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Dichotomization of ICU length of stay based on model calibration

Dichotomization of ICU length of stay based on model calibration. Marion Verduijn , Niels Peek, Frans Voorbraak, Evert de Jonge, Bas de Mol. ICU length of stay (LOS). Important outcome after cardiac surgery Predictive models for identification of high risk patients

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Dichotomization of ICU length of stay based on model calibration

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  1. Dichotomization of ICU length of stay based on model calibration Marion Verduijn, Niels Peek, Frans Voorbraak, Evert de Jonge, Bas de Mol

  2. ICU length of stay (LOS) Important outcome after cardiac surgery Predictive models for • identification of high risk patients • case load planning and resource allocation

  3. Main objective Development of a predictive model to estimate the risk of long ICU LOS using the method of class probability trees

  4. Data 2063 patients (Academic Medical Center, Amsterdam, 1997-2001) • preoperative (e.g., age, gender) • operative (e.g., surgery type, duration) • first 24h ICU stay (e.g., blood pressure, temperature) (122 patients died: 5.2%)

  5. Problem of outcome definition How should we define the outcome ‘long ICU LOS’? Literature: outcome dichotomized based on threshold values of 2-10 days without motivation or based on simple statistics

  6. Objective of this study Selection of the threshold value to dichotomize ICU LOS in a structured fashion based on data analysis

  7. Approach • Development of tree models for outcomes defined with different threshold values • Calculation of the model performance • Selection of the best model

  8. First results † patients with ICU LOS higher than the threshold value of death ‡ determined using 10-fold cross-validation

  9. Distances between probabilities I

  10. Distances between probabilities I

  11. Distances between probabilities I

  12. ALOR distance Distance between two probabilities for a given x Absolute Log-Odds Ratio

  13. Distances between probabilities II Property of ALOR: approximate proportional equivalence

  14. MALOR statistic Distance measure for all elementsin F Mean value of the Absolute Log-Odds Ratio (MALOR)  quantifies model calibration

  15. Procedure of threshold selection 1) define a set of possible threshold values T 2) for all threshold values t in T do a) define the dichotomized outcome Yt using threshold t b) build a predictive model Mtfor outcome Yt c) compute DMALOR(Mt , Pt) 3) select threshold value with minimal MALOR statistic

  16. Additional results † patients with ICU LOS higher than the threshold value of death ‡ determined using 10-fold cross-validation

  17. Tree model for ‘ICU LOS>5 days or death’

  18. Discussion and conclusions • Class probability trees to identify high risk groups • Performance measure should be insensitive to class unbalance when comparing models for different prediction problems

  19. Marion Verduijn m.verduijn@amc.uva.nl

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