Patient Preferences for Treatment‑Free Intervals in Multiple Myeloma: Mixed‑Methods Insights on Monitoring Trade‑offs and Supportive Care Improvements

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This mixed‑methods study quantified patient preferences for treatment‑free intervals (TFIs), monitoring trade‑offs, and actionable service improvements across inpatient and outpatient care. Methods A 12-month, single-center mixed-methods survey (n = 100) at a German tertiary center collected sociodemographics, treatment exposure, and structured items on TFI importance, preferred TFI length, monitoring intensity, relapse-risk trade-offs, inpatient stressors, and patient-generated improvement proposals. Quantitative analyses comprised descriptive statistics and hypothesis‑driven group comparisons; qualitative data were analyzed using inductive thematic analysis with double‑coding and consensus. Results TFIs between inpatient treatments were rated 10/10 “very important” by 49.5% and 0/10 “not important” by 9.1%. Predominant inpatient stressors were separation from family (27%), treatment‑related symptoms/side effects (23%), fatigue (17%), food quality (15%), waiting times (14%), and shared rooms (12%). In the outpatient setting, 77% found regular check‑ups reassuring, while 13% found them burdensome. When trading TFI length against relapse risk, 67% prioritized risk minimization and 7% would accept higher risk for longer TFIs. Preferred minimal TFIs were 8 weeks (39%). Narratives emphasized physical/emotional recovery, autonomy, restoration of routines, and pragmatic levers (single rooms, better food, shorter waits, flexible scheduling, staffing). Conclusion People living with MM value TFIs - especially post‑inpatient - for recovery, while favoring vigilant, risk‑adapted monitoring. Preference‑sensitive TFI planning with explicit safety triggers and hybrid in‑person/telehealth follow‑up may enhance quality of life without compromising safety. Multiple myeloma treatment-free intervals patient preferences monitoring supportive care outpatient follow-up Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Continuous or prolonged therapy remains central to disease control in MM, yet appointment schedules and cumulative toxicities impose time and psychosocial burden that can erode daily functioning [ 1 – 6 ]. Planned TFIs may offer patient-centered recovery phases if safety is preserved through appropriate monitoring and rapid re-evaluation. Emerging finite-course approaches, including BCMA-directed CAR-T, create clinical space for intentional pauses without ongoing maintenance in selected patients, as randomized data show superior progression-free outcomes for idecabtagene vicleucel versus standard regimens in triple-class-exposed RRMM and support vigilant survivorship pathways after deep responses [ 7 – 9 ]. Ciltacabtagene autoleucel further reinforces this paradigm with improved clinical outcomes in lenalidomide‑refractory settings and structured post‑treatment follow‑up in trial programs [ 10 – 13 ]. Despite this rationale, several supportive-care questions remain insufficiently characterized: how people living with MM prioritize TFI length; how they balance reassurance from regular follow-up against concerns about relapse risk; and which modifiable inpatient and outpatient service factors most affect experience and feasibility. Practice-relevant evidence that quantifies these preferences and translates them into implementable improvements (environment, scheduling, staffing, hybrid visits with explicit safety triggers) is needed to operationalize TFIs safely in routine care. This mixed-methods survey addresses these gaps by combining structured preference items and qualitative narratives to: quantify the perceived importance of TFIs; characterize trade-offs between TFI length and relapse risk in relation to monitoring intensity; and derive pragmatic, high-yield changes across inpatient and outpatient settings. The objective is to inform preference-sensitive scheduling aligned with clinical risk and depth of response while mapping feasible environmental and process improvements that enhance patient experience without compromising safety. METHODS Design This was a 12-month, single-center, mixed-methods survey of adults with multiple myeloma (MM) at a German tertiary center. Setting and participants Consecutive patients were approached during scheduled inpatient admissions and outpatient visits at University Hospital Würzburg. Eligibility criteria were age ≥ 18 years, confirmed MM (per IMWG criteria), capacity to consent, and sufficient German proficiency to complete the survey. Exclusion criteria were acute psychiatric crisis, decision-relevant cognitive impairment, intensive-care admission, or insufficient language proficiency. Enrollment continued until 100 complete datasets were obtained. The sample size of 100 participants was determined based on a power calculation designed to ensure sufficient statistical power to detect clinically meaningful differences in key outcome measures, particularly the perceived importance of TFIs and the associated patient preferences. A target power of 80% and a significance level of 0.05 were selected to ensure robust results. The calculation assumed an effect size of 0.5 (Cohen’s d), which is considered a moderate effect size commonly observed in patient preference and healthcare utilization studies. The sample size was adjusted to account for potential dropouts and missing data, with an additional margin included to enhance the precision of estimates. This ensured that the study would be adequately powered to detect significant differences in TFI preferences and the trade-offs between relapse risk and monitoring intensity, providing reliable and actionable insights for clinical practice. Endpoints The primary endpoint was perceived importance of treatment-free intervals (TFIs). Secondary endpoints included: inpatient stressors and care experience, reassurance versus burden of outpatient visits, preferred monitoring intensity and cadence, willingness to accept relapse-risk trade-offs for longer TFIs, and patient-generated proposals for pragmatic service improvements across care settings. Survey content The survey instrument was specifically developed for this study, drawing on prior work on patient-reported outcomes and preferences in multiple myeloma and on clinical and psychosocial expertise within the study team [ 14 , 15 ]. The instrument comprised: sociodemographics and treatment exposure (for example, recent treatment intensity, inpatient days in the prior year), structured items on TFI importance (0–10), preferred minimal TFI length and conditions, monitoring frequency/cadence preferences, and relapse-risk trade-offs, structured checklists of inpatient stressors and outpatient barriers, free-text fields for narratives on challenges, needs, and improvement proposals. Data collection and quality assurance Surveys were completed on paper or via a secure hospital platform. Trained staff provided assistance when needed. Completeness was checked at collection; data were de-identified, consistency-checked, and stored on protected institutional systems with role-based access. Statistical analysis Analyses were conducted using IBM SPSS Statistics (version 29.0). Continuous variables were summarized as mean (SD) and/or median (IQR), and categorical variables as n (%). Group comparisons used chi-square tests for categorical data and Mann–Whitney U tests or ANOVA for continuous data, as appropriate. Assumptions were assessed (normality by Shapiro–Wilk; homogeneity by Levene’s test); if violated, non-parametric tests were applied. A pre-specified MANCOVA examined associations between demographic/clinical predictors and TFI preference metrics (covariates: age, sex, recent treatment intensity (past 6 months), inpatient days (past year)); model diagnostics included residual inspection and assessment of homogeneity of covariance matrices. Two-sided α = 0.05 defined statistical significance. Effect sizes (Cohen’s d, r, η²) and 95% confidence intervals were reported where applicable. No adjustment for multiplicity was applied given the primarily descriptive/exploratory aim; multivariable results are summarized in Supplementary Tables. No imputation was performed; available-case analyses with explicit denominators were used. Qualitative analysis Free-text responses underwent inductive qualitative content analysis. Two independent raters coded all narratives, iteratively refined a consensus codebook, and resolved discrepancies by discussion. Thematic saturation was assessed pragmatically. Representative quotations were extracted to illustrate recurrent and divergent themes. Mixed-methods integration Triangulation at interpretation linked quantitative distributions (for example, TFI preferences, risk trade-offs) to qualitative explanations and mapped themes to implementation levers (environment, scheduling, staffing, hybrid contacts). Convergence, complementarity, and dissonance were explicitly examined. Subgroups and sensitivity Exploratory subgroup inspections considered recent treatment intensity and inpatient exposure to contextualize preference heterogeneity. Sensitivity checks contrasted parametric versus non-parametric tests for borderline