the only drug combination with dose-dense options outlined in the guidelines throughout the study period, a sensitivity analysis was conducted among women receiving guideline administration schedules of ACT (n=3,252).
Additional sensitivity analyses were conducted stratified by nodal status (all nodes negative, any positive nodes), stage (stage I, stage II+), and restricted to women without comorbidities, in order to understand the interplay between patient factors and other potential drivers of treatment decisions.
Results
Overall, women aged 50-64 years made up 47.9% of the study population, while 12.6% were aged 65-74 years, and 1.1% were aged 75 or older (Table 2).
Table 2.
Characteristics of women receiving adjuvant chemotherapy with dose-dense scheduling opportunity
| Characteristic | Received chemotherapy N (%) |
|---|---|
| TOTAL | 4685 |
| Sociodemographics | |
| Age at diagnosis (years) | |
| 18-39 | 482 (10.3) |
| 40-49 | 1,316 (28.1) |
| 50-64 | 2,244 (47.9) |
| 65-74 | 590 (12.6) |
| 75+ | 53 (1.1) |
| Race and Ethnicity | |
| Non-Hispanic White | 2,591 (55.3) |
| Non-Hispanic Black/African American | 390 (8.3) |
| Non-Hispanic Asian | 841 (18.0) |
| Hispanic | 798 (17.0) |
| Non-Hispanic Native Hawaiian or Other Pacific Islander | 37 (0.8) |
| Non-Hispanic American Indian or Alaskan Native | 28 (0.6) |
| Patient characteristics | |
| BMI at diagnosis (kg/m2) a | |
| <18.5 | 49 (1.0) |
| 18.5-<25 | 1,410 (30.1) |
| 25-<30 | 1,495 (31.9) |
| 30-<35 | 941 (20.1) |
| ≥35 | 790 (16.9) |
| Tumor/cancer characteristics | |
| Year of diagnosis | |
| 2005-2011 | 2,493 (53.2) |
| 2012-2019 | 2,192 (46.8) |
| Stage | |
| Stage I | 974 (20.8) |
| Stage II | 2,804 (59.9) |
| Stage IIIA | 907 (19.4) |
| Grade | |
| Grade 1 | 465 (9.9) |
| Grade 2 | 1,891 (40.4) |
| Grade 3 | 2,329 (49.7) |
| Hormone-receptor status | |
| Estrogen receptor and progesterone receptor − | 1,366 (29.2) |
| Estrogen receptor + and/or progesterone receptor + | 3,319 (70.8) |
| HER2 status b | |
| HER2− | 4,114 (87.8) |
| HER2+ | 571 (12.2) |
BMI, body mass index
HER2, human epidermal growth factor receptor-2
Of these patients receiving eligible drug combinations, 60% received dose dense administration schedules while 40% received conventional administration schedules (Table 3). Age was strongly associated with receipt of conventional schedule regimens (PRatio75+vs18-39: 2.97; 95% CI: 2.35-3.75; p-trend<0.001). Renal impairment was also positively associated with conventional schedule regimens (PRatio: 1.55; 95% CI: 1.11-2.17). Those with hormone receptor positivity (PRatio: 1.22; 95% CI: 1.13-1.32) and HER2+ status (PRatio: 1.50; 95% CI: 1.38-1.62) were more likely to receive conventional schedule regimens. Those with higher median household income were less likely to receive conventional schedules (PRatioQ4 vs Q1: 0.73; 95% CI: 0.67-0.80; p-trend<0.001), as were those diagnosed in later years (PRatio: 0.44; 95% CI: 0.41-0.48). Higher stage was also associated with a lower likelihood of conventional schedule regimens (PRatiostage IIIA vs stage I: 0.51; 95% CI: 0.46-0.58; p-trend<0.001). Other comorbidities (including diabetes, cardiovascular disease, hepatic impairment, neutropenia, and thrombocytopenia) were not significantly associated with use of conventional vs. dose-dense regimens. Nodal status did not meaningfully impact associations between patient factors and conventional vs. dose-dense regimens (eTable 1), with the exception of hormone receptor positivity (PRatioER+ and/or PR+ vs. ER− and PR− for node−: 1.48; 95% CI: 1.33, 1.64; PRatioER+ and/or PR+ vs. ER− and PR− for node+: 1.12; 95% CI: 0.98, 1.28). Some difference in association was also observed for HER2+ status (PRatioHER2+ vs. HER2− for node−: 1.45; 95% CI: 1.30, 1.61; PRatioHER2+ vs. HER2− for node+: 1.85; 95% CI: 1.64, 2.08), and combined HR/HER2 status, particularly for those with HR+/HER2− disease (i.e., ER+ or PR+/HER2− disease) (PRatioHR+/HER2− vs HR−/HER2− for node−: 1.66; 95% CI: 1.47, 1.87; PRatioHR+/HER2− vs HR−/HER2− for node+: 1.22; 95% CI: 1.03, 1.44), although notably conclusions remained comparable across groups.
