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The clinical and disease characteristics of patients in the model derivation sample are presented in Table 1.
Our statistical power to identify important risk factors was limited with only 43 patients developing ≥ grade 2 HFSR in our model derivation sample.
For the home care model derivation sample, the baseline hospital-ED visit rates (which reflect utilization in the 90 days prior to the independent variable measures) were significantly higher than the rates at the time of the follow-up assessment: hospital stay – 37.7% vs. 25.3%; ED – 19.8% vs. 15.7%.
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The clinical and disease characteristics of patients in the model derivation and validation samples are presented in Table 1.
A potential limitation of our investigation is that the model derivation dataset included samples obtained at different time points ranging from 1 hour to 8 weeks after MI [ 10].
If so, we derived the final prediction model from the full derivation sample [ 19].
Using the derivation sample, we modeled the overall cost of service (our dependent variable).
A CM model was developed based on a derivation sample (n = 182) and tested with a replication sample (n = 135) of adults aged 18+ with known DSL who were living in the community.
Since a model tends to perform best in the derivation sample, called 'overfitting', we used bootstrapping techniques to internally validate the model.
We assessed the calibration of predictions obtained in the EFFECT Follow-up sample (the validation sample) using models developed in the EFFECT Baseline sample (the derivation sample) in three different ways.
Second, we assessed model performance using the EFFECT Baseline sample as the derivation sample and the EFFECT Follow-up sample as the validation.
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