library(tidyverse)
library(gtsummary)
library(janitor)
library(skimr)This document contains a reproducible analysis of a simulated acute appendicitis cohort (n = 300). It was developed as a learning exercise in R/tidyverse for surgical outcomes research. Variables were designed to reflect a realistic acute care surgery dataset including patient demographics, operative characteristics, and postoperative outcomes.
set.seed(42)
n <- 300
appendicitis <- tibble(
patient_id = 1:n,
age = round(rnorm(n, mean = 38, sd = 15)) |> pmax(18) |> pmin(89),
sex = sample(c("Male", "Female"), n, replace = TRUE),
bmi = round(rnorm(n, mean = 28, sd = 6), 1) |> pmax(17) |> pmin(55),
asa_class = sample(c("I", "II", "III", "IV"), n, replace = TRUE,
prob = c(0.20, 0.45, 0.30, 0.05)),
wbc = round(rnorm(n, mean = 13.5, sd = 3.5), 1) |> pmax(4) |> pmin(30),
ct_performed = sample(c(TRUE, FALSE), n, replace = TRUE, prob = c(0.85, 0.15)),
perforation = sample(c(TRUE, FALSE), n, replace = TRUE, prob = c(0.25, 0.75)),
operative_approach = sample(c("Laparoscopic", "Open", "Converted"), n, replace = TRUE,
prob = c(0.78, 0.12, 0.10)),
op_time_min = round(rnorm(n, mean = 68, sd = 22)) |> pmax(25) |> pmin(180),
ssi = sample(c(TRUE, FALSE), n, replace = TRUE, prob = c(0.08, 0.92)),
los_days = round(rnorm(n, mean = 2.1, sd = 1.4), 1) |> pmax(0.5) |> pmin(14),
readmission_30d = sample(c(TRUE, FALSE), n, replace = TRUE, prob = c(0.06, 0.94))
) |>
mutate(
sex = factor(sex, levels = c("Male", "Female")),
asa_class = factor(asa_class, levels = c("I", "II", "III", "IV"),
ordered = TRUE),
operative_approach = factor(operative_approach,
levels = c("Laparoscopic", "Open", "Converted"))
)Four derived variables were created from the raw dataset to support downstream analysis.
appendicitis <- appendicitis |>
mutate(
# Binary integer for modeling
ssi_int = as.integer(ssi),
# Leukocytosis threshold at 11.0 x10³/µL
leukocytosis = factor(
case_when(
wbc >= 11.0 ~ "Elevated",
wbc < 11.0 ~ "Normal"),
levels = c("Normal", "Elevated")),
# WHO BMI classification
bmi_class = factor(
case_when(
bmi < 18.5 ~ "Underweight",
bmi >= 18.5 & bmi < 25 ~ "Normal",
bmi >= 25 & bmi < 30 ~ "Overweight",
bmi >= 30 ~ "Obese"),
levels = c("Underweight", "Normal",
"Overweight", "Obese"),
ordered = TRUE),
# High risk composite: perforated AND leukocytosis
high_risk = perforation == TRUE & wbc >= 11.0
)Initial dataset structure and distributional characteristics were examined prior to formal analysis.
