Prognostic implications of thrombocytopenia in Chinese patients with newly diagnosed multiple myeloma

Prognostic implications of thrombocytopenia in Chinese patients with newly diagnosed multiple myeloma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic implications of thrombocytopenia in Chinese patients with newly diagnosed multiple myeloma Xiaojing Li, Xiaoxi Xu, Xiaohui Lai, Xiaolin Wang, Qiang Liu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5872364/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Thrombocytopenia is less common but shows high risk of early mortality in newly diagnosed multiple myeloma (NDMM) patients. In the era of novel agents-based induction therapy (NAIT), it is unclear whether NAIT can overcome the poor prognosis associated with thrombocytopenia. Objectives To evaluate the prognostic implications of thrombocytopenia in NDMM patients. Methods We retrospectively analyzed 1363 NDMM patients baseline characteristics, treatment response and survival, further performed regression analysis, constructed a nomogram model to predict progression free survival (PFS), and further internally validated this model. Results Overall, 211 (15.48%) NDMM patients were harboring thrombocytopenia, with advanced disease stages and worse outcomes. Their PFS (15 months vs 21.5 months, P < 0.001)and overall survival (47 months vs 77 months, P < 0.001) were significantly inferior compared with patients without thrombocytopenia. In NDMM receiving NAIT, the overall response (87.8% vs 92.4%, P = 0.33) but not deep response or survival could be improved between patients with and without thrombocytopenia. Five important variables (thrombocytopenia, R-ISS stage III, NAIT, deep response and autologous stem-cell transplantation) in multivariate Cox analysis were incorporated in the nomogram, which was further validated by internal datasets. The Calibration curve and time-dependent Receiver operating characteristic showed that the model accurately predicted the 12- and 24- months PFS of NDMM patients. Conclusions Thrombocytopenia has an indispensable prognostic effect in decreasing responses to induction therapy and survival in NDMM patients. Thrombocytopenia might need to be regarded as an independent prognostic factor in risk stratification of Chinese NDMM patients. Multiple Myeloma Prognosis Thrombocytopenia Figures Figure 1 Figure 2 Figure 3 1. Introduction Multiple myeloma (MM) is the second most common hematological malignancy characterized by abnormal plasma cell proliferation and consequently leading to end organ damage [ 1 – 4 ]. Anemia and thrombocytopenia can be seen in a subset of newly diagnosed MM (NDMM) patients [ 5 ]. Thrombocytopenia is less common but shows high risk of early mortality [ 6 ]. In the development of the International Staging System (ISS) for MM in 2005, platelet count was applied in the preliminary prognostic factor analysis, univariate and multivariate survival analysis, and was found to be a powerful predictor of survival [ 7 ]. However, platelet count was not incorporated into ISS and the prognostic value of thrombocytopenia in NDMM patients might have been underestimated. After nearly two decades with a great revolution in myeloma treatment and improvement of survival, Mao et al developed an individualized and weighted myeloma prognostic score system (MPSS) in NDMM patients [ 8 ]. Thrombocytopenia was integrated into MPSS and assigned a point equal to that of ISS stage III and two or more high-risk cytogenetic abnormalities (HRCA). Recent studies presented similar results that thrombocytopenia at diagnosis was linked to poor prognosis in MM patients [ 9 , 10 ]. These previous reports highlighted the importance of thrombocytopenia in the risk stratification of NDMM patients. Current international guidelines favor triplet or quadruplet induction regimens consisting of proteasome inhibitors, immunomodulatory agents and monoclonal antibodies. Adequate induction therapy greatly improves the prognosis and survival of NDMM patients [ 11 , 12 ]. Proteasome inhibitors combined with lenalidomide and dexamethasone are strongly recommended as the standard frontline induction regimens based on the superior progression free survival (PFS) and overall survival (OS) in the previously reported blockbuster studies in NDMM patients [ 13 – 19 ]. Recent studies have focused on whether the addition of monoclonal antibodies to triplet induction regimens can further improve the efficacy. Overall, the quadruplet regimens achieved better outcomes than the triplet regimens [ 19 – 22 ]. However, it is unclear whether triplet or quadruplet induction regimens are sufficient for NDMM patients with thrombocytopenia. This study aimed to evaluate the prognostic implications of thrombocytopenia in NDMM patients. We analyzed the baseline clinical features, responses to frontline induction therapy and survival between NDMM patients with and without thrombocytopenia. Based on the results of Cox regression analysis, we constructed a nomogram to predict 12- and 24-month PFS, and further validated this model. 2. Methods 2.1. Study design and participants We conducted a retrospective, multi-center study and enrolled 1363 NDMM patients who received induction therapies from three hospitals in China between January 2015 and December 2023. Diagnosis was in accordance with the International Myeloma Working Group (IMWG) criteria [ 23 ]. Patients diagnosed as primary amyloidosis (PAL), plasma cell leukemia (PCL), monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM) were excluded. The primary endpoint was PFS and the secondary endpoints were responses and OS. The study was approved by the Ethical Committee of Qilu Hospital of Shandong University and conducted in accordance with the Declaration of Helsinki. Informed consents were obtained from patients before recruitment. Thrombocytopenia was defined as an absolute platelet count less than 100 000/uL in peripheral blood in NDMM patients. Novel agents accessible to Chinese MM patients included proteasome inhibitors (bortezomib, ixazomib and carfilzomib), immunomodulatory agents (thalidomide, lenalidomide and pomalidomide) and anti-CD38 monoclonal antibodies (daratumumab). In this study, novel agents-based induction therapy (NAIT) was defined as triplet or quadruplet induction regimen consisting of at least two novel agents and accompanying steroids. IMWG consensus criteria for response assessment was used to evaluate the response and progression [ 24 ]. Patients were categorized as having stringent complete response (sCR), complete response (CR), very good partial response (VGPR), partial response (PR), stable disease (SD) and progressive disease (PD). PFS was defined as the duration from diagnosis to disease progression, first relapse, death, or the end of follow-up, whichever comes first. OS was defined as the duration from diagnosis to death or the end of follow-up. Moreover, if patients’ outcomes were not present at the end of follow-up, such case information was defined as censored data [ 25 ]. 2.2. Statistical analysis Baseline clinical characteristics of NDMM patients with low and normal platelet count were compared using Chi-square test and Fisher’s exact test. Probabilities for PFS and OS were estimated using the Kaplan-Meier curve, and differences were tested for statistical significance using two-sided log-rank test. We used univariate logistic regression analysis to investigate the impact of induction regimens and platelet count on efficacy, as well as univariate cox regression analysis to evaluate the effects of variables on PFS and OS. Moreover, variables with statistical significance in the univariate Cox regression analysis and meeting the proportionality assumption were included in the subsequent multivariate Cox regression analysis. Missing data were considered as dummy variables. The dataset was interpolated to form a complete dataset by random forest interpolation and later divided into the training and validation sets in a ratio of 7:3. Based on the results of multivariate Cox regression analysis, we constructed a nomogram for PFS in complete dataset. Calibration plot and time-dependent Receiver operating characteristic (ROC) were used to evaluate the predictive accuracy and conformity in training and validation datasets. All statistical tests were two-sided and P values less than 0.05 was considered as significant. All statistical analysis were performed in SPSS software version 26.0 (IBBM Corp). The Kaplan-Meier survival curves, the nomogram, time-dependent ROC curve and the calibration curves were constructed in R software version 4.3.3 (R Project for Statistical Computing, Vienna, Austria). 3. Results 3.1. Patient characteristics As shown in Fig. 1 , 1363 NDMM patients were analyzed. The incidence of thrombocytopenia in NDMM patients was 15.48% (211/1363, Table 1 ). The gerontal NDMM patients with thrombocytopenia were more than those with normal platelet count, especially those over 70 years (20.4% vs 11.8%, P = 0.003). NDMM patients with thrombocytopenia presented a larger number of bone marrow plasma cells ( P < 0.001), more advanced Durie-Salmon (DS) stage (more severe anemia and hypercalcemia, P < 0.001), ISS stage [lower albumin and higher β2-microglobulin (β2-MG) levels, P < 0.001] and revised ISS (R-ISS) stage [higher lactate dehydrogenase (LDH) levels and more patients with HRCA, P < 0.001] than patients with normal platelet count (Table 1 ). NDMM patients with and without thrombocytopenia received comparable induction regimens. The most commonly used induction therapy was bortezomib, lenalidomide and dexamethasone (VRD), the second most commonly used therapy was bortezomib, thalidomide and dexamethasone (VTD), and then the triplet therapy consisting of daratumumab and proteasome inhibitors. After induction therapy, patients with normal platelet count receiving autologous stem cell transplantation (ASCT) were a little more than patients with thrombocytopenia (25.3% vs 18.5%, P = 0.033). Interestingly, the ratio of males to females in NDMM patients with thrombocytopenia was 1.78, while this ratio was 1.16 in patients with normal platelet count. Table 1 Characteristics of 1363 patients with newly diagnosed multiple myeloma. Patients' characteristics All patients (N = 1363) Patients without thrombocytopenia (N = 1152) Patients with thrombocytopenia (N = 211) P value Sex 0.006 Female 610 534 (46.4%) 76 (36.0%) male 753 618 (53.6%) 135 (64.0%) Age (Years) 0.003 70 179 136 (11.8%) 43 (20.4%) BMPCs (%) < 0.001 < 60 1147 995 (86.4%) 152 (72.0%) ≥ 60 182 131 (11.4%) 51 (24.2%) Missing 34 26 (2.3%) 8 (3.8%) Serum calcium (mmol/L) 0.183 < 2.65 1200 1020 (88.5%) 