distributional assumptions; inferential emphasis remained on prespecified analyses. Missing data Item-level missingness was documented; analyses used non-missing observations with reported denominators. Ethics and data protection Ethics approval was obtained from the University of Würzburg (AZ 246/22). All participants provided written informed consent. Data were de-identified and processed according to institutional policies and applicable regulations. RESULTS Participants and baseline characteristics A total of 100 adults with MM completed the survey. Median age was 67.2 years (IQR 60.8–72.3); 64% were male. Median inpatient days in the prior year were 31 (IQR 12–57). Supplementary Table 1 summarizes demographics and clinical exposure. Primary endpoint importance of TFIs Between inpatient treatments, TFIs were rated 10/10 “very important” by 49.5% and 0/10 “not important” by 9.1%. Figure 1 (left panel) illustrates inpatient TFI importance; the corresponding outpatient distribution is shown in Fig. 1 (right panel). Full distributions for both settings are provided in Supplementary Table 2 . Qualitative accounts highlighted physical/emotional recovery, family reconnection, restoration of routines, vacation/social activities, and autonomy as core drivers. Inpatient stressors and care experience Most frequent stressors were separation from family (27%), treatment-related symptoms/side effects (23%), fatigue (17%), food quality (15%), waiting times (14%), and shared rooms (12%); additional issues included climate control (10%), communication/empathy gaps (11%), health-related limitations (9%), and lack of physical activity (7%). Supplementary Table 3 provides details. Effective coping included family support, online task management, prior preparation, and delegation (Supplementary Table 4) . Impact on daily life domains Home management: 67% reported no major backlog of home tasks (planning/delegation/online management); 33% reported difficulties, especially with frequent admissions, citing cognitive strain and stress. Social relationships: 32% reported negative effects (cancellations, infection precautions, exhaustion); others maintained/strengthened contacts via proactive planning and digital communication. Professional activities: Among non-retired participants, 53.6% reported work limitations (fatigue, immunocompromise, side effects); flexible scheduling and supportive workplace policies mitigated some constraints. Outpatient TFIs, monitoring reassurance, and risk trade‑offs Preferences for longer outpatient TFIs were heterogeneous (10/10: 30.3%; 0/10: 16.2%) and are shown in Fig. 1 (right panel) and Supplementary Table 2 . Regular check-ups were reassuring for 77% and burdensome for 13% (Fig. 2 a). Preferences regarding increased visit frequency are shown in Fig. 2 b. When trading TFI length against relapse risk, 67% prioritized risk minimization; 7% would accept higher relapse risk for longer TFIs (Fig. 2 c). Preferred minimal TFI length Preferred minimal TFIs were 8 weeks (39%), unspecified (18%). The distribution is shown in Fig. 3 . Explanations referenced recent treatment intensity, recovery needs, travel logistics, and personal circumstances. Patient‑generated improvement proposals High-yield, implementable suggestions included more (quieter) single rooms, improved food quality, reduced waiting times, flexible appointment scheduling, and adequate staffing; additional proposals included clearer communication and access to rehabilitative activities (e.g., physiotherapy, yoga). Factors affecting maintenance-therapy attendance are summarized in Supplementary Table 5 . DISCUSSION This mixed-methods survey demonstrates that people living with MM place substantial value on TFIs, particularly following intensive inpatient treatments, primarily to enable physical and psychosocial recovery, regain autonomy, and restore everyday routines. At the same time, most patients in our cohort preferred vigilant, structured follow-up in order to minimize relapse risk rather than maximizing time off therapy at the expense of perceived safety. Taken together, these findings support a pragmatic model in which TFIs and surveillance are not competing but complementary goals: time off treatment is desired, but only under conditions of transparent monitoring, clear safety triggers, and rapid access pathways. The marked heterogeneity in preferred TFI length underscores the need for individualized scheduling aligned with recent treatment intensity, symptom burden, functional status, and logistical constraints rather than a one-size-fits-all cadence. From an implementation perspective, three practice-relevant insights emerge from the convergence of quantitative distributions and qualitative narratives. First, TFIs function as restorative phases that buffer cumulative physical toxicities and psychosocial strain. Patients consistently described TFIs - especially post-inpatient - as periods to recover from fatigue, gastrointestinal and neurotoxic side effects, and sleep disruption; to reconnect with family and friends; and to re-establish valued activities such as travel, work, or hobbies. These observations complement prior work showing that patients weigh quality of life and progression-free survival (PFS) as equally important outcomes in MM clinical trials [ 15 ]. Second, when asked to explicitly trade TFI length against relapse risk, safety-oriented preferences predominated: two-thirds of respondents prioritized risk minimization, and regular check-ups were experienced as reassuring rather than burdensome by the majority. Third, patients identified concrete environmental and process factors - single rooms, food quality, waiting times, flexible scheduling, adequate staffing, and consistent communication - as high-yield levers to improve the lived experience and feasibility of both TFIs und maintenance phases. Notably, several of these levers are resource-intensive and may increase costs for providers and payers, indicating a need to balance preference-sensitive improvements with considerations of efficiency and equity. Emerging finite-course therapeutics create genuine clinical space for intentional TFIs without ongoing maintenance in selected patients. BCMA-directed CAR T-cell products such as idecabtagene vicleucel (ide-cel) and ciltacabtagene autoleucel (cilta-cel) exemplify this paradigm: they are administered as one-time or short-course interventions with no protocol-mandated maintenance thereafter. In randomized settings, ide-cel (KarMMa-3) significantly improved PFS compared with standard regimens in triple-class-exposed relapsed/refractory MM (RRMM), with hazard ratios around 0.5 for PFS and clinically meaningful absolute gains, thereby supporting structured survivorship pathways after deep responses [ 7 , 8 ]. Similarly, cilta-cel in CARTITUDE-4 improved key clinical outcomes - including PFS and depth of response - versus pomalidomide-based standard therapies in lenalidomide-refractory patients after one to three prior lines, with sustained QoL benefits in patient-reported outcomes [ 10 , 11 , 16 ]. Extended follow-up and integrated analyses across studies suggest that a subset of patients can achieve durable, multi-year remissions following BCMA-directed CAR T-cell therapy, reinforcing the clinical plausibility of finite-course treatment followed by periods off therapy when aligned with individual disease risk and depth of response [ 17 ]. Parallel to CAR T-cell approaches, BCMA-directed T-cell redirecting bispecific antibodies have expanded the therapeutic armamentarium for triple-class-exposed RRMM and beyond. Teclistamab is a first-in-class, off-the-shelf, BCMA × CD3 bispecific antibody that has shown high response rates and deep remissions in heavily pretreated populations and is now being moved into earlier-line combinations [ 9 ]. In the phase 3 MajesTEC-3 trial, teclistamab combined with subcutaneous daratumumab and dexamethasone significantly improved PFS compared with daratumumab-based standard regimens (DPd/DVd) in patients with one to three prior lines of therapy. At a median follow-up of approximately 34–35 months, the estimated 36-mont PFS was 83.4 % in the teclistamab–daratumumab grop versus 29.7 % in the DPd/DVd group (hazard atio 0.17; 95 % CI 0.12–0.23; p < 0.001) [ 18 ]. Overall survival was likewise significantly prolonged. These results underscore the potential of teclistamab-based regimens to deliver durable disease control; however, the safety profile is characterized by a distinct spectrum of adverse events - including higher rates of infections and cytopenias - rather than a uniformly more favorable toxicity profile compared with traditional daratumumab-based combinations [ 18 ]. From a TFI perspective, such continuous or frequently administered regimens may effectively compress the intervals between contacts and limit extended periods completely off therapy, even as they improve disease outcomes. Within supportive care, oncology telehealth frameworks and hybrid follow-up models offer pragmatic tools to reconcile patients’ desire for TFIs with the need for vigilance. Structured hybrid pathways - combining scheduled in-person assessments at clinically meaningful junctures with telehealth contacts (video, telephone, or secure messaging) in between - can reduce