TABLE II.

The Variables Selected by Lasso Penalized Logistic Regression and Corresponding Coefficients for Predicting Each of the Outcomes^*

Variable	Model
	Periprosthetic Joint Infection	Dislocation	Periprosthetic Fracture	Death
Intercept	−8.576	−6.801	−9.138	−7.017
ASA class (per class)	0.387	0.459	0.404	0.491
Male sex (1 if yes, 0 if no)	0.444	—	—	—
Age (per 10 years)	—	—	0.244	0.104
BMI (per kg/m2)	0.103	—	—	—
Preoperative diagnosis: fracture (1 if yes, 0 if no)	—	0.861	—	0.878
Previous contributing operations (1 if any, 0 if no)	—	0.675	—	—
Surgical approach: posterior (1 if yes, 0 if no)	—	0.606	—	—
Anesthesia: general (1 if yes, 0 if no)	0.636	—	—	—
Fixation: cementless (1 if yes, 0 if no)	—	—	1.479	—
Head diameter 32 mm (1 if yes, 0 if no)	—	0.355	—	—
Example calculations†
Raw score (sum of patient value × coefficient)	4.681	2.844	4.350	3.058
Transformed score = $\frac{1}{1+{\text{ e}}^{-(\text{Intercept }+\text{ Raw score})}}$	0.020 or 2.0%	0.019 or 1.9%	0.008 or 0.8%	0.019 or 1.9%

^*The coefficients indicate the impact of 1-unit change in a predictor variable, given in parentheses, on the response variable when the other predictors are held constant. Fields without a numerical value indicate that the indicated variable is not needed for predicting the risk of the designated outcome (i.e., regression coefficient equals zero).

^†Example calculations are given for a 68-year-old female patient with ASA class III, BMI of 28 kg/m², preoperative fracture diagnosis, and no previous contributing operations for a surgical procedure performed using a posterior surgical approach, general anesthesia, and cementless fixation to install an implant with a head diameter of >32 mm.