glimpse(appendicitis)## Rows: 300
## Columns: 17
## $ patient_id <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, …
## $ age <dbl> 59, 30, 43, 47, 44, 36, 61, 37, 68, 37, 58, 72, 18,…
## $ sex <fct> Female, Female, Female, Female, Female, Male, Femal…
## $ bmi <dbl> 20.2, 26.5, 29.0, 25.6, 28.6, 26.1, 37.7, 32.3, 45.…
## $ asa_class <ord> II, IV, II, I, II, III, III, II, II, II, III, II, I…
## $ wbc <dbl> 16.8, 12.6, 13.8, 12.0, 21.1, 4.0, 13.8, 13.9, 14.2…
## $ ct_performed <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, TR…
## $ perforation <lgl> FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALS…
## $ operative_approach <fct> Laparoscopic, Converted, Laparoscopic, Laparoscopic…
## $ op_time_min <dbl> 70, 34, 64, 59, 79, 56, 79, 80, 62, 67, 120, 102, 1…
## $ ssi <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FA…
## $ los_days <dbl> 1.3, 3.4, 2.0, 0.8, 1.2, 1.3, 2.4, 2.9, 1.5, 1.6, 2…
## $ readmission_30d <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FA…
## $ ssi_int <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ leukocytosis <fct> Elevated, Elevated, Elevated, Elevated, Elevated, N…
## $ bmi_class <ord> Normal, Overweight, Overweight, Overweight, Overwei…
## $ high_risk <lgl> FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FAL…skim(appendicitis)| Name | appendicitis |
| Number of rows | 300 |
| Number of columns | 17 |
| _______________________ | |
| Column type frequency: | |
| factor | 5 |
| logical | 5 |
| numeric | 7 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| sex | 0 | 1 | FALSE | 2 | Fem: 152, Mal: 148 |
| asa_class | 0 | 1 | TRUE | 4 | II: 140, III: 86, I: 55, IV: 19 |
| operative_approach | 0 | 1 | FALSE | 3 | Lap: 235, Ope: 37, Con: 28 |
| leukocytosis | 0 | 1 | FALSE | 2 | Ele: 224, Nor: 76 |
| bmi_class | 0 | 1 | TRUE | 4 | Obe: 104, Ove: 100, Nor: 77, Und: 19 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| ct_performed | 0 | 1 | 0.83 | TRU: 249, FAL: 51 |
| perforation | 0 | 1 | 0.25 | FAL: 224, TRU: 76 |
| ssi | 0 | 1 | 0.07 | FAL: 278, TRU: 22 |
| readmission_30d | 0 | 1 | 0.07 | FAL: 279, TRU: 21 |
| high_risk | 0 | 1 | 0.19 | FAL: 243, TRU: 57 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| patient_id | 0 | 1 | 150.50 | 86.75 | 1.0 | 75.75 | 150.5 | 225.25 | 300.0 | ▇▇▇▇▇ |
| age | 0 | 1 | 38.37 | 13.60 | 18.0 | 28.00 | 38.0 | 48.00 | 79.0 | ▇▇▅▂▁ |
| bmi | 0 | 1 | 27.66 | 5.83 | 17.0 | 24.00 | 27.6 | 31.83 | 47.4 | ▅▇▆▂▁ |
| wbc | 0 | 1 | 13.51 | 3.68 | 4.0 | 10.85 | 13.8 | 15.90 | 25.7 | ▂▆▇▃▁ |
| op_time_min | 0 | 1 | 68.17 | 22.24 | 25.0 | 50.75 | 67.0 | 85.00 | 147.0 | ▅▇▆▂▁ |
| los_days | 0 | 1 | 2.22 | 1.28 | 0.5 | 1.20 | 2.1 | 3.00 | 7.0 | ▇▆▃▁▁ |
| ssi_int | 0 | 1 | 0.07 | 0.26 | 0.0 | 0.00 | 0.0 | 0.00 | 1.0 | ▇▁▁▁▁ |
Length of stay was non-normally distributed and is reported as median with interquartile range.
appendicitis |>
group_by(operative_approach) |>
summarise(
n = n(),
median_los = round(median(los_days), 2),
q25 = round(quantile(los_days, 0.25), 2),
q75 = round(quantile(los_days, 0.75), 2)
)## # A tibble: 3 × 5
## operative_approach n median_los q25 q75
## <fct> <int> <dbl> <dbl> <dbl>
## 1 Laparoscopic 235 2.1 1.2 3.05
## 2 Open 37 2.2 1.4 2.9
## 3 Converted 28 2.05 1.1 3.02appendicitis |>
group_by(perforation) |>
summarise(
n = n(),
ssi_n = sum(ssi_int),
ssi_prop = round(mean(ssi_int) * 100, 1)
)## # A tibble: 2 × 4
