## Warning: package 'ggplot2' was built under R version 4.0.5
## Warning: package 'GGally' was built under R version 4.0.5

table one # of patients age gender days elapsed avg follow up

BY when second surgery occurred

Lumped together

Table 1: Patient info and time data
variable Total Population Separate Day Surgery Same Day Surgery
Volume
Patients 54 28 26
Patient Info
Age mean (SD) 61.4 / 10.9 61.2 / 10.9 61.6 / 11.1
Male N(%) 23 / 43 15 / 54 8 / 31
Interval Time
Days Elapsed 105.6 / 186.1 203.6 / 217.4 0 / 0
Follow up Time 653.9 / 814.9 721.4 / 901.9 581.2 / 720.2

table two Pain 2 ROM 2 Pain 8 ROM 8 Pain 4mo ROM 4mo Revision All cause revision

BY when second surgery occurred

Lumped together

Table 2: Clinical Outcomes
variable Total Population Separate Day Surgery Same Day Surgery
Volume
Patients 54 28 26
Clinical Outcomes
Pain at Week 2 5.8 / 2.3 5.4 / 2.2 6.3 / 2.4
ROM at Week 2 88.8 / 19 88.5 / 15.2 89.1 / 22.6
Pain at Week 8 4.2 / 2.6 3.5 / 2.4 5.1 / 2.6
ROM at Week 8 107 / 18 106.2 / 17.9 108 / 18.5
Pain at Month 4 3.2 / 3.2 3.1 / 3.1 3.2 / 3.3
ROM at Month 4 120.9 / 16.5 120 / 19.4 121.8 / 13.5
Revision 10 4 6
All Cause Revision 12 6 6

Table 3: staged surgery patients

Lumped together

Table 3: Clinical Outcomes base on time between surgery
variable Total Population Within 4 Months 4 Month Delay
Volume
Patients 28 12 16
Clinical Outcomes
Pain at Week 2 5.4 / 2.2 4.7 / 2.2 6 / 2.1
ROM at Week 2 88.5 / 15.2 87 / 11.8 89.5 / 17.5
Pain at Week 8 3.5 / 2.4 3 / 2.3 4.1 / 2.5
ROM at Week 8 106.2 / 17.9 107 / 19 105.6 / 17.7
Pain at Month 4 3.1 / 3.1 1 / 0.8 4.3 / 3.4
ROM at Month 4 120 / 19.4 121.2 / 23.4 118.9 / 16.4
Revision 4 2 2
All Cause Revision 6 3 3 
#pain trend
data_pain2$ID <- as.factor(data_pain2$ID)

data_pain2 %>%
  ggplot( aes(x=week, y=number, group=ID, color=ID)) +
    geom_line() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14)
    ) +
    ggtitle("Pain trend: 16 weeks post op") +
  theme_minimal()
## Warning: Removed 60 row(s) containing missing values (geom_path).

#ROM trend
data_rom2$ID <- as.factor(data_rom2$ID)

data_rom2 %>%
  ggplot( aes(x=week, y=number, group=ID, color=ID)) +
    geom_line() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14)
    ) +
    ggtitle("Range of motion trend: 16 weeks post op") +
  theme_minimal()
## Warning: Removed 34 row(s) containing missing values (geom_path).

log model

logmodel <- glm(revision ~ #Age +
                gender +
                same_day,
                family = binomial,
                data = data_select2)
summary(logmodel)
## 
## Call:
## glm(formula = revision ~ gender + same_day, family = binomial, 
##     data = data_select2)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.8459  -0.6674  -0.6162  -0.4785   2.1091  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  -2.1097     0.7135  -2.957  0.00311 **
## gender        0.5447     0.7346   0.741  0.45842   
## same_day      0.7213     0.7420   0.972  0.33101   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 51.750  on 53  degrees of freedom
## Residual deviance: 50.506  on 51  degrees of freedom
## AIC: 56.506
## 
## Number of Fisher Scoring iterations: 4
odds_ci_overall <- as.data.frame(exp(cbind(Odds = coef(logmodel),
          confint(logmodel,
                  level = 0.95))))
## Waiting for profiling to be done...