180 (85.3%) ≥ 2.65 163 132 (11.5%) 31 (14.7%) Serum creatinine (umol/L) < 0.001 < 177 1125 970 (84.2%) 155 (73.5%) ≥ 177 238 182 (15.8%) 56 (26.5%) Hb (g/L) < 0.001 ≥ 85 857 803 (69.7%) 54 (25.6%) < 85 506 349 (30.3%) 157 (74.4%) Bone destruction 0.704 < 3 sites 686 578 (50.2%) 108 (51.2%) ≥ 3 sites 570 486 (42.2%) 84 (39.8%) Missing 107 88 (7.6%) 19 (9.0%) DS stage < 0.001 I 94 92 (8.0%) 2 (0.9%) II 338 315 (27.3%) 23 (10.9%) III 907 721 (62.6%) 186 (88.2%) missing 24 24 (2.1%) 0 (0.0%) β2-MG (mg/L) < 0.001 < 5.5 784 716 (62.2%) 68 (32.2%) ≥ 5.5 575 433 (37.6%) 142 (67.3%) missing 4 3 (0.3%) 1 (0.5%) Albumin (g/L) < 0.001 ≥ 35 804 710 (61.6%) 94 (44.5%) ˂ 35 559 442 (38.4%) 117 (55.5%) ISS stage < 0.001 I 356 337 (29.3%) 19 (9.0%) II 428 379 (32.9%) 49 (23.2%) III 575 433 (37.6%) 142 (67.3%) missing 4 3 (0.3%) 1 (0.5%) LDH < 0.001 normal 1047 911 (79.1%) 136 (64.5%) elevated a 316 241 (20.9%) 75 (35.5%) HRCA b < 0.001 No 406 364 (31.6%) 42 (19.9%) Yes 503 403 (35.0%) 100 (47.4%) Missing 454 385 (33.4%) 69 (32.7%) R-ISS stage < 0.001 I 160 155 (13.5%) 5 (2.4%) II 697 615 (53.4%) 82 (38.9%) III 271 188 (16.3%) 83 (39.3%) Missing 235 194 (16.8%) 41 (19.4%) Treatment 0.183 Non-NAIT 621 516 (44.8%) 105 (49.8%) NAIT 742 636 (55.2%) 106 (50.2%) VRD 418 373 (58.6%) 45 (42.5%) VTD 170 140 (22.0%) 30 (28.3%) VPD 13 11 (1.7%) 2 (1.9%) IRD/ITD 33 27 (4.2%) 6 (5.7%) KRD/KPD 13 11 (1.7%) 2 (1.9%) DVD/DKD/DID 53 40 (6.3%) 13 (12.3%) DRD/DPD 15 13 (2.0%) 2 (1.9%) DVRD/DKRD 27 21 (3.3%) 6 (5.7%) ASCT 0.033 No 1032 860 (74.7%) 172 (81.5%) Yes 331 292 (25.3%) 39 (18.5%) Response < 0.001 < PR 122 95 (8.2%) 27 (12.8%) PR 263 210 (18.2%) 53 (25.1%) VGPR 410 353 (30.6%) 57 (27.0%) CR and sCR 480 430 (37.3%) 50 (23.7%) missing 88 64 (5.6%) 24 (11.4%) BMPCs, bone marrow plasma cells; Hb, hemoglobin; DS, Durie-Salmon staging system; β2-MG, β2 microglobulin; ISS, international staging system; LDH, lactate dehydrogenase; HRCA: high risk cytogenetic aberration; R-ISS, revised international staging system; NAIT: novel agents-based induction therapy; VRD, bortezomib, lenalidomide and dexamethasone; VTD, bortezomib, thalidomide and dexamethasone; VPD, bortezomib, pomalidomide and dexamethasone; IRD, ixazomib, lenalidomide and dexamethasone; ITD, ixazomib, thalidomide and dexamethasone; KRD, carfilzomib, lenalidomide and dexamethasone; DVD, daratumumab, bortezomib and dexamethasone; DKD, daratumumab, carfilzomib and dexamethasone; DID, daratumumab, ixazomib and dexamethasone; DRD, daratumumab, lenalidomide and dexamethasone; DPD, daratumumab, pomalidomide and dexamethasone; DVRD, daratumumab, bortezomib, lenalidomide and dexamethasone; DKRD, daratumumab, carfilzomib, lenalidomide and dexamethasone; ASCT, autologous stem cell transplantation; PR, partial response; VGPR, very good partial response; CR, complete response; sCR, stringent complete response. a. LDH > 230 U/L in Qilu Hospital of Shandong University and Shandong Provincial Hospital Affiliated to Shandong First Medical University, LDH > 250 U/L in Fujian Medical University Union Hospital. b. HRCA: del (17p), t (4;14), t (14;16), t (14;20), p53 mutation and 1q21 gain/amplification. 3.2. Responses to induction therapy The overall response rates (ORR) were 86.1% and 75.8% in NDMM patients with normal and low platelet count, respectively ( P = 0.015, Fig. 2 A and 2 C). NDMM patients with normal platelet counts achieved significantly better deep response (≥ VGPR) than thrombocytopenic patients (67.9% vs 50.7%, P < 0.001, Fig. 2 A and 2 C). The rate of sCR plus CR (≥ CR) in patients with normal platelet count was also significantly higher than that in patients with thrombocytopenia (37.3% vs 23.7%, P = 0.001, Fig. 2 A and 2 C). These results suggest that NDMM patients with thrombocytopenia have significantly worse efficacy. NAIT regimens induced remarkable superior efficacy than non-NAIT therapies in NDMM patients with normal and low platelet count (Figure S1 and Table S1 ). To evaluate whether NAIT regimens showed benefits in NDMM patients with thrombocytopenia, we next analyzed the responses to NAIT in NDMM patients and found that the deep response (≥ VGPR) rate in patients with thrombocytopenia was significantly lower than that in patients with normal platelet count (60.4% vs 76.4%, P = 0.001, Fig. 2 B and 2 C), however, no significant difference was observed in OR rates between patients with or without thrombocytopenia (87.8% vs 92.4%, P = 0.33, Fig. 2 B and 2 C). This indicates that NAIT can improve overall response but not deep response in NDMM patients with thrombocytopenia. In NDMM patients receiving non-NAIT regimens, both overall and deep response rates in patients with thrombocytopenia were significantly lower than patients without thrombocytopenia (ORR: 63.8% vs 78.5%, P = 0.039; ≥ VGPR: 40.9% vs 57.6%, P = 0.018; respectively, Fig. 2 B and 2 C). 3.3. Survival outcomes With a median follow-up of 27 months, both PFS and OS in NDMM patients with thrombocytopenia were significantly worse than patients with normal platelet counts (median PFS: 15 months vs 21.5 months, P < 0.001, median OS: 47 months vs 77 months, P < 0.001, respectively. Figure 2 D and 2 E). In subgroup analyses of survival, PFS and OS of patients in different DS, ISS and R-ISS stages were compared between patients with and without thrombocytopenia (Figure S2). In general, the outcomes in patients with thrombocytopenia at different stages were inferior to patients with normal platelet count. Induction regimens and platelet counts were included in Kaplan-Meier analysis to further investigate the impacts of these two factors on PFS and OS. As shown in Fig. 2 F and 2 G, among patients with NAIT regimens, both PFS and OS of patients with thrombocytopenia were significantly shorter than those of patients with normal platelet counts (median PFS: 16 months vs 25 months, P < 0.001, median OS: 53 months vs 79 months, P < 0.001, respectively). In patients with non-NAIT regimens, the outcomes of patients with thrombocytopenia were also significantly worse than those of patients with normal platelet counts (median PFS: 14 months vs 18 months, P < 0.001, median OS: 45 months vs 71 months, P < 0.001, respectively). Neither NAIT nor non-NAIT regimens improved the survival of patients with thrombocytopenia. Univariate and multivariate Cox analyses for PFS and OS were presented in Table 2 and Table S2, respectively. In multivariate analysis for PFS (Table 2 ), thrombocytopenia [hazard ratio (95% confidence interval, 95% CI) 1.40 (1.14–1.72), P = 0.001] and R-ISS III [1.60 (1.13–2.28), P = 0.009] were associated with worse PFS. In contrast, NAIT [0.52 (0.45–0.60), P < 0.001], ASCT [0.71 (0.60–0.85), P < 0.001] and achieving deep response (≥ VGPR) [0.53 (0.45-0. 62), P < 0.001] were associated with better PFS. Similarly, in multivariate analysis for OS (Table S2), thrombocytopenia [1.74 (1.29–2.35), P < 0.001] and R-ISS stage III [2.07 (1.10–3.90), P = 0.023] were associated with worse OS. In contrast, ASCT [0.69 (0.49–0.97), P = 0.033] and achieving deep response (≥ VGPR) [0.57 (0.45–0.73), P < 0.001] were associated with better OS. Table 2 Univariate and multivariate Cox analyses for PFS. Univariate analysis Multivariate analysis Variables Hazard Ratio (95% CI) P value Hazard Ratio (95% CI) P value Male 1.01 (0.88–1.16) 0.867 - - Age > 60 years 0.95 (0.83–1.09) 0.488 - - Thrombocytopenia 1.76 (1.46–2.13) < 0.001 1.40 (1.14–1.72) 0.001 Ca ≥ 2.65mmol/L 1.34 (1.09–1.65) 0.006 1.20 (0.96–1.50) 0.107 Cr ≥ 177umol/L 1.40 (1.17–1.67) < 0.001 0.99 (0.81–1.22) 0.94 Hb < 85g/L 1.46 (1.27–1.69) < 0.001 1.18 (1.00-1.39) 0.055 Bone destruction < 3 sites reference Bone destruction ≥ 3 sites 1.10 (0.95–1.27) 0.19 - - missing 1.26 (0.96–1.65) 0.09 ISS I reference ISS II 1.10 (0.91–1.32) 0.326 1.04 (0.80–1.35) 0.764 ISS III 1.60 (1.35–1.90) < 0.001 1.01 (0.77–1.31) 0.953 R-ISS I reference R-ISS II 1.37 (1.09–1.74) 0.008 1.13 (0.83–1.55) 0.431 R-ISS III 2.38 (1.84–3.09) < 0.001 1.60 (1.13–2.28) 0.009 missing 1.59 (1.22–2.08) 0.001 1.13 (0.84–1.52) 0.427 NAIT 0.47 (0.41–0.54) < 0.001 0.52 (0.45–0.60) < 0.001 ASCT 0.59 (0.50–0.70) < 0.001 0.71 (0.60–0.85) < 0.001 < VGPR reference ≥ VGPR 0.44 (0.38–0.52) < 0.001 0.53 (0.45-0. 62) < 0.001 missing 0.82 (0.59–1.13) 0.215 0.83 (0.60–1.14) 0.244 PFS, progression free survival; 95% CI, 95% confidence interval; Hb, hemoglobin; ISS, international staging system; R-ISS, revised international staging system; NAIT: novel agents-based induction therapy; ASCT, autologous stem cell transplantation; VGPR, very good partial response. 3.4. Nomogram construction and validation Based on the results of multivariate regression analysis, thrombocytopenia, R-ISS stage, NAIT, ASCT and deep response (≥ VGPR) were used to construct a nomogram predicting 12-month and 24-month PFS in the complete dataset (Fig. 3 A). The baseline characteristics of training group and validation group were presented in Table S3 with no significant bias. ROC analysis was used to assess the discrimination of the nomogram. The area under the curve (AUC) for the training nomogram model demonstrated values of 0.712 at 12 months and 0.792 at 24 months, whereas the validation nomogram model yielded AUCs of 0.711 and 0.750 at 12-month and 24-month intervals, respectively (Fig. 3 B and 3 C). The calibration curves for the probability of 12-month and 24-month PFS demonstrated a good agreement between the actual reported and the predicated PFS (Fig. 3 D-G). Our nomogram model effectively predicted PFS for NDMM patients. 