travel and time burden without sacrificing safety when deployed in risk-adapted fashion. In these pathways, explicit escalation routes, predefined symptom and laboratory thresholds, and clear response windows are essential to maintain safety during TFIs, particularly after high-intensity treatments such as CAR T-cell therapy or prolonged multi-agent regimens. Building on our data and the evolving therapeutic landscape, an operational TFI pathway should integrate three core components. First, risk stratification must incorporate disease biology (e.g., cytogenetic risk, early relapse), depth of response (including minimal residual disease, MRD, where available), recent treatment intensity, and comorbidities. This risk profile should determine a baseline permissible TFI range and minimum monitoring cadence. Second, preference-sensitive scheduling should individualize the exact length of TFIs within this range based on recovery needs, functional status, work and caregiving responsibilities, mobility, and travel logistics. Third, safety should be codified a priori through explicit triggers: core symptom thresholds (e.g., new bone pain, recurrent infections, B-symptoms), key laboratory cut-offs (e.g., hemoglobin, creatinine, paraprotein or free light chain dynamics), and clearly communicated unscheduled alert routes with defined response times. Follow-up should adopt a hybrid cadence calibrated to risk and logistics, integrating in-person assessments (e.g., at initiation of TFIs, after CAR T-cell therapy, or around predefined milestones) with structured remote contacts and mandatory re-evaluation checkpoints. For high-risk patients or those with aggressive prior relapse, TFIs may need to be shorter and more tightly surveilled; for lower-risk patients in deep, sustained responses, longer TFIs with less frequent in-person visits but robust remote monitoring may be acceptable. At the system level, several modifiable factors identified by patients appear to have particularly high leverage for making TFIs both feasible and restorative. Quieter single room capacity (or, where this is not possible, clear rules for noise and visitor restrictions in shared rooms) can reduce noise and peak stress. Reliable, scheduled appointments with minimized waiting times reduce the time burden per contact and facilitate the acceptance of more frequent, risk-adapted monitoring visits. Improved nutritional offerings, access to rehabilitative measures (e.g., physical therapy, lightly adapted exercise, yoga), as well as sufficient staff resources and consistent, empathetic communication address key stressors identified in our sample. Continuous quality monitoring should systematically track unplanned contacts (emergency admissions, unscheduled inpatient admissions), therapy adherence, functional status, and patient-reported experiences to iteratively adjust TFI cadence and trigger criteria. The present study has several limitations. Its single-center, cross-sectional design at a tertiary academic institution limits the generalizability of findings to other settings and healthcare systems. Selection bias is possible, as patients with strong preferences or better functional status may have been more likely to participate. All quantitative data were self-reported and are susceptible to recall and social desirability biases; clinical details and objective outcomes were not systematically cross-validated against medical records. Preference dynamics likely evolve over the course of the disease and across treatment milestones, whereas our survey captures a single time-point snapshot. Although the questionnaire was iteratively developed by a multidisciplinary team with clinical and psychosocial expertise, and informed by prior work on patient-reported outcomes and preferences in MM [ 1 ], it did not undergo formal psychometric validation. Therefore, some composite preference metrics (e.g., global TFI importance scores) should be interpreted as exploratory. Furthermore, while our survey explicitly asked about trade-offs between TFI length and relapse risk, actual behavior under real-world risk-benefit conditions may differ from stated preferences. Strengths of this work include its practice-proximal mixed-methods design, the explicit integration of qualitative narratives with quantitative distributions, and the direct mapping of patient-generated levers to concrete, potentially implementable process changes across inpatient and outpatient care. Importantly, our findings do not suggest that longer TFIs are universally preferred or clinically appropriate; rather, they advocate for preference-sensitive, risk-aligned TFI planning with transparent communication about uncertainty and trade-offs. Future multicenter, prospective evaluations should test whether such pathways - combining risk stratification, preference-sensitive scheduling, codified safety triggers, and hybrid follow-up - improve patient experience, functional outcomes, adherence, and unplanned healthcare utilization. Simultaneously, health economic and equity analyses will be needed to define sustainable ways of investing in the environmental and process changes (e.g., single rooms, telehealth infrastructure, staffing) that patients in our cohort identified as most impactful. CONCLUSION People living with MM value time off therapy - especially after inpatient treatments - primarily for recovery and psychosocial reasons, while most simultaneously favor vigilant follow‑up to minimize relapse risk. The heterogeneity of preferred TFI length supports preference‑sensitive scheduling aligned to clinical risk, depth of response, and personal context. A risk‑aligned TFI model that codifies safety triggers, embeds hybrid in‑person/telehealth follow‑up, and targets modifiable system factors (quiet single rooms, predictable scheduling with shorter waits, improved food quality, adequate staffing, consistent communication) offers a pragmatic pathway to enhance patient experience without compromising safety. Future multicenter, prospective evaluations should test whether such pathways improve patient experience, functional outcomes, adherence, and unplanned utilization, and refine alert thresholds and cadence to balance reassurance with resource stewardship. Declarations Acknowledgements: The authors would like to thank the patients for their active involvement in this study. Funding: Anna Fleischer was supported by the Else Kröner-Fresenius Foundation, Leo Rasche was supported by the Paula and Rodger Riney Foundation and the German Cancer Aid via the MSNZ programme. KMK was supported by the Stifterverband. Sofie-Katrin Kadel was supported by the Else Kröner-Fresenius Foundation. Jessica Peter was supported by the Else Kröner-Fresenius Foundation. C.R. was supported by a fellowship (project number ZZ-40) of the Interdisciplinary Center for Clinical Research (IZKF). Conflicts of interest: AF received honoraria from GSK, BMS and Janssen. The other authors report no conflict of interests. KMK received honoraria from Abbvie, Beigene, BMS, GSK, Johnson&Johnson, Menarini, Novartis, Pfizer and Sanofi. SKK received honoraria from Janssen. J.M.W. reports personal fees or consultancy for Johnson & Johnson, Sanofi, Takeda, Pfizer, Oncopeptides, Menarini-Stemline, Skyline Dx, Abbvie, Amgen and GSK. C.R. received honoraria from Janssen and Takeda. Ethics approval: The study was approved by the ethics committee of Würzburg (AZ 246/22) and conducted in accordance with the Declaration of Helsinki. Consent to participate: All participants provided written informed consent for study participation and publication of anonymized data. Consent for publication: All included patients gave their consent for publication of the collected data. Data Availability Statement: The data supporting the findings of this study are available from the corresponding author upon reasonable request. Author Contributions: Anna Fleischer : Conceptualization, Investigation, Funding acquisition, Writing – original draft, Methodology, Validation, Visualization, Writing – review & editing, Software, Formal analysis, Project administration, Data curation, Supervision, Resources. Mohammad Barakat : Conceptualization, Investigation, Methodology, Formal analysis, Data curation, Writing – original draft, Writing – review & editing. Jessica Peter, Sofie Kadel, Christine Riedhammer, Patrick-Pascal Strunz, Julia Mersi, Johannes Waldschmidt, K. Martin Kortüm, Hermann Einsele, Imad Maatouk : Writing – original draft, Writing – review & editing. Leo Rasche : Conceptualization, Investigation, Funding acquisition, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing, Software, Formal analysis, Project administration, Data curation, Supervision, Resources. 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Costa, L.J., et al., Teclistamab plus Daratumumab in Relapsed or Refractory Multiple Myeloma . N Engl J Med, 2025. Additional Declarations Competing interest reported. Conflicts of interest: AF received honoraria from GSK, BMS and Janssen. The other authors report no conflict of interests. KMK received honoraria from Abbvie, Beigene, BMS, GSK, Johnson&Johnson, Menarini, Novartis, Pfizer and Sanofi. SKK received honoraria from Janssen. J.M.W. reports personal fees or consultancy for Johnson & Johnson, Sanofi, Takeda, Pfizer, Oncopeptides, Menarini-Stemline, Skyline Dx, Abbvie, Amgen and GSK. C.R. received honoraria from Janssen and Takeda. Supplementary Files SupplementaryTables.