Table 3.
Patient factors associated with conventional schedule chemotherapy vs. dose dense chemotherapy
| Characteristic | Among those receiving drug combinations with dose-dense and conventional administration schedules | Minimally adjusteda | Fully adjustedb | |||
|---|---|---|---|---|---|---|
|
| ||||||
| Dose dense chemotherapy (N, %) | Conventional schedule chemotherapy (N, %) | PRatio | 95% CI | PRatio | 95% CI | |
| TOTAL | 2789 (60) | 1896 (40) | - | - | - | - |
| Sociodemographics | ||||||
| Age at diagnosis (years) | ||||||
| 18-39 | 358 (74.3) | 124 (25.7) | REF | - | REF | - |
| 40-49 | 838 (63.7) | 478 (36.3) | 1.28 | 1.09, 1.49 | 1.24 | 1.06, 1.45 |
| 50-64 | 1,313 (58.5) | 931 (41.5) | 1.47 | 1.27, 1.71 | 1.44 | 1.24, 1.67 |
| 65-74 | 268 (45.4) | 322 (54.6) | 2.00 | 1.71, 2.24 | 1.99 | 1.70, 2.33 |
| 75+ | 12 (22.6) | 41 (77.4) | 2.89 | 2.29, 3.66 | 2.97 | 2.35, 3.75 |
| p-trend<0.001 | p-trend<0.001 | |||||
| Race and ethnicity | ||||||
| Non-Hispanic White | 1,534 (59.2) | 1,057 (40.8) | REF | - | REF | - |
| Non-Hispanic Black/AA | 211 (54.1) | 179 (45.9) | 1.15 | 1.03, 1.28 | 1.11 | 0.99, 1.24 |
| Non-Hispanic Asian | 518 (61.6) | 323 (38.4) | 1.01 | 0.92, 1.10 | 1.00 | 0.91, 1.09 |
| Hispanic | 478 (59.9) | 320 (40.1) | 1.08 | 0.99, 1.18 | 1.04 | 0.95, 1.14 |
| Other | 48 (73.8) | 17 (26.2) | 0.94 | 0.65, 1.37 | 0.86 | 0.60, 1.24 |
| global p=0.08 | global p=0.32 | |||||
| Neighborhood level SES | ||||||
| Median Household Income c | ||||||
| Q1: <$66,392 | 691 (55.9) | 546 (44.1) | REF | - | REF | - |
| Q2: $66,392-<$89,107 | 679 (58.8) | 476 (41.2) | 0.91 | 0.84, 0.99 | 0.90 | 0.83, 0.98 |
| Q3: $89,107-<$117,278 | 723 (60.3) | 476 (39.7) | 0.86 | 0.79, 0.94 | 0.84 | 0.77, 0.92 |
| Q4: ≥$117,278 | 696 (63.6) | 398 (36.4) | 0.74 | 0.68, 0.81 | 0.73 | 0.67, 0.80 |
| p-trend<0.001 | p-trend<0.001 | |||||
| Patient characteristics | ||||||
| BMI at diagnosis (kg/m2) | ||||||
| <18.5 | 27 (55.1) | 22 (44.9) | 1.09 | 0.80, 1.48 | 1.07 | 0.78, 1.47 |
| 18.5-<25 | 838 (59.4) | 572 (40.6) | REF | - | REF | - |
| 25-<30 | 923 (61.7) | 572 (38.3) | 0.95 | 0.88, 1.03 | 0.93 | 0.86, 1.01 |
| 30-<35 | 545 (57.9) | 396 (42.1) | 1.05 | 0.96, 1.14 | 0.99 | 0.91, 1.09 |
| ≥35 | 456 (57.7) | 334 (42.3) | 1.06 | 0.97, 1.17 | 1.01 | 0.92, 1.11 |
| p-trend=0.12 | p-trend=0.79 | |||||
| Diabetes | ||||||
