Development and validation of a new risk assessment model for immunomodulatory drug-associated venous thrombosis among Chinese patients with multiple myeloma.

BACKGROUND: Individuals with multiple myeloma (MM) receiving immunomodulatory drugs (IMiDs) are at risk of developing venous thromboembolism (VTE), a serious complication. There is no established clinical model for predicting VTE in the Chinese population. We develop a new risk assessment model (RAM) for IMiD-associated VTE in Chinese MM patients. METHODS: We retrospectively selected 1334 consecutive MM patients receiving IMiDs from 16 medical centers in China and classified them randomly into the derivation and validation cohorts. A multivariate Cox regression model was used for analysis. RESULTS: The overall incidence of IMiD-related VTE in Chinese MM patients was 6.1%. Independent predictive factors of VTE (diabetes, ECOG performance status, erythropoietin-stimulating agent use, dexamethasone use, and VTE history or family history of thrombosis) were identified and merged to develop the RAM. The model identified approximately 30% of the patients in each cohort at high risk for VTE. The hazard ratios (HRs) were 6.08 (P < 0.001) and 6.23 (P < 0.001) for the high-risk subcohort and the low-risk subcohort, respectively, within both the derivation and validation cohorts. The RAM achieved satisfactory discrimination with a C statistic of 0.64. The stratification approach of the IMWG guidelines yielded respective HRs of 1.77 (P = 0.053) and 1.81 (P = 0.063). The stratification approach of the SAVED score resulted in HRs of 3.23 (P = 0.248) and 1.65 (P = 0.622), respectively. The IMWG guideline and the SAVED score-based method yielded C statistics of 0.58 and 0.51, respectively. CONCLUSIONS: The new RAM outperformed the IMWG guidelines and the SAVED score and could potentially guide the VTE prophylaxis strategy for Chinese MM patients.

Establishment of a prediction model for disease progression within one year in newly diagnosed multiple myeloma patients.

Multiple myeloma is still an incurable disease In the past decade, with the continuous progress of treatment methods, the progression-free survival of patients has been prolonged, but some patients still progress in the early stage of the disease. Our research analyses the clinical laboratory indicators of newly diagnosed multiple myeloma (NDMM) patients, to obtain the relevant factors of disease progression within one year in MM patients and to establish a prediction model.108 MM patients treated in our hospital from January 2015 to January 2020 were retrospectively analyzed. After univariate and multivariate logistic regression analyses, the related factors of disease progression within one year in NDMM patients were obtained, and a prediction model was established.Treatment regimen containing at least two targeted drugs (OR = 0.226, 95% CI 0.068-0.753), increased lactate dehydrogenase(LDH, OR = 3.452, 95% CI 1.101-10.826) and increased serum corrected calcium(OR = 4.466, 95% CI 1.346-14.811) were identified as potential predictors by statistical analysis. The prediction model was obtained: x = -2.042-1.489 × treatment regimen (including at least two targeted drug assignment as 1, otherwise 0) + 1.239 ×LDH (U/L, lactate dehydrogenase elevation assignment as 1, normal as 0) +1.496 × serum corrected calcium (mmol/L, serum corrected calcium elevation assignment as 1, normal as 0). Receiver operating characteristic curve analysis showed that the model has good predictive performance.The possibility of disease progression within one year can be predicted by the prediction model. The model can be used as a reference for clinicians to make individualized treatment plans for patients so that patients can obtain better treatment effects.

Development and validation of a nomogram for steroid-resistance prediction in immune thrombocytopenia patients.

OBJECTIVES: Corticosteroid is first-line therapy in immune thrombocytopenia. However, nearly 30% of patients appear in steroid-resistance. Our research analyses the relevant indicators of patients and develops a risk prediction model to predict the poor response to steroid-therapy in ITP patients. METHODS: We collected data from 111 ITP patients admitted to Xiamen University Zhongshan Hospital from 2013 to 2019 as the training cohort and 65 ITP patients during 2019-2020 as the external validation cohort. Screening significant factors(P < 0.05) in univariate analysis, and further identified to be independent variables in multivariable logistic regression analysis. Incorporated the significant risk factors in and presented them with a nomogram based on independent risk predictors. The nomogram was assessed by receiver operating characteristics curves and decision curve analysis. RESULTS: We constructed a steroid-resistance prediction model based on the potential predictors including age, serum ferritin and expression of HBsAg. As a result, based on the area under the ROC curves, the training cohort (AUC: 0.718, 95% CI: 0.615-0.821) and the external validation cohort (AUC:0.799,95%CI:0.692-0.905), which displayed good discrimination. The decision curve showed that predicting the steroid-refractory risk in ITP patients using this nomogram with a range of the threshold probability between >16% and <70%. The nomogram appears good performance in predicting steroid-refractory ITP patients. CONCLUSION: Prediction model shows that elder patients with a high level of ferritin and positive expression of HBsAg may appear a high possibility of steroid-resistance. For these patients, TPO-RAs can be considered to help patients to get better treatment effects and develop a better health-related quality of life.