## perforation n ssi_n ssi_prop
## <lgl> <int> <int> <dbl>
## 1 FALSE 224 16 7.1
## 2 TRUE 76 6 7.9appendicitis |>
summarise(
n = n(),
ssi_prop = round(mean(ssi_int) * 100, 1),
readmit_prop = round(mean(readmission_30d)*100, 1),
mean_op_time = round(mean(op_time_min),1),
perf_prop = round(mean(perforation)*100, 1)
)## # A tibble: 1 × 5
## n ssi_prop readmit_prop mean_op_time perf_prop
## <int> <dbl> <dbl> <dbl> <dbl>
## 1 300 7.3 7 68.2 25.3appendicitis |>
group_by(perforation, operative_approach) |>
summarize(
n = n(),
ssi_prop = round(mean(ssi_int) * 100, 1),
.groups = "drop"
)## # A tibble: 6 × 4
## perforation operative_approach n ssi_prop
## <lgl> <fct> <int> <dbl>
## 1 FALSE Laparoscopic 177 6.8
## 2 FALSE Open 26 11.5
## 3 FALSE Converted 21 4.8
## 4 TRUE Laparoscopic 58 10.3
## 5 TRUE Open 11 0
## 6 TRUE Converted 7 0###Module 4 Exercise Plan ###Exercise 4.1 — pivot_longer(): Reshape all binary complication variables into long format ###Exercise 4.2 — group_by() + summarize() on long data: Compute complication proportions across all outcomes simultaneously — a powerful pattern for complication profile tables ###Exercise 4.3 — pivot_wider(): Reshape back to wide format — confirming round-trip integrity ###Exercise 4.4 — Applied clinical task: Generate a complication profile stratified by operative approach using the long format
##Exercise 4.1 - piovt_longer()
# Identify our binary complication variables
complications_long <- appendicitis |>
select(patient_id, operative_approach, perforation,
ssi, readmission_30d, ct_performed) |>
pivot_longer(
cols = c(ssi, readmission_30d, ct_performed),
names_to = "complication",
values_to = "value"
)
# Inspect the result
glimpse(complications_long)## Rows: 900
## Columns: 5
## $ patient_id <int> 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, …
## $ operative_approach <fct> Laparoscopic, Laparoscopic, Laparoscopic, Converted…
## $ perforation <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FA…
## $ complication <chr> "ssi", "readmission_30d", "ct_performed", "ssi", "r…
## $ value <lgl> FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALS…head(complications_long, 12)## # A tibble: 12 × 5
## patient_id operative_approach perforation complication value
## <int> <fct> <lgl> <chr> <lgl>
## 1 1 Laparoscopic FALSE ssi FALSE
## 2 1 Laparoscopic FALSE readmission_30d FALSE
## 3 1 Laparoscopic FALSE ct_performed TRUE
## 4 2 Converted FALSE ssi FALSE
## 5 2 Converted FALSE readmission_30d FALSE
## 6 2 Converted FALSE ct_performed TRUE
## 7 3 Laparoscopic FALSE ssi FALSE
## 8 3 Laparoscopic FALSE readmission_30d FALSE
## 9 3 Laparoscopic FALSE ct_performed TRUE
## 10 4 Laparoscopic FALSE ssi FALSE
## 11 4 Laparoscopic FALSE readmission_30d FALSE
## 12 4 Laparoscopic FALSE ct_performed TRUE####This is where long format becomes powerful. Instead of writing a separate summarize() call for each complication, you compute all proportions in one pipeline:
complications_long |>
filter(complication %in% c("ssi", "readmission_30d")) |>
mutate(complication = factor(complication,
levels = c("ssi", "readmission_30d"),
labels = c("SSI", "30-day Readmission"))) |>
group_by(complication) |>
summarise(
n = n(),
events = sum(value),
prop = round(mean(value) * 100, 1)
)## # A tibble: 2 × 4
## complication n events prop
## <fct> <int> <int> <dbl>
## 1 SSI 300 22 7.3
## 2 30-day Readmission 300 21 7###Exercise 4.3 — Complication Proportion Stratified by Operative Approach ####Same pattern, add operative_approach to group_by():
complications_long |>
