Abstract
Introduction: Heart failure (HF) is an uncommon but one of the most serious types of cardiovascular complications after allogeneic hematopoietic stem cell transplantation (allo-HSCT). However, the prognostic factors of HF after allo-HSCT remain unknown, and there is currently no prognostic model for HF after allo-HSCT. In this study, we investigated the clinical features and prognosis of patients with HF after allo-HSCT. A prognostic model based on clinical biomarkers, which combined variables that are readily available in clinical practice, that is capable of predicting 2-month mortality of HF patients after allo-HSCT was developed and validated.
Methods: In our retrospective study, 154 patients with HF after allo-HSCT from 2008 to 2021 were identified. One hundred patients were assigned to the derivation cohort and the remaining 54 patients were assigned to the external validation cohort according to the time of transplantation. We first calculated the univariable association of each variable with 2-month mortality in the derivation cohort. Second, the variables with p values less than 0.1 in the univariate analysis were further included as candidate predictors in the multivariate analysis using a backward stepwise logistic regression model. The variables that remained in the final model based on the outcomes of the multivariate analysis in the derivation cohort were identified as independent prognostic factors. A scoring system to predict the prognosis of HF after allo-HSCT was also established, and scores were assigned to the prognostic factors based on the regression coefficient.
Results: Using multivariable logistic regression methods with stepwise variable selection, four highly significant independent prognostic factors for the two-month mortality of HF were identified: pulmonary infection (p=0.005; odds ratio [OR], 6.89; 95% confidence interval [CI], 1.93-29.13), Grades III to IV acute graft-versus-host disease (severe aGVHD) (p=0.033; odds ratio [OR], 3.62; 95% confidence interval [CI], 1.15-12.69), lactate dehydrogenase (LDH)>426 U/L (p=0.049; odds ratio [OR], 3.02; 95% confidence interval [CI], 1.00-9.32) and brain natriuretic peptide (BNP)>1799 pg/ml (p=0.026; odds ratio [OR], 3.44; 95% confidence interval [CI], 1.16-10.56). We also included cardiac troponin I (cTNI) (p=0.069; odds ratio [OR], 2.93, 95% confidence interval [CI], 0.94-29.13) in our final 5-predictor prognostic model. A risk grading model termed the BLIPS score (BNP, LDH, cardiac troponin I, pulmonary infection, severe aGVHD) was constructed according to the regression coefficients. BNP level>1799 pg/mL, LDH level>426 U/L, cTNI level>100 pg/ml, and severe aGVHD were each assigned one point, and pulmonary infection was assigned two points. The points scored for each of these five factors were added to yield the overall risk score, which ranged from zero to six. Patients were stratified into a low-risk group (0-2 points), an intermediate-risk group (3-4 points) and a high-risk group (5-6 points). The validated internal c-statistic was 0.870 (95% CI, 0.798-0.942), and the external c-statistic was 0.882 (95% CI, 0.791-0.973). According to the calibration plots, the model-predicted probabilities showed a good correlation with the actual observed frequencies. Decision curve analysis indicated that the clinical implementation of the prognostic model could benefit HF patients. The Kaplan‒Meier estimations of overall survival revealed good separation between these risk groups. We generated a risk heatmap for the BLIPS model to visualize the risk estimates derived from the individual prognostic factor profile.
Conclusions: Patients who develop HF after allo-HSCT have a poor prognosis. An integrated prognostic model based on clinical biomarkers (BLIPS) was developed and externally validated, and this is the first straightforward scoring model that incorporates clinical and laboratory risk factors to evaluate the 2-month mortality of patients with HF after receiving allo-HSCT to treat hematological malignancies. This model can be effectively utilized to help improve the survival and prognosis of HF patients by accelerating the early identification of patients at a high risk of mortality and contributing to the appropriate implementation of urgent medical support.
Disclosures: No relevant conflicts of interest to declare.
Disclosures
No relevant conflicts of interest to declare.
Author notes
Asterisk with author names denotes non-ASH members.
