Patients complexes aux soins intensifs: modèle de prédiction de risque et les résultats des soins de longue durée


Jason Shahin

Institut de recherche du Centre universitaire de santé McGill (CUSM)


Domaine : services des populations

Programme chercheurs-boursiers cliniciens - Junior 1

Concours 2016-2017

On a daily basis, doctors who work in the Intensive care unit (ICU) are called upon to make "life-or-death" decisions for patients. Each of these decisions is based on a combination of clinical experience and medical knowledge. Prediction tools have become important in helping physicians make these important decisions.

There already exists good tools to predict death in the general ICU patient, but no good tools exist for the patient who is chronic critically ill. The chronic critically ill patient is someone who has suffered a severe sickness, but is unable to fully recover. For these patients a tracheostomy is often placed to facilitate their care. However, some patients who receive a tracheostomy will become dependent on their ventilator spending months in the ICU with a poor outcome and quality of life if ever discharged home from the ICU-in effect a miserable outcome.

As most patients are too ill to make their own medical decisions, it is up to the surrogate decision makers, usually family members, to speak for the patient. Despite the importance of the tracheostomy decision, studies have shown that surrogate decision makers feel that they receive very little information on issues that matter most: long-term mobility problems, intellectual capacity and caregiver needs. These studies show us that we need not only better risk prediction tools but also communication aids to improve physician and patient/family communication about tracheostomy insertion and prolonged ICU care.

The objective of this research program is to 1) develop a risk prediction tools to predict how patients with tracheostomies will do in the long-term and 2) examine how physicians and families communicate while discussing tracheostomies. Both the prediction tool and communication studies will help patients, caregivers and physicians to make important decisions and in turn reduce patient suffering.