| No | 2,552 (60.0) | 1,698 (40.0) | REF | - | REF | - |
| Yes | 237 (54.5) | 198 (45.5) | 1.11 | 1.01, 1.23 | 1.09 | 0.99, 1.20 |
| CVD | ||||||
| No | 2,718 (59.6) | 1,843 (40.4) | REF | - | REF | - |
| Yes | 71 (57.3) | 53 (42.7) | 0.98 | 0.82, 1.18 | 0.96 | 0.80, 1.15 |
| Renal impairment | ||||||
| No | 2,785 (59.6) | 1,884 (40.4) | REF | - | REF | - |
| Yes | 4 (25.0) | 12 (75.0) | 1.61 | 1.15, 2.24 | 1.55 | 1.11, 2.17 |
| Hepatic impairment | ||||||
| No | 2,727 (59.5) | 1,853 (40.5) | REF | - | REF | - |
| Yes | 62 (59.0) | 43 (41.0) | 1.03 | 0.83, 1.28 | 1.03 | 0.83, 1.26 |
| Neutropenia | ||||||
| No | 2,735 (59.4) | 1,867 (40.6) | REF | - | REF | - |
| Yes | 54 (65.1) | 29 (34.9) | 1.06 | 0.81, 1.39 | 1.08 | 0.83, 1.41 |
| Thrombocytopenia | ||||||
| No | 2,761 (59.4) | 1,886 (40.6) | REF | - | REF | - |
| Yes | 28 (73.7) | 10 (26.3) | 0.74 | 0.45, 1.22 | 0.74 | 0.44, 1.25 |
| Tumor/cancer characteristics | ||||||
| Year of diagnosis | ||||||
| Early (2005-2011) | 1,108 (44.4) | 1,385 (55.6) | REF | - | REF | - |
| Late (2012-2019) | 1,681 (76.7) | 511 (23.3) | 0.42 | 0.39, 0.46 | 0.44 | 0.41, 0.48 |
| Stage | ||||||
| Stage I | 449 (46.1) | 525 (53.9) | REF | - | REF | - |
| Stage II | 1,687 (60.2) | 1,117 (39.8) | 0.71 | 0.67, 0.76 | 0.71 | 0.67, 0.76 |
| Stage IIIA | 653 (72.0) | 254 (28.0) | 0.52 | 0.47, 0.58 | 0.51 | 0.46, 0.58 |
| p-trend<0.001 | p-trend<0.001 | |||||
| Grade | ||||||
| Grade 1 | 239 (51.4) | 226 (48.6) | REF | - | REF | - |
| Grade 2 | 1,086 (57.4) | 805 (42.6) | 0.96 | 0.87, 1.05 | 0.95 | 0.86, 1.05 |
| Grade 3 | 1,464 (62.9) | 865 (37.1) | 0.88 | 0.80, 0.97 | 0.90 | 0.81, 1.00 |
| p-trend=0.002 | p-trend=0.03 | |||||
| Hormone-receptor status | ||||||
| ER− and PR− | 875 (64.1) | 491 (35.9) | REF | - | REF | - |
| ER+ and/or PR+ | 1,914 (57.7) | 1,405 (42.3) | 1.22 | 1.13, 1.32 | 1.22 | 1.13, 1.32 |
| HER2 status | ||||||
| HER2− | 2,554 (62.1) | 1,560 (37.9) | REF | - | REF | - |
| HER2+ | 235 (41.2) | 336 (58.8) | 1.43 | 1.33, 1.55 | 1.50 | 1.38, 1.62 |
| Surgery type | ||||||
| Mastectomy | 1,579 (64.0) | 888 (36.0) | REF | - | REF | - |
| Breast conserving surgery | 1,210 (54.6) | 1,008 (45.4) | 1.09 | 1.02, 1.16 | 1.11 | 1.04, 1.19 |
Minimally adjusted: Age, stage, study site, year of diagnosis. Outcome is conventional regimen (as 1) versus dose dense regimen (as 0).