4. Discussion In this multicenter retrospective study, we analyzed the clinical characteristics, responses to induction therapy and survival of NDMM patients with and without thrombocytopenia, performed regression analysis and constructed a nomogram model to predict PFS. We found that NDMM patients with thrombocytopenia showed a larger number of bone marrow plasma cells, more advanced disease stages and worse outcomes than patients without thrombocytopenia. NAIT could improve overall response but not deep response or survival in NDMM patients with thrombocytopenia. Multivariate regression analysis proposed that thrombocytopenia together with R-ISS stage III, NAIT, ASCT and deep response were significantly correlated with survival. Thrombocytopenia is less common in NDMM patients and its cut-off value varies across different studies. This study conducted in China defined thrombocytopenia as platelet count less than 100 000/uL. In this situation, the incidence of thrombocytopenia in NDMM patients was approximately 15%, which was in line with previously reported data [ 7 , 8 , 10 ]. The presence of thrombocytopenia in NDMM patients significantly correlated with invasive clinical manifestations, including high myeloma burden, severe anemia, low albumin levels, renal failure, and elevated β2-MG and LDH levels. These factors along with HRCA are important indicators for MM disease staging and risk stratification. Charalampous et al analyzed the association of thrombocytopenia with disease burden, HRCA and survival in NDMM patients from Mayo Clinic and found that thrombocytopenia was associated with mortality independently [ 10 ]. They demonstrated that thrombocytopenia was significantly associated with t(4;14) and t(14;16) [ 10 ]. Due to the cost and accessibility of fluorescence in situ hybridization (FISH) test, part of NDMM patients in our study failed to complete FISH test, resulting in some missing data in the risk stratification of cytogenetics. We failed to present the exact correlation between thrombocytopenia and a specific cytogenetic abnormality. However, we found that NDMM patients with thrombocytopenia had a higher proportion of HRCA based on the available data. Interestingly, our study demonstrated that male NDMM patients were more likely to have thrombocytopenia. Other studies reported similar results that the percentage of male patients with thrombocytopenia was higher than that of female myeloma patients [ 9 , 10 ], suggesting that male patients were susceptible to thrombocytopenia at diagnosis. NDMM patients with or without thrombocytopenia in our study received comparable induction regimens. However, the percentage of NDMM patients with normal platelet count receiving ASCT was a little higher than patients with thrombocytopenia, mainly due to the better performance status and younger age in patients with normal platelet count. Survival in MM patients has improved significantly during the past two decades in China and around the world [ 26 – 29 ]. Numerous combinations for initial therapy have been developed based on novel agents which have shown apparent efficacy [ 12 , 30 ]. Recent studies have established and further consolidated triplet and quadruplet regimens in the management of MM patients [ 31 – 34 ]. The most commonly recommended induction regimens are triplet and quadruplet regimens consisting of proteasome inhibitors, immunomodulatory agents and monoclonal antibodies [ 3 , 29 ]. To investigate whether NAIT can overcome the poor prognosis associated with thrombocytopenia, we compared the outcomes and survival of NDMM patients with and without thrombocytopenia receiving NAIT regimens. We found that NDMM patients with thrombocytopenia had poor outcomes and survival. NAIT significantly improved ORR of patients with thrombocytopenia close to that of patients with normal platelet count. But unfortunately, NAIT failed to achieve a satisfactory deep response and thus did not prolong their survival in NDMM patients with thrombocytopenia. NDMM patients with normal platelet count who received NAIT had the longest PFS and OS, followed by those with normal platelet count receiving non-NAIT and those with thrombocytopenia receiving NAIT, respectively. NDMM patients with thrombocytopenia who received non-NAIT had the worst survival. NAIT regimens induced better outcomes and improved survival of NDMM patients compared with non-NAIT treatments. The improvement of quality of response is associated with better disease control and longer survival [ 35 – 37 ]. The achievement of maximal response should be strongly considered in eligible patients. The prognosis evaluation and risk stratification of MM patients were complex and variable [ 38 – 42 ]. We performed univariate and multivariate analyses for PFS and OS, and found that thrombocytopenia, R-ISS stage III, NAIT, ASCT and deep response were significantly correlated with survival. These five factors were used to construct a nomogram to predict 12-month and 24-month PFS with reliable predictive ability. Recently, Maura F et al integrated clinical, genomic and therapeutic data to build a model predicting individualized risk in NDMM patients [ 43 ]. This model is an online available tool including patients’ demographics, ISS, IGH translocations, genomics, induction and post-induction therapies. They developed an individualized risk-prediction model enabling personally tailored therapeutic decisions for NDMM patients. In our study, induction therapy, response to induction therapy and ASCT were also included in the construction of nomogram model based on the results of multivariate Cox regression analysis and these factors played important roles in predicting PFS. Most existing risk stratification models in multiple myeloma have not included platelet count as a laboratory feature[ 29 , 44 ], unless the MPSS risk model, which incorporates platelet count and improves the risk estimation in NDMM patients [ 8 ]. The inclusion of thrombocytopenia as a high-risk factor in the prognosis of multiple myeloma is controversial. Our results suggest that NDMM patients with thrombocytopenia have poor prognosis, similar to that of patients with high-risk MM [ 39 , 44 ]. Our findings are based on a retrospective observational study, which has certain limitations. Firstly, the most commonly used two induction therapies in our study are VRD and VTD. The percentage of patients receiving induction regimens consisting of daratumumab and carfilzomib is relatively low. Therefore, the effects of regimens composed by monoclonal antibodies, new generation proteasome inhibitors and immunomodulatory agents in NDMM patients with thrombocytopenia need to be further confirmed. Secondly, we used random forest interpolation in constructing the nomogram and dummy variables in multifactor Cox regression to deal with some missing data. Although good internal verification results are obtained, this model needs to be further validated by external data. In conclusion, thrombocytopenia in NDMM patients significantly affects responses to induction therapy and survival. Thrombocytopenia should be regarded as an independent prognostic factor in the risk stratification of Chinese NDMM patients. Declarations Acknowledgments Thanks to all the authors for their contributions to this manuscript. Author Contributions HZ and PC conceived and designed the study. XJL XXX, XHL, XLW and XL collected and assembled the data. XJL XLW, QL, XL and ZS analyzed and verified the data. LQW, PC, JP and HZ verified and interpreted the data. All authors wrote and approved of the article and are accountable for publication. Funding This work was supported by grants from National Natural Science Foundation of China (No. 82370132, No. 82070122, No. 82030005), Taishan Scholar Foundation of Shandong Province (No.ts20221157), Joint Funds for the Innovation of Science and Technology, Fujian Province (No. 2020Y9097), China Postdoctoral Science Foundation (2023M732098), and Postdoctoral Innovation Project of Shandong Province (SDCX-ZG-202302029). Data availability Data can be obtained from the corresponding author upon reasonable request. Ethics approval and consent to participate The study was approved by the Ethical Committee of Qilu Hospital of Shandong University and conducted in accordance with the Declaration of Helsinki. Informed consents were obtained from patients before recruitment. Consent for publication Not Applicable Competing interests All authors have no competing interests to disclose. References Cowan AJ, Allen C, Barac A, Basaleem H, Bensenor I, Curado MP, Foreman K, Gupta R, Harvey J, Hosgood HD, et al. Global Burden of Multiple Myeloma: A Systematic Analysis for the Global Burden of Disease Study 2016. JAMA Oncol. 2018;4(9):1221–7. van de Donk N, Pawlyn C, Yong KL. Multiple myeloma. Lancet. 2021;397(10272):410–27. Cowan AJ, Green DJ, Kwok M, Lee S, Coffey DG, Holmberg LA, Tuazon S, Gopal AK, Libby EN. Diagnosis and Management of Multiple Myeloma: A Review. JAMA. 2022;327(5):464–77. Liu J, Liu W, Mi L, Zeng X, Cai C, Ma J, Wang L. 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The evolving diagnosis and treatment paradigms of multiple myeloma in China: 15 years' experience of 1256 patients in a national medical center. Cancer Med. 2023;12(8):9604–14. Bazarbachi AH, Al Hamed R, Malard F, Bazarbachi A, Harousseau JL, Mohty M. Induction therapy prior to autologous stem cell transplantation (ASCT) in newly diagnosed multiple myeloma: an update. Blood Cancer J. 2022;12(3):47. Durie BGM, Hoering A, Sexton R, Abidi MH, Epstein J, Rajkumar SV, Dispenzieri A, Kahanic SP, Thakuri MC, Reu FJ, et al. Longer term follow-up of the randomized phase III trial SWOG S0777: bortezomib, lenalidomide and dexamethasone vs. lenalidomide and dexamethasone in patients (Pts) with previously untreated multiple myeloma without an intent for immediate autologous stem cell transplant (ASCT). Blood Cancer J. 2020;10(5):53. Durie BGM, Hoering A, Abidi MH, Rajkumar SV, Epstein J, Kahanic SP, Thakuri M, Reu F, Reynolds CM, Sexton R, et al. Bortezomib with lenalidomide and dexamethasone versus lenalidomide and dexamethasone alone in patients with newly diagnosed myeloma without intent for immediate autologous stem-cell transplant (SWOG S0777): a randomised, open-label, phase 3 trial. Lancet. 2017;389(10068):519–27. Attal M, Lauwers-Cances V, Hulin C, Leleu X, Caillot D, Escoffre M, Arnulf B, Macro M, Belhadj K, Garderet L, et al. Lenalidomide, Bortezomib, and Dexamethasone with Transplantation for Myeloma. N Engl J Med. 2017;376(14):1311–20. Jakubowiak AJ, Dytfeld D, Griffith KA, Lebovic D, Vesole DH, Jagannath S, Al-Zoubi A, Anderson T, Nordgren B, Detweiler-Short K, et al. A phase 1/2 study of carfilzomib in combination with lenalidomide and low-dose dexamethasone as a frontline treatment for multiple myeloma. Blood. 2012;120(9):1801–9. Gay F, Musto P, Rota-Scalabrini D, Bertamini L, Belotti A, Galli M, Offidani M, Zamagni E, Ledda A, Grasso M, et al. Carfilzomib with cyclophosphamide and dexamethasone or lenalidomide and dexamethasone plus autologous transplantation or carfilzomib plus lenalidomide and dexamethasone, followed by maintenance with carfilzomib plus lenalidomide or lenalidomide alone for patients with newly diagnosed multiple myeloma (FORTE): a randomised, open-label, phase 2 trial. Lancet Oncol. 2021;22(12):1705–20. Facon T, Venner CP, Bahlis NJ, Offner F, White DJ, Karlin L, Benboubker L, Rigaudeau S, Rodon P, Voog E, et al. Oral ixazomib, lenalidomide, and dexamethasone for transplant-ineligible patients with newly diagnosed multiple myeloma. Blood. 2021;137(26):3616–28. !!!. INVALID CITATION !!!. Moreau P, Attal M, Hulin C, Arnulf B, Belhadj K, Benboubker L, Bene MC, Broijl A, Caillon H, Caillot D, et al. Bortezomib, thalidomide, and dexamethasone with or without daratumumab before and after autologous stem-cell transplantation for newly diagnosed multiple myeloma (CASSIOPEIA): a randomised, open-label, phase 3 study. Lancet. 