Early organ response predictive model in newly diagnosed systemic immunoglobulin light chain amyloidosis patients 

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Methods The clinical data of 90 patients newly diagnosed with AL amyloidosis between April 2015 to January 2026 at Mianyang Central Hospital were collected retrospectively. Based on the achievement of early organ response within 3 months, patients were divided into the early organ response group and the early organ non-response group. Univariate analysis was used to screen the potential influencing factors of early organ response, and the logistic regression model was used to identify the final model variables. The model was visualized with a nomogram. Calibration was assessed using the Hosmer-Lemeshow test. The ROC curve of the predictive model was plotted, and AUC, sensitivity, specificity and 95%CI were calculated. The clinical value of the model was evaluated by clinical decision curve (DCA). Results The overall early organ response rate in 90 AL amyloidosis patients was 35.6%. The median follow-up was 13 months, 26 patients died during the follow-up period. Overall survival was significantly longer in patients with early organ response than in those with early organ non-response( P ＜0.001). Univariate analysis showed that there were significant differences in male gender, dFLC(difference between involved and uninvolved free light chains)≥180mg/L, decreased albumin and estimated glomerular filtration rate, increased lactate dehydrogenase and uric acid level, interventricular septal thickening and advanced Mayo 2004 Staging System stage between the two groups(all P <0.05). In multivariate analysis, dFLC≥180 mg/L, lower albumin level and interventricular septal thickening were identified as independent risk factors for early organ response in patients with AL amyloidosis, and a predictive model was established based on these variables. ROC curve analysis showed that the AUC of this model for predicting early organ response was0.843 (95% CI: 0.763-0.923), with the sensitivity 80.6%, the specificity 76.8%, and the Youden index was 0.574. The C-index of the nomogram for predicting early organ response was 0.843 (95% CI 0.762-0.924). The Hosmer-Lemeshow test results indicated that the early organ response risk prediction model has a good goodness-of-fit (Chi-Square=5.7756, P =0.672). Decision curve analysis showed that the predictive model had a good net benefit in the threshold probability between 0.1 and 0.75. Conclusions In the study cohort, the established nomogram model demonstrates a strong capability for predicting the early organ response rate in newly diagnosed AL amyloidosis patients, providing valuable guidance to clinicians in assessing prognosis and selecting appropriate treatment strategies. systemic immunoglobulin light chain amyloidosis early organ response risk factors nomogram predictive model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Systemic immunoglobulin light chain (AL) amyloidosis is a rare plasma cell dyscrasia characterized by the deposition of amyloid fibrils in organs, causing vital organ dysfunction and eventually death. The goal of treatment is to achieve a deep and rapid hematologic response, which in turn translates into organ response. Recently, with the application of novel therapeutic drugs and autologous hematopoietic stem cell transplantation(ASCT), the treatment outcomes for patients with AL amyloidosis have significantly improved. However, up to 30% patients die within 6 months of diagnosis[ 1 ], which highlights the importance of early organ response for improving survival outcomes. Early organ response not only effectively prevents further deterioration of organ function but may also be closely related to long-term survival. It is of great clinical significance to accurately identify high-risk patients in advance and enable them to derive benefits from the adjustment of initial treatment strategies. At present, commonly used clinical prognostic assessment tools (such as the Mayo Staging System[ 2 , 3 ] and Renal Prognostic Staging System[ 4 ]) focus primarily on survival prognosis prediction and have limited predictive value for early organ response. However, there is a lack of simple-to-use clinical models to predict early organ response in de novo AL patients. Therefore, to explore the factors associated with early organ response and better guide clinical treatment. we performed a retrospective analysis of the clinical data of newly diagnosed patients with AL amyloidosis treated at our center from 2015 to 2026, with the objectives of investigating the correlates of early organ response, constructing a predictive model and providing a quantitative tool for individualized treatment decision-making. Material and methods Study population A total of 90 newly diagnosed AL amyloidosis patients in the department of hematology of our hospital were retrospectively enrolled from April 2015 to January 2026. The inclusion criteria were as follows: (1)Diagnosis of AL amyloidosis was confirmed by the presence of amyloid deposits with apple-green birefringence on tissue biopsy stained with Congo red, and the amyloid deposits were composed of immunoglobulin light chains[ 5 ]; (2) Organ involvement (kidneys. heart, liver and/or peripheral nervous system) was based on amyloidosis consensus criteria[ 6 ]; (3) Enabling clear evaluation of the early organ response status. Exclusion criteria were as follows: (1) Localized amyloidosis; (2) Patients aged < 14 years. This study was approved by the Ethics Committee of Mianyang Central Hospital, Sichuan Province, China (2022KY018). Informed verbal and written consent were obtained from all patients. Organ response definition and treatment Renal response was defined as ≥ 30% decrease in proteinuria or drop of proteinuria below 0.5g/24h in the absence of renal progression; Cardiac response as N-terminal pro-brain natriuretic peptide (NT-proBNP) response (> 30% and > 300 ng/L decrease in patients with baseline NT-proBNP ≥ 650 ng/L) or New York Heart Association (NYHA) class response (≥ 2 class decrease in subjects with baseline NYHA class 3 or 4); Hepatic response as 50% decrease in abnormal alkaline phosphatase value and/or decrease in liver size radiographically at least 2 cm; and peripheral nervous system response as improvement in electromyogram nerve conduction velocity. Early organ response is defined as the achievement of response in the involved organs (any of kidneys, heart, liver and peripheral nervous system) within 3 months. Patients were divided into the early organ response group and the early non-organ response group based on whether early organ response was achieved. Daratumumab-based and bortezomib-based treatment are the common treatment regimens for AL amyloidosis[ 7 – 9 ]. Daratumumab was administered as an intravenous infusion at a dosage of 16 mg/kg or a subcutaneous injection at a fixed dose of 1800mg. The treatment schedule involved weekly administration in cycles 1 to 2, every 2 weeks in cycles 3 to 6, and every 4 weeks thereafter. Bortezomib was administered subcutaneously at a dosage of 1.3 mg/m 2 on days 1, 8, 15, and 22 of each cycle. For some patients, the drug combination and dosing interval may be adjusted by physicians according to changes in their disease status. Follow-up Follow-up was conducted by reviewing patients' outpatient medical records and telephone interviews, with the follow-up cut-off date of January 6, 2026, and a median follow-up duration of 13 months. Overall survival was defined as the time from the date of initial diagnosis of AL amyloidosis to the date of death or last follow-up. Statistical analysis Statistical analysis was performed using SPSS software (version 24.0) and R statistical software(version 4.0.2). Categorical variables were expressed as percentages (frequencies) and compared between the groups using the chi-square test or Fisher's exact test. Continuous variables were presented as median and interquartile range (25% to 75%) and compared between the groups using Student’s t-test or Mann–Whitney U test. Univariate Logistic regression analysis was used to screen potential influencing factors of early organ response, and variables with P < 0.05 in univariate analysis were included in multivariate Logistic regression analysis (entry criterion: P 0.15) to identify independent influencing factors. The predictive efficacy of the model was evaluated by means of ROC curves and decision curve analysis (DCA). The Hosmer-Lemeshow test was used to evaluate the calibration of the model. Estimates of survival curves were calculated according to the Kaplan-Meier curve and compared by means of the log-rank test. P < 0.05 was considered statistically significant. Results Baseline characteristics A total of 90 patients were enrolled, including 51 males (56.7%) and 39 females (43.3%), with a median age of 62.5 years. There were 53 patients (58.9%) with cardiac involvement, 56 patients (62.2%) with renal involvement, 7 patients (7.8%) with neurological involvement, and 5 patients (5.6%) with hepatic involvement. 