Fully adjusted: age, income, renal impairment, diabetes, year of diagnosis, HER2, stage, grade, hormone receptor status, surgery type, study site. Outcome is conventional regimen (as 1) versus dose dense regimen (as 0).
Median neighborhood household income: calculated at neighborhood level
Abbreviations: SES, socioeconomic status; BMI, body mass index; ER: estrogen receptor; PR: progesterone receptor; HER2, human epidermal growth factor receptor-2; CVD, cardiovascular disease
When stratifying by time, we found that 56% of participants were treated with conventional regimens in early years (2005-2011) compared with 23% in more recent years (2012-2019) (Table 4). The predictors of conventional regimen use changed over time, with significant interactions observed for age (p-interaction<0.001), income (p-interaction=0.003), renal impairment (p-interaction=0.007), stage (p-interaction=0.002) and HER2 status (p-interaction<0.001). Specifically, the association with age strengthened over time, with a PRatio of 5.09 for those 75+ years in the later years (95%CI: 3.46-7.49; p-trend<0.001) compared to 2.13 (95% CI: 1.63-2.76; p-trend<0.001) in the early years. The inverse association with income strengthened over time, with a PRatio of 0.58 for Q4 in the later years (95% CI:0.47-0.72; p-trend<0.001) compared to 0.80 (95% CI: 0.73-0.89; p-trend<0.001) in earlier years. The association between renal impairment and use of conventional regimens was stronger in later years (PRatio: 2.36; 95% CI: 1.30-4.30) vs earlier years (PRatio: 1.25; 95% CI:0.86-1.82). The association by stage attenuated a little over time, with PRatioStage IIIA vs I going from 0.45 in early years (95% CI: 0.39-0.51; p-trend<0.001) to 0.70 in later years (95% CI: 0.55-0.88; p-trend=0.002). Women with HER2+ status were more likely to receive conventional schedule regimens in later years (PRatio: 2.21; 95% CI: 1.82-2.67) than earlier years (PRatio: 1.34; 95% CI: 1.23-1.45).
Table 4.
Factors associated with conventional schedule vs. dose dense: how associations varied over time
| Characteristic | Early Years (2005-2011) | Late Years (2012-2019) | p-interaction | ||
|---|---|---|---|---|---|
|
| |||||
| N (%) | PRatio (95% CI)a | N (%) | PRatio (95% CI)a | ||
| TOTAL | 2493 (53) | - | 2192 (47) | - | - |
| Dose dense | 1,108 (44) | - | 1,681 (77) | - | - |
| Conventional schedule | 1,385 (56) | - | 511 (23) | - | - |
| Sociodemographics | |||||
| Age at diagnosis (years) | |||||
| 18-39 | 198 (7.9) | REF | 284 (13.0) | REF | <0.001 |
| 40-49 | 707 (28.4) | 1.16 (0.99, 1.38) | 609 (27.8) | 1.40 (1.02, 1.92) | |
| 50-64 | 1,228 (49.3) | 1.35 (1.15, 1.58) | 1,016 (46.4) | 1.61 (1.20, 2.18) | |
| 65-74 | 332 (13.3) | 1.81 (1.53, 2.14) | 258 (11.8) | 2.39 (1.72, 3.33) | |
| 75+ | 28 (1.1) | 2.13 (1.63, 2.76) | 25 (1.1) | 5.09 (3.46, 7.49) | |
| p-trend<0.001 | p-trend<0.001 | ||||
| Race and ethnicity | |||||
| Non-Hispanic White | 1,458 (58.5) | REF | 1,133 (51.7) | REF | 0.15 |
| Non-Hispanic Black/AA | 206 (8.3) | 1.05 (0.93, 1.18) | 184 (8.4) | 1.28 (1.01, 1.62) | |
| Non-Hispanic Asian | 421 (16.9) | 0.99 (0.90, 1.09) | 420 (19.2) | 1.03 (0.84, 1.26) | |
| Hispanic | 382 (15.3) | 1.04 (0.94, 1.14) | 416 (19.0) | 1.01 (0.82, 1.23) | |
| Other | 26 (1.0) | 1.07 (0.75, 1.53) | 39 (1.8) | 0.58 (0.25, 1.39) | |
