1 INTRODUCTION

Breast cancer-related lymphedema (BCRL) results in quality-of-life altering treatment sequelae for approximately 20%-30% of breast cancer survivors (BCS).¹ The identification of initial increases in lymphatic fluid (subclinical lymphedema) after cancer treatment, when coupled with an early short-term compression intervention, is thought to reduce rates of progression to clinical lymphedema.^{2, 3} Thus, since the publication of the Stout et al study in 2008, consensus has grown that prospective surveillance after breast cancer treatment is needed to promote early identification of subclinical lymphedema to facilitate noninvasive interventions that reduce burden and cost of clinical lymphedema.^4-6 Prospective surveillance requires longitudinal assessments of patients’ arm status, preferably with baseline measurements before treatment commences and serially thereafter.

Biomarkers are increasingly being studied in oncology and serve as objective indicators,⁷ “…reflecting an interaction between a biological system and an environmental agent, which may be chemical, physical or biological.”⁸ In the case of lymphedema, Lymphedema index (L-Dex) units^® and arm volume function as biomarkers. An L-Dex unit change of ≥6.5 from presurgical baseline and a tape measure (TM) volume difference change of ≥5% but <10% are considered subclinical BCRL and values exceeding these thresholds for each measure represent clinical lymphedema.⁹

Little is known about the longitudinal patterns of change in L-Dex units and arm volume difference in relation to subclinical BCRL.^{1, 10} This contributes to the absence of both standardized monitoring intervals and monitoring duration for BCRL prospective surveillance.¹¹ In the case of lymphedema biomarkers, trajectory analysis of change in L-Dex units and arm volume percent difference from baseline, combined with symptoms, could provide information to fill this gap.^{12, 13} The objectives of this secondary study were to examine: (a) the trajectories of objective biomarkers (change from presurgical baseline values of L-Dex units and in percentage arm volume difference) and subjective symptom clusters over 24 months after surgery; and (b) associations of objective biomarkers and subjective symptom clusters. Secondarily, patient/treatment characteristics influencing the trajectories of interest were explored. It was hypothesized that longitudinal patterns in biomarkers and symptom clusters would be identified.

2 PATIENTS AND METHODS

3 RESULTS

3.2 Trajectories

Three trajectories were observed for each of the four measures: changes in L-Dex obtained by BIS, percent volume difference change by TM, and symptom scores using LSIDS-A and FACT-B + 4. Each biomarker trajectory was categorized as “Decreasing”, “Stable” or “Increasing” over the 24 months. Symptom trajectories were characterized as “Stable”, “Slight Increase/Decrease” or “Increasing” as there was not a definitive decreasing trajectory. The patterns are illustrated in Figures 1-4 and summarized below.

3.2.1 L-Dex

Slightly more than half (56%) demonstrated stable values from baseline through 24 months (Figure 1). Approximately a third demonstrated decreasing L-Dex values (values lower than baseline, 35%), while 9% demonstrated patterns consistent with increasing L-Dex values (n = 25 of 263). In the group with increasing L-Dex values over time, increases were initially noted at approximately 3 months postsurgery, and at 15 months postsurgery, a second increase was noted (see Figure 1). Those patients in the decreasing L-Dex values trajectory group had higher baseline L-Dex values (median = 2.4, IQR = −1, 5) than did those in the other trajectory groups (stable: median = −1.1, IQR = −4, 2; increasing: median = −3.5, IQR = −6, 0, P < .001).

3.2.2 Percent arm volume difference

Slightly more than half (56%) of the patients demonstrated stable arm volume difference, 27% demonstrated a pattern of decreasing difference between arm volumes, and 17% (n = 41 of 245) showed increasing arm volume difference (Figure 2). Increases were initially noted at approximately 3 months postsurgery with a steadily increasing slope over time. The percentage of patients assessed with TM, demonstrating the increasing trajectory pattern (17%), was statistically significantly higher than the percentage of those assessed via BIS with increasing trajectories (9%, P = .023). Similar to those patients randomized to the BIS assessments, patients with TM assessments, and in the decreasing percent arm volume difference trajectory, had higher baseline values (median = 3.0%, IQR = 0, 6) than did those in the other trajectories (stable: median = −0.6%, IQR = −4, 2; increasing: median = −2.1%, IQR = −5, 2, P < .001.

3.2.3 LISDS—A soft tissue

Almost two-thirds of the patients (64%) had stable soft tissue symptoms over 24 months (Figure 3). Another 27% had slightly increased symptom reports during the first 12 months after surgery that decreased to near presurgery level by 24 months. The remaining 9% (44 of 508) had an increasing trajectory characterized by steady rise in symptoms from baseline to 6 months postsurgery and remained well above baseline through 24 months postsurgery.

3.2.4 FACTB+4

Slightly more than half of the patients (53%) had little to no symptoms throughout; 38% demonstrated a pattern of slightly increased symptoms the first 9-12 months after surgery that stabilized and trended downward toward presurgery levels by 24 months (Figure 4). Finally, 9% (n = 44 of 508) demonstrated an increasing trajectory of symptoms from baseline to 12 months postsurgery and remained well above baseline through 24 months. Consistent with the similar patterns and proportions of patients in each pattern for the two self-report measures, the contingency coefficient for the association was 0.59 (P < .001).