2019;394(10192):29–38. Voorhees PM, Kaufman JL, Laubach J, Sborov DW, Reeves B, Rodriguez C, Chari A, Silbermann R, Costa LJ, Anderson LD Jr., et al. Daratumumab, lenalidomide, bortezomib, and dexamethasone for transplant-eligible newly diagnosed multiple myeloma: the GRIFFIN trial. Blood. 2020;136(8):936–45. Costa LJ, Chhabra S, Medvedova E, Dholaria BR, Schmidt TM, Godby KN, Silbermann R, Dhakal B, Bal S, Giri S, et al. Daratumumab, Carfilzomib, Lenalidomide, and Dexamethasone With Minimal Residual Disease Response-Adapted Therapy in Newly Diagnosed Multiple Myeloma. J Clin Oncol. 2022;40(25):2901–12. Rajkumar SV, Dimopoulos MA, Palumbo A, Blade J, Merlini G, Mateos MV, Kumar S, Hillengass J, Kastritis E, Richardson P, et al. International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. 2014;15(12):e538–548. Kumar S, Paiva B, Anderson KC, Durie B, Landgren O, Moreau P, Munshi N, Lonial S, Blade J, Mateos MV, et al. International Myeloma Working Group consensus criteria for response and minimal residual disease assessment in multiple myeloma. Lancet Oncol. 2016;17(8):e328–46. Rajkumar SV, Harousseau JL, Durie B, Anderson KC, Dimopoulos M, Kyle R, Blade J, Richardson P, Orlowski R, Siegel D et al. Consensus recommendations for the uniform reporting of clinical trials: report of the International Myeloma Workshop Consensus Panel 1. Blood 2011, 117(18):4691–4695. Chinese Hematology A. Chinese Society of H: [Guidelines for the diagnosis and management of multiple myeloma in China (2022 revision)]. Zhonghua nei ke za zhi. 2022;61(5):480–7. Goel U, Usmani S, Kumar S. Current approaches to management of newly diagnosed multiple myeloma. Am J Hematol. 2022;97(Suppl 1):S3–25. Dimopoulos MA, Moreau P, Terpos E, Mateos MV, Zweegman S, Cook G, Delforge M, Hajek R, Schjesvold F, Cavo M, et al. Multiple myeloma: EHA-ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up(dagger). Ann Oncol. 2021;32(3):309–22. Malard F, Neri P, Bahlis NJ, Terpos E, Moukalled N, Hungria VTM, Manier S, Mohty M. Multiple myeloma. Nat Rev Dis Primers. 2024;10(1):45. Rajkumar SV. Multiple myeloma: 2022 update on diagnosis, risk stratification, and management. Am J Hematol. 2022;97(8):1086–107. Facon T, Kumar S, Plesner T, Orlowski RZ, Moreau P, Bahlis N, Basu S, Nahi H, Hulin C, Quach H, et al. Daratumumab plus Lenalidomide and Dexamethasone for Untreated Myeloma. N Engl J Med. 2019;380(22):2104–15. Richardson PG, Jacobus SJ, Weller EA, Hassoun H, Lonial S, Raje NS, Medvedova E, McCarthy PL, Libby EN, Voorhees PM, et al. Triplet Therapy, Transplantation, and Maintenance until Progression in Myeloma. N Engl J Med. 2022;387(2):132–47. Kaiser MF, Hall A, Walker K, Sherborne A, De Tute RM, Newnham N, Roberts S, Ingleson E, Bowles K, Garg M, et al. Daratumumab, Cyclophosphamide, Bortezomib, Lenalidomide, and Dexamethasone as Induction and Extended Consolidation Improves Outcome in Ultra-High-Risk Multiple Myeloma. J Clin Oncol. 2023;41(23):3945–55. Sonneveld P, Dimopoulos MA, Boccadoro M, Quach H, Ho PJ, Beksac M, Hulin C, Antonioli E, Leleu X, Mangiacavalli S, et al. Daratumumab, Bortezomib, Lenalidomide, and Dexamethasone for Multiple Myeloma. N Engl J Med. 2024;390(4):301–13. Lonial S, Anderson KC. Association of response endpoints with survival outcomes in multiple myeloma. Leukemia. 2014;28(2):258–68. Mailankody S, Korde N, Lesokhin AM, Lendvai N, Hassoun H, Stetler-Stevenson M, Landgren O. Minimal residual disease in multiple myeloma: bringing the bench to the bedside. Nat Rev Clin Oncol. 2015;12(5):286–95. Landgren O, Iskander K. Modern multiple myeloma therapy: deep, sustained treatment response and good clinical outcomes. J Intern Med. 2017;281(4):365–82. Yan Y, Mao X, Liu J, Fan H, Du C, Li Z, Yi S, Xu Y, Lv R, Liu W, et al. The impact of response kinetics for multiple myeloma in the era of novel agents. Blood Adv. 2019;3(19):2895–904. Hagen P, Zhang J, Barton K. High-risk disease in newly diagnosed multiple myeloma: beyond the R-ISS and IMWG definitions. Blood Cancer J. 2022;12(5):83. Abdallah NH, Binder M, Rajkumar SV, Greipp PT, Kapoor P, Dispenzieri A, Gertz MA, Baughn LB, Lacy MQ, Hayman SR, et al. A simple additive staging system for newly diagnosed multiple myeloma. Blood Cancer J. 2022;12(1):21. Chng WJ, Dispenzieri A, Chim CS, Fonseca R, Goldschmidt H, Lentzsch S, Munshi N, Palumbo A, Miguel JS, Sonneveld P, et al. IMWG consensus on risk stratification in multiple myeloma. Leukemia. 2014;28(2):269–77. Mikhael JR, Dingli D, Roy V, Reeder CB, Buadi FK, Hayman SR, Dispenzieri A, Fonseca R, Sher T, Kyle RA, et al. Management of newly diagnosed symptomatic multiple myeloma: updated Mayo Stratification of Myeloma and Risk-Adapted Therapy (mSMART) consensus guidelines 2013. Mayo Clin Proc. 2013;88(4):360–76. Maura F, Rajanna AR, Ziccheddu B, Poos AM, Derkach A, Maclachlan K, Durante M, Diamond B, Papadimitriou M, Davies F, et al. Genomic Classification and Individualized Prognosis in Multiple Myeloma. J Clin Oncol. 2024;42(11):1229–40. Rees MJ, Kumar S. High-risk multiple myeloma: Redefining genetic, clinical, and functional high-risk disease in the era of molecular medicine and immunotherapy. Am J Hematol. 2024;99(8):1560–75. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5872364","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":406269666,"identity":"aaf4ba96-5bb0-4af5-95e7-e8d4a0dacc7b","order_by":0,"name":"Xiaojing Li","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojing","middleName":"","lastName":"Li","suffix":""},{"id":406269667,"identity":"77526ab3-dac3-4f64-be0f-c0013e565f12","order_by":1,"name":"Xiaoxi Xu","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxi","middleName":"","lastName":"Xu","suffix":""},{"id":406269668,"identity":"68036bd0-242d-4591-b7af-8c7236e29216","order_by":2,"name":"Xiaohui Lai","email":"","orcid":"","institution":"Fujian Institute of Hematology, Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaohui","middleName":"","lastName":"Lai","suffix":""},{"id":406269669,"identity":"43bb23f2-3501-42b4-81b8-101e459f184d","order_by":3,"name":"Xiaolin Wang","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xiaolin","middleName":"","lastName":"Wang","suffix":""},{"id":406269670,"identity":"efb31ef4-a366-4245-a8e1-7d22099909c3","order_by":4,"name":"Qiang Liu","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Liu","suffix":""},{"id":406269671,"identity":"e5832010-4700-45c7-ab15-6ef42646d14c","order_by":5,"name":"Xin Liu","email":"","orcid":"","institution":"Shandong Provincial Hospital, Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Liu","suffix":""},{"id":406269672,"identity":"9b3b5604-d555-46aa-9222-131e9231a5fd","order_by":6,"name":"Luqun Wang","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Luqun","middleName":"","lastName":"Wang","suffix":""},{"id":406269673,"identity":"9228be37-d52d-492d-8ab5-79b351348897","order_by":7,"name":"Zi Sheng","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Zi","middleName":"","lastName":"Sheng","suffix":""},{"id":406269674,"identity":"395cb8a1-48c4-40cb-8d60-f414938286f1","order_by":8,"name":"Jun Peng","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Peng","suffix":""},{"id":406269675,"identity":"94539ee0-e3e0-49d1-8c70-e3fa5ef04cfc","order_by":9,"name":"Ping Chen","email":"","orcid":"","institution":"Fujian Institute of Hematology, Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Chen","suffix":""},{"id":406269676,"identity":"5cabe322-aa97-4cf0-9624-3b8e9d9095ac","order_by":10,"name":"Hai Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYDACCQYGZjB5gPnAgQ8/SNPClnhwZg/xWoDgAI/xYQ42InTIz24+Jl1QY2HPdyPnw2EGHgZ5frED+LUwzjmWJj3jmETizBu5Gw4XWDAYzpydgF8Ls0SOmTQPm0SCAUjLDB6GBIPbBLSwgbX8k7A3uJHz4DAPGxFaeEBaeNskGDfcyGEgTouERFqyNW8f0C9nnhkAA1mCsF/kZyQfvM3zrc6e73jy4w8fftjI80sT0IJhK2nKR8EoGAWjYBRgBwB6I0GOckG4SwAAAABJRU5ErkJggg==","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Hai","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2025-01-21 09:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5872364/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5872364/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74690898,"identity":"24b2cf23-ff55-4611-bcdc-4a2139984124","added_by":"auto","created_at":"2025-01-24 18:38:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47702,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe flow diagram of the study.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: NDMM, newly diagnosed multiple myeloma; PAL, primary amyloidosis; PCL, plasma cell leukemia; MGUS, monoclonal gammopathy of undetermined significance; SMM, smoldering multiple myeloma; PFS, progression free survival; ROC, Receiver operating characteristic analysis.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-5872364/v1/a6d0c48cb4c9713ac4434dbe.png"},{"id":74690904,"identity":"fdb3bbee-a7c9-4824-adec-6d3e285dba57","added_by":"auto","created_at":"2025-01-24 18:38:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":98371,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResponses and survival of NDMM patients with and without thrombocytopenia. \u003c/strong\u003e(A) Responses to induction therapy among patients with normal and low platelet count. (B) Responses of patients with and without thrombocytopenia receiving non-NAIT and NAIT regimens, respectively. (C) Univariate logistic analysis of the effect of platelet count on efficacy in all patients, patients receiving non-NAIT and NAIT regimens, respectively. (D) Progression free survival (PFS) between NDMM patients with normal and low platelet count. (E) Overall survival (OS) between NDMM patients with normal and low platelet count. (F) Progression free survival (PFS) of NDMM patients with normal and low platelet count receiving non-NAIT and NAIT regimens, respectively. (G) Overall survival (OS) of NDMM patients with normal and low platelet count receiving non-NAIT and NAIT regimens, respectively.\u003c/p\u003e\n\u003cp\u003eNDMM, newly diagnosed multiple myeloma; NAIT, novel agents-based induction therapy; CR, complete response; VGPR, very good partial response; PR, partial response; OR, overall response.