65 patients (72.2%) had involvement of two or more major organs. The overall early organ response rate was 35.6% (32/90). The baseline characteristics were presented in Table 1 . Table 1 Baseline characteristics of the patients with systemic light chain amyloidosis without concurrent multiple myeloma. Baseline characteristics All patients (N = 90) Age, years, median(IQR) 62.5(54–70) Male, %(n)9 56.7(51) Organs involved, %(n) Heart 58.9(53) Kidney 62.2(56) Liver 5.6(5) Peripheral nervous system 7.8(7) ≥2 major organs involved 72.2(65) Bone marrow plasma cells count, %, median(IQR) 3.5(2-5.6) M protein isotype, %(n) IgG 41.1(37) IgA 26.7(24) IgM 1.1(1) IgD 3.3(3) Light chain only 21.1(19) Non-secretory 6.7(6) Serum M protein, g/L, median(IQR) 3.59(0.82–8.41) Urinary protein excretion, g/24hrs, median(IQR) 1.91(0.38–4.04) Serum free lambda light chain, mg/L, median(IQR) 142.8 (28.9-545.9) dFLC, mg/L, median(IQR) 187 (35.9–641) eGFR, mL/min/1.73 m 2 , median(IQR) 59(38.5–87.1) Creatinine, µmol/L, median(IQR) 93.5(68.5-129.8) Alkaline phosphatase, U/L, median(IQR) 86(63.3-122.8) Albumin, g/L, median(IQR) 33.5(27.6–39.8) Lactate dehydrogenase, U/L, median(IQR) 224.5(188-264.8) Uric acid, µmol/L, median(IQR) 366.7(256.3-471.2) Serum calcium, mmol/L, median(IQR) 2.16(2.04–2.3) β2-Microglobulin, ug/L, median (IQR) 3493.6(2417–6058) High-sensitivity cardiac troponin T, ng/L, median (IQR) 25(13–84) NT-proBNP, ng/L, median (IQR) 914.1(286-8184.3) Interventricular septum, mm, median(IQR) 12(10–13) Left ventricular ejection fraction, %, median(IQR) 62(60–65) FISH, %(n) 1q21 55.3 (n = 21, N = 38) Del(13q14) 28.9 (n = 11, N = 38) t(11;14) 13.2 (n = 5, N = 38) t(4;14) 13.2 (n = 5, N = 38) Mayo 2004 Staging System with European Modifications, %(n) Stage I 44.4(40) Stage II 23.3(21) Stage IIIA 2.2(2) Stage IIIB 30.0(27) Mayo 2012 Staging System, %(n) Stage I 33.3(30) Stage II 20.0(18) Stage III 21.1(19) Stage IV 25.6(23) Induction therapy, %(n) Bortezomib-based 53.3(48) Daratumumab-based 34.4(31) Supportive Treatment 12.2 (11) Early cardiac response, %(n) 26.4 (n = 14, N = 53) Early renal response, %(n) 41.1 (n = 23, N = 56) Early hepatic response, %(n) 60 (n = 3, N = 5) Early peripheral nervous system response, %(n) 85.7 (n = 6, N = 7) Early organ response, %(n) 35.6(32) Note : IQR, interquartile range; dFLC, difference between involved and uninvolved free light chains; eGFR, estimated glomerular filtration rate; NT-proBNP, N-terminal pro-brain natriuretic peptide; FISH, fluorescence in situ hybridization. Analysis of influencing factors of early organ response Univariate analysis indicated that male gender, dFLC≥180mg/L decreased albumin and estimated glomerular filtration rate, increased lactate dehydrogenase and uric acid level, interventricular septal thickening and advanced Mayo 2004 Staging System stage were identified as candidate variables (all P < 0.05). Detailed results were shown in Table 2 . Statistically significant variables from the univariate analysis were included in the multivariate logistic regression analysis. Multivariate logistic regression analysis showed that dFLC≥180mg/L(OR = 0.2, 95%CI: 0.058–0.69, P = 0.011), low serum albumin(Odds Ratio(OR) = 0.907, 95%CI: 0.842–0.978, P = 0.011) and interventricular septal thickening (OR = 1.335, 95%CI: 1.017–1.752, P = 0.038) were all independent risk factors for early organ response in patients with AL amyloidosis ( P < 0.05) (Table 3 ). Table 2 Comparison of clinical characteristics in patients with different organ response groups. Variables organ response (N = 32) organ non-response (N = 58) P value Age, years, median(IQR) 61(47–71) 64(56–68) 0.204 Male, %(n) 37.5(12) 67.2(39) 0.006 dFLC≥180mg/L, %(n) 25.0(8) 69.0(40) < 0.001 Bone marrow plasma cells count, %, median(IQR) 3 (2-4.5) 4 (2-6.5) 0.302 Alkaline phosphatase, U/L, median(IQR) 80.5 (59.8-109.3) 94.5 (68.5-130.8) 0.099 Serum albumin, g/L, median (IQR) 39.3 (32.1–44.4) 30.8 (26–37) < 0.001 Lactate dehydrogenase, U/L, median(IQR) 205.5 (169.3–228) 240.5 (196.5–282) 0.009 eGFR, mL/min/1.73 m2, median(IQR) 76.6 (42.6–91.9) 54.6 (33.5–81.8) 0.034 Uric acid,µmol/L, median(IQR) 319.4 (250-397.5) 379.3 (302.4-484.3) 0.023 β2-Microglobulin, ug/L, median (IQR) 2637.4 (2062.4-4409.5) 3726.5 (2696.4-6137.5) 0.109 Interventricular septum,mm, median(IQR) 10 (8.5–12) 12 (10-13.3) 0.001 Mayo 2004 Staging System with European Modifications, Stage I, %(n) 65.6(21) 32.8(19) 0.003 Induction therapy, %(n) 0.992 Daratumumab-based 34.4(11) 34.5(20) Other regimen 65.6(21) 65.5(38) Note : IQR, interquartile range; dFLC, difference between involved and uninvolved free light chains; eGFR, estimated glomerular filtration rate. Table 3 Multivariable analysis of the baseline features to predict early organ response in patients with systemic immunoglobulin light chain. Variables β SE Walds OR (95%CI) P value Male 0.927 0.618 2.252 0.396 (0.118–1.328) 0.133 dFLC 1.609 0.633 6.475 0.200 (0.058–0.691) 0.011 Serum albumin -0.098 0.038 6.507 0.907 (0.842–0.978) 0.011 Lactate dehydrogenase 0.001 0.003 0.055 1.001(0.996–1.006) 0.814 eGFR -0.001 0.013 0.004 0.999(0.974–1.025) 0.950 Uric acid 0.001 0.003 0.062 1.001(0.996–1.006) 0.803 Interventricular septum 0.289 0.139 4.317 1.335 (1.017–1.752) 0.038 Mayo 2004 Staging System with European Modifications -0.458 0.781 0.344 0.632(0.137–2.923) 0.558 Note : dFLC, difference between involved and uninvolved free light chains; eGFR, estimated glomerular filtration rate. Relationship between early organ response and prognosis The median follow-up time of all patients was 13 months (range:1-63months). 26 patients died during the follow-up period. Overall survival was significantly longer with early organ response than with early organ non-response ( P <0.001), See Fig. 1 . Construction and evaluation of predictive model The construction of the predictive model involved selecting independent risk factors from the multivariate logistic regression analysis. The regression equation for predicting the risk of early organ response in patients with systemic immunoglobulin light chain is as follows: Logit(P) = 1.528–1.686×dFLC + 0.094×Albumin-0.277×Interventricular septum. The selected variables were visually presented (Fig. 2 ). The area under the curve for predicting early organ response was 0.843 (95% CI: 0.763–0.923), with the sensitivity 80.6%, the specificity 76.8%, the Youden index was 0.574 (Fig. 3 ). The C-index of the nomogram for predicting early organ response was 0.843 (95% CI 0.762–0.924). DCA was performed to assess the validity and clinical usefulness of the prediction model. The net benefit of the model was significantly higher than that of the two extreme strategies across a broad range of risk thresholds, spanning from approximately 10% to 75%(Fig. 4 ). The Hosmer-Lemeshow test results indicate that the early organ response risk prediction model has a good goodness-of-fit (Chi-Square = 5.7756, P = 0.672). The calibration curve of the nomogram indicates that the model performs well in terms of predictive accuracy (Fig. 5 ). Discussion AL amyloidosis is a disorder characterized by the abnormal deposition of amyloid fibrils derived from monoclonal light chains in multiple organs and tissues, which leads to progressive organ dysfunction and high mortality. Its clinical manifestations are heterogeneous, and the prognosis varies significantly. The primary therapeutic goal of AL amyloidosis is to inhibit the production of monoclonal light chains, thereby preventing further organ damage. While haematologic response is a key indicator of treatment effectiveness, a considerable number of patients still experience organ dysfunction despite achieving a deep haematologic response[ 10 ], highlighting the need to focus on the speed of overall organ response. Early organ response is increasingly recognized as a critical determinant for evaluating therapeutic efficacy and predicting long-term prognosis in patients with AL amyloidosis[ 1 , 11 , 12 ]. Consistent with this, our cohort study identified a trend toward a statistically significant difference in overall survival between patients with early organ response and those without, emphasizing