filter(complication %in% c("ssi", "readmission_30d")) |>
mutate(complication = factor(complication,
levels = c("ssi", "readmission_30d"),
labels = c("SSI", "30-day Readmission"))) |>
group_by(operative_approach, complication) |>
summarise(
n = n(),
events = sum(value),
prop_percent = round(mean(value) * 100, 1),
.groups = "drop"
)## # A tibble: 6 × 5
## operative_approach complication n events prop_percent
## <fct> <fct> <int> <int> <dbl>
## 1 Laparoscopic SSI 235 18 7.7
## 2 Laparoscopic 30-day Readmission 235 14 6
## 3 Open SSI 37 3 8.1
## 4 Open 30-day Readmission 37 4 10.8
## 5 Converted SSI 28 1 3.6
## 6 Converted 30-day Readmission 28 3 10.7prop.test(x = 1, n = 28, conf.level = 0.95)
prop.test(x = 1, n = 28, conf.level = 0.95)
prop.test(x = 4, n = 100, conf.level = 0.95)
prop.test(x = 36, n = 1000, conf.level = 0.95)
prop.test(x = 1, n = 28, p = 0.08, conf.level = 0.95)
###Adding CI Reporting to Your R Markdown Document
# CI width narrows as N increases - same proportion
prop.test(x = 1, n = 28, conf.level = 0.95) # n=28##
## 1-sample proportions test with continuity correction
##
## data: 1 out of 28, null probability 0.5
## X-squared = 22.321, df = 1, p-value = 2.306e-06
## alternative hypothesis: true p is not equal to 0.5
## 95 percent confidence interval:
## 0.001867118 0.202390833
## sample estimates:
## p
## 0.03571429prop.test(x = 4, n = 100, conf.level = 0.95) # n=100##
## 1-sample proportions test with continuity correction
##
## data: 4 out of 100, null probability 0.5
## X-squared = 82.81, df = 1, p-value < 2.2e-16
## alternative hypothesis: true p is not equal to 0.5
## 95 percent confidence interval:
## 0.01289087 0.10511152
## sample estimates:
## p
## 0.04prop.test(x = 36, n = 1000, conf.level = 0.95) # n=1000##
## 1-sample proportions test with continuity correction
##
## data: 36 out of 1000, null probability 0.5
## X-squared = 859.33, df = 1, p-value < 2.2e-16
## alternative hypothesis: true p is not equal to 0.5
## 95 percent confidence interval:
## 0.02569362 0.05000931
## sample estimates:
## p
## 0.036# Clinically meaningful null - does converted SSI differ from overall cohort?
prop.test(x = 1, n = 28, p = 0.08, conf.level = 0.95)## Warning in prop.test(x = 1, n = 28, p = 0.08, conf.level = 0.95): Chi-squared
## approximation may be incorrect##
## 1-sample proportions test with continuity correction
##
## data: 1 out of 28, null probability 0.08
## X-squared = 0.26572, df = 1, p-value = 0.6062
## alternative hypothesis: true p is not equal to 0.08
## 95 percent confidence interval:
## 0.001867118 0.202390833
## sample estimates:
## p
## 0.03571429###Framework 2 — The Relative Width Rule #### Some epidemiologists use relative CI width as a rough heuristic: ##### Relative Width = (CI upper−CI lower)/point estimate #### For your converted SSI example: #### (17.7−0.6)/3.6 =4.75 #### The CI spans nearly 5 times the point estimate — by any standard this is uninformative. A commonly cited informal threshold is <1.0 reasonably precise, 1-2 Moderate uncertainty, report with caution, > 2.0 Wide, conclusion unreliable and if > 4.0 then the result is essentially uninformative. ##### This heuristic (mental shortcut) is not formally codified in the statistical literature to my knowledge — use it as a rapid screen, not a rigid rule.
