60-Day Mortality Prediction
Derived variable
Equations
The model uses:
R = PaO₂ / PaCO₂
Define the hinge function:
h(u) = max(0, u)
The Yeo–Johnson transformation for x ≥ 0 is:
YJλ(x) = ((x + 1)λ − 1) / λ when λ ≠ 0.
The model linear predictor is:
η = 0.1803450722 − 0.0239113110 × h(124.8493196754 − YJ1.1395665474(Age))
+ 4.5690181283 × h(0.5970780861 − YJ−1.1051799726(R))
+ 4.2596007650 × h(YJ−1.0302359828(Lactate) − 0.2843467765)
− 0.5602665090 × h(3.3011808232 − YJ0.1891305588(Days))
+ 4.0352484115 × h(7.3 − pH)
Predicted probability of death by 60 days:
P = 1 / (1 + e−η)
Predicted 60-day mortality = 100 × P
References
- Friedman JH. Multivariate adaptive regression splines. Ann Stat. 1991;19(1):1-67. doi:10.1214/aos/1176347963.
- Yeo IK, Johnson RA. A new family of power transformations to improve normality or symmetry. Biometrika. 2000;87(4):954-959. doi:10.1093/biomet/87.4.954.
- McLay S, et al. Five-variable diagnosis-free MARS model for prediction of 60-day mortality following referral for extracorporeal membrane oxygenation. Internal derivation and nested cross-validation analysis. 2026. Unpublished.