4 DISCUSSION

This study provides longitudinal data regarding the postsurgical patterns of subclinical BCRL needed to inform prospective surveillance guidelines and clinical practice. One trajectory for each biomarker demonstrated slight decreases from baseline; in a second, there was little to no change from baseline measures. These two trajectories suggest that cancer treatment does not always lead to poorer arm status during the first 24 months. Subclinical lymphedema was identified in a third trajectory for both BIS and TM groups that continued to rise beyond 12 months after surgery. When looking at the characteristics of patients in the increasing trajectory groups for both BIS and TM, it appears that these cohorts include higher rates of regional nodal irradiation, tri-modality therapy with taxanes, mastectomies, and patients with elevated Body mass index (BMI)’s as compared to decreasing and stable trajectories (Table 1).

The increasing trajectories for both biomarkers demonstrate that a subset of patients are at high-risk of subclinical lymphedema during the first 24 months after surgery; however, due to censoring of participants when subclinical lymphedema was noted, patterns beyond the date of censoring are unknown for either biomarker. Previous trajectory work covering 12 months post operatively, using BIS ratios, identified three trajectories, all described as being “relatively stable” over 12 months and suggested BIS ratios 1 month postsurgery might predict lymphedema.¹³ In this study, spanning 24 months post surgery, only one stable trajectory was maintained over time. Of clinical importance is the trajectory increase noted in L-Dex units at 15 months. Long-term prospective surveillance is needed through at least 24 months postsurgery and frequent monitoring of patients every 3 months at least through 15 months postsurgery should be considered. Additionally, patients with characteristics similar to those noted in the third trajectories for both BIS and TM (eg, regional nodal irradiation, tri-modality therapy with taxanes, mastectomies, and elevated BMI’s) should be encouraged to contact their providers between scheduled visits if they become symptomatic (new onset swelling or altered sensations in the arm).

Some patient/treatment factors were associated with trajectories for biomarkers and symptoms. Age, use of NSAIDS, and a history of prior cardiac conditions were associated with increasing L-Dex trajectories, consistent with previous research.^{19, 20} GERD was associated with percent difference change in arm volume. No known studies support an association between GERD and BCRL. This is likely a spurious finding. Until a well-tested, predictive model that identifies patients who absolutely fall into an increasing trajectory is proven accurate, prospective surveillance using objective biomarkers for all BCS is indicated.

Regardless of trajectory group, patients reported few to no baseline symptoms. Both symptom clusters were indicative of ongoing issues for about 10% of participants through 24 months after surgery. Being younger, respiratory health issues, type of complete treatment received, and regional nodal irradiation contributed to symptom severity. Younger BCS report more symptoms than do older survivors, therefore the age related findings are not surprising.²¹ Deep breathing improves lymph flow, thus respiratory conditions that constrain breathing might contribute to lymph stasis.²² Axillary lymph node dissection and radiation are known to be related to lymphedema, and additional research regarding the extent of surgery to the axilla +/− the extent of radiation to the regional nodes, L-Dex increases, and symptoms is in preparation.²³

This study found a second peak in increasing L-Dex at 15 months. McDuff has previously shown that the hazard rate peak for lymphedema after axillary treatment varies by treatment complexity.¹⁰ In patients who received an axillary dissection alone, the hazard rate of lymphedema development was greatest in the first 6-12 months. For patients who received axillary dissection with regional node irradiation, the hazard rate peaked between 18 and 24 months. Whereas in patients undergoing sentinel node biopsy with regional node irradiation, the hazard rate peaked between 36 and 48 months, presumably due to the slower onset of radiation induced changes such as fibrosis. Therefore, clinicians may wish to advise patients with these characteristics to contact them between visits.

Self-reported symptoms were associated with L-Dex changes, but not percent change in arm volume difference. Symptoms have long been considered warning signs in a subset of patients at risk for BCRL.^{24, 25} In 2017, the American Physical Therapists Association (APTA) recommended that: (a) self-reported numbness, heaviness, etc. in at-risk patients be assessed using an objective measure, and (b) BIS be used as a measurement tool for diagnosis of subclinical (stage 0) lymphedema.²⁶ Symptom severity, from two symptom tools, was statistically significantly associated with L-Dex changes indicative of subclinical lymphedema, but not with percent change in arm volume difference. This supports APTA’s recommendation that BIS be used for early identification of subclinical lymphedema, is consistent with previous research,²⁷ and is clinically relevant for two primary reasons: First, the symptoms included in this study offer new information regarding symptoms associated with subclinical lymphedema. Second, BIS measurement captured early subclinical lymphedema when self-reports of symptoms were present which provides evidence to support that BIS can identify subclinical lymphedema. Therefore, L-Dex can be considered as a confirmatory biomarker for early identification of subclinical lymphedema, especially in the presence of patient-reported symptoms.

5 CONCLUSIONS

New onset subclinical lymphedema was experienced by patients in the increasing trajectory across both biomarkers across 24 months postsurgery. These data support the need for prospective surveillance through at least 24 months postsurgery with 3-month assessments in this population, through at least 15 months postsurgery. Statistically significant convergence of symptom cluster scores with L-Dex unit change support BIS as beneficial in early identification of subclinical lymphedema.

AUTHOR CONTRIBUTIONS

Conceptualization and study design—Ridner, Dietrich, & Shah. Data collection—Ridner, Boyages, Koelmeyer, Ajkay, DeSnyder, & McLaughlin. Statistical Analysis—Dietrich. Manuscript development and review: Ridner, Shah, Boyages, Koelmeyer, Ajkay, DeSnyder, McLaughlin, & Dietrich.