the clinical significance of early organ response. Palladini et al[ 13 ] reported that the organ response rate at 3 months was 37.5%(6/16) in patients with AL amyloidosis. In contrast, the early organ response rate observed in our study was 35.6%, which was slightly lower than that rate reported in this prior literature. This discrepancy may be attributed to the non-administration of daratumumab in some patients in our cohort, as daratumumab-based regimens have been shown to improve organ response rates in AL amyloidosis. Basset et al[ 12 ] demonstrated that the cardiac response rate was only 8% at 90 days, which was significantly lower than our findings, possibly due to the exclusive enrollment of patients with stage IIIb cardiac AL amyloidosis and the older age of the study population in their research. To date, the influencing factors and predictive models for early organ response in newly diagnosed AL amyloidosis patients remain inadequately established. Our study addressed this gap by identifying three independent risk factors for poor early organ response: higher baseline dFLC level, low serum albumin, and interventricular septal thickening. Consistent with existing findings, our study showed that patients with baseline dFLC < 180 mg/L had a higher rate of early organ response, and those who achieved early organ response had more favorable survival outcomes. dFLC has been widely used to assess treatment response and prognostic stratification in patients with AL amyloidosis. Kumar et al[ 3 ] identified dFLC as a prognostic risk factor in the Mayo 2012 Staging System for AL amyloidosis due to its adverse impact on overall survival. Nguyen et al[ 14 ] demonstrated that low free light chains level at initial diagnosis are associated with prolonged survival in AL amyloidosis patients. Dittrich et al[ 15 ] reported that newly diagnosed AL amyloidosis patients with baseline dFLC<50mg/L had significantly better median OS regardless of treatment type, while Saunders et al[ 16 ] noted that a baseline dFLC < 180 mg/L correlates with better prognosis in patients treated with bortezomib-based treatment. Zhang et al[ 17 ] further confirmed that high dFLC levels are independently associated with inferior survival. Pinney et al[ 18 ] also indicated that independent factor associated with death in patients with renal AL amyloidosis was a higher absolute baseline amyloidogenic free light chains concentration at presentation. Similar to the existing findings, we observed that patients with dFLC<180mg/L at first diagnosis had higher rates of early organ response, and those who achieved early organ response obtained more favorable survival outcomes. Pinney et al[ 18 ] found, by retrospectively analyzing data from 923 patients with renal AL amyloidosis, that low serum albumin level was independently associated with renal response in multivariable analyses. A retrospective analysis of 242 newly diagnosed AL amyloidosis patients from the Mayo Clinic revealed that serum albumin level was an independent predictor of survival[ 19 ]. Our findings also demonstrated that low serum albumin level is an independent risk factor affecting early organ response in patients with AL amyloidosis. Mean wall thickness>12mm is recognized as an indicator of cardiac involvement according to the international amyloidosis consensus criteria[ 6 ]. Gertz et al[ 5 ] reported that echocardiographic wall thickening was associated with prognosis. Law et al[ 20 ] identified that mortality was predicted by interventricular septum at end-diastole thickness>12mm in AL amyloidosis patients who underwent renal transplantation. Similar with previous studies, our findings showed that interventricular septal thickening was closely correlated with early organ response. Therefore, higher baseline dFLC levels, low serum albumin, and interventricular septal thickening are three independent risk factors for poor early organ response. More intensive treatment regimens plus adequate supportive care[ 21 ] may be required for newly diagnosed AL amyloidosis patients with baseline dFLC ≥ 180 mg/L, low serum albumin, and interventricular septal thickening to rapidly reduce light chain load and improve the early organ response rate. To the best of our knowledge, this is the first study to explore a predictive model specifically for early organ response in newly diagnosed AL amyloidosis patients. We found that dFLC, low serum albumin, and interventricular septal thickening are three independent risk factors for poor early organ response in patients with AL amyloidosis. Accordingly, a nomogram model to predict early organ response was constructed with good discrimination and calibration, and decision curve analysis showed clinically significant results. The predictive model established in this study incorporated three readily accessible clinical indicators with simple operation and favorable predictive efficacy, helping clinicians early identify high-risk factors for early organ non-response and providing a reference for physicians to select optimal therapeutic strategies. However, this study has certain limitations: first, as a single-center retrospective study, it may be subject to selection bias; second, the sample size of this single-center retrospective study was relatively small due to the rarity of the disease, and the predictive model was not externally validated. Future multicenter prospective studies are necessary to optimize the predictive model and conduct external validation to improve the reliability and clinical application value of the model. Conclusions In conclusion, early organ response in newly diagnosed AL amyloidosis patients may be associated with baseline dFLC, serum albumin level, and interventricular septal thickening. The nomogram model constructed in this study can effectively predict the early organ response rate in newly diagnosed AL amyloidosis patients, which provides a reliable reference for clinicians in developing personalized treatment strategies. Conflict of Interest: All the authors declare that they have no conflicts of interest. Abbreviations AL systemic immunoglobulin light chain DCA decision curve analysis ASCT autologous hematopoietic stem cell transplantation NT-proBNP N-terminal pro-brain natriuretic peptide NYHA New York Heart Association IQR interquartile range dFLC difference between involved and uninvolved free light chains eGFR estimated glomerular filtration rate FISH fluorescence in situ hybridization Declarations Conflict of Interest ： All the authors declare that they have no conflicts of interest. Ethics approval and consent to participate : This study was approved by the Ethics Committee of Mianyang Central Hospital, Sichuan Province, China (2022KY018). All study participants provided informed consent prior to being enrolled in the study. The procedures used in this study adhered to the tenets of the Declaration of Helsinki. Consent to publication : Informed consent was obtained from all individual participants included in the study. Data availability statement : The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. Funding : The authors received no financial support for the research. Authors contributions : Conception and design of the data: Jing Yue, Kai Tang and Fang Xu; Analysis and interpretation of the data: Ya Zhang and Jingjing Wen; Draft the paper: Jing Yue and Kai Tang; Revise it critically for intellectual content: Qiaolin Zhou and Fang Xu; All authors read and approved the final manuscript. Acknowledgements : All authors are the guarantors of the study and take full responsibility for the work including the study design, access to data, and the decision to submit and publish the manuscript. ORCID ： Jing Yue 0000-0003-3557-2538·Kai Tang 0009-0005-2083-1260·Fang Xu 0000-0002-6731-1116·Ya Zhang 0000-0002-0426-333X·Qiaolin Zhou 0000-0003-0022-0567·Jingjing Wen 0000-0002-6994-9025. References Zanwar S, Gertz MA, Muchtar E. 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Zhonghua Xue Ye Xue Za Zhi. 2016;37(11):942–5. 10.3760/cma.j.issn.0253-2727.2016.11.003 . Pinney JH, Lachmann HJ, Bansi L, Wechalekar AD, Gilbertson JA, Rowczenio D, et al. Outcome in renal Al amyloidosis after chemotherapy. J Clin Oncol. 2011;29(6):674–81. 10.1200/jco.2010.30.5235 . Dispenzieri A, Gertz MA, Kyle RA, Lacy MQ, Burritt MF, Therneau TM, et al. Serum cardiac troponins and N-terminal pro-brain natriuretic peptide: a staging system for primary systemic amyloidosis. J Clin Oncol. 2004;22(18):3751–7. 10.1200/jco.2004.03.029 . Law S, Cohen O, Lachmann HJ, Rezk T, Gilbertson JA, Rowczenio D, et al. Renal transplant outcomes in amyloidosis. Nephrol Dial Transpl. 