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 10 May, 2026 Reviews received at journal 07 May, 2026 Reviews received at journal 03 May, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviews received at journal 05 Mar, 2026 Reviewers agreed at journal 27 Feb, 2026 Reviewers invited by journal 06 Jan, 2026 Editor assigned by journal 06 Jan, 2026 Submission checks completed at journal 29 Dec, 2025 First submitted to journal 15 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8366273","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":570876577,"identity":"41f4879a-3e70-4506-8ffc-8ac2f652e523","order_by":0,"name":"Anna Fleischer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIie2PMQrCQBAAVw5is5rWgCRfuCBoma/EJh+wNOCJn7DwMRcWTJMHBCIYEKwsYiOx0ksUy1xKixsOrtlhdgEMhj8kSAFk+2CwLdtfahTOfgoj3lv5jlnRpKfCkqSOT+5C4HyNGcE4Fd1KwKyQ8HidTSXOC8wJnEyT4Qw5DQQt99AoVQE8D3WKXSVPQZs92I9Vq5xLbQXkSFA4URWGeVPpNpRicXUL+WqxmXPIXuhkusWGu8u9jslTFb+6HSN3nEpN5of3OQH7zhsMBoOhgzfC5Uhp95/tRgAAAABJRU5ErkJggg==","orcid":"","institution":"University Hospital Würzburg","correspondingAuthor":true,"prefix":"","firstName":"Anna","middleName":"","lastName":"Fleischer","suffix":""},{"id":570876578,"identity":"60a70425-3bb8-49c8-af29-7b7e891adb7f","order_by":1,"name":"Mohammad Barakat","email":"","orcid":"","institution":"University Hospital Würzburg","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Barakat","suffix":""},{"id":570876579,"identity":"91195bc1-b859-47fc-84c4-0205f311cbaa","order_by":2,"name":"Jessica Peter","email":"","orcid":"","institution":"University Hospital Würzburg","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Peter","suffix":""},{"id":570876580,"identity":"dc9874cb-f291-4b58-9f29-4d9681a4f1ce","order_by":3,"name":"Sofie-Katrin Kadel","email":"","orcid":"","institution":"University Hospital Würzburg","correspondingAuthor":false,"prefix":"","firstName":"Sofie-Katrin","middleName":"","lastName":"Kadel","suffix":""},{"id":570876581,"identity":"39cd6095-5085-4ae6-a319-ed77bb47b850","order_by":4,"name":"Christine Riedhammer","email":"","orcid":"","institution":"University Hospital Würzburg","correspondingAuthor":false,"prefix":"","firstName":"Christine","middleName":"","lastName":"Riedhammer","suffix":""},{"id":570876582,"identity":"d4ec8c1c-362b-49c4-91db-18040d9f1080","order_by":5,"name":"Patrick-Pascal Strunz","email":"","orcid":"","institution":"University Hospital Würzburg","correspondingAuthor":false,"prefix":"","firstName":"Patrick-Pascal","middleName":"","lastName":"Strunz","suffix":""},{"id":570876583,"identity":"ee2237b2-bf75-4691-950e-b5db750b510d","order_by":6,"name":"Julia Mersi","email":"","orcid":"","institution":"University Hospital Würzburg","correspondingAuthor":false,"prefix":"","firstName":"Julia","middleName":"","lastName":"Mersi","suffix":""},{"id":570876584,"identity":"cb12c5ce-3fcd-45aa-b732-a0cdf5a2f583","order_by":7,"name":"Johannes Waldschmidt","email":"","orcid":"","institution":"University Hospital Würzburg","correspondingAuthor":false,"prefix":"","firstName":"Johannes","middleName":"","lastName":"Waldschmidt","suffix":""},{"id":570876585,"identity":"aff195f8-6dce-4de7-b51e-51b6521a333f","order_by":8,"name":"K. Martin Kortüm","email":"","orcid":"","institution":"University Hospital Würzburg","correspondingAuthor":false,"prefix":"","firstName":"K.","middleName":"Martin","lastName":"Kortüm","suffix":""},{"id":570876586,"identity":"b873566c-371c-4f19-85b3-1618c7b624ce","order_by":9,"name":"Hermann Einsele","email":"","orcid":"","institution":"University Hospital Würzburg","correspondingAuthor":false,"prefix":"","firstName":"Hermann","middleName":"","lastName":"Einsele","suffix":""},{"id":570876587,"identity":"2d698864-ead5-4625-abd6-d5a38a196eb6","order_by":10,"name":"Imad Maatouk","email":"","orcid":"","institution":"University Hospital Würzburg","correspondingAuthor":false,"prefix":"","firstName":"Imad","middleName":"","lastName":"Maatouk","suffix":""},{"id":570876588,"identity":"c8d5748c-afde-4d90-bd58-b2a0a8c00b02","order_by":11,"name":"Leo Rasche","email":"","orcid":"","institution":"University Hospital Würzburg","correspondingAuthor":false,"prefix":"","firstName":"Leo","middleName":"","lastName":"Rasche","suffix":""}],"badges":[],"createdAt":"2025-12-15 12:39:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8366273/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8366273/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100007140,"identity":"4cf9fda4-0083-473e-8f1a-3bc41da6b34c","added_by":"auto","created_at":"2026-01-12 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05:49:50","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":105269,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8366273/v1/304151ae72d39ad66e8c4b7c.html"},{"id":100361824,"identity":"b1fb0388-1d94-4533-85f2-8bd42dca0ad2","added_by":"auto","created_at":"2026-01-16 07:45:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":312664,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImportance of TFIs: inpatient (left) and outpatient (right); proportions of respondents across 0–10 ratings\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8366273/v1/dc76b575359fcf30c7311b67.png"},{"id":100007137,"identity":"26ecc36f-9640-4169-bad4-b6f42029082d","added_by":"auto","created_at":"2026-01-12 05:49:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":357585,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMonitoring perceptions and trade-offs: (a) perceived reassurance of regular check-ups; (b) preference for increased check-up frequency; (c) willingness to accept higher relapse risk for extended TFIs\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8366273/v1/1b01246fca8f2e4b7f32e6ca.png"},{"id":100007138,"identity":"078eb0fa-3b97-43f4-b8fd-d2331611e6d0","added_by":"auto","created_at":"2026-01-12 05:49:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":327398,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePreferred minimal length of TFIs from the patient perspective; category distribution (\u0026lt;4 weeks, 4–8 weeks, \u0026gt;8 weeks, unspecified)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8366273/v1/f3d23754ef98660526b71f84.png"},{"id":100380875,"identity":"8fb7e3a2-6354-4b44-8101-d6d73a7a0c0d","added_by":"auto","created_at":"2026-01-16 10:36:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2052813,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8366273/v1/9fa6f390-a9a6-4793-ae70-52b14c335a47.pdf"},{"id":100007135,"identity":"2dc0be6c-5d18-46b7-bd61-f32861d524e8","added_by":"auto","created_at":"2026-01-12 05:49:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21816,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8366273/v1/2a175187ea80923274ca6d9a.docx"}],"financialInterests":"Competing interest reported. Conflicts of interest: AF received honoraria from GSK, BMS and Janssen. The other authors report no conflict of interests. KMK received honoraria from Abbvie, Beigene, BMS, GSK, Johnson\u0026Johnson, Menarini, Novartis, Pfizer and Sanofi. SKK received honoraria from Janssen. J.M.W. reports personal fees or consultancy for Johnson \u0026 Johnson, Sanofi, Takeda, Pfizer, Oncopeptides, Menarini-Stemline, Skyline Dx, Abbvie, Amgen and GSK. C.R. received honoraria from Janssen and Takeda.","formattedTitle":"Patient Preferences for Treatment‑Free Intervals in Multiple Myeloma: Mixed‑Methods Insights on Monitoring Trade‑offs and Supportive Care Improvements","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eContinuous or prolonged therapy remains central to disease control in MM, yet appointment schedules and cumulative toxicities impose time and psychosocial burden that can erode daily functioning [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Planned TFIs may offer patient-centered recovery phases if safety is preserved through appropriate monitoring and rapid re-evaluation.\u003c/p\u003e \u003cp\u003eEmerging finite-course approaches, including BCMA-directed CAR-T, create clinical space for intentional pauses without ongoing maintenance in selected patients, as randomized data show superior progression-free outcomes for idecabtagene vicleucel versus standard regimens in triple-class-exposed RRMM and support vigilant survivorship pathways after deep responses [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Ciltacabtagene autoleucel further reinforces this paradigm with improved clinical outcomes in lenalidomide‑refractory settings and structured post‑treatment follow‑up in trial programs [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite this rationale, several supportive-care questions remain insufficiently characterized: how people living with MM prioritize TFI length; how they balance reassurance from regular follow-up against concerns about relapse risk; and which modifiable inpatient and outpatient service factors most affect experience and feasibility. Practice-relevant evidence that quantifies these preferences and translates them into implementable improvements (environment, scheduling, staffing, hybrid visits with explicit safety triggers) is needed to operationalize TFIs safely in routine care.