\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-5872364/v1/06b43d45a49e10abf538e793.png"},{"id":74690899,"identity":"fa32ab2c-deb9-4bcd-8bdc-1c0a9b9384e3","added_by":"auto","created_at":"2025-01-24 18:38:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":62526,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe nomogram for the prediction of PFS in NDMM patients. \u003c/strong\u003e(A) Thrombocytopenia, R-ISS stage, NAIT, ASCT and deep response (≥ VGPR) were used to construct a nomogram predicting 12-month and 24-month PFS. (B) The area under the curve (AUC) for the training nomogram model demonstrated values of 0.712 at 12 months and 0.792 at 24 months. (C) The validation nomogram model yielded AUC of 0.711 at 12 months and 0.750 at 24 months. (D-G) Calibration curves of nomogram in terms of the agreement between predicted and observed 12-month in training group (D) and validation group (E) and 24-month in training group (F) and in validation group (G), respectively.\u003c/p\u003e\n\u003cp\u003ePFS, progression free survival; NDMM, newly diagnosed multiple myeloma; R-ISS, Revised International Staging System; NAIT, novel agents-based induction therapy; ASCT, autologous stem cell transplantation; VGPR, very good partial response.\u003c/p\u003e","description":"","filename":"Figure13.png","url":"https://assets-eu.researchsquare.com/files/rs-5872364/v1/2cb32840eea24eb00ea21906.png"},{"id":83354857,"identity":"a053be34-49cc-40cf-ada4-8e691cafa075","added_by":"auto","created_at":"2025-05-23 14:53:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1388988,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5872364/v1/dd98dd49-0afe-4c57-aa58-ca8890f2ea63.pdf"},{"id":74690903,"identity":"472bdf5b-78fa-4e6b-ae44-0fd53d39d8c3","added_by":"auto","created_at":"2025-01-24 18:38:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":868698,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiguresandTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-5872364/v1/322134cfb7fa3ef847a17a6a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePrognostic implications of thrombocytopenia in Chinese patients with newly diagnosed multiple myeloma\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMultiple myeloma (MM) is the second most common hematological malignancy characterized by abnormal plasma cell proliferation and consequently leading to end organ damage [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Anemia and thrombocytopenia can be seen in a subset of newly diagnosed MM (NDMM) patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Thrombocytopenia is less common but shows high risk of early mortality [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In the development of the International Staging System (ISS) for MM in 2005, platelet count was applied in the preliminary prognostic factor analysis, univariate and multivariate survival analysis, and was found to be a powerful predictor of survival [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, platelet count was not incorporated into ISS and the prognostic value of thrombocytopenia in NDMM patients might have been underestimated. After nearly two decades with a great revolution in myeloma treatment and improvement of survival, Mao et al developed an individualized and weighted myeloma prognostic score system (MPSS) in NDMM patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Thrombocytopenia was integrated into MPSS and assigned a point equal to that of ISS stage III and two or more high-risk cytogenetic abnormalities (HRCA). Recent studies presented similar results that thrombocytopenia at diagnosis was linked to poor prognosis in MM patients [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These previous reports highlighted the importance of thrombocytopenia in the risk stratification of NDMM patients.\u003c/p\u003e \u003cp\u003e Current international guidelines favor triplet or quadruplet induction regimens consisting of proteasome inhibitors, immunomodulatory agents and monoclonal antibodies. Adequate induction therapy greatly improves the prognosis and survival of NDMM patients [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Proteasome inhibitors combined with lenalidomide and dexamethasone are strongly recommended as the standard frontline induction regimens based on the superior progression free survival (PFS) and overall survival (OS) in the previously reported blockbuster studies in NDMM patients [\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Recent studies have focused on whether the addition of monoclonal antibodies to triplet induction regimens can further improve the efficacy. Overall, the quadruplet regimens achieved better outcomes than the triplet regimens [\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, it is unclear whether triplet or quadruplet induction regimens are sufficient for NDMM patients with thrombocytopenia.\u003c/p\u003e \u003cp\u003eThis study aimed to evaluate the prognostic implications of thrombocytopenia in NDMM patients. We analyzed the baseline clinical features, responses to frontline induction therapy and survival between NDMM patients with and without thrombocytopenia. Based on the results of Cox regression analysis, we constructed a nomogram to predict 12- and 24-month PFS, and further validated this model.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study design and participants\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective, multi-center study and enrolled 1363 NDMM patients who received induction therapies from three hospitals in China between January 2015 and December 2023. Diagnosis was in accordance with the International Myeloma Working Group (IMWG) criteria [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Patients diagnosed as primary amyloidosis (PAL), plasma cell leukemia (PCL), monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM) were excluded. The primary endpoint was PFS and the secondary endpoints were responses and OS. The study was approved by the Ethical Committee of Qilu Hospital of Shandong University and conducted in accordance with the Declaration of Helsinki. Informed consents were obtained from patients before recruitment.\u003c/p\u003e \u003cp\u003eThrombocytopenia was defined as an absolute platelet count less than 100 000/uL in peripheral blood in NDMM patients. Novel agents accessible to Chinese MM patients included proteasome inhibitors (bortezomib, ixazomib and carfilzomib), immunomodulatory agents (thalidomide, lenalidomide and pomalidomide) and anti-CD38 monoclonal antibodies (daratumumab). In this study, novel agents-based induction therapy (NAIT) was defined as triplet or quadruplet induction regimen consisting of at least two novel agents and accompanying steroids.\u003c/p\u003e \u003cp\u003eIMWG consensus criteria for response assessment was used to evaluate the response and progression [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Patients were categorized as having stringent complete response (sCR), complete response (CR), very good partial response (VGPR), partial response (PR), stable disease (SD) and progressive disease (PD).\u003c/p\u003e \u003cp\u003ePFS was defined as the duration from diagnosis to disease progression, first relapse, death, or the end of follow-up, whichever comes first. OS was defined as the duration from diagnosis to death or the end of follow-up. Moreover, if patients\u0026rsquo; outcomes were not present at the end of follow-up, such case information was defined as censored data [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Statistical analysis\u003c/h2\u003e \u003cp\u003eBaseline clinical characteristics of NDMM patients with low and normal platelet count were compared using Chi-square test and Fisher\u0026rsquo;s exact test. Probabilities for PFS and OS were estimated using the Kaplan-Meier curve, and differences were tested for statistical significance using two-sided log-rank test. We used univariate logistic regression analysis to investigate the impact of induction regimens and platelet count on efficacy, as well as univariate cox regression analysis to evaluate the effects of variables on PFS and OS. Moreover, variables with statistical significance in the univariate Cox regression analysis and meeting the proportionality assumption were included in the subsequent multivariate Cox regression analysis. Missing data were considered as dummy variables.\u003c/p\u003e \u003cp\u003eThe dataset was interpolated to form a complete dataset by random forest interpolation and later divided into the training and validation sets in a ratio of 7:3. Based on the results of multivariate Cox regression analysis, we constructed a nomogram for PFS in complete dataset. Calibration plot and time-dependent Receiver operating characteristic (ROC) were used to evaluate the predictive accuracy and conformity in training and validation datasets.\u003c/p\u003e \u003cp\u003eAll statistical tests were two-sided and P values less than 0.05 was considered as significant. All statistical analysis were performed in SPSS software version 26.0 (IBBM Corp). The Kaplan-Meier survival curves, the nomogram, time-dependent ROC curve and the calibration curves were constructed in R software version 4.3.3 (R Project for Statistical Computing, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Patient characteristics\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 1363 NDMM patients were analyzed. The incidence of thrombocytopenia in NDMM patients was 15.48% (211/1363, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The gerontal NDMM patients with thrombocytopenia were more than those with normal platelet count, especially those over 70 years (20.4% \u003cem\u003evs\u003c/em\u003e 11.8%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). NDMM patients with thrombocytopenia presented a larger number of bone marrow plasma cells (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), more advanced Durie-Salmon (DS) stage (more severe anemia and hypercalcemia, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ISS stage [lower albumin and higher β2-microglobulin (β2-MG) levels, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001] and revised ISS (R-ISS) stage [higher lactate dehydrogenase (LDH) levels and more patients with HRCA, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001] than patients with normal platelet count (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). NDMM patients with and without thrombocytopenia received comparable induction regimens. The most commonly used induction therapy was bortezomib, lenalidomide and dexamethasone (VRD), the second most commonly used therapy was bortezomib, thalidomide and dexamethasone (VTD), and then the triplet therapy consisting of daratumumab and proteasome inhibitors. After induction therapy, patients with normal platelet count receiving autologous stem cell transplantation (ASCT) were a little more than patients with thrombocytopenia (25.3% \u003cem\u003evs\u003c/em\u003e 18.5%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033). Interestingly, the ratio of males to females in NDMM patients with thrombocytopenia was 1.78, while this ratio was 1.16 in patients with normal platelet count.