ICU {vcdExtra}

ICU data set

Description

The ICU data set consists of a sample of 200 subjects who were part of a much larger study on survival of patients following admission to an adult intensive care unit (ICU), derived from Hosmer, Lemeshow and Sturdivant (2013) and Friendly (2000).

The major goal of this study was to develop a logistic regression model to predict the probability of survival to hospital discharge of these patients and to study the risk factors associated with ICU mortality. The clinical details of the study are described in Lemeshow, Teres, Avrunin, and Pastides (1988).

This data set is often used to illustrate model selection methods for logistic regression.

Usage

data(ICU)

Format

A data frame with 200 observations on the following 22 variables.

died

Died before discharge?, a factor with levels No Yes

age

Patient age, a numeric vector

sex

Patient sex, a factor with levels Female Male

race

Patient race, a factor with levels Black Other White. Also represented here as white.

service

Service at ICU Admission, a factor with levels Medical Surgical

cancer

Cancer part of present problem?, a factor with levels No Yes

renal

History of chronic renal failure?, a factor with levels No Yes

infect

Infection probable at ICU admission?, a factor with levels No Yes

cpr

Patient received CPR prior to ICU admission?, a factor with levels No Yes

systolic

Systolic blood pressure at admission (mm Hg), a numeric vector

hrtrate

Heart rate at ICU Admission (beats/min), a numeric vector

previcu

Previous admission to an ICU within 6 Months?, a factor with levels No Yes

admit

Type of admission, a factor with levels Elective Emergency

fracture

Admission with a long bone, multiple, neck, single area, or hip fracture? a factor with levels No Yes

po2

PO2 from initial blood gases, a factor with levels >60 <=60

ph

pH from initial blood gases, a factor with levels >=7.25 <7.25

pco

PCO2 from initial blood gases, a factor with levels <=45 >45

bic

Bicarbonate (HCO3) level from initial blood gases, a factor with levels >=18 <18

creatin

Creatinine, from initial blood gases, a factor with levels <=2 >2

coma

Level of unconsciousness at admission to ICU, a factor with levels None Stupor Coma

white

a recoding of race, a factor with levels White Non-white

uncons

a recoding of coma a factor with levels No Yes

Details

Patient ID numbers are the rownames of the data frame.

Note that the last two variables white and uncons are a recoding of respectively race and coma to binary variables.

Source

M. Friendly (2000), Visualizing Categorical Data, Appendix B.4. SAS Institute, Cary, NC.

Hosmer, D. W. Jr., Lemeshow, S. and Sturdivant, R. X. (2013) Applied Logistic Regression, NY: Wiley, Third Edition.

References

Lemeshow, S., Teres, D., Avrunin, J. S., Pastides, H. (1988). Predicting the Outcome of Intensive Care Unit Patients. Journal of the American Statistical Association, 83, 348-356.

Examples

data(ICU)
# remove redundant variables (race, coma)
ICU1 <- ICU[,-c(4,20)]

# fit full model
icu.full <- glm(died ~ ., data=ICU1, family=binomial)
summary(icu.full)

# simpler model (found from a "best" subsets procedure)
icu.mod1 <- glm(died ~ age + sex + cancer + systolic + admit + uncons, 
  data=ICU1, 
  family=binomial)
summary(icu.mod1)

# even simpler model
icu.mod2 <- glm(died ~ age + cancer  + admit + uncons, 
  data=ICU1, 
  family=binomial)
summary(icu.mod2)

anova(icu.mod2, icu.mod1, icu.full, test="Chisq")

## Reproduce Fig 6.12 from VCD

icu.fit <- data.frame(ICU, prob=predict(icu.mod2, type="response"))

# combine categorical risk factors to a single string
risks <- ICU[, c("cancer", "admit", "uncons")]
risks[,1] <- ifelse(risks[,1]=="Yes", "Cancer", "")
risks[,2] <- ifelse(risks[,2]=="Emergency", "Emerg", "")
risks[,3] <- ifelse(risks[,3]=="Yes", "Uncons", "")
risks <- apply(risks, 1, paste, collapse="")
risks[risks==""] <- "(none)"
icu.fit$risks <- risks

library(ggplot2)
ggplot(icu.fit, aes(x=age, y=prob, color=risks)) +
	geom_point(size=2) +
	geom_line(size=1.25, alpha=0.5) +
	theme_bw() + ylab("Probability of death")