\u003c/p\u003e \u003cp\u003eThis mixed-methods survey addresses these gaps by combining structured preference items and qualitative narratives to: quantify the perceived importance of TFIs; characterize trade-offs between TFI length and relapse risk in relation to monitoring intensity; and derive pragmatic, high-yield changes across inpatient and outpatient settings. The objective is to inform preference-sensitive scheduling aligned with clinical risk and depth of response while mapping feasible environmental and process improvements that enhance patient experience without compromising safety.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign\u003c/h2\u003e \u003cp\u003eThis was a 12-month, single-center, mixed-methods survey of adults with multiple myeloma (MM) at a German tertiary center.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSetting and participants\u003c/h3\u003e\n\u003cp\u003eConsecutive patients were approached during scheduled inpatient admissions and outpatient visits at University Hospital W\u0026uuml;rzburg. Eligibility criteria were age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, confirmed MM (per IMWG criteria), capacity to consent, and sufficient German proficiency to complete the survey. Exclusion criteria were acute psychiatric crisis, decision-relevant cognitive impairment, intensive-care admission, or insufficient language proficiency. Enrollment continued until 100 complete datasets were obtained.\u003c/p\u003e \u003cp\u003eThe sample size of 100 participants was determined based on a power calculation designed to ensure sufficient statistical power to detect clinically meaningful differences in key outcome measures, particularly the perceived importance of TFIs and the associated patient preferences. A target power of 80% and a significance level of 0.05 were selected to ensure robust results. The calculation assumed an effect size of 0.5 (Cohen\u0026rsquo;s d), which is considered a moderate effect size commonly observed in patient preference and healthcare utilization studies. The sample size was adjusted to account for potential dropouts and missing data, with an additional margin included to enhance the precision of estimates. This ensured that the study would be adequately powered to detect significant differences in TFI preferences and the trade-offs between relapse risk and monitoring intensity, providing reliable and actionable insights for clinical practice.\u003c/p\u003e\n\u003ch3\u003eEndpoints\u003c/h3\u003e\n\u003cp\u003eThe primary endpoint was perceived importance of treatment-free intervals (TFIs).\u003c/p\u003e \u003cp\u003eSecondary endpoints included:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003einpatient stressors and care experience,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ereassurance versus burden of outpatient visits,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003epreferred monitoring intensity and cadence,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ewillingness to accept relapse-risk trade-offs for longer TFIs, and\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003epatient-generated proposals for pragmatic service improvements across care settings.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eSurvey content\u003c/h3\u003e\n\u003cp\u003eThe survey instrument was specifically developed for this study, drawing on prior work on patient-reported outcomes and preferences in multiple myeloma and on clinical and psychosocial expertise within the study team [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe instrument comprised:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003esociodemographics and treatment exposure (for example, recent treatment intensity, inpatient days in the prior year),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003estructured items on TFI importance (0\u0026ndash;10), preferred minimal TFI length and conditions, monitoring frequency/cadence preferences, and relapse-risk trade-offs,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003estructured checklists of inpatient stressors and outpatient barriers,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003efree-text fields for narratives on challenges, needs, and improvement proposals.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eData collection and quality assurance\u003c/h3\u003e\n\u003cp\u003eSurveys were completed on paper or via a secure hospital platform. Trained staff provided assistance when needed. Completeness was checked at collection; data were de-identified, consistency-checked, and stored on protected institutional systems with role-based access.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAnalyses were conducted using IBM SPSS Statistics (version 29.0). Continuous variables were summarized as mean (SD) and/or median (IQR), and categorical variables as n (%). Group comparisons used chi-square tests for categorical data and Mann\u0026ndash;Whitney U tests or ANOVA for continuous data, as appropriate. Assumptions were assessed (normality by Shapiro\u0026ndash;Wilk; homogeneity by Levene\u0026rsquo;s test); if violated, non-parametric tests were applied. A pre-specified MANCOVA examined associations between demographic/clinical predictors and TFI preference metrics (covariates: age, sex, recent treatment intensity (past 6 months), inpatient days (past year)); model diagnostics included residual inspection and assessment of homogeneity of covariance matrices. Two-sided α\u0026thinsp;=\u0026thinsp;0.05 defined statistical significance. Effect sizes (Cohen\u0026rsquo;s d, r, η\u0026sup2;) and 95% confidence intervals were reported where applicable. No adjustment for multiplicity was applied given the primarily descriptive/exploratory aim; multivariable results are summarized in Supplementary Tables. No imputation was performed; available-case analyses with explicit denominators were used.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQualitative analysis\u003c/h3\u003e\n\u003cp\u003eFree-text responses underwent inductive qualitative content analysis. Two independent raters coded all narratives, iteratively refined a consensus codebook, and resolved discrepancies by discussion. Thematic saturation was assessed pragmatically. Representative quotations were extracted to illustrate recurrent and divergent themes.\u003c/p\u003e\n\u003ch3\u003eMixed-methods integration\u003c/h3\u003e\n\u003cp\u003eTriangulation at interpretation linked quantitative distributions (for example, TFI preferences, risk trade-offs) to qualitative explanations and mapped themes to implementation levers (environment, scheduling, staffing, hybrid contacts). Convergence, complementarity, and dissonance were explicitly examined.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSubgroups and sensitivity\u003c/h2\u003e \u003cp\u003eExploratory subgroup inspections considered recent treatment intensity and inpatient exposure to contextualize preference heterogeneity. Sensitivity checks contrasted parametric versus non-parametric tests for borderline distributional assumptions; inferential emphasis remained on prespecified analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMissing data\u003c/h2\u003e \u003cp\u003eItem-level missingness was documented; analyses used non-missing observations with reported denominators.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEthics and data protection\u003c/h2\u003e \u003cp\u003eEthics approval was obtained from the University of W\u0026uuml;rzburg (AZ 246/22). All participants provided written informed consent. Data were de-identified and processed according to institutional policies and applicable regulations.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and baseline characteristics\u003c/h2\u003e \u003cp\u003eA total of 100 adults with MM completed the survey. Median age was 67.2 years (IQR 60.8\u0026ndash;72.3); 64% were male. Median inpatient days in the prior year were 31 (IQR 12\u0026ndash;57). \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e summarizes demographics and clinical exposure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePrimary endpoint importance of TFIs\u003c/h2\u003e \u003cp\u003eBetween inpatient treatments, TFIs were rated 10/10 \u0026ldquo;very important\u0026rdquo; by 49.5% and 0/10 \u0026ldquo;not important\u0026rdquo; by 9.1%. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (left panel) illustrates inpatient TFI importance; the corresponding outpatient distribution is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (right panel). Full distributions for both settings are provided in \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e. Qualitative accounts highlighted physical/emotional recovery, family reconnection, restoration of routines, vacation/social activities, and autonomy as core drivers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eInpatient stressors and care experience\u003c/h2\u003e \u003cp\u003eMost frequent stressors were separation from family (27%), treatment-related symptoms/side effects (23%), fatigue (17%), food quality (15%), waiting times (14%), and shared rooms (12%); additional issues included climate control (10%), communication/empathy gaps (11%), health-related limitations (9%), and lack of physical activity (7%). \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e provides details. Effective coping included family support, online task management, prior preparation, and delegation \u003cb\u003e(Supplementary Table\u0026nbsp;4)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eImpact on daily life domains\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eHome management: 67% reported no major backlog of home tasks (planning/delegation/online management); 33% reported difficulties, especially with frequent admissions, citing cognitive strain and stress.