2021;36(2):355–65. 10.1093/ndt/gfaa293 . Muchtar E, Grogan M, Aus dem Siepen F, Waddington-Cruz M, Misumi Y, Carroll AS, et al. Supportive care for systemic amyloidosis: International Society of Amyloidosis (ISA) expert panel guidelines. Amyloid. 2025;32(2):93–116. 10.1080/13506129.2025.2463678 . Additional Declarations No competing interests reported. Supplementary Files Table3.doc Table2.doc Table1.doc Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9454468","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631099045,"identity":"9b80ceed-d039-4a42-b3b2-066c0d4093dc","order_by":0,"name":"Jing Yue","email":"","orcid":"","institution":"Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Yue","suffix":""},{"id":631099054,"identity":"d2294701-dd61-45ca-8cee-7f33f3311a5d","order_by":1,"name":"Kai Tang","email":"","orcid":"","institution":"Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Tang","suffix":""},{"id":631099055,"identity":"1bfb854e-6ec3-4f9e-98d8-11a2bcd2f0fb","order_by":2,"name":"Fang Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIie3PMQrCMBTG8RcKcQnOrxTaKwQKxSHgQVzSJZNOrh0igpMX8Bi9QSRQl4BrRyEXEJwVCy5uTTfB/Of3g/cBxGI/GIXrwz9fgi0vOpDMiak4oyoHZwJJnhiOjNoSehn6GDVyMKrenXzbQyNW44QZc+Mo6n2mtgvo1EaPEjxrLrmqD9m6QqJtACk8oJG2PqYulEAHqTa2RGTBxNGSaJVzNmyRIVsK7RJPtGB8Ztv+3ohx8h1HOeX8Q6aKWCwW+4/exVg9yGhyvHoAAAAASUVORK5CYII=","orcid":"","institution":"Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China","correspondingAuthor":true,"prefix":"","firstName":"Fang","middleName":"","lastName":"Xu","suffix":""},{"id":631099058,"identity":"b3f4924a-f933-406c-8b30-6420165d4744","order_by":3,"name":"Ya Zhang","email":"","orcid":"","institution":"Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Ya","middleName":"","lastName":"Zhang","suffix":""},{"id":631099060,"identity":"f8623875-3d07-4eb1-b65f-eb36e02e9e5c","order_by":4,"name":"Qiaolin Zhou","email":"","orcid":"","institution":"Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Qiaolin","middleName":"","lastName":"Zhou","suffix":""},{"id":631099062,"identity":"c4f0b35d-ff9f-4867-8c24-45a78c31c03a","order_by":5,"name":"Jingjing Wen","email":"","orcid":"","institution":"Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Wen","suffix":""}],"badges":[],"createdAt":"2026-04-18 05:53:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9454468/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9454468/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108405091,"identity":"7c8a555c-8558-4133-83bf-ffd5ba8a18ee","added_by":"auto","created_at":"2026-05-04 09:37:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":106622,"visible":true,"origin":"","legend":"\u003cp\u003eOS according to\u003cstrong\u003e \u003c/strong\u003eearly organ response.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9454468/v1/f952ca9be43456a799b948d6.png"},{"id":108493495,"identity":"eef9c259-6f03-41b1-948f-81d0c36373df","added_by":"auto","created_at":"2026-05-05 10:00:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47173,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram of early organ response prediction model.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9454468/v1/263e127eef01e569e2a1b0ce.png"},{"id":108405096,"identity":"0a072956-7986-40cb-8338-b9fd8fb16ca3","added_by":"auto","created_at":"2026-05-04 09:37:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":63609,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve of the model.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9454468/v1/7686cc46435ef2afdaa78059.png"},{"id":108493080,"identity":"c9613eee-a5e4-46c3-b3b3-80a5823f9637","added_by":"auto","created_at":"2026-05-05 09:59:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":179001,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis of the prediction model.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9454468/v1/94c908f3695314121dd8422d.png"},{"id":108405098,"identity":"fb50152c-638e-475e-aec0-96c4b2b24995","added_by":"auto","created_at":"2026-05-04 09:37:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":190159,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of the nomogram for predicting the risk of early organ response.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9454468/v1/ce4ece4f4dd6485b6da2adec.png"},{"id":109458206,"identity":"4c95852c-25ab-4986-bcc5-1073a5a93e27","added_by":"auto","created_at":"2026-05-18 10:26:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":956528,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9454468/v1/62d46302-f4b8-4fda-9aeb-cd6efe82e12b.pdf"},{"id":108493181,"identity":"29981d87-9026-4f77-902e-334d0e4e4d9f","added_by":"auto","created_at":"2026-05-05 09:59:34","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":37888,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.doc","url":"https://assets-eu.researchsquare.com/files/rs-9454468/v1/a4a559cc1407f0616b4801f2.doc"},{"id":108492891,"identity":"8353d705-2a31-48c2-a76b-a244db6653e5","added_by":"auto","created_at":"2026-05-05 09:58:54","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":41984,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.doc","url":"https://assets-eu.researchsquare.com/files/rs-9454468/v1/7168bfedbe8de284bd78bf37.doc"},{"id":108803593,"identity":"9eaf4a7f-7aa9-41f6-9651-7d814a5dd00b","added_by":"auto","created_at":"2026-05-08 14:59:53","extension":"doc","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":72192,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.doc","url":"https://assets-eu.researchsquare.com/files/rs-9454468/v1/7d6a9276710273c45f2a403f.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early organ response predictive model in newly diagnosed systemic immunoglobulin light chain amyloidosis patients ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSystemic immunoglobulin light chain (AL) amyloidosis is a rare plasma cell dyscrasia characterized by the deposition of amyloid fibrils in organs, causing vital organ dysfunction and eventually death. The goal of treatment is to achieve a deep and rapid hematologic response, which in turn translates into organ response. Recently, with the application of novel therapeutic drugs and autologous hematopoietic stem cell transplantation(ASCT), the treatment outcomes for patients with AL amyloidosis have significantly improved. However, up to 30% patients die within 6 months of diagnosis[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], which highlights the importance of early organ response for improving survival outcomes. Early organ response not only effectively prevents further deterioration of organ function but may also be closely related to long-term survival. It is of great clinical significance to accurately identify high-risk patients in advance and enable them to derive benefits from the adjustment of initial treatment strategies. At present, commonly used clinical prognostic assessment tools (such as the Mayo Staging System[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and Renal Prognostic Staging System[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]) focus primarily on survival prognosis prediction and have limited predictive value for early organ response. However, there is a lack of simple-to-use clinical models to predict early organ response in de novo AL patients. Therefore, to explore the factors associated with early organ response and better guide clinical treatment. we performed a retrospective analysis of the clinical data of newly diagnosed patients with AL amyloidosis treated at our center from 2015 to 2026, with the objectives of investigating the correlates of early organ response, constructing a predictive model and providing a quantitative tool for individualized treatment decision-making.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eA total of 90 newly diagnosed AL amyloidosis patients in the department of hematology of our hospital were retrospectively enrolled from April 2015 to January 2026. The inclusion criteria were as follows: (1)Diagnosis of AL amyloidosis was confirmed by the presence of amyloid deposits with apple-green birefringence on tissue biopsy stained with Congo red, and the amyloid deposits were composed of immunoglobulin light chains[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; (2) Organ involvement (kidneys. heart, liver and/or peripheral nervous system) was based on amyloidosis consensus criteria[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]; (3) Enabling clear evaluation of the early organ response status. Exclusion criteria were as follows: (1) Localized amyloidosis; (2) Patients aged\u0026thinsp;\u0026lt;\u0026thinsp;14 years. This study was approved by the Ethics Committee of Mianyang Central Hospital, Sichuan Province, China (2022KY018). Informed verbal and written consent were obtained from all patients.