Background: Administration of autologous bone marrow-derived mononuclear cells (BM-MNC) resulted in favorable outcomes after myocardial infarction (MI) compared to placebo in the REPAIR-AMI trial. Pre-clinical studies have shown that individual risk factors negatively influence cell function. To date, it is not known how these risk factors modify treatment effect in clinical trials and if potential (non-)responders can be identified. Aim: To investigate the effect of individual risk factors on functional outcome after BM-MNC therapy and to establish a prediction model for treatment response in patients with MI. Methods: Data from the REPAIR-AMI trial were used, consisting of 186 patients who had complete baseline and follow-up measurements. We performed univariable and multivariable linear regression with 18 predefined baseline characteristics using the difference in baseline EF and after 4 months (ΔEF4) as primary outcome. Our main goal was to identify interaction terms of predictors with BM-MNC treatment; i.e. effect modifiers of treatment response. An individual estimate of treatment effect over placebo (ΔΔEF4) was created by extrapolating an ‘untreated’ reference value for ΔEF4, based on the placebo-arm and comparing it to the observed value after treatment. Treatment response was defined as a ΔΔEF4 of >5% (so 5% over a predicted placebo value). Discrimination was quantified by the area under the ROC-curve (AUC). Results: The predictors age, weight, baseline EF and ESV showed an interaction with cell treatment in multivariable analysis with backward selection. Subsequently, the prediction model for individual ΔΔEF4 included age (-0.18/year, p=0.008), weight (+0.16/kg, p=0.01), baseline EF (-0.46/%, p=0.0001) and ESV (-0.09/ml, p=0.06). The predictive capacity for response to therapy showed an AUC of 79.2% (95% CI 69.7-88.6), which retained 74.8% (95% CI 65.0-84.5) after shrinkage. Conclusion: Response to BM-MNC administration after MI is influenced by age, weight, and baseline cardiac parameters. Using these continuous predictors, potential treatment responders can be accurately identified. Interestingly, an adverse risk-factor profile was associated with greater response, with potential implications for future patient selection.