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Friday 16 September 2016

Predicting Performance Status 1 Year After Critical Illness in Patients 80 Years or Older: Development of a Multivariable Clinical Prediction Model

Predicting Performance Status 1 Year After Critical Illness in Patients 80 Years or Older: Development of a Multivariable Clinical Prediction Model


Critical Care Medicine:
September 2016 - Volume 44 - Issue 9 - p 1718–1726

Heyland, Daren K et al

Objective: We sought to develop and internally validate a clinical prediction model to estimate the outcome of very elderly patients 12 months after being admitted to the ICU. Design: Prospective, longitudinal cohort study. Setting: Twenty-two Canadian ICUs. Patients: We recruited 527 patients 80 years or older who had a medical or urgent surgical diagnosis and were admitted to an ICU for at least 24 hours. Measurements and Main Results: At baseline, we completed a comprehensive geriatric assessment of enrolled patients; survival and functional status was determined 12 months later. We defined recovery from critical illness as Palliative Performance Scale score of greater than or equal to 60. We used logistic regression analysis to examine factors associated with this outcome. Of the 434 patients (82%) whose Palliative Performance Scale was known at 12 months, 50% had died and 29% (126/434) had a score of greater than or equal to 60. In the multivariable model, we found that being married, having a primary diagnosis of emergency coronary artery bypass grafting or valve replacement, and higher baseline Palliative Performance Scale were independently predictive of a 12-month Palliative Performance Scale score of greater than or equal to 60. Male sex, primary diagnosis of stroke, and higher Acute Physiology and Chronic Health Evaluation II score, Charlson comorbidity index, or clinical frailty scale were independently predictive of Palliative Performance Scale score of less than 60. Conclusion: Approximately one-quarter of very old ICU patients achieve a reasonable level of function 1 year after admission. This prediction model applied to individual patients may be helpful in decision making about the utility of life support for very elderly patients who are admitted to the ICU.

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