| global p=0.86 | global p=0.17 | ||||
| Neighborhood level SES | |||||
| Median Household Income b | |||||
| Q1: <$66,392 | 657 (26.4) | REF | 580 (26.5) | REF | 0.003 |
| Q2: $66,392-<$89,107 | 614 (24.6) | 1.01 (0.93, 1.10) | 541 (24.7) | 0.68 (0.56, 0.83) | |
| Q3: $89,107-<$117,278 | 632 (25.4) | 0.92 (0.84, 1.00) | 567 (25.9) | 0.72 (0.59, 0.87) | |
| Q4: ≥$117,278 | 590 (23.7) | 0.80 (0.73, 0.89) | 504 (23.0) | 0.58 (0.47, 0.72) | |
| p-trend<0.001 | p-trend<0.001 | ||||
| Patient characteristics | |||||
| BMI at diagnosis (kg/m2) | |||||
| <18.5 | 32 (1.3) | 0.98 (0.69, 1.39) | 17 (0.8) | 1.53 (0.86, 2.71) | 0.06 |
| 18.5-<25 | 769 (30.8) | REF | 641 (29.2) | REF | |
| 25-<30 | 806 (32.3) | 0.99 (0.91, 1.08) | 689 (31.4) | 0.79 (0.66, 0.96) | |
| 30-<35 | 488 (19.6) | 1.03 (0.93, 1.13) | 453 (20.7) | 0.89 (0.73, 1.09) | |
| ≥35 | 398 (16.0) | 1.11 (1.01, 1.22) | 392 (17.9) | 0.80 (0.64, 1.01) | |
| p-trend=0.04 | p-trend=0.07 | ||||
| Diabetes | |||||
| No | 2,286 (91.7) | REF | 1,964 (89.6) | REF | 0.85 |
| Yes | 207 (8.3) | 1.11 (1.00, 1.23) | 228 (10.4) | 1.04 (0.83, 1.29) | |
| CVD | |||||
| No | 2,404 (96.4) | REF | 2,157 (98.4) | REF | 0.75 |
| Yes | 89 (3.6) | 0.96 (0.79, 1.16) | 35 (1.6) | 1.20 (0.69, 2.09) | |
| Renal impairment | |||||
| No | 2,482 (99.6) | REF | 2,187 (99.8) | REF | 0.007 |
| Yes | 11 (0.4) | 1.25 (0.86, 1.82) | 5 (0.2) | 2.36 (1.30, 4.30) | |
| Hepatic impairment | |||||
| No | 2,438 (97.8) | REF | 2,142 (97.7) | REF | 0.20 |
| Yes | 55 (2.2) | 0.93 (0.74, 1.16) | 50 (2.3) | 1.30 (0.84, 2.00) | |
| Neutropenia | |||||
| No | 2,467 (99.0) | REF | 2,135 (97.4) | REF | 0.82 |
| Yes | 26 (1.0) | 1.03 (0.76, 1.40) | 57 (2.6) | 1.12 (0.73, 1.71) | |
| Thrombocytopenia | |||||
| No | 2,480 (99.5) | REF | 2,167 (98.9) | REF | 0.81 |
| Yes | 13 (0.5) | 0.81 (0.45, 1.48) | 25 (1.1) | 0.68 (0.27, 1.74) | |
| Tumor/cancer characteristics | |||||
| Stage | |||||
| Stage I | 501 (20.1) | REF | 473 (21.6) | REF | 0.002 |
| Stage II | 1,556 (62.4) | 0.66 (0.62, 0.71) | 1,248 (56.9) | 0.85 (0.72, 1.02) | |
| Stage IIIA | 436 (17.5) | 0.45 (0.39, 0.51) | 471 (21.5) | 0.70 (0.55, 0.88) | |
| p-trend<0.001 | p-trend=0.002 | ||||
| Grade | |||||
| Grade 1 | 301 (12.1) | REF | 164 (7.5) | REF | 0.40 |
| Grade 2 | 1,039 (41.7) | 0.99 (0.89, 1.09) | 852 (38.9) | 0.84 (0.64, 1.11) | |
| Grade 3 | 1,153 (46.2) | 0.90 (0.81, 1.01) | 1,176 (53.6) | 0.84 (0.63, 1.12) | |
| p-trend=0.02 | p-trend=0.42 | ||||
| Hormone-receptor status | |||||
| ER− and PR− | 676 (27.1) | REF | 690 (31.5) | REF | 0.74 |
| ER+ and/or PR+ | 1,817 (72.9) | 1.20 (1.10, 1.30) | 1,502 (68.5) | 1.19 (0.99, 1.43) | |
| HER2 status | |||||
| HER2− | 2,104 (84.4) | REF | 2,010 (91.7) | REF | <0.001 |
| HER2+ | 389 (15.6) | 1.34 (1.23, 1.45) | 182 (8.3) | 2.21 (1.82, 2.67) | |
| Surgery | |||||
| Breast conserving surgery | 1,245 (49.9) | REF | 1,222 (55.7) | REF | 0.60 |
| Mastectomy | 1,248 (50.1) | 1.10 (1.03, 1.18) | 970 (44.3) | 1.12 (0.96, 1.30) | |
Fully adjusted: age, income, renal impairment, diabetes, HER2, stage, grade, hormone receptor status, surgery type, study site. Outcome is conventional regimen (as 1) versus dose dense regimen (as 0).
Median neighborhood household income: calculated at neighborhood level