\u003c/p\u003e \u003cp\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\u003eCharacteristics of 1363 patients with newly diagnosed multiple myeloma.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatients' characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll patients\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1363)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatients without thrombocytopenia (N\u0026thinsp;=\u0026thinsp;1152)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePatients with thrombocytopenia (N\u0026thinsp;=\u0026thinsp;211)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u003eSex\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e534 (46.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76 (36.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\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\u003e753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e618 (53.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e135 (64.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Years)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e577 (50.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91 (43.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e439 (38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77 (36.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e136 (11.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMPCs (%)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e995 (86.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e152 (72.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51 (24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum calcium (mmol/L)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1020 (88.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180 (85.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31 (14.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine (umol/L)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e\u0026lt;\u0026thinsp;177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e970 (84.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e155 (73.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e182 (15.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb (g/L)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e\u0026ge;\u0026thinsp;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e803 (69.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54 (25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e349 (30.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e157 (74.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone destruction\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3 sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e578 (50.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108 (51.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3 sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e486 (42.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84 (39.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88 (7.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (9.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS stage\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e315 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e721 (62.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186 (88.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ2-MG (mg/L)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e\u0026lt;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e716 (62.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68 (32.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e433 (37.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e142 (67.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e710 (61.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94 (44.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e˂ 35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e442 (38.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e117 (55.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISS stage\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e337 (29.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (9.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e379 (32.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49 (23.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e433 (37.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e142 (67.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003enormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e911 (79.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e136 (64.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eelevated\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e241 (20.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRCA\u003csup\u003eb\u003c/sup\u003e\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e364 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42 (19.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e403 (35.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e385 (33.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69 (32.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-ISS stage\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155 (13.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e615 (53.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82 (38.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e188 (16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83 (39.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e194 (16.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-NAIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e516 (44.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105 (49.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e636 (55.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e106 (50.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e373 (58.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45 (42.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVTD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIRD/ITD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKRD/KPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDVD/DKD/DID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (12.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRD/DPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDVRD/DKRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASCT\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e860 (74.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e172 (81.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e292 (25.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39 (18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003e\u0026lt; PR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e210 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53 (25.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVGPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e353 (30.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57 (27.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR and sCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e430 (37.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBMPCs, bone marrow plasma cells; Hb, hemoglobin; DS, Durie-Salmon staging system; β2-MG, β2 microglobulin; ISS, international staging system; LDH, lactate dehydrogenase; HRCA: high risk cytogenetic aberration; R-ISS, revised international staging system; NAIT: novel agents-based induction therapy; VRD, bortezomib, lenalidomide and dexamethasone; VTD, bortezomib, thalidomide and dexamethasone; VPD, bortezomib, pomalidomide and dexamethasone; IRD, ixazomib, lenalidomide and dexamethasone; ITD, ixazomib, thalidomide and dexamethasone; KRD, carfilzomib, lenalidomide and dexamethasone; DVD, daratumumab, bortezomib and dexamethasone; DKD, daratumumab, carfilzomib and dexamethasone; DID, daratumumab, ixazomib and dexamethasone; DRD, daratumumab, lenalidomide and dexamethasone; DPD, daratumumab, pomalidomide and dexamethasone; DVRD, daratumumab, bortezomib, lenalidomide and dexamethasone; DKRD, daratumumab, carfilzomib, lenalidomide and dexamethasone; ASCT, autologous stem cell transplantation; PR, partial response; VGPR, very good partial response; CR, complete response; sCR, stringent complete response.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ea. LDH\u0026thinsp;\u0026gt;\u0026thinsp;230 U/L in Qilu Hospital of Shandong University and Shandong Provincial Hospital Affiliated to Shandong First Medical University, LDH\u0026thinsp;\u0026gt;\u0026thinsp;250 U/L in Fujian Medical University Union Hospital.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eb. HRCA: del (17p), t (4;14), t (14;16), t (14;20), p53 mutation and 1q21 gain/amplification.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Responses to induction therapy\u003c/h2\u003e \u003cp\u003eThe overall response rates (ORR) were 86.1% and 75.8% in NDMM patients with normal and low platelet count, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). NDMM patients with normal platelet counts achieved significantly better deep response (\u0026ge;\u0026thinsp;VGPR) than thrombocytopenic patients (67.9% \u003cem\u003evs\u003c/em\u003e 50.7%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The rate of sCR plus CR (\u0026ge;\u0026thinsp;CR) in patients with normal platelet count was also significantly higher than that in patients with thrombocytopenia (37.3% \u003cem\u003evs\u003c/em\u003e 23.7%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). These results suggest that NDMM patients with thrombocytopenia have significantly worse efficacy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNAIT regimens induced remarkable superior efficacy than non-NAIT therapies in NDMM patients with normal and low platelet count (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). To evaluate whether NAIT regimens showed benefits in NDMM patients with thrombocytopenia, we next analyzed the responses to NAIT in NDMM patients and found that the deep response (\u0026ge;\u0026thinsp;VGPR) rate in patients with thrombocytopenia was significantly lower than that in patients with normal platelet count (60.4% \u003cem\u003evs\u003c/em\u003e 76.4%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), however, no significant difference was observed in OR rates between patients with or without thrombocytopenia (87.8% \u003cem\u003evs\u003c/em\u003e 92.4%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.33, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). This indicates that NAIT can improve overall response but not deep response in NDMM patients with thrombocytopenia.