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOrgan response definition and treatment\u003c/h3\u003e\n\u003cp\u003eRenal response was defined as \u0026ge;\u0026thinsp;30% decrease in proteinuria or drop of proteinuria below 0.5g/24h in the absence of renal progression; Cardiac response as N-terminal pro-brain natriuretic peptide (NT-proBNP) response (\u0026gt;\u0026thinsp;30% and \u0026gt;\u0026thinsp;300 ng/L decrease in patients with baseline NT-proBNP\u0026thinsp;\u0026ge;\u0026thinsp;650 ng/L) or New York Heart Association (NYHA) class response (\u0026ge;\u0026thinsp;2 class decrease in subjects with baseline NYHA class 3 or 4); Hepatic response as 50% decrease in abnormal alkaline phosphatase value and/or decrease in liver size radiographically at least 2 cm; and peripheral nervous system response as improvement in electromyogram nerve conduction velocity. Early organ response is defined as the achievement of response in the involved organs (any of kidneys, heart, liver and peripheral nervous system) within 3 months. Patients were divided into the early organ response group and the early non-organ response group based on whether early organ response was achieved. Daratumumab-based and bortezomib-based treatment are the common treatment regimens for AL amyloidosis[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Daratumumab was administered as an intravenous infusion at a dosage of 16 mg/kg or a subcutaneous injection at a fixed dose of 1800mg. The treatment schedule involved weekly administration in cycles 1 to 2, every 2 weeks in cycles 3 to 6, and every 4 weeks thereafter. Bortezomib was administered subcutaneously at a dosage of 1.3 mg/m\u003csup\u003e2\u003c/sup\u003e on days 1, 8, 15, and 22 of each cycle. For some patients, the drug combination and dosing interval may be adjusted by physicians according to changes in their disease status.\u003c/p\u003e\n\u003ch3\u003eFollow-up\u003c/h3\u003e\n\u003cp\u003eFollow-up was conducted by reviewing patients' outpatient medical records and telephone interviews, with the follow-up cut-off date of January 6, 2026, and a median follow-up duration of 13 months. Overall survival was defined as the time from the date of initial diagnosis of AL amyloidosis to the date of death or last follow-up.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using SPSS software (version 24.0) and R statistical software(version 4.0.2). Categorical variables were expressed as percentages (frequencies) and compared between the groups using the chi-square test or Fisher's exact test. Continuous variables were presented as median and interquartile range (25% to 75%) and compared between the groups using Student\u0026rsquo;s t-test or Mann\u0026ndash;Whitney U test. Univariate Logistic regression analysis was used to screen potential influencing factors of early organ response, and variables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis were included in multivariate Logistic regression analysis (entry criterion: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; removal criterion: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.15) to identify independent influencing factors. The predictive efficacy of the model was evaluated by means of ROC curves and decision curve analysis (DCA). The Hosmer-Lemeshow test was used to evaluate the calibration of the model. Estimates of survival curves were calculated according to the Kaplan-Meier curve and compared by means of the log-rank test. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eA total of 90 patients were enrolled, including 51 males (56.7%) and 39 females (43.3%), with a median age of 62.5 years. There were 53 patients (58.9%) with cardiac involvement, 56 patients (62.2%) with renal involvement, 7 patients (7.8%) with neurological involvement, and 5 patients (5.6%) with hepatic involvement. 65 patients (72.2%) had involvement of two or more major organs. The overall early organ response rate was 35.6% (32/90). The baseline characteristics were presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the patients with systemic light chain amyloidosis without concurrent multiple myeloma.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll patients\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;90)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.5(54\u0026ndash;70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, %(n)9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.7(51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrgans involved, %(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.9(53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.2(56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.6(5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral nervous system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.8(7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;2 major organs involved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.2(65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone marrow plasma cells count, %, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5(2-5.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM protein isotype, %(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.1(37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.7(24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.3(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight chain only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.1(19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-secretory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7(6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum M protein, g/L, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.59(0.82\u0026ndash;8.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary protein excretion, g/24hrs, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.91(0.38\u0026ndash;4.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum free lambda light chain, mg/L, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142.8 (28.9-545.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edFLC, mg/L, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187 (35.9\u0026ndash;641)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59(38.5\u0026ndash;87.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, \u0026micro;mol/L, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.5(68.5-129.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlkaline phosphatase, U/L, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86(63.3-122.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/L, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.5(27.6\u0026ndash;39.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate dehydrogenase, U/L, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e224.5(188-264.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid, \u0026micro;mol/L, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e366.7(256.3-471.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum calcium, mmol/L, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.16(2.04\u0026ndash;2.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ2-Microglobulin, ug/L, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3493.6(2417\u0026ndash;6058)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-sensitivity cardiac troponin T, ng/L, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(13\u0026ndash;84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNT-proBNP, ng/L, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e914.1(286-8184.