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. 2020 Dec 15;44(2):321–325. doi: 10.2337/dc20-0150

Table 3.

Multivariate logistic regression model for long-term diabetes remission after RYGB considering baseline variables and postoperative weight loss data

Variables OR 95% CI P value z value
Sex (male vs. female) 1.22 0.72–2.06 0.465 0.730
Age at surgery 0.98 0.95–1.00 0.088 1.707
BMI before surgery 1.01 0.98–1.04 0.550 0.597
Percent total weight loss in long term 1.05 1.03–1.08 <0.001 4.317
Duration of diabetes 0.86 0.81–0.91 <0.001 5.093
Preoperative insulin use (yes vs. no) 0.32 0.18–0.57 <0.001 3.825
Preoperative number of diabetes medications 0.49 0.36–0.67 <0.001 4.526
Poor glycemic control (HbA1c >7%) (yes vs. no) 0.45 0.26–0.76 0.003 2.978
Steatosis (yes vs. no) 2.21 1.14–4.31 0.020 2.335
Lobular inflammation (yes vs. no) 1.19 0.66–2.11 0.565 0.576
Hepatocyte ballooning (yes vs. no) 0.67 0.35–1.31 0.244 1.165
Fibrosis (yes vs. no) 0.97 0.53–1.77 0.910 0.113

N = 505, as long-term weight loss data could not be retrieved for 14 patients. z value is the absolute value of regression coefficient divided by its SE. A larger z value indicates a stronger statistical relation of factor on outcome. As a rule of thumb, if the absolute value of the z value is larger than cutoff value of 2.0, the variable is significant. Significant P values (P < 0.05) and z values (z > 2.0) are presented in bold.