Development and Validation of a Risk Tool for Predicting Severe Toxicity in Older Adults Receiving Chemotherapy for Early-Stage Breast Cancer
Authors: Abrahm Levi
Efrat Dotan
Hyman B Muss
William Dale
Harvey J Cohen
Vani Katheria
Anait Arsenyan
William P Tew
Can-Lan Sun
Arti Hurria
Supriya Mohile
Tracey O&#x27;Connor
Rachel A Freedman
Tanya M Wildes
Heidi D Klepin
Allison Magnuson
Cary P Gross
Mina S Sedrak
Heeyoung Kim
Journal: Journal of Clinical Oncology (2021)
Discipline: CLINICAL MEDICINE
Keywords: chemotherapy toxicity; older adults; breast cancer; predictive model; risk assessment
Affiliations: University of Rochester, Rochester, NY., City of Hope, Duarte, CA., Yale School of Medicine, New Haven, CT., Memorial Sloan Kettering Cancer Center, New York, NY., Wake Forest School of Medicine, Winston-Salem, NC., Washington University School of Medicine, St. Louis, MO., UNC Lineberger Comprehensive Cancer Center, Chapel Hill, NC., Fox Chase Cancer Center, Philadelphia, PA., Dana-Farber Cancer Institute, Boston, MA., Roswell Park Cancer Institute, Buffalo, NY., Duke University Medical Center, Durham, NC., Deceased.

Abstract:  Purpose: Limited tools exist to predict the risk of chemotherapy toxicity in older adults with early-stage breast cancer. Methods: Patients of age ≥ 65 years with stage I-III breast cancer from 16 institutions treated with neoadjuvant or adjuvant chemotherapy were prospectively evaluated for geriatric and clinical features predictive of grade 3-5 chemotherapy toxicity. Logistic regression with best-subsets selection was used to identify and incorporate independent predictors of toxicity into a model with weighted variable scoring. Model performance was evaluated using area under the ROC curve (AUC) and goodness-of-fit statistics. The model was internally and externally validated. Results: In 473 patients (283 in development and 190 in validation cohort), 46% developed grade 3-5 chemotherapy toxicities. Eight independent predictors were identified (each assigned weighted points): anthracycline use (1 point), stage II or III (3 points), planned treatment duration &gt; 3 months (4 points), abnormal liver function (3 points), low hemoglobin (3 points), falls (4 points), limited walking (3 points), and lack of social support (3 points). We calculated risk scores for each patient and defined three risk groups: low (0-5 points), intermediate (6-11 points), or high (≥ 12 points). In the development cohort, the rates of grade 3-5 chemotherapy toxicity for these three groups were 19%, 54%, and 87%, respectively (P &lt; .01). In the validation cohort, the corresponding toxicity rates were 27%, 45%, and 76%. The AUC was 0.75 (95% CI, 0.70 to 0.81) in the development cohort and 0.69 (95% CI, 0.62 to 0.77) in the validation cohort. Risk groups were also associated with hospitalizations and reduced dose intensity (P &lt; .01). Conclusion: The Cancer and Aging Research Group-Breast Cancer (CARG-BC) score was developed and validated to predict grade 3-5 chemotherapy toxicity in older adults with early-stage breast cancer. 