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterventricular septum, mm, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(10\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft ventricular ejection fraction, %, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62(60\u0026ndash;65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFISH, %(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1q21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.3\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;21, N\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDel(13q14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;11, N\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et(11;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5, N\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et(4;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5, N\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayo 2004 Staging System with European\u003c/p\u003e \u003cp\u003eModifications, %(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.4(40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.3(21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage IIIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage IIIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.0(27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayo 2012 Staging System, %(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.3(30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.0(18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.1(19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.6(23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInduction therapy, %(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBortezomib-based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.3(48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaratumumab-based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.4(31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupportive Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.2 (11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly cardiac response, %(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;14, N\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly renal response, %(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.1\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;23, N\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly hepatic response, %(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3, N\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly peripheral nervous system response, %(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.7\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;6, N\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly organ response, %(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.6(32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cem\u003eNote\u003c/em\u003e: IQR, interquartile range; dFLC, difference between involved and uninvolved free light chains; eGFR, estimated glomerular filtration rate; NT-proBNP, N-terminal pro-brain natriuretic peptide; FISH, fluorescence in situ hybridization.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalysis of influencing factors of early organ response\u003c/h3\u003e\n\u003cp\u003eUnivariate analysis indicated that male gender, dFLC\u0026ge;180mg/L decreased albumin and estimated glomerular filtration rate, increased lactate dehydrogenase and uric acid level, interventricular septal thickening and advanced Mayo 2004 Staging System stage were identified as candidate variables (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Detailed results were shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Statistically significant variables from the univariate analysis were included in the multivariate logistic regression analysis. Multivariate logistic regression analysis showed that dFLC\u0026ge;180mg/L(OR\u0026thinsp;=\u0026thinsp;0.2, 95%CI: 0.058\u0026ndash;0.69, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), low serum albumin(Odds Ratio(OR)\u0026thinsp;=\u0026thinsp;0.907, 95%CI: 0.842\u0026ndash;0.978, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) and interventricular septal thickening (OR\u0026thinsp;=\u0026thinsp;1.335, 95%CI: 1.017\u0026ndash;1.752, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038) were all independent risk factors for early organ response in patients with AL amyloidosis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of clinical characteristics in patients with different organ response groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eorgan response\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eorgan non-response\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61(47\u0026ndash;71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64(56\u0026ndash;68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, %(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.5(12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.2(39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edFLC\u0026ge;180mg/L, %(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.0(8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.0(40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone marrow plasma cells count, %, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2-4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2-6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlkaline phosphatase, U/L, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.5 (59.8-109.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.5 (68.5-130.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum\u0026nbsp;albumin, g/L, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.3 (32.1\u0026ndash;44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.8 (26\u0026ndash;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate dehydrogenase, U/L, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e205.5 (169.3\u0026ndash;228)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e240.5 (196.5\u0026ndash;282)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, mL/min/1.73 m2, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.6 (42.6\u0026ndash;91.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.6 (33.5\u0026ndash;81.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid,\u0026micro;mol/L, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e319.4 (250-397.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e379.3 (302.4-484.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ2-Microglobulin, ug/L, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2637.4 (2062.4-4409.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3726.5 (2696.4-6137.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterventricular septum,mm, median(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (8.5\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (10-13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayo 2004 Staging System with European\u003c/p\u003e \u003cp\u003eModifications, Stage I, %(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.6(21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.8(19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInduction therapy, %(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaratumumab-based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.4(11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.5(20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.6(21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.5(38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote\u003c/em\u003e: IQR, interquartile range; dFLC, difference between involved and uninvolved free light chains; eGFR, estimated glomerular filtration rate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable analysis of the baseline features to predict early organ response in patients with systemic immunoglobulin light chain.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWalds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.396 (0.118\u0026ndash;1.328)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edFLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.200 (0.058\u0026ndash;0.691)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum albumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.907 (0.842\u0026ndash;0.978)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate dehydrogenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.001(0.996\u0026ndash;1.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.999(0.974\u0026ndash;1.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.001(0.996\u0026ndash;1.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterventricular septum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.335 (1.017\u0026ndash;1.752)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayo 2004 Staging System with European\u003c/p\u003e \u003cp\u003eModifications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.632(0.137\u0026ndash;2.923)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote\u003c/em\u003e: dFLC, difference between involved and uninvolved free light chains; eGFR, estimated glomerular filtration rate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eRelationship between early organ response and prognosis\u003c/h3\u003e\n\u003cp\u003eThe median follow-up time of all patients was 13 months (range:1-63months). 