\u003c/p\u003e \u003cp\u003eIn NDMM patients receiving non-NAIT regimens, both overall and deep response rates in patients with thrombocytopenia were significantly lower than patients without thrombocytopenia (ORR: 63.8% \u003cem\u003evs\u003c/em\u003e 78.5%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039; \u0026ge; VGPR: 40.9% \u003cem\u003evs\u003c/em\u003e 57.6%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018; respectively, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Survival outcomes\u003c/h2\u003e \u003cp\u003eWith a median follow-up of 27 months, both PFS and OS in NDMM patients with thrombocytopenia were significantly worse than patients with normal platelet counts (median PFS: 15 months \u003cem\u003evs\u003c/em\u003e 21.5 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, median OS: 47 months \u003cem\u003evs\u003c/em\u003e 77 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). In subgroup analyses of survival, PFS and OS of patients in different DS, ISS and R-ISS stages were compared between patients with and without thrombocytopenia (Figure S2). In general, the outcomes in patients with thrombocytopenia at different stages were inferior to patients with normal platelet count.\u003c/p\u003e \u003cp\u003eInduction regimens and platelet counts were included in Kaplan-Meier analysis to further investigate the impacts of these two factors on PFS and OS. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG, among patients with NAIT regimens, both PFS and OS of patients with thrombocytopenia were significantly shorter than those of patients with normal platelet counts (median PFS: 16 months \u003cem\u003evs\u003c/em\u003e 25 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, median OS: 53 months \u003cem\u003evs\u003c/em\u003e 79 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). In patients with non-NAIT regimens, the outcomes of patients with thrombocytopenia were also significantly worse than those of patients with normal platelet counts (median PFS: 14 months \u003cem\u003evs\u003c/em\u003e 18 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, median OS: 45 months \u003cem\u003evs\u003c/em\u003e 71 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). Neither NAIT nor non-NAIT regimens improved the survival of patients with thrombocytopenia.\u003c/p\u003e \u003cp\u003eUnivariate and multivariate Cox analyses for PFS and OS were presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table S2, respectively. In multivariate analysis for PFS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), thrombocytopenia [hazard ratio (95% confidence interval, 95% CI) 1.40 (1.14\u0026ndash;1.72), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001] and R-ISS III [1.60 (1.13\u0026ndash;2.28), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009] were associated with worse PFS. In contrast, NAIT [0.52 (0.45\u0026ndash;0.60), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001], ASCT [0.71 (0.60\u0026ndash;0.85), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001] and achieving deep response (\u0026ge;\u0026thinsp;VGPR) [0.53 (0.45-0. 62), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001] were associated with better PFS. Similarly, in multivariate analysis for OS (Table S2), thrombocytopenia [1.74 (1.29\u0026ndash;2.35), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001] and R-ISS stage III [2.07 (1.10\u0026ndash;3.90), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023] were associated with worse OS. In contrast, ASCT [0.69 (0.49\u0026ndash;0.97), P\u0026thinsp;=\u0026thinsp;0.033] and achieving deep response (\u0026ge;\u0026thinsp;VGPR) [0.57 (0.45\u0026ndash;0.73), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001] were associated with better OS.\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\u003eUnivariate and multivariate Cox analyses for PFS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard Ratio (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHazard Ratio (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.88\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.83\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThrombocytopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.76 (1.46\u0026ndash;2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.40 (1.14\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u0026thinsp;\u0026ge;\u0026thinsp;2.65mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34 (1.09\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20 (0.96\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u0026thinsp;\u0026ge;\u0026thinsp;177umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40 (1.17\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.81\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb\u0026thinsp;\u0026lt;\u0026thinsp;85g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.46 (1.27\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18 (1.00-1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone destruction\u0026thinsp;\u0026lt;\u0026thinsp;3 sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone destruction\u0026thinsp;\u0026ge;\u0026thinsp;3 sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10 (0.95\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26 (0.96\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISS I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISS II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10 (0.91\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.80\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISS III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60 (1.35\u0026ndash;1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.77\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-ISS I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-ISS II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37 (1.09\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13 (0.83\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-ISS III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.38 (1.84\u0026ndash;3.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.60 (1.13\u0026ndash;2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59 (1.22\u0026ndash;2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13 (0.84\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47 (0.41\u0026ndash;0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52 (0.45\u0026ndash;0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59 (0.50\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71 (0.60\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; VGPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; VGPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.44 (0.38\u0026ndash;0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53 (0.45-0. 62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82 (0.59\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83 (0.60\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ePFS, progression free survival; 95% CI, 95% confidence interval; Hb, hemoglobin; ISS, international staging system; R-ISS, revised international staging system; NAIT: novel agents-based induction therapy; ASCT, autologous stem cell transplantation; VGPR, very good partial response.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Nomogram construction and validation\u003c/h2\u003e \u003cp\u003eBased on the results of multivariate regression analysis, thrombocytopenia, R-ISS stage, NAIT, ASCT and deep response (\u0026ge;\u0026thinsp;VGPR) were used to construct a nomogram predicting 12-month and 24-month PFS in the complete dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The baseline characteristics of training group and validation group were presented in Table S3 with no significant bias. ROC analysis was used to assess the discrimination of the nomogram. The area under the curve (AUC) for the training nomogram model demonstrated values of 0.712 at 12 months and 0.792 at 24 months, whereas the validation nomogram model yielded AUCs of 0.711 and 0.750 at 12-month and 24-month intervals, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The calibration curves for the probability of 12-month and 24-month PFS demonstrated a good agreement between the actual reported and the predicated PFS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-G). Our nomogram model effectively predicted PFS for NDMM patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this multicenter retrospective study, we analyzed the clinical characteristics, responses to induction therapy and survival of NDMM patients with and without thrombocytopenia, performed regression analysis and constructed a nomogram model to predict PFS. We found that NDMM patients with thrombocytopenia showed a larger number of bone marrow plasma cells, more advanced disease stages and worse outcomes than patients without thrombocytopenia. NAIT could improve overall response but not deep response or survival in NDMM patients with thrombocytopenia. Multivariate regression analysis proposed that thrombocytopenia together with R-ISS stage III, NAIT, ASCT and deep response were significantly correlated with survival.\u003c/p\u003e \u003cp\u003eThrombocytopenia is less common in NDMM patients and its cut-off value varies across different studies. This study conducted in China defined thrombocytopenia as platelet count less than 100 000/uL. In this situation, the incidence of thrombocytopenia in NDMM patients was approximately 15%, which was in line with previously reported data [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The presence of thrombocytopenia in NDMM patients significantly correlated with invasive clinical manifestations, including high myeloma burden, severe anemia, low albumin levels, renal failure, and elevated β2-MG and LDH levels. These factors along with HRCA are important indicators for MM disease staging and risk stratification. Charalampous et al analyzed the association of thrombocytopenia with disease burden, HRCA and survival in NDMM patients from Mayo Clinic and found that thrombocytopenia was associated with mortality independently [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. They demonstrated that thrombocytopenia was significantly associated with t(4;14) and t(14;16) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Due to the cost and accessibility of fluorescence in situ hybridization (FISH) test, part of NDMM patients in our study failed to complete FISH test, resulting in some missing data in the risk stratification of cytogenetics. We failed to present the exact correlation between thrombocytopenia and a specific cytogenetic abnormality. However, we found that NDMM patients with thrombocytopenia had a higher proportion of HRCA based on the available data. Interestingly, our study demonstrated that male NDMM patients were more likely to have thrombocytopenia. Other studies reported similar results that the percentage of male patients with thrombocytopenia was higher than that of female myeloma patients [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], suggesting that male patients were susceptible to thrombocytopenia at diagnosis. NDMM patients with or without thrombocytopenia in our study received comparable induction regimens. However, the percentage of NDMM patients with normal platelet count receiving ASCT was a little higher than patients with thrombocytopenia, mainly due to the better performance status and younger age in patients with normal platelet count.