Abbreviations: SES, socioeconomic status; BMI, body mass index; ER: estrogen receptor; PR: progesterone receptor; HER2, human epidermal growth factor receptor-2; CVD, cardiovascular disease
When restricted to guideline administration schedules of ACT only, we found that effect estimates between patient factors and conventional schedules were comparable (data not shown). Similar results were also found by comparing women with stage I vs. stage II+ breast cancer (eTable 2) or restricting to women without comorbidities (eTable 3).
Discussion
Use of conventional schedule regimens vs dose-dense regimens in breast cancer chemotherapy is associated with several patient factors, including older age, renal impairment, positive hormone receptor status and positive HER2 status (all p-values<0.01). Those with higher stage, higher median neighborhood household income and later years of diagnosis (all p-values<0.01) were less likely to receive conventional schedule regimens. The use of conventional schedule therapy in this population was associated with characteristics associated with lower risk of recurrence and mortality and higher risk of toxicity and chemotherapy intolerance.
We found that those living in higher income neighborhoods were less likely to receive conventional schedule chemotherapy, and this was more pronounced in later years. Yabroff et al found that financial toxicity among cancer patients is worsening over time, due in part to broader economic shifts which disproportionately affect those living with lower household income.21 This may in part drive this finding even within an integrated healthcare system, where despite comparable insurance coverage, cost, accessibility, and access may still vary.21 A lower frequency of hospital attendance may be preferable for patients in lower income neighborhoods, who may live in more rural areas and have challenges attending appointments, or have lower job security. These associations present an area for potential disparities and warrant further investigation.
The association between older age and conventional schedule regimens may be driven by concern regarding the risk of neutropenia, which is a dose-limiting toxicity for many chemotherapeutic agents.22,23 Previous studies have reported that older adults are more vulnerable to neutropenia, which is a severe and potentially life-threatening complication of chemotherapy.24 Risk of neutropenia is higher with dose-dense than conventional regimens, although this risk can be mitigated with use of granulocyte-colony stimulating factor administration. While our finding of older age with higher likelihood of convention regimens has not previously been reported, older age is known to be associated with factors that would impact administration of dose-dense regimens (e.g., delays and overall lower doses).25,26 This may occur by delays in treatment between cycles or by dose reduction, which have similar effects on chemotherapy exposure as using conventional schedules vs dose dense regimens. Notably, we did not observe an association with pre-existing neutropenia and use of dose-dense vs conventional schedule regimens, although we did not account for neutropenia that may have developed between chemotherapy cycles, as we were focused on the intended regimen at the outset, not the regimen actually received.