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSocial relationships: 32% reported negative effects (cancellations, infection precautions, exhaustion); others maintained/strengthened contacts via proactive planning and digital communication.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eProfessional activities: Among non-retired participants, 53.6% reported work limitations (fatigue, immunocompromise, side effects); flexible scheduling and supportive workplace policies mitigated some constraints.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eOutpatient TFIs, monitoring reassurance, and risk trade‑offs\u003c/h2\u003e \u003cp\u003ePreferences for longer outpatient TFIs were heterogeneous (10/10: 30.3%; 0/10: 16.2%) and are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (right panel) and \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e. Regular check-ups were reassuring for 77% and burdensome for 13% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Preferences regarding increased visit frequency are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb. When trading TFI length against relapse risk, 67% prioritized risk minimization; 7% would accept higher relapse risk for longer TFIs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePreferred minimal TFI length\u003c/h2\u003e \u003cp\u003ePreferred minimal TFIs were \u0026lt;\u0026thinsp;4 weeks (23%), 4\u0026ndash;8 weeks (19%), \u0026gt;\u0026thinsp;8 weeks (39%), unspecified (18%). The distribution is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Explanations referenced recent treatment intensity, recovery needs, travel logistics, and personal circumstances.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePatient‑generated improvement proposals\u003c/h2\u003e \u003cp\u003eHigh-yield, implementable suggestions included more (quieter) single rooms, improved food quality, reduced waiting times, flexible appointment scheduling, and adequate staffing; additional proposals included clearer communication and access to rehabilitative activities (e.g., physiotherapy, yoga). Factors affecting maintenance-therapy attendance are summarized in \u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis mixed-methods survey demonstrates that people living with MM place substantial value on TFIs, particularly following intensive inpatient treatments, primarily to enable physical and psychosocial recovery, regain autonomy, and restore everyday routines. At the same time, most patients in our cohort preferred vigilant, structured follow-up in order to minimize relapse risk rather than maximizing time off therapy at the expense of perceived safety. Taken together, these findings support a pragmatic model in which TFIs and surveillance are not competing but complementary goals: \u003cem\u003etime off treatment\u003c/em\u003e is desired, but only under conditions of transparent monitoring, clear safety triggers, and rapid access pathways. The marked heterogeneity in preferred TFI length underscores the need for individualized scheduling aligned with recent treatment intensity, symptom burden, functional status, and logistical constraints rather than a one-size-fits-all cadence.\u003c/p\u003e \u003cp\u003eFrom an implementation perspective, three practice-relevant insights emerge from the convergence of quantitative distributions and qualitative narratives. First, TFIs function as restorative phases that buffer cumulative physical toxicities and psychosocial strain. Patients consistently described TFIs - especially post-inpatient - as periods to recover from fatigue, gastrointestinal and neurotoxic side effects, and sleep disruption; to reconnect with family and friends; and to re-establish valued activities such as travel, work, or hobbies. These observations complement prior work showing that patients weigh quality of life and progression-free survival (PFS) as equally important outcomes in MM clinical trials [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Second, when asked to explicitly trade TFI length against relapse risk, safety-oriented preferences predominated: two-thirds of respondents prioritized risk minimization, and regular check-ups were experienced as reassuring rather than burdensome by the majority. Third, patients identified concrete environmental and process factors - single rooms, food quality, waiting times, flexible scheduling, adequate staffing, and consistent communication - as high-yield levers to improve the lived experience and feasibility of both TFIs und maintenance phases. Notably, several of these levers are resource-intensive and may increase costs for providers and payers, indicating a need to balance preference-sensitive improvements with considerations of efficiency and equity.\u003c/p\u003e \u003cp\u003eEmerging finite-course therapeutics create genuine clinical space for intentional TFIs without ongoing maintenance in selected patients. BCMA-directed CAR T-cell products such as idecabtagene vicleucel (ide-cel) and ciltacabtagene autoleucel (cilta-cel) exemplify this paradigm: they are administered as one-time or short-course interventions with no protocol-mandated maintenance thereafter. In randomized settings, ide-cel (KarMMa-3) significantly improved PFS compared with standard regimens in triple-class-exposed relapsed/refractory MM (RRMM), with hazard ratios around 0.5 for PFS and clinically meaningful absolute gains, thereby supporting structured survivorship pathways after deep responses [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Similarly, cilta-cel in CARTITUDE-4 improved key clinical outcomes - including PFS and depth of response - versus pomalidomide-based standard therapies in lenalidomide-refractory patients after one to three prior lines, with sustained QoL benefits in patient-reported outcomes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Extended follow-up and integrated analyses across studies suggest that a subset of patients can achieve durable, multi-year remissions following BCMA-directed CAR T-cell therapy, reinforcing the clinical plausibility of finite-course treatment followed by periods off therapy when aligned with individual disease risk and depth of response [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eParallel to CAR T-cell approaches, BCMA-directed T-cell redirecting bispecific antibodies have expanded the therapeutic armamentarium for triple-class-exposed RRMM and beyond. Teclistamab is a first-in-class, off-the-shelf, BCMA \u0026times; CD3 bispecific antibody that has shown high response rates and deep remissions in heavily pretreated populations and is now being moved into earlier-line combinations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In the phase 3 MajesTEC-3 trial, teclistamab combined with subcutaneous daratumumab and dexamethasone significantly improved PFS compared with daratumumab-based standard regimens (DPd/DVd) in patients with one to three prior lines of therapy. At a median follow-up of approximately 34\u0026ndash;35 months, the estimated 36-mont PFS was 83.4 % in the teclistamab\u0026ndash;daratumumab grop versus 29.7 % in the DPd/DVd group (hazard atio 0.17; 95 % CI 0.12\u0026ndash;0.23; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Overall survival was likewise significantly prolonged. These results underscore the potential of teclistamab-based regimens to deliver durable disease control; however, the safety profile is characterized by a distinct spectrum of adverse events - including higher rates of infections and cytopenias - rather than a uniformly more favorable toxicity profile compared with traditional daratumumab-based combinations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. From a TFI perspective, such continuous or frequently administered regimens may effectively compress the intervals between contacts and limit extended periods completely off therapy, even as they improve disease outcomes.\u003c/p\u003e \u003cp\u003eWithin supportive care, oncology telehealth frameworks and hybrid follow-up models offer pragmatic tools to reconcile patients\u0026rsquo; desire for TFIs with the need for vigilance. Structured hybrid pathways - combining scheduled in-person assessments at clinically meaningful junctures with telehealth contacts (video, telephone, or secure messaging) in between - can reduce travel and time burden without sacrificing safety when deployed in risk-adapted fashion. In these pathways, explicit escalation routes, predefined symptom and laboratory thresholds, and clear response windows are essential to maintain safety during TFIs, particularly after high-intensity treatments such as CAR T-cell therapy or prolonged multi-agent regimens.