###The Rapid Interpretation Algorithm ####In practice, when you see a CI in a paper or your own output, run through this sequence in order: ###1. Does the CI span a clinically meaningful difference? #####YES → too wide, unreliable ###2. Are there fewer than 5 events in the subgroup? ####YES → unstable, flag or combine groups ###3. Does the CI include the null and are the bounds wide? ####YES → data insufficient to conclude presence or absence of effect ###4. Is the relative width > 2? ####YES → report with explicit caution ###All NO → CI is acceptably precise for the point estimate
###exercise 4.4 return to wide fomat
complications_wide <- complications_long |>
pivot_wider(
names_from = complication,
values_from = value
)
# Confirm dimensions match original
glimpse(complications_wide)## Rows: 300
## Columns: 6
## $ patient_id <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, …
## $ operative_approach <fct> Laparoscopic, Converted, Laparoscopic, Laparoscopic…
## $ perforation <lgl> FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALS…
## $ ssi <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FA…
## $ readmission_30d <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FA…
## $ ct_performed <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, TR…###What gtsummary Does - gtsummary inspects your variable types and
applies appropriate summary statistics automatically: ### Variable Type
Default Summary ###Continuous
# Define display labels for all analysis variables
var_labels <- list(
age = "Age, years",
sex = "Sex",
bmi = "BMI, kg/m²",
asa_class = "ASA Physical Status",
wbc = "WBC, ×10³/µL",
ct_performed = "CT Obtained",
perforation = "Perforated Appendicitis",
operative_approach = "Operative Approach",
op_time_min = "Operative Time, minutes",
ssi = "Surgical Site Infection",
los_days = "Length of Stay, days",
readmission_30d = "30-day Readmission",
leukocytosis = "Leukocytosis (WBC ≥11.0)",
bmi_class = "BMI Classification",
high_risk = "High Risk (Perforated + Leukocytosis)"
)###table 1a overall cohort descriptive table
appendicitis |>
select(age, sex, bmi, bmi_class, asa_class,
wbc, leukocytosis, ct_performed,
perforation, operative_approach,
op_time_min, ssi, los_days, readmission_30d) |>
tbl_summary(
label = var_labels,
missing = "no"
) |>
bold_labels() |>
italicize_levels() |>
modify_caption("**Table 1. Cohort Characteristics (N=300)**")| Characteristic | N = 3001 |
|---|---|
| Age, years | 38 (28, 48) |
| Sex | |
| Male | 148 (49%) |
| Female | 152 (51%) |
| BMI, kg/m² | 27.6 (24.0, 31.9) |
| BMI Classification | |
| Underweight | 19 (6.3%) |
| Normal | 77 (26%) |
| Overweight | 100 (33%) |
| Obese | 104 (35%) |
| ASA Physical Status | |
| I | 55 (18%) |
| II | 140 (47%) |
| III | 86 (29%) |
| IV | 19 (6.3%) |
| WBC, ×10³/µL | 13.8 (10.8, 15.9) |
| Leukocytosis (WBC ≥11.0) | |
| Normal | 76 (25%) |
| Elevated | 224 (75%) |
| CT Obtained | 249 (83%) |
| Perforated Appendicitis | 76 (25%) |
| Operative Approach | |
| Laparoscopic | 235 (78%) |
| Open | 37 (12%) |
| Converted | 28 (9.3%) |
| Operative Time, minutes | 67 (51, 85) |
| Surgical Site Infection | 22 (7.3%) |
| Length of Stay, days | 2.10 (1.20, 3.00) |
| 30-day Readmission | 21 (7.0%) |
| 1 Median (Q1, Q3); n (%) | |
###table 1b stratified by perforation status
appendicitis |>
select(age, sex, bmi, bmi_class, asa_class,
wbc, leukocytosis, ct_performed,
operative_approach, op_time_min,
ssi, los_days, readmission_30d,
perforation) |>
tbl_summary(
by = perforation,
label = var_labels,
missing = "no"
) |>
bold_labels() |>
