Predicting adverse events for risk stratification of chemotherapy based stem cell mobilization in multiple myeloma - Scorecard - MDSpire

Predicting adverse events for risk stratification of chemotherapy based stem cell mobilization in multiple myeloma

  • By

  • F. Schwarz

  • L. Levien

  • M. Maulhardt

  • G. Wulf

  • N. Brökers

  • E. Aydilek

  • February 3, 2026

  • 0 min

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Clinical Scorecard: Forecasting Complications for Risk Assessment of Chemotherapy-Induced Stem Cell Mobilization in Multiple Myeloma

At a Glance

CategoryDetail
ConditionMultiple myeloma undergoing autologous stem-cell transplantation
Key MechanismsChemotherapy-based stem-cell mobilization with risk of severe adverse events requiring hospitalization
Target PopulationPatients with multiple myeloma eligible for autologous stem-cell transplantation
Care SettingInpatient and potential outpatient stem-cell mobilization protocols

Key Highlights

  • 97% of patients achieved successful stem-cell collection despite high incidence of severe adverse events (69%) requiring hospitalization.
  • Machine learning models can predict certain adverse events (e.g., elevated creatinine with ROC-AUC 1.0) and forecast timing of adverse event onset with a mean error of about one day.
  • Risk-stratified outpatient stem-cell mobilization protocols could reduce inpatient bed usage by at least one third without compromising patient safety.

Guideline-Based Recommendations

Diagnosis

  • Assess eligibility for autologous stem-cell transplantation in multiple myeloma patients.
  • Monitor renal function and neutropenic fever risk during stem-cell mobilization.

Management

  • Consider inpatient chemotherapy-based stem-cell mobilization as standard care.
  • Implement risk-stratified outpatient mobilization protocols guided by predictive modeling to optimize resource use and patient safety.

Monitoring & Follow-up

  • Closely monitor for severe adverse events requiring hospitalization during mobilization.
  • Use predictive models to anticipate adverse event onset timing for optimized ward management.

Risks

  • High incidence of severe adverse events (69%) including neutropenic fever and elevated creatinine.
  • Challenges remain in accurately predicting neutropenic fever (ROC-AUC 0.67).

Patient & Prescribing Data

109 multiple myeloma patients undergoing chemotherapy-based stem-cell mobilization

High success rate of stem-cell collection with significant risk of severe adverse events; predictive analytics can guide safe outpatient mobilization.

Clinical Best Practices

  • Use machine learning risk stratification to identify patients suitable for outpatient stem-cell mobilization.
  • Maintain inpatient care for high-risk patients to manage severe adverse events promptly.
  • Integrate predictive timing of adverse events into clinical workflows to optimize hospital resource allocation.

References

Original Source(s)

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