\u003c/p\u003e \u003cp\u003eBuilding on our data and the evolving therapeutic landscape, an operational TFI pathway should integrate three core components. First, risk stratification must incorporate disease biology (e.g., cytogenetic risk, early relapse), depth of response (including minimal residual disease, MRD, where available), recent treatment intensity, and comorbidities. This risk profile should determine a \u003cem\u003ebaseline permissible TFI range\u003c/em\u003e and minimum monitoring cadence. Second, preference-sensitive scheduling should individualize the exact length of TFIs within this range based on recovery needs, functional status, work and caregiving responsibilities, mobility, and travel logistics. Third, safety should be codified \u003cem\u003ea priori\u003c/em\u003e through explicit triggers: core symptom thresholds (e.g., new bone pain, recurrent infections, B-symptoms), key laboratory cut-offs (e.g., hemoglobin, creatinine, paraprotein or free light chain dynamics), and clearly communicated unscheduled alert routes with defined response times. Follow-up should adopt a hybrid cadence calibrated to risk and logistics, integrating in-person assessments (e.g., at initiation of TFIs, after CAR T-cell therapy, or around predefined milestones) with structured remote contacts and mandatory re-evaluation checkpoints. For high-risk patients or those with aggressive prior relapse, TFIs may need to be shorter and more tightly surveilled; for lower-risk patients in deep, sustained responses, longer TFIs with less frequent in-person visits but robust remote monitoring may be acceptable.\u003c/p\u003e \u003cp\u003eAt the system level, several modifiable factors identified by patients appear to have particularly high leverage for making TFIs both feasible and restorative. Quieter single room capacity (or, where this is not possible, clear rules for noise and visitor restrictions in shared rooms) can reduce noise and peak stress. Reliable, scheduled appointments with minimized waiting times reduce the time burden per contact and facilitate the acceptance of more frequent, risk-adapted monitoring visits. Improved nutritional offerings, access to rehabilitative measures (e.g., physical therapy, lightly adapted exercise, yoga), as well as sufficient staff resources and consistent, empathetic communication address key stressors identified in our sample. Continuous quality monitoring should systematically track unplanned contacts (emergency admissions, unscheduled inpatient admissions), therapy adherence, functional status, and patient-reported experiences to iteratively adjust TFI cadence and trigger criteria.\u003c/p\u003e \u003cp\u003eThe present study has several limitations. Its single-center, cross-sectional design at a tertiary academic institution limits the generalizability of findings to other settings and healthcare systems. Selection bias is possible, as patients with strong preferences or better functional status may have been more likely to participate. All quantitative data were self-reported and are susceptible to recall and social desirability biases; clinical details and objective outcomes were not systematically cross-validated against medical records. Preference dynamics likely evolve over the course of the disease and across treatment milestones, whereas our survey captures a single time-point snapshot. Although the questionnaire was iteratively developed by a multidisciplinary team with clinical and psychosocial expertise, and informed by prior work on patient-reported outcomes and preferences in MM [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], it did not undergo formal psychometric validation. Therefore, some composite preference metrics (e.g., global TFI importance scores) should be interpreted as exploratory. Furthermore, while our survey explicitly asked about trade-offs between TFI length and relapse risk, actual behavior under real-world risk-benefit conditions may differ from stated preferences.\u003c/p\u003e \u003cp\u003eStrengths of this work include its practice-proximal mixed-methods design, the explicit integration of qualitative narratives with quantitative distributions, and the direct mapping of patient-generated levers to concrete, potentially implementable process changes across inpatient and outpatient care. Importantly, our findings do not suggest that longer TFIs are universally preferred or clinically appropriate; rather, they advocate for preference-sensitive, risk-aligned TFI planning with transparent communication about uncertainty and trade-offs. Future multicenter, prospective evaluations should test whether such pathways - combining risk stratification, preference-sensitive scheduling, codified safety triggers, and hybrid follow-up - improve patient experience, functional outcomes, adherence, and unplanned healthcare utilization. Simultaneously, health economic and equity analyses will be needed to define sustainable ways of investing in the environmental and process changes (e.g., single rooms, telehealth infrastructure, staffing) that patients in our cohort identified as most impactful.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003ePeople living with MM value time off therapy - especially after inpatient treatments - primarily for recovery and psychosocial reasons, while most simultaneously favor vigilant follow‑up to minimize relapse risk. The heterogeneity of preferred TFI length supports preference‑sensitive scheduling aligned to clinical risk, depth of response, and personal context. A risk‑aligned TFI model that codifies safety triggers, embeds hybrid in‑person/telehealth follow‑up, and targets modifiable system factors (quiet single rooms, predictable scheduling with shorter waits, improved food quality, adequate staffing, consistent communication) offers a pragmatic pathway to enhance patient experience without compromising safety. Future multicenter, prospective evaluations should test whether such pathways improve patient experience, functional outcomes, adherence, and unplanned utilization, and refine alert thresholds and cadence to balance reassurance with resource stewardship.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e The authors would like to thank the patients for their active involvement in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eAnna Fleischer was supported by the Else Kröner-Fresenius Foundation, Leo Rasche was supported by the Paula and Rodger Riney Foundation and the German Cancer Aid via the MSNZ programme. KMK was supported by the Stifterverband. Sofie-Katrin Kadel was supported by the Else Kröner-Fresenius Foundation. Jessica Peter was supported by the Else Kröner-Fresenius Foundation. C.R. was supported by a fellowship (project number ZZ-40) of the Interdisciplinary Center\u0026nbsp;for\u0026nbsp;Clinical Research (IZKF).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u003c/strong\u003e AF received honoraria from GSK, BMS and Janssen. The other authors report no conflict of interests. KMK received honoraria from Abbvie, Beigene, BMS, GSK, Johnson\u0026amp;Johnson, Menarini, Novartis, Pfizer and Sanofi. SKK received honoraria from Janssen. J.M.W. reports personal fees or consultancy for Johnson \u0026amp; Johnson, Sanofi, Takeda, Pfizer, Oncopeptides, Menarini-Stemline, Skyline Dx, Abbvie, Amgen and GSK. C.R. received honoraria from Janssen and Takeda.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThe study was approved by the ethics committee of Würzburg (AZ 246/22) and conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e All participants provided written informed consent for study participation and publication of anonymized data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e All included patients gave their consent for publication of the collected data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnna Fleischer\u003c/strong\u003e: Conceptualization, Investigation, Funding acquisition, Writing – original draft, Methodology, Validation, Visualization, Writing – review \u0026amp; editing, Software, Formal analysis, Project administration, Data curation, Supervision, Resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMohammad Barakat\u003c/strong\u003e: Conceptualization, Investigation, Methodology, Formal analysis, Data curation, Writing – original draft, Writing – review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJessica Peter, Sofie Kadel, Christine Riedhammer, Patrick-Pascal Strunz, Julia Mersi, Johannes Waldschmidt, K. Martin Kortüm, Hermann Einsele, Imad Maatouk\u003c/strong\u003e: Writing – original draft, Writing – review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLeo Rasche\u003c/strong\u003e: Conceptualization, Investigation, Funding acquisition, Methodology, Validation, Visualization, Writing – original draft, Writing – review \u0026amp; editing, Software, Formal analysis, Project administration, Data curation, Supervision, Resources.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFleischer, A., et al., \u003cem\u003eA patient survey indicates quality of life and progression-free survival as equally important outcome measures in multiple myeloma clinical trials\u003c/em\u003e. J Cancer Res Clin Oncol, 2023. 149(14): p. 12897\u0026ndash;12902.