Risk factor analysis and prediction model construction for delayed erythroid hematopoietic reconstitution after allogeneic hematopoietic stem cell transplantation: a single-center retrospective study - Report - MDSpire
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Risk factor analysis and prediction model construction for delayed erythroid hematopoietic reconstitution after allogeneic hematopoietic stem cell transplantation: a single-center retrospective study
Clinical Report: Predictive Model for Delayed Erythroid Recovery Post-HSCT
Overview
This study identifies independent risk factors for delayed erythroid hematopoietic recovery (DEHR) following allogeneic hematopoietic stem cell transplantation (allo-HSCT) and constructs a predictive model. Key factors include pre-transplant ferritin levels, anemia severity, and donor-recipient ABO compatibility.
Background
Delayed erythroid hematopoietic reconstitution (DEHR) is a significant clinical issue following allo-HSCT, impacting patient recovery and outcomes. Understanding the risk factors associated with DEHR is important for identifying high-risk patients.
Data Highlights
Risk Factor
Odds Ratio (OR)
95% Confidence Interval (CI)
Ferritin Level Before Transplantation
1.001
1.000-1.001
Severity of Anemia Before Transplantation
3.740
1.115-12.541
ABO Compatibility
3.283
1.262-8.540
Key Findings
Ferritin level before transplantation is an independent risk factor for DEHR (OR = 1.001).
Severity of anemia before transplantation significantly increases the risk of DEHR (OR = 3.740).
Compatibility of donor and recipient ABO blood types is associated with DEHR risk (OR = 3.283).
The predictive model achieved an AUC of 0.835, indicating good discrimination.
Calibration curve slope approached 1, indicating good calibration of the model.
Nagelkerke R² value of 0.349 suggests moderate explanatory power of the model.
Clinical Implications
The identification of these risk factors allows for monitoring of patients undergoing allo-HSCT.
Conclusion
The developed nomogram provides a tool for predicting DEHR after allo-HSCT, aiding in the identification of patients at high risk.