\u003c/p\u003e \u003cp\u003eSurvival in MM patients has improved significantly during the past two decades in China and around the world [\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Numerous combinations for initial therapy have been developed based on novel agents which have shown apparent efficacy [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Recent studies have established and further consolidated triplet and quadruplet regimens in the management of MM patients [\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The most commonly recommended induction regimens are triplet and quadruplet regimens consisting of proteasome inhibitors, immunomodulatory agents and monoclonal antibodies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. To investigate whether NAIT can overcome the poor prognosis associated with thrombocytopenia, we compared the outcomes and survival of NDMM patients with and without thrombocytopenia receiving NAIT regimens. We found that NDMM patients with thrombocytopenia had poor outcomes and survival. NAIT significantly improved ORR of patients with thrombocytopenia close to that of patients with normal platelet count. But unfortunately, NAIT failed to achieve a satisfactory deep response and thus did not prolong their survival in NDMM patients with thrombocytopenia. NDMM patients with normal platelet count who received NAIT had the longest PFS and OS, followed by those with normal platelet count receiving non-NAIT and those with thrombocytopenia receiving NAIT, respectively. NDMM patients with thrombocytopenia who received non-NAIT had the worst survival. NAIT regimens induced better outcomes and improved survival of NDMM patients compared with non-NAIT treatments. The improvement of quality of response is associated with better disease control and longer survival [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The achievement of maximal response should be strongly considered in eligible patients.\u003c/p\u003e \u003cp\u003eThe prognosis evaluation and risk stratification of MM patients were complex and variable [\u003cspan additionalcitationids=\"CR39 CR40 CR41\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. We performed univariate and multivariate analyses for PFS and OS, and found that thrombocytopenia, R-ISS stage III, NAIT, ASCT and deep response were significantly correlated with survival. These five factors were used to construct a nomogram to predict 12-month and 24-month PFS with reliable predictive ability. Recently, Maura F et al integrated clinical, genomic and therapeutic data to build a model predicting individualized risk in NDMM patients [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This model is an online available tool including patients\u0026rsquo; demographics, ISS, IGH translocations, genomics, induction and post-induction therapies. They developed an individualized risk-prediction model enabling personally tailored therapeutic decisions for NDMM patients. In our study, induction therapy, response to induction therapy and ASCT were also included in the construction of nomogram model based on the results of multivariate Cox regression analysis and these factors played important roles in predicting PFS. Most existing risk stratification models in multiple myeloma have not included platelet count as a laboratory feature[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], unless the MPSS risk model, which incorporates platelet count and improves the risk estimation in NDMM patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The inclusion of thrombocytopenia as a high-risk factor in the prognosis of multiple myeloma is controversial. Our results suggest that NDMM patients with thrombocytopenia have poor prognosis, similar to that of patients with high-risk MM [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur findings are based on a retrospective observational study, which has certain limitations. Firstly, the most commonly used two induction therapies in our study are VRD and VTD. The percentage of patients receiving induction regimens consisting of daratumumab and carfilzomib is relatively low. Therefore, the effects of regimens composed by monoclonal antibodies, new generation proteasome inhibitors and immunomodulatory agents in NDMM patients with thrombocytopenia need to be further confirmed. Secondly, we used random forest interpolation in constructing the nomogram and dummy variables in multifactor Cox regression to deal with some missing data. Although good internal verification results are obtained, this model needs to be further validated by external data.\u003c/p\u003e \u003cp\u003eIn conclusion, thrombocytopenia in NDMM patients significantly affects responses to induction therapy and survival. Thrombocytopenia should be regarded as an independent prognostic factor in the risk stratification of Chinese NDMM patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks to all the authors for their contributions to this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHZ and PC conceived and designed the study. XJL XXX, XHL, XLW and XL collected and assembled the data. XJL XLW, QL, XL\u0026nbsp;and ZS analyzed and verified the data. LQW, PC, JP and HZ verified and interpreted the data. All authors wrote and approved of the article and are accountable for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from National Natural Science Foundation of China (No. 82370132, No. 82070122, No. 82030005), Taishan Scholar Foundation of Shandong Province (No.ts20221157), Joint Funds for the Innovation of Science and Technology, Fujian Province (No. 2020Y9097), China Postdoctoral Science Foundation (2023M732098), and Postdoctoral Innovation Project of Shandong Province (SDCX-ZG-202302029).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethical Committee of Qilu Hospital of Shandong University and conducted in accordance with the Declaration of Helsinki. Informed consents were obtained from patients before recruitment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no competing interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCowan AJ, Allen C, Barac A, Basaleem H, Bensenor I, Curado MP, Foreman K, Gupta R, Harvey J, Hosgood HD, et al. Global Burden of Multiple Myeloma: A Systematic Analysis for the Global Burden of Disease Study 2016. JAMA Oncol. 2018;4(9):1221\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan de Donk N, Pawlyn C, Yong KL. Multiple myeloma. 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Am J Hematol. 2024;99(8):1560\u0026ndash;75.\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":"Multiple Myeloma, Prognosis, Thrombocytopenia","lastPublishedDoi":"10.21203/rs.3.rs-5872364/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5872364/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThrombocytopenia is less common but shows high risk of early mortality in newly diagnosed multiple myeloma (NDMM) patients. In the era of novel agents-based induction therapy (NAIT), it is unclear whether NAIT can overcome the poor prognosis associated with thrombocytopenia.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eTo evaluate the prognostic implications of thrombocytopenia in NDMM patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe retrospectively analyzed 1363 NDMM patients baseline characteristics, treatment response and survival, further performed regression analysis, constructed a nomogram model to predict progression free survival (PFS), and further internally validated this model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall, 211 (15.48%) NDMM patients were harboring thrombocytopenia, with advanced disease stages and worse outcomes. Their PFS (15 months \u003cem\u003evs\u003c/em\u003e 21.5 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)and overall survival (47 months \u003cem\u003evs\u003c/em\u003e 77 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly inferior compared with patients without thrombocytopenia. In NDMM receiving NAIT, the overall response (87.8% \u003cem\u003evs\u003c/em\u003e 92.4%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.33) but not deep response or survival could be improved between patients with and without thrombocytopenia. Five important variables (thrombocytopenia, R-ISS stage III, NAIT, deep response and autologous stem-cell transplantation) in multivariate Cox analysis were incorporated in the nomogram, which was further validated by internal datasets. The Calibration curve and time-dependent Receiver operating characteristic showed that the model accurately predicted the 12- and 24- months PFS of NDMM patients.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThrombocytopenia has an indispensable prognostic effect in decreasing responses to induction therapy and survival in NDMM patients. Thrombocytopenia might need to be regarded as an independent prognostic factor in risk stratification of Chinese NDMM patients.\u003c/p\u003e","manuscriptTitle":"Prognostic implications of thrombocytopenia in Chinese patients with newly diagnosed multiple myeloma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-24 18:38:01","doi":"10.21203/rs.3.rs-5872364/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":"25d72f90-646e-4073-93d7-8cbaec1df360","owner":[],"postedDate":"January 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-23T14:53:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-24 18:38:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5872364","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5872364","identity":"rs-5872364","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}