Right Heart Catheterization Dataset

This dataset was used in Connors et al. (1996): The effectiveness of RHC in the initial care of critically ill patients. J American Medical Association 276:889-897. The dataset pertains to day 1 of hospitalization, i.e., the "treatment" variable swang1 is whether or not a patient received a RHC (also called the Swan-Ganz catheter) on the first day in which the patient qualified for the SUPPORT study (see above). The dataset is suitable for use in papers submitted in response to the call for papers on causal inference, by the journal Health Services and Outcomes Research Methodology. The original analysis by Connors et al. used binary logistic model to develop a propensity score that was then used for matching RHC patients with non-RHC patients. A sensitivity analysis was also done. The results provided some evidence that patients receiving RHC had decreased survival time, and the sensitivity analysis indicated that any unmeasured confounder would have to be somewhat strong to explain away the results. See Lin DY, Psaty BM, Kronmal RA (1998): Assessing the sensitivity of regression results to unmeasured confounders in observational studies. Biometrics 54:948-963 for useful methods for sensitivity analysis, one of which was applied to the RHC results.

Here is a .zip containing the original SAS code used to do the published analyses, from Charles Thomas. A reverse chronological directory of the SAS code files is here.

The S-Plus dataset is easy to use if you have the Hmisc library in effect. If you don't, you need to define the following S-Plus functions.

ddmmmyy <- function(x)
{
	y <- month.day.year(trunc(oldUnclass(x)), attr(x,"origin"))
	yr <- y$year
	m <- c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct",
		"Nov","Dec")[y$month]
	ifelse(yr<1900 | yr>=2000, paste(y$day,m,yr,sep=""),
		paste(y$day,m,yr-1900,sep=""))
}

"[.labelled"<- function(x, ...) {
  lab <- attr(x, "label")
  x <- NextMethod("[")
  attr(x, "label") <- lab
  if(!inherits(x,'labelled'))
    attr(x,'class') <- c("labelled", attr(x,'class'))
  x
}

as.data.frame.labelled <- function(x, ...) {
  y <- x
  cy <- attr(y,'class')
  cy <- if(length(cy)>1) cy[cy!='labelled'] else NULL
  if(length(cy)==0) cy <- NULL  # handles wierd case e.g. class=rep('lab..',2)
  attr(y,'class') <- cy
  d <- data.class(y)
  methodname <- paste("as.data.frame", d, sep = '.')
  if(exists(methodname, mode = "function"))
            (get(methodname, mode = "function"))(x, ...)
  else {
	if(options()$check)
		warning(paste("no method for coercing",d,"to data.frame"))
	as.data.frame.AsIs(y, ...)
  }
}

If using S-Plus 5.x or 6.x, you don't need to define the last two functions. Instead you will need to run the data frame through a special function cleanup.import to remove old S-Plus classes.


August 11, 2000

Division of Biostatistics and Epidemiology
Department of Health Evaluation Sciences
University of Virginia Health System

This documents the SAS transport data file and the S-Plus data file for The Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments (SUPPORT).

  1. First unzip either of the two files
  2. The SAS file may be transferred to another computing platform using SAS PROC CIMPORT.
  3. The S-plus data file was created using S-plus function data.dump, so it may be restored by using function data.restore to put the data into your local _Data directory.
  4. The variable descriptions are presented in the following table.

 

Table 1: SUPPORT Variable Description

Variable name

Variable Definition

Age

Age

Sex

Sex

Race

Race

Edu

Years of education

Income

Income

Ninsclas

Medical insurance

Cat1

Primary disease category

Cat2

Secondary disease category

Categories of admission diagnosis:

 

Resp

Respiratory Diagnosis

Card

Cardiovascular Diagnosis

Neuro

Neurological Diagnosis

Gastr

Gastrointestinal Diagnosis

Renal

Renal Diagnosis

Meta

Metabolic Diagnosis

Hema

Hematologic Diagnosis

Seps

Sepsis Diagnosis

Trauma

Trauma Diagnosis

Ortho

Orthopedic Diagnosis

   

Adld3p

ADL

Das2d3pc

DASI ( Duke Activity Status Index)

Dnr1

DNR status on day1

Ca

Cancer

Surv2md1

Support model estimate of the prob. of surviving 2 months

Aps1

APACHE score

Scoma1

Glasgow Coma Score

Wtkilo1

Weight

Temp1

Temperature

Meanbp1

Mean blood pressure

Resp1

Respiratory rate

Hrt1

Heart rate

Pafi1

PaO2/FIO2 ratio

Paco21

PaCo2

Ph1

PH

Wblc1

WBC

Hema1

Hematocrit

Sod1

Sodium

Pot1

Potassium

Crea1

Creatinine

Bili1

Bilirubin

Alb1

Albumin

Urin1

Urine output

Categories of comorbidities illness:

 

Cardiohx

Acute MI, Peripheral Vascular Disease, Severe Cardiovascular Symptoms (NYHA-Class III), Very Severe Cardiovascular Symptoms (NYHA-Class IV)

Chfhx

Congestive Heart Failure

Dementhx

Dementia, Stroke or Cerebral Infarct, Parkinsonís Disease

Psychhx

Psychiatric History, Active Psychosis or Severe Depression

Chrpulhx

Chronic Pulmonary Disease, Severe Pulmonary Disease, Very Severe Pulmonary Disease

Renalhx

Chronic Renal Disease, Chronic Hemodialysis or Peritoneal Dialysis

Liverhx

Cirrhosis, Hepatic Failure

Gibledhx

Upper GI Bleeding

Malighx

Solid Tumor, Metastatic Disease, Chronic Leukemia/Myeloma, Acute Leukemia, Lymphoma

Immunhx

Immunosupperssion, Organ Transplant, HIV Positivity, Diabetes Mellitus Without End Organ Damage, Diabetes Mellitus With End Organ Damage, Connective Tissue Disease

Transhx

Transfer (> 24 Hours) from Another Hospital

Amihx

Definite Myocardial Infarction

 

 

Swang1

Right Heart Catheterization (RHC)

Sadmdte

Study Admission Date

Dthdte

Date of Death

Lstctdte

Date of Last Contact

Dschdte

Hospital Discharge Date

Death

Death at any time up to 180 Days

Ptid

Patient ID


Frank E Harrell Jr
Last modified: Fri Dec 27 16:14:18 EST 2002