Daily assessment of severity of illness and mortality prediction for individual patients
Issue date
2001Suggested citation
Rué i Monné, Montserrat;
Quintana, Salvador;
Álvarez, Manuel;
Artigas, Antoni;
.
(2001)
.
Daily assessment of severity of illness and mortality prediction for individual patients.
Critical Care Medicine, 2001, vol. 29, núm. 1, p. 45-50.
http://hdl.handle.net/10459.1/57005.
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Show full item recordAbstract
Objective: To refine the prognosis of critically ill patients using
a statistical model that incorporates the daily probabilities of
hospital mortality during the first week of stay in the intensive
care unit (ICU).
Design: Prospective inception cohort.
Setting: Fifteen adult medical and surgical ICUs in Spain.
Patients: A total of 1,441 patients aged >18 yrs who were
consecutively admitted from April 1, 1995, through July 31, 1995.
Interventions: Prospective data collection during the stay of
the patient in the ICU. Data collected included vital status at
hospital discharge as well as all variables necessary for computing
the Mortality Probability Models II system at admission and
during the first 7 days of stay in the ICU.
Measurements and Main Results: Four logistic regression
models were obtained. These models contained survival status at
hospital discharge as a dependent variable and the following
explanatory variables: (model 1) only the probability of dying at
admission; (model 2) only the probability of dying during the
current day; (model 3) the probability of dying at admission and
during the current day; and (model 4) the probabilities of dying at
admission and during the previous and current days.
Models were evaluated using the Hosmer-Lemeshow statistic
and the area under the receiver operating characteristic curve.
For survivor and nonsurvivor patients, mortality probabilities obtained
using the aforementioned models were compared using
linear regression and the paired Student’s t-test.
Although severity at admission was a statistically significant
variable, models 2 and 3 produced almost the same probabilities
of hospital mortality, as shown with the linear regression and
paired Student’s t-test results.
Conclusions: To have an accurate measurement of the prognosis,
it is necessary to update the severity measure. The best
estimate of hospital mortality was the probability of death on
the current day. Severity at admission and at previous days did
not improve the assessment of prognosis. (Crit Care Med 2001;
29:45–50)
KEY WORDS: probability models; logistic regression models; hospital
mortality; mortality prediction; prognosis; daily assessment;
severity of illness index; Mortality Probability Models II system;
intensive care; critical illness