Table 1.
Model input parameters for transitional probabilities
| Transitional Probability | Input Data | AVF | AVG | CVC | Distribution Typea |
|---|---|---|---|---|---|
| Probability of access infection | Monthly rate of bacteremia per 1000 patient-yr, mean (SD) (20) | 70.3 (23.5) | 127 (44.2) | 258.7 (97.2) | γ |
| Probability of compromised patency of access | Accesses requiring intervention over 12 mo (8,21), % | 35 | 34.3 | 47.5b | β |
| Probability of successful intervention to restore patency | Accesses with patency after intervention (22), % | 29 | 29 | N/A | β |
| Probability of access maturation | Mature accesses (of eligible accesses) (4), % | Cycle dependentc | Cycle dependentc | N/A | β |
| Probability of death | Death hazard ratio (95% CI) (23) | Reference | 1.39 (1.32 to 1.47) | 2.18 (2.11 to 2.26) | Log normal |
Data sources for input parameters are indicated by references. AVF, arteriovenous fistula; AVG, arteriovenous graft; CVC, central venous catheter; N/A, not applicable; 95% CI, 95% confidence interval.
For our Monte Carlo simulations, most input parameters were converted into probability distributions. The probability distribution types used were γ, β, and log normal. We converted infection rate into a γ-probability distribution by using mean (and SD) infection rates to calculate shape and rate parameter (α and β) values. We converted compromised patency, treatment of patency, and access maturation proportions into β-probability distributions by using the proportions as α (or β)-parameter values. We converted risk of death into a log-normal probability distribution by using the natural log of a hazard ratio and 95% CI to determine mean and SD parameter values. These parameter values were applied in specific formulas to create the probability distributions.
There is no distribution for probability of compromised CVC patency.
The probability of access maturation changed with each cycle of the Markov model.