26 patients died during the follow-up period. Overall survival was significantly longer with early organ response than with early organ non-response (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), See Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and evaluation of predictive model\u003c/h2\u003e \u003cp\u003eThe construction of the predictive model involved selecting independent risk factors from the multivariate logistic regression analysis. The regression equation for predicting the risk of early organ response in patients with systemic immunoglobulin light chain is as follows: Logit(P)\u0026thinsp;=\u0026thinsp;1.528\u0026ndash;1.686\u0026times;dFLC\u0026thinsp;+\u0026thinsp;0.094\u0026times;Albumin-0.277\u0026times;Interventricular septum. The selected variables were visually presented (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The area under the curve for predicting early organ response was 0.843 (95% CI: 0.763\u0026ndash;0.923), with the sensitivity 80.6%, the specificity 76.8%, the Youden index was 0.574 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The C-index of the nomogram for predicting early organ response was 0.843 (95% CI 0.762\u0026ndash;0.924). DCA was performed to assess the validity and clinical usefulness of the prediction model. The net benefit of the model was significantly higher than that of the two extreme strategies across a broad range of risk thresholds, spanning from approximately 10% to 75%(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The Hosmer-Lemeshow test results indicate that the early organ response risk prediction model has a good goodness-of-fit (Chi-Square\u0026thinsp;=\u0026thinsp;5.7756, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.672). The calibration curve of the nomogram indicates that the model performs well in terms of predictive accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAL amyloidosis is a disorder characterized by the abnormal deposition of amyloid fibrils derived from monoclonal light chains in multiple organs and tissues, which leads to progressive organ dysfunction and high mortality. Its clinical manifestations are heterogeneous, and the prognosis varies significantly. The primary therapeutic goal of AL amyloidosis is to inhibit the production of monoclonal light chains, thereby preventing further organ damage. While haematologic response is a key indicator of treatment effectiveness, a considerable number of patients still experience organ dysfunction despite achieving a deep haematologic response[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], highlighting the need to focus on the speed of overall organ response.\u003c/p\u003e \u003cp\u003eEarly organ response is increasingly recognized as a critical determinant for evaluating therapeutic efficacy and predicting long-term prognosis in patients with AL amyloidosis[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Consistent with this, our cohort study identified a trend toward a statistically significant difference in overall survival between patients with early organ response and those without, emphasizing the clinical significance of early organ response. Palladini et al[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] reported that the organ response rate at 3 months was 37.5%(6/16) in patients with AL amyloidosis. In contrast, the early organ response rate observed in our study was 35.6%, which was slightly lower than that rate reported in this prior literature. This discrepancy may be attributed to the non-administration of daratumumab in some patients in our cohort, as daratumumab-based regimens have been shown to improve organ response rates in AL amyloidosis. Basset et al[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] demonstrated that the cardiac response rate was only 8% at 90 days, which was significantly lower than our findings, possibly due to the exclusive enrollment of patients with stage IIIb cardiac AL amyloidosis and the older age of the study population in their research.\u003c/p\u003e \u003cp\u003eTo date, the influencing factors and predictive models for early organ response in newly diagnosed AL amyloidosis patients remain inadequately established. Our study addressed this gap by identifying three independent risk factors for poor early organ response: higher baseline dFLC level, low serum albumin, and interventricular septal thickening. Consistent with existing findings, our study showed that patients with baseline dFLC\u0026thinsp;\u0026lt;\u0026thinsp;180 mg/L had a higher rate of early organ response, and those who achieved early organ response had more favorable survival outcomes. dFLC has been widely used to assess treatment response and prognostic stratification in patients with AL amyloidosis. Kumar et al[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] identified dFLC as a prognostic risk factor in the Mayo 2012 Staging System for AL amyloidosis due to its adverse impact on overall survival. Nguyen et al[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] demonstrated that low free light chains level at initial diagnosis are associated with prolonged survival in AL amyloidosis patients. Dittrich et al[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] reported that newly diagnosed AL amyloidosis patients with baseline dFLC\u0026lt;50mg/L had significantly better median OS regardless of treatment type, while Saunders et al[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] noted that a baseline dFLC\u0026thinsp;\u0026lt;\u0026thinsp;180 mg/L correlates with better prognosis in patients treated with bortezomib-based treatment. Zhang et al[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] further confirmed that high dFLC levels are independently associated with inferior survival. Pinney et al[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] also indicated that independent factor associated with death in patients with renal AL amyloidosis was a higher absolute baseline amyloidogenic free light chains concentration at presentation. Similar to the existing findings, we observed that patients with dFLC\u0026lt;180mg/L at first diagnosis had higher rates of early organ response, and those who achieved early organ response obtained more favorable survival outcomes. Pinney et al[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] found, by retrospectively analyzing data from 923 patients with renal AL amyloidosis, that low serum albumin level was independently associated with renal response in multivariable analyses. A retrospective analysis of 242 newly diagnosed AL amyloidosis patients from the Mayo Clinic revealed that serum albumin level was an independent predictor of survival[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Our findings also demonstrated that low serum albumin level is an independent risk factor affecting early organ response in patients with AL amyloidosis. Mean wall thickness\u0026gt;12mm is recognized as an indicator of cardiac involvement according to the international amyloidosis consensus criteria[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Gertz et al[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] reported that echocardiographic wall thickening was associated with prognosis. Law et al[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] identified that mortality was predicted by interventricular septum at end-diastole thickness\u0026gt;12mm in AL amyloidosis patients who underwent renal transplantation. Similar with previous studies, our findings showed that interventricular septal thickening was closely correlated with early organ response. Therefore, higher baseline dFLC levels, low serum albumin, and interventricular septal thickening are three independent risk factors for poor early organ response. More intensive treatment regimens plus adequate supportive care[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] may be required for newly diagnosed AL amyloidosis patients with baseline dFLC\u0026thinsp;\u0026ge;\u0026thinsp;180 mg/L, low serum albumin, and interventricular septal thickening to rapidly reduce light chain load and improve the early organ response rate.