SURvival PRediction In SEverely Ill Patients Study-The Prediction of Survival in Critically Ill Patients by ICU Physicians

Abstract

The surprise question, "Would I be surprised if this patient died in the next 12 months?" is a tool to identify patients at high risk of death in the next year. Especially in the situation of an ICU admission, it is important to recognize patients who could and could not have the benefits of an intensive treatment in the ICU department.

Design and setting: A single-center, prospective, observational cohort study was conducted between April 2013 and April 2018, in ICU Gelre hospitals, location Apeldoorn.

Patients: A total of 3,140 patients were included (57% male) with a mean age of 63.5 years. Seven-hundred thirteen patients (23%) died within 1 year.

Interventions: The physician answered three different surprise question's with either "yes" or "no": "I expect that the patient is going to survive the ICU admission" (surprise question 1), "I expect that the patient is going to survive the hospital stay" (surprise question 2), and "I expect that the patient is going to survive one year after ICU admission" (surprise question 3). We tested positive and negative predicted values of the surprise questions, the mean accuracy of the surprise questions, and kappa statistics.

Measurements and main results: The positive and negative predictive values of the surprise questions for ICU admission, hospital admission, and 1-year survival were, respectively, 64%/94%, 59%/92%, and 60%/86%. Accordingly, the mean accuracy and kappa statistics were 93% (95% CI, 92-94%), κ equals to 0.43, 89% (95% CI, 88-90%), κ equals to 0.40, and 81% (95% CI, 80-82%), κ equals to 0.43.

Conclusions: The frequently overlooked simple and cheap surprise question is probably an useful tool to evaluate the prognosis of acutely admitted critically ill patients.

Keywords: (hospital) mortality; critical care; intensive care units; palliative medicine; patient-centered care; survival analysis.

Figure 1.

Flowchart inclusions.

Figure 2.

Blue: Receiver operating characteristic (ROC) curve basic model (model 1). Area under the curve 0.778. Contains variables: age, diabetes, chronic renal insufficiency, chronic dialysis, confirmed infection, cerebrovascular accident, intracranial mass, immunocompromised, neoplasm, cirrhosis, hematologic malignancy, cardiovascular insufficiency, cardiopulmonary resuscitation, respiratory insufficiency, mechanical ventilation at start, admission type. Red: ROC curve basic model with addition of the surprise question (SQ) (model 2). Area under the curve 0.822. Contains all variables of the basic model and SQ3.