Dose density has been associated with a higher incidence of non-hematologic toxic effects than conventional schedule chemotherapy27. These toxic effects may be more likely to occur in patients with pre-existing comorbidities. Several studies have examined the incidence of neutropenia with chronic comorbid conditions and none have reported an association between diabetes or renal disease and likelihood of neutropenia in breast cancer.28–30 Regardless, the presence of these comorbidities may affect treatment decisions around delivery of dose-dense chemotherapy because of the interplay among chemotherapy drugs, comorbidities and neutropenia. The drug classes commonly administered in a dose-dense fashion are taxanes and anthracyclines.31 Taxanes can cause peripheral neuropathy, and the risk of this is highest among people with diabetes.32,33 Anthracyclines are known to rarely cause nephrotic syndrome with focal segmental glomerulosclerosis and significant renal lesions.34 When stratified by time, we found a higher likelihood of conventional schedule regimens in people with pre-existing renal disease in later years, which may reflect a greater body of evidence on both the baseline understanding of renal insufficiency in cancer patients and the risk of nephrotoxicity of chemotherapy agents.35
Higher stage at diagnosis is associated with poorer prognosis and may explain the use of dose dense regimens, suggesting that higher risk of recurrence and mortality or more advanced or aggressive disease informs chemotherapy regimen selection. In these patients, providers are more likely to administer dose-dense regimens. Interestingly, higher stage disease has been associated with higher incidence of neutropenia in other studies,29 but potentially, the risk due to more advanced stage may be the predominant driver of dose-dense use regardless of risk of neutropenia. The change in association with stage over time suggests that, while dose-dense regimens are still more commonly used in higher stage disease, this prescribing pattern has reduced over time. The association with hormone receptor positive status may also reflect a response to a patient’s disease risk. Patients with hormone receptor positive disease typically have a better prognosis than their negative counterparts, which may influence the decision to give conventional schedule regimens.36
We found that HER2+ disease was positively associated with use of conventional schedule regimens. This may be driven by lack of solid clinical evidence of administrating adjuvant dose-dense treatment in patients with HER2+ disease.37–39 Even so, it is possibly that clinical patterns may shift with evolving evidence.40 HER2+ disease is traditionally treated with trastuzumab +/− pertuzumab. These drugs are administered over one year with variations in administration schedule.31 In OBCD, we observed a large number of non-guideline regimens for ACTH,18,20 which may reflect more alterations of the trastuzumab scheduling, potentially reflecting desire to minimize number of infusion visits. When stratified by time, we found a higher likelihood of conventional schedule regimens in HER2+ disease in later years compared to earlier years. Given these drugs are cornerstones of HER2+ breast cancer treatment,41 dosing schedules may be constructed around their planned delivery, in preference to the use of dose-dense chemotherapy for other drugs.