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerrot, A., et al., \u003cem\u003eSustained Improvement in Health-Related Quality of Life in Transplant-Ineligible Newly Diagnosed Multiple Myeloma Treated With Daratumumab, Lenalidomide, and Dexamethasone: MAIA Final Analysis of Patient-Reported Outcomes\u003c/em\u003e. Eur J Haematol, 2025. 114(5): p. 883\u0026ndash;889.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCaughan, G., et al., \u003cem\u003eManagement of patients with newly diagnosed multiple myeloma who are eligible for autologous stem cell transplantation: position statement of the Medical and Scientific Advisory Group of Myeloma Australia\u003c/em\u003e. Intern Med J, 2025. 55(10): p. 1741\u0026ndash;1751.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoccadoro, M., et al., \u003cem\u003eSupporting treatment decision-making for patients with multiple myeloma post-DRd in Italy: a multi-criteria decision framework\u003c/em\u003e. BMC Cancer, 2025. 25(1): p. 1676.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMettias, S., et al., \u003cem\u003eMultiple Myeloma: Improved Outcomes Resulting from a Rapidly Expanding Number of Therapeutic Options\u003c/em\u003e. Target Oncol, 2025. 20(2): p. 247\u0026ndash;267.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFleischer, A., et al., \u003cem\u003eNeuropsychiatric manifestations following chimeric antigen receptor T cell therapy for cancer: a systematic review of clinical outcomes and management strategies\u003c/em\u003e. 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Discov Oncol, 2025. 16(1): p. 1573.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMina, R., et al., \u003cem\u003ePatient-reported outcomes following ciltacabtagene autoleucel or standard of care in patients with lenalidomide-refractory multiple myeloma (CARTITUDE-4): results from a randomised, open-label, phase 3 trial\u003c/em\u003e. Lancet Haematol, 2025. 12(1): p. e45\u0026ndash;e56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePuig, N., et al., \u003cem\u003eComparative Efficacy of Ciltacabtagene Autoleucel Versus Standard-of-Care Treatments for Patients with Previously Treated Relapsed or Refractory Multiple Myeloma: A Matching-Adjusted Indirect Comparison\u003c/em\u003e. Adv Ther, 2025. 42(7): p. 3223\u0026ndash;3239.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMina, R., et al., \u003cem\u003ePatient-reported outcomes following ciltacabtagene autoleucel or standard of care in patients with lenalidomide-refractory multiple myeloma (CARTITUDE-4): results from a randomised, open-label, phase 3 trial\u003c/em\u003e. The Lancet Haematology, 2025. 12(1): p. e45\u0026ndash;e56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJagannath, S., et al., \u003cem\u003eLong-Term (\u0026ge;\u0026thinsp;5-Year) Remission and Survival After Treatment With Ciltacabtagene Autoleucel in CARTITUDE-1 Patients With Relapsed/Refractory Multiple Myeloma\u003c/em\u003e. J Clin Oncol, 2025. 43(25): p. 2766\u0026ndash;2771.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFleischer, A., et al., \u003cem\u003eTalquetamab-Related Dysgeusia in Multiple Myeloma Compared to BCMA-Targeted Bispecifics and High-Dose Melphalan\u003c/em\u003e. Cancer Med, 2025. 14(23): p. e71401.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFleischer, A., et al., \u003cem\u003eA patient survey indicates quality of life and progression-free survival as equally important outcome measures in multiple myeloma clinical trials\u003c/em\u003e. J Cancer Res Clin Oncol, 2023. 149(14): p. 12897\u0026ndash;12902.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTouzeau, C., et al., \u003cem\u003eComparative Effectiveness of Ciltacabtagene Autoleucel in CARTITUDE-4 Versus Real-World Physician's Choice of Therapy from the Flatiron Registry in Lenalidomide-Refractory Multiple Myeloma\u003c/em\u003e. Adv Ther, 2025. 42(10): p. 5023\u0026ndash;5041.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePleitez, H.G., et al., \u003cem\u003eFrom Trials to Practice: A 2025 Review of Idecabtagene Vicleucel and Ciltacabtagene Autoleucel Efficacy Across Clinical Studies and Real-World Evidence\u003c/em\u003e. Eur J Haematol, 2025. 115(6): p. 533\u0026ndash;546.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCosta, L.J., et al., \u003cem\u003eTeclistamab plus Daratumumab in Relapsed or Refractory Multiple Myeloma\u003c/em\u003e. N Engl J Med, 2025.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"supportive-care-in-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jscc","sideBox":"Learn more about [Supportive Care in Cancer](https://www.springer.com/journal/520)","snPcode":"520","submissionUrl":"https://submission.nature.com/new-submission/520/3","title":"Supportive Care in Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Multiple myeloma, treatment-free intervals, patient preferences, monitoring, supportive care, outpatient follow-up","lastPublishedDoi":"10.21203/rs.3.rs-8366273/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8366273/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eContinuous therapy in multiple myeloma (MM) can impose cumulative toxicities and psychosocial burden. This mixed‑methods study quantified patient preferences for treatment‑free intervals (TFIs), monitoring trade‑offs, and actionable service improvements across inpatient and outpatient care.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA 12-month, single-center mixed-methods survey (n\u0026thinsp;=\u0026thinsp;100) at a German tertiary center collected sociodemographics, treatment exposure, and structured items on TFI importance, preferred TFI length, monitoring intensity, relapse-risk trade-offs, inpatient stressors, and patient-generated improvement proposals. Quantitative analyses comprised descriptive statistics and hypothesis‑driven group comparisons; qualitative data were analyzed using inductive thematic analysis with double‑coding and consensus.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTFIs between inpatient treatments were rated 10/10 \u0026ldquo;very important\u0026rdquo; by 49.5% and 0/10 \u0026ldquo;not important\u0026rdquo; by 9.1%. Predominant inpatient stressors were separation from family (27%), treatment‑related symptoms/side effects (23%), fatigue (17%), food quality (15%), waiting times (14%), and shared rooms (12%). In the outpatient setting, 77% found regular check‑ups reassuring, while 13% found them burdensome. When trading TFI length against relapse risk, 67% prioritized risk minimization and 7% would accept higher risk for longer TFIs. Preferred minimal TFIs were \u0026lt;\u0026thinsp;4 weeks (23%), 4\u0026ndash;8 weeks (19%), and \u0026gt;\u0026thinsp;8 weeks (39%). Narratives emphasized physical/emotional recovery, autonomy, restoration of routines, and pragmatic levers (single rooms, better food, shorter waits, flexible scheduling, staffing).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePeople living with MM value TFIs - especially post‑inpatient - for recovery, while favoring vigilant, risk‑adapted monitoring. Preference‑sensitive TFI planning with explicit safety triggers and hybrid in‑person/telehealth follow‑up may enhance quality of life without compromising safety.\u003c/p\u003e","manuscriptTitle":"Patient Preferences for Treatment‑Free Intervals in Multiple Myeloma: Mixed‑Methods Insights on Monitoring Trade‑offs and Supportive Care Improvements","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 05:49:45","doi":"10.21203/rs.3.rs-8366273/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-11T01:10:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T17:56:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T09:23:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"323899371381420665576301064458964586622","date":"2026-04-13T23:14:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25833559950669719772818200919766342447","date":"2026-04-13T17:31:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-05T22:16:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98038445151614686104384021370260511051","date":"2026-02-27T16:04:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-06T23:59:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-06T23:53:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-30T02:31:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Supportive Care in Cancer","date":"2025-12-15T12:25:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"supportive-care-in-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jscc","sideBox":"Learn more about [Supportive Care in Cancer](https://www.springer.com/journal/520)","snPcode":"520","submissionUrl":"https://submission.nature.com/new-submission/520/3","title":"Supportive Care in Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b06e75ab-014c-4583-87e0-8d1a5f7636eb","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-11T01:10:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T17:56:13+00:00","index":34,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T09:23:35+00:00","index":33,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T01:24:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 05:49:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8366273","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8366273","identity":"rs-8366273","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}