Retracted: No

PubPeer comments:
 Certain findings obtained by Magnuson and colleagues can be interpreted in a pharmacologically realistic light, by means of a thought-experiment. Imagine that in some parallel universe, the HOPE study ([NCT01472094] ) was conducted as a dose-titration trial. Such a trial would yield for each patient an estimated *individualized* maximum tolerated dose of the assigned adjuvant chemotherapy regimenu2014what I have elsewhere termed an u2018MTD$_i$u2019.$^1$ In that parallel universe, many of the Grade 3+ toxicities tallied in Magnuson et al. Fig 1 might not have occurred, and most Grade 4+ toxicities may have been avoided altogether. Yet the statistician denizens of that universe still could reconstruct that figure by a thought experiment of their own, if they were willing to imagine a universe where one-size-fits-all chemotherapy dosing was standard-of-care. They would simply partition their observed MTD$_i$ distribution into 3 groups according to categories of some clinical score, then examine how some fixed, one-size-fits-all dose bisects the groups. Figure 1 below shows how the 27%, 45% and 76% bars of Magnuson et al. Fig 1A might arise from such an exercise. ![file] **Figure 1:** Distribution of MTD$_i$ by CARG-BC score category, versus u2018theu2019 (one-size-fits-all) chemotherapy dose. The area under each group s curve to the *left* of the chemo dose line represents the fraction of that group who experience a Gr3+ toxicity. Several components of variation$^2$ are depicted in Figure 1. As indicated by the breadth of the several group-wise densities, there remains within each group much residual inter-individual variation. Clinical dose titration is well suited to identifying an idiosyncratic variance component of this kind. As for the *predictable* component of variationu2014evident in the leftward march of group-wise means correlating with increasing clinical scoreu2014this points to an opportunity to individualize the starting doses for these titrations. Thus, the results of Magnuson et al. show geriatric assessment yields information useful for initiating patient-centered dose titrations of adjuvant chemotherapy in older patients with breast cancer. Whether their CARG-BC score incorporates that information suitably for this purpose is doubtful, however, because CARG-BC development was carried out within a problem framing motivated by the statistician s preoccupation with *passively predicting* toxicities, not the clinician s interest in *actively averting* them. The authors elide this crucial distinction when suggesting that their findings  may be useful to clinicians for predicting individual probability of chemotherapy toxicity *and directing therapy* [emphasis added]. But how exactly to rationalize such therapeutic direction remains imponderable without a genuinely *clinical* problem framing. I contend that *dose titration algorithm tuning* (DTAT) provides just such a framework. Not only does DTAT s grounding in *recursive filtering* lend an apt formalism, but its humanistic conception of dose titration  in terms of reflective, organic decision-making that takes into view  the diversity of [patients ] individual values and goals of care $^1$ accords fully with a holistic outlook so essential to Geriatric Oncology. If CARG-BC is not obviously fit-for-purpose in the clinic, then whose purposes might it serve? The 27% bar in the authors Fig 1A provides a clue, as it matches typical u2018target toxicity ratesu2019 for phase 1 oncology trials.$^3$ Thus, in a sense, the CARG-BC u2264 5 group includes just those patients whom strict exclusion criteria are intended to select. However much the authors might abhor the use of CARG-BC to harden trial exclusion criteria, that is unfortunately the most natural application of a toxicity risk prediction model developed expressly and merely to *predict* toxicity. The authors predicament in this respect has arisen perhaps from unrecognized causal structure of the problem.$^4$ Magnuson et al. observe in their Discussion that  patients receiving an RDI 85% experience poorer relapse-free survival. Absent an acknowledgment of *baseline health status* as a likely counfounder of this relation, and indeed of *toxicities* as a mediator (Figure 2, below), this induces a blinkered and uncontextualized focus on RDI as a sole determinant of outcome. In turn, such narrow focus tends to discount the value of *prospective* adjustments to chemotherapy regimen. Yet basic pharmacologic intuition readily rejects any notion that one-size-fits-all dosing followed by *unplanned* (reactive) dose adjustments might constitute better care or yield better outcomes than model-based first-dose determination followed by a *planned* (proactive) clinical dose titration. ![file] **Figure 2:** Directed acyclic graph (DAG) positing likely causal relations between key factors in the Discussion of Magnuson et al (2021). The possibility of using geriatric assessment as an input to prospective dose adjustment is depicted in dashed portions of the graph. [RDI: relative dose intensity.] Deeper analysis of the HOPE trial still surely holds opportunities for better advancing Dr. Hurria s humanistic vision of personalized, multidisciplinary care of older patients with breast cancer.$^5$ I would urge efforts to identify severally the predictable and idiosyncratic components of variance exhibited in Figure 1 above, contextualizing these in a clinical frame of *first-dose selection and subsequent titration*. Although HOPE was carried out as an observational trial, the clinical data collected will include time-series measurements of crucial hematologic dose-limiting toxicities; and the occurrence of dose reductions may well serve to interval-censor the latent MTD$_i$ of some patients. It is worth bearing in mind that, in conjunction with realistic modeling, Bayesian methods may sometimes recover a surprising amount of information.$^6$ ## References 1. Norris DC. Dose Titration Algorithm Tuning (DTAT) should supersede u2018theu2019 Maximum Tolerated Dose (MTD) in oncology dose-finding trials. *F1000Research.* 2017;6:112. doi:[10.12688 f1000research.10624.3] 2. Senn S. Mastering variation: variance components and personalised medicine. *Stat Med.* 2016;35(7):966-977. doi:[10.1002 sim.6739](http: onlinelibrary.wiley.com doi 10.1002 sim.6739 abstract) 3. Thall PF, Lee S-J. Practical model-based dose-finding in phase I clinical trials: methods based on toxicity. *Int J Gynecol Cancer.* 2003;13(3):251-261. [PMID: 12801254] 4. Shapiro DD, Msaouel P. Causal Diagram Techniques for Urologic Oncology Research. *Clinical Genitourinary Cancer.* Published online August 2020:S1558767320301932. doi:[10.1016 j.clgc.2020.08.003] 5. Karuturi MS, Sedrak MS, Magnuson A, et al. Breast cancer and aging: Standing on the shoulders of a giant. *J Geriatr Oncol.* 2020;11(2):212-216. doi:[10.1016 j.jgo.2019.05.019] 6. Norris DC. 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