This study had several strengths. There is limited evidence on which patient populations are not receiving dose-dense regimens, which is known to be associated with improved outcomes for breast cancer patients. This study contains detailed treatment and demographic data, including the intended regimen, and thus we were able to define dose dense and conventional schedule based on the originally planned regimen. This meant that patients who experienced delays or early treatment discontinuation were still counted as dose-dense where appropriate. Therefore, we were able to accurately reflect which patient factors are associated with the intention to administer anti-cancer drugs on a conventional or a dose-dense schedule. We were also able to restrict our analysis to those exclusively receiving guideline ACT regimen, which had guideline dose dense options throughout to the study period, to mitigate any concerns that use of non-guideline administration schedules may be driving results. This study was conducted in an integrated healthcare system where all participants have healthcare coverage, thus minimizing the role of insurance coverage as a confounder. However, variation in insurance coverage, such as deductibles and copays, may still exist within the population. We also had several additional limitations. As with all observational studies, we cannot rule out residual confounding, although we have attempted to mitigate this with careful covariate adjustment. Additionally, it is possible that participants’ intended regimens were classified incorrectly. That said, we used extensive chart abstraction for ambiguous treatment patterns to reduce the likelihood of this error, and any such error would be expected to attenuate effect estimates. Lastly, small number of patients with renal impairment (CrCl ≥30 vs <30) was observed, which may impact the reliability of association between renal impairment and conventional vs dose dense chemotherapy. However, further exploration of the association between a three-level renal impairment (CrCl ≥60, CrCl 30 to <60, and CrCl <30) and conventional vs dose dense chemotherapy supported our selection of CrCl <30 as cut-off point, as it showed that clinicians are much more selective in dose dense chemotherapy in patients with more severely impacted renal function.
The use of conventional schedule regimens persists in certain patient populations. Many of these patient populations represent people at lower risk of recurrence and mortality, or those at highest risk of complications of chemotherapy, including exacerbation and development of renal disease and peripheral neuropathy. Despite hypotheses suggesting dose-dense regimens would be beneficial across a broader range of patient populations,8 there is limited evidence of using dose-dense regimens among them in real-world settings. Whether these patterns occur in care settings outside that of the community settings of KPNC and KPWA should be determined. For instance, in other settings, differential coverage may impact access granulocyte-colony stimulating factor administration, which may further impact the prescription of dose-dense regimens; however, this is unexpected to play a role in KPNC and KPWA, given that both are integrated healthcare delivery systems. Further study is needed to determine why some high-risk patients do not receive dose-dense chemotherapy, to ensure the highest standard of care can be delivered to patients treated with chemotherapy for breast cancer.
Here, we observed several factors to be associated with an increased/decreased likelihood of conventional regimens. While factors such as age, and comorbidity may very well reflect warranted clinical considerations, we should note that patients in higher income neighborhoods were less likely to receive conventional regimens. Given that all patients had healthcare coverage in these integrated healthcare delivery systems, we do not expect differential healthcare coverage to be driving the observed association. It is important that we better understand the drivers behind these decisions, and the extent to which factors such as distance, time off work, time away from child/eldercare may impact treatment decisions; identifying these drivers may provide opportunities to reduce disparities.
Supplementary Material
Acknowledgement of research support:
This work was supported by grants R37CA222793, U24CA171524, U01CA195565, P30CA008748, and P01CA154292 from the National Cancer Institute of the National Institutes of Health, as well as the Geoffrey Beene Cancer Research Center at Memorial Sloan Kettering Cancer Center. Erin Bowles’s time was supported by the National Cancer Institute (R50CA211115).
Conflict of interest statement:
Dr. Wang was former employee of Daiichi Sankyo, Inc (6/2022-4/2023). Dr. Bandera served as member of Pfizer’s Advisory Board to enhance minority participation in clinical trials (7/2021-8/2023). Dr. Liu served as member of Pfizer’s think tank on real-world evidence sponsored by Pfizer on 11/28/23. Dr. Liu’s research funding (institutional) unrelated to this current work: Genentech, AstraZeneca, Bristol Myers Squibb, Exact Sciences, Biotheranostics, and Beigene. Other authors have no COI to declare.
Footnotes
Ethical Considerations: IRB approval was obtained from Memorial Sloan Kettering Cancer Center, Rutgers University, KPNC, and KPWA with a waiver of informed consent to extract and analyze patient data at KPNC and KPWA.
Data Availability Statement:
The data underlying this article were provided by KPNC and KPWA under license/by permission. Data contain potentially identifiable information such as dates of events that cannot be shared openly without appropriate human subjects approval and data use agreements. Data will be shared on request to the corresponding author with permission of KPNC and KPWA.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data underlying this article were provided by KPNC and KPWA under license/by permission. Data contain potentially identifiable information such as dates of events that cannot be shared openly without appropriate human subjects approval and data use agreements. Data will be shared on request to the corresponding author with permission of KPNC and KPWA.