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first study to explore a predictive model specifically for early organ response in newly diagnosed AL amyloidosis patients. We found that dFLC, low serum albumin, and interventricular septal thickening are three independent risk factors for poor early organ response in patients with AL amyloidosis. Accordingly, a nomogram model to predict early organ response was constructed with good discrimination and calibration, and decision curve analysis showed clinically significant results. The predictive model established in this study incorporated three readily accessible clinical indicators with simple operation and favorable predictive efficacy, helping clinicians early identify high-risk factors for early organ non-response and providing a reference for physicians to select optimal therapeutic strategies. However, this study has certain limitations: first, as a single-center retrospective study, it may be subject to selection bias; second, the sample size of this single-center retrospective study was relatively small due to the rarity of the disease, and the predictive model was not externally validated. Future multicenter prospective studies are necessary to optimize the predictive model and conduct external validation to improve the reliability and clinical application value of the model.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, early organ response in newly diagnosed AL amyloidosis patients may be associated with baseline dFLC, serum albumin level, and interventricular septal thickening. The nomogram model constructed in this study can effectively predict the early organ response rate in newly diagnosed AL amyloidosis patients, which provides a reliable reference for clinicians in developing personalized treatment strategies.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflict of Interest:\u003c/strong\u003e \u003cp\u003eAll the authors declare that they have no conflicts of interest.\u003c/p\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAL\u003c/strong\u003e systemic immunoglobulin light chain \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDCA\u003c/strong\u003e decision curve analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eASCT\u0026nbsp;\u003c/strong\u003eautologous hematopoietic stem cell transplantation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNT-proBNP\u003c/strong\u003e N-terminal pro-brain natriuretic peptide\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNYHA\u0026nbsp;\u003c/strong\u003eNew York Heart Association\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIQR\u0026nbsp;\u003c/strong\u003einterquartile range\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003edFLC\u0026nbsp;\u003c/strong\u003edifference between involved and uninvolved free light chains\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eeGFR\u0026nbsp;\u003c/strong\u003eestimated glomerular filtration rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFISH\u0026nbsp;\u003c/strong\u003efluorescence in situ hybridization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eAll the authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e: This study was approved by the Ethics Committee of Mianyang Central Hospital, Sichuan Province, China (2022KY018). All study participants provided informed consent prior to being enrolled in the study. The procedures used in this study adhered to the tenets of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publication\u003c/strong\u003e: Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e: The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: The authors received no financial support for the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e: Conception and design of the data: Jing Yue, Kai Tang and Fang Xu; Analysis and interpretation of the data: Ya Zhang and Jingjing Wen; Draft the paper: Jing Yue and Kai Tang; Revise it critically for intellectual content: Qiaolin Zhou and Fang Xu; All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: All authors are the guarantors of the study and take full responsibility for the work including the study design, access to data, and the decision to submit and publish the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJing Yue 0000-0003-3557-2538·Kai Tang 0009-0005-2083-1260·Fang Xu 0000-0002-6731-1116·Ya Zhang 0000-0002-0426-333X·Qiaolin Zhou 0000-0003-0022-0567·Jingjing Wen 0000-0002-6994-9025.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZanwar S, Gertz MA, Muchtar E. 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Amyloid. 2025;32(2):93\u0026ndash;116. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/13506129.2025.2463678\u003c/span\u003e\u003cspan address=\"10.1080/13506129.2025.2463678\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"systemic immunoglobulin light chain amyloidosis, early organ response, risk factors, nomogram, predictive model","lastPublishedDoi":"10.21203/rs.3.rs-9454468/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9454468/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eTo explore risk factors for early organ response in patients with newly diagnosed systemic immunoglobulin light chain (AL) amyloidosis and to build a predictive model for early organ response.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eThe clinical data of 90 patients newly diagnosed with AL amyloidosis between April 2015 to January 2026 at Mianyang Central Hospital were collected retrospectively. Based on the achievement of early organ response within 3 months, patients were divided into the early organ response group and the early organ non-response group. Univariate analysis was used to screen the potential influencing factors of early organ response, and the logistic regression model was used to identify the final model variables. The model was visualized with a nomogram. Calibration was assessed using the Hosmer-Lemeshow test. The ROC curve of the predictive model was plotted, and AUC, sensitivity, specificity and 95%CI were calculated. The clinical value of the model was evaluated by clinical decision curve (DCA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eThe overall early organ response rate in 90 AL amyloidosis patients was 35.6%. The median follow-up was 13 months, 26 patients died during the follow-up period. Overall survival was significantly longer in patients with early organ response than in those with early organ non-response(\u003cem\u003eP\u003c/em\u003e＜0.001). Univariate analysis showed that there were significant differences in male gender, dFLC(difference between involved and uninvolved free light chains)≥180mg/L, decreased albumin and estimated glomerular filtration rate, increased lactate dehydrogenase and uric acid level, interventricular septal thickening and advanced Mayo 2004 Staging System stage between the two groups(all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). In multivariate analysis, dFLC≥180 mg/L, lower albumin level and interventricular septal thickening were identified as independent risk factors for early organ response in patients with AL amyloidosis, and a predictive model was established based on these variables.\u003c/p\u003e\n\u003cp\u003eROC curve analysis showed that the AUC of this model for predicting early organ response was0.843 (95% CI: 0.763-0.923), with the sensitivity 80.6%, the specificity 76.8%, and the Youden index was 0.574. The C-index of the nomogram for predicting early organ response was 0.843 (95% CI 0.762-0.924). The Hosmer-Lemeshow test results indicated that the early organ response risk prediction model has a good goodness-of-fit (Chi-Square=5.7756, \u003cem\u003eP\u003c/em\u003e=0.672). Decision curve analysis showed that the predictive model had a good net benefit in the threshold probability between 0.1 and 0.75.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eIn the study cohort,\u003cstrong\u003e \u003c/strong\u003ethe established nomogram model demonstrates a strong capability for predicting the early organ response rate in newly diagnosed\u003cstrong\u003e \u003c/strong\u003eAL amyloidosis patients, providing valuable guidance to clinicians in assessing prognosis and selecting appropriate treatment strategies.\u003c/p\u003e","manuscriptTitle":"Early organ response predictive model in newly diagnosed systemic immunoglobulin light chain amyloidosis patients ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 09:37:32","doi":"10.21203/rs.3.rs-9454468/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"70764e28-c22b-4f71-9fc5-93ce7b89485e","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-18T10:12:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T09:29:56+00:00","index":81,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T17:41:58+00:00","index":80,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T10:26:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 09:37:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9454468","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9454468","identity":"rs-9454468","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}