italicize_levels() |>
modify_header(
stat_1 ~ "**Non-Perforated** \nN={n}",
stat_2 ~ "**Perforated** \nN={n}"
) |>
modify_caption("**Table 1. Cohort Characteristics Stratified by Perforation Status**")| Characteristic | Non-Perforated N=2241 |
Perforated N=761 |
|---|---|---|
| Age, years | 37 (27, 46) | 40 (30, 51) |
| Sex | ||
| Male | 104 (46%) | 44 (58%) |
| Female | 120 (54%) | 32 (42%) |
| BMI, kg/m² | 27.0 (23.3, 31.9) | 28.9 (25.9, 31.9) |
| BMI Classification | ||
| Underweight | 15 (6.7%) | 4 (5.3%) |
| Normal | 66 (29%) | 11 (14%) |
| Overweight | 67 (30%) | 33 (43%) |
| Obese | 76 (34%) | 28 (37%) |
| ASA Physical Status | ||
| I | 42 (19%) | 13 (17%) |
| II | 105 (47%) | 35 (46%) |
| III | 65 (29%) | 21 (28%) |
| IV | 12 (5.4%) | 7 (9.2%) |
| WBC, ×10³/µL | 13.8 (10.7, 16.1) | 14.1 (11.1, 15.8) |
| Leukocytosis (WBC ≥11.0) | ||
| Normal | 57 (25%) | 19 (25%) |
| Elevated | 167 (75%) | 57 (75%) |
| CT Obtained | 187 (83%) | 62 (82%) |
| Operative Approach | ||
| Laparoscopic | 177 (79%) | 58 (76%) |
| Open | 26 (12%) | 11 (14%) |
| Converted | 21 (9.4%) | 7 (9.2%) |
| Operative Time, minutes | 67 (53, 86) | 69 (49, 81) |
| Surgical Site Infection | 16 (7.1%) | 6 (7.9%) |
| Length of Stay, days | 2.10 (1.20, 3.10) | 2.10 (1.30, 2.95) |
| 30-day Readmission | 16 (7.1%) | 5 (6.6%) |
| 1 Median (Q1, Q3); n (%) | ||
###Table 1C — Add Overall Column and P-values
appendicitis |>
select(age, sex, bmi, bmi_class, asa_class,
wbc, leukocytosis, ct_performed,
operative_approach, op_time_min,
ssi, los_days, readmission_30d,
perforation) |>
tbl_summary(
by = perforation,
label = var_labels,
missing = "no"
) |>
add_overall() |>
add_p() |>
bold_labels() |>
italicize_levels() |>
bold_p(t = 0.05) |>
modify_header(
stat_1 ~ "**Non-Perforated** \nN={n}",
stat_2 ~ "**Perforated** \nN={n}"
) |>
modify_caption("**Table 1. Cohort Characteristics Stratified by Perforation Status**")| Characteristic | Overall N = 3001 |
Non-Perforated N=2241 |
Perforated N=761 |
p-value2 |
|---|---|---|---|---|
| Age, years | 38 (28, 48) | 37 (27, 46) | 40 (30, 51) | 0.11 |
| Sex | 0.084 | |||
| Male | 148 (49%) | 104 (46%) | 44 (58%) | |
| Female | 152 (51%) | 120 (54%) | 32 (42%) | |
| BMI, kg/m² | 27.6 (24.0, 31.9) | 27.0 (23.3, 31.9) | 28.9 (25.9, 31.9) | 0.11 |
| BMI Classification | 0.035 | |||
| Underweight | 19 (6.3%) | 15 (6.7%) | 4 (5.3%) | |
| Normal | 77 (26%) | 66 (29%) | 11 (14%) | |
| Overweight | 100 (33%) | 67 (30%) | 33 (43%) | |
| Obese | 104 (35%) | 76 (34%) | 28 (37%) | |
| ASA Physical Status | 0.7 | |||
| I | 55 (18%) | 42 (19%) | 13 (17%) | |
| II | 140 (47%) | 105 (47%) | 35 (46%) | |
| III | 86 (29%) | 65 (29%) | 21 (28%) | |
| IV | 19 (6.3%) | 12 (5.4%) | 7 (9.2%) | |
| WBC, ×10³/µL | 13.8 (10.8, 15.9) | 13.8 (10.7, 16.1) | 14.1 (11.1, 15.8) | >0.9 |
| Leukocytosis (WBC ≥11.0) | >0.9 | |||
| Normal | 76 (25%) | 57 (25%) | 19 (25%) | |
| Elevated | 224 (75%) | 167 (75%) | 57 (75%) | |
| CT Obtained | 249 (83%) | 187 (83%) | 62 (82%) | 0.7 |
| Operative Approach | 0.8 | |||
| Laparoscopic | 235 (78%) | 177 (79%) | 58 (76%) | |
| Open | 37 (12%) | 26 (12%) | 11 (14%) | |
| Converted | 28 (9.3%) | 21 (9.4%) | 7 (9.2%) | |
| Operative Time, minutes | 67 (51, 85) | 67 (53, 86) | 69 (49, 81) | 0.4 |
| Surgical Site Infection | 22 (7.3%) | 16 (7.1%) | 6 (7.9%) | 0.8 |
| Length of Stay, days | 2.10 (1.20, 3.00) | 2.10 (1.20, 3.10) | 2.10 (1.30, 2.95) | 0.9 |
| 30-day Readmission | 21 (7.0%) | 16 (7.1%) | 5 (6.6%) | 0.9 |
| 1 Median (Q1, Q3); n (%) | ||||
| 2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test | ||||