Table 2.

Logistic regression analysis to identify independent pre-operative predictors of good to excellent satisfaction with hospital stay

Predictors in model	Odds ratio	95% CI		p value
		Lower	Upper
Gender	1.09	0.82	1.37	0.57
Age	1.01	1.00	1.03	0.15
Comorbidity
Heart disease	0.76	0.53	1.12	0.14
Hypertension	0.86	0.65	1.14	0.30
Lung disease	0.89	0.57	1.33	0.60
Vascular disease	0.71	0.38	1.33	0.28
Neurological disease	1.19	0.58	1.90	0.64
Diabetes mellitus	0.85	0.56	1.25	0.42
Gastric ulceration	1.40	0.66	2.16	0.38
Kidney disease	0.38	0.16	1.27	0.03
Liver disease	10.47	1.13	12.70	0.05
Anaemia	1.74	0.87	2.43	0.12
Back pain	0.75	0.56	1.04	0.049
Depression	0.80	0.53	1.21	0.28
Length of stay	0.99	0.94	1.03	0.56
Prosthesis
PFC	Reference
Triathlon	0.91	0.69	1.22	0.53
Kinemax	1.25	0.76	2.05	0.38
Functional measures
OKS	1.01	0.99	1.04	0.27
SF-12 PCS	1.00	0.97	1.02	0.82
SF-12 MCS	1.03	1.02	1.04	<0.001

All variables (in Table 1) were all entered into the model using “enter” methodology (Nagelkerke R ² = 0.06)

Significant values (p < 0.05) have been highlighted in bold