3. Any thing can predict 90day newonset encephalopathy? (initial tips diameter? Begininig portosystemic pressure? Post portosystemic pressure? Or changes in portosystemic pressure? Pre MELD)
fit <- glm(newen90d~initialtipsdiameter, subset(dat, pretipsencephalopathy==0), family="binomial" )
summary(fit)$coeff
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.9796764 3.4579338 0.5725027 0.5669815
## initialtipsdiameter -0.3563711 0.5073927 -0.7023575 0.4824563
fit <- glm(newen90d~beginningportosystemicpressure, subset(dat, pretipsencephalopathy==0), family="binomial" )
summary(fit)$coeff
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 6.3493124 3.4959249 1.816204 0.0693391
## beginningportosystemicpressure -0.3210636 0.1720536 -1.866068 0.0620319
fit <- glm(newen90d~endportosystemicpressure, subset(dat, pretipsencephalopathy==0), family="binomial" )
summary(fit)$coeff
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.7803696 1.9023462 0.4102143 0.6816488
## endportosystemicpressure -0.1124009 0.1822702 -0.6166721 0.5374510
fit <- glm(newen90d~changeportalpressure, subset(dat, pretipsencephalopathy==0), family="binomial" )
summary(fit)$coeff
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.4446686 1.6291978 1.500535 0.13347584
## changeportalpressure -0.2601186 0.1547656 -1.680727 0.09281605
fit <- glm(newen90d~premeldscore, subset(dat, pretipsencephalopathy==0), family="binomial" )
summary(fit)$coeff
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.01341996 1.1012104 -0.01218656 0.9902768
## premeldscore -0.03243652 0.0701083 -0.46266297 0.6436060
fit <- glm(newen90d~preproceduralcreatinine, subset(dat, pretipsencephalopathy==0), family="binomial" )
summary(fit)$coeff
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1725482 0.8278545 0.2084282 0.8348946
## preproceduralcreatinine -0.4836378 0.6596466 -0.7331771 0.4634504
fit <- glm(newen90d~pretotalbilirubinmgdl, subset(dat, pretipsencephalopathy==0), family="binomial" )
summary(fit)$coeff
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.48930248 0.52693253 -0.9285866 0.3531034
## pretotalbilirubinmgdl 0.03403622 0.08279709 0.4110799 0.6810140
fit <- glm(newen90d~preinr, subset(dat, pretipsencephalopathy==0), family="binomial" )
summary(fit)$coeff
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4899400 1.967112 -0.24906570 0.8033100
## preinr 0.0889831 1.396271 0.06372911 0.9491859
