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. Author manuscript; available in PMC: 2018 Mar 4.
Published in final edited form as: South Med J. 2016 Sep;109(9):583–587. doi: 10.14423/SMJ.0000000000000526

Table 2.

Conditional logistic regression modeling differentiating readmitted patients from controls

Full model Final model Utilization mediation
OR (95% CI) P OR (95% CI) P OR (95% CI) P
Complications
 Avascular necrosis 6.49 (1.97–21.3)A 0.002 6.89 (2.23–21.3) <0.001 2.39 (0.957–5.99) 0.062
 Acute chest syndrome 4.00 (0.918– 17.4) 0.065 5.026 (1.24–20.4) 0.024 2.57 (0.943–7.01) 0.065
 Stroke 1.45 (0.317– 6.62) 0.632
 Deep vein thrombosis 1.47 (0.375–5.74) 0.582
 Chronic kidney disease 0.0649 (0.003– 1.41) 0.065 0.075 (0.004–1.54) 0.093 0.310 (0.046–2.09) 0.310
Comorbidities
 Asthma 1.67 (0.496– 5.63) 0.407
 Psychiatric history 3.35 (0.975– 11.5) 0.055 3.25 (0.996–10.6) 0.051 1.79 (0.719–4.44) 0.211
 Substance use disorder 2.33 (0.684– 7.90) 0.176 2.59 (0.813–8.26) 0.107 1.35 (0.474–3.86) 0.573
Utilization
 Admissions 2010 1.94 (1.40–2.68) <0.001
R2 0.233 0.226 0.336

Full model was fitted with all studied predictors. The final model was developed after an automated backward stepwise model selection procedure. The mediation by utilization model is the final model with prior year utilization added. R2 figures are adjusted for multiple predictors. CI, confidence interval; OR, odds ratio.

A

Pls delineate between italic and bold values—usually indicate statistical significance, but they both can’t mean that.