Estimating Long-Term Survival of Critically Ill Patients: The PREDICT Model
Author Information
Author(s): Ho Kwok M., Knuiman Matthew, Finn Judith, Webb Steven A.
Primary Institution: University of Western Australia and Royal Perth Hospital
Hypothesis
Can a prognostic model accurately estimate long-term survival of critically ill patients?
Conclusion
The PREDICT model can estimate long-term survival probabilities for critically ill patients based on age, co-morbidities, and severity of illness.
Supporting Evidence
- Age and co-morbidity are the most important determinants of long-term prognosis.
- The model provides median survival time and long-term survival probabilities.
- Discrimination performance of the model was assessed with a c-index of 0.757.
- Patients with higher age and co-morbidities had worse survival outcomes.
- The model is the first to estimate survival probabilities up to 15 years after critical illness.
Takeaway
Doctors can use a special tool to guess how long critically ill patients might live after their treatment, based on their age and health problems.
Methodology
This was a retrospective linked data cohort study involving critically ill patients who survived more than 5 days in an ICU.
Potential Biases
The model may not fully capture the impact of patients' wishes and quality of life on treatment decisions.
Limitations
The model's predictions should be considered average estimates and not used for individual patients; it may not account for all factors affecting survival.
Participant Demographics
The cohort included 11,930 critically ill patients, with a mean age of 53.8 years, and 62.8% were male.
Statistical Information
Confidence Interval
95% confidence interval 0.745–0.769
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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