The simple observational critical care studies: estimations by students, nurses, and physicians of in-hospital and 6-month mortality

Abstract

Background Prognostic assessments of the mortality of critically ill patients are frequently performed in daily clinical practice and provide prognostic guidance in treatment decisions. In contrast to several sophisticated tools, prognostic estimations made by healthcare providers are always available and accessible, are performed daily, and might have an additive value to guide clinical decision-making. The aim of this study was to evaluate the accuracy of students’, nurses’, and physicians’ estimations and the association of their combined estimations with in-hospital mortality and 6-month follow-up. Methods The Simple Observational Critical Care Studies is a prospective observational single-center study in a tertiary teaching hospital in the Netherlands. All patients acutely admitted to the intensive care unit were included. Within 3 h of admission to the intensive care unit, a medical or nursing student, a nurse, and a physician independently predicted in-hospital and 6-month mortality. Logistic regression was used to assess the associations between predictions and the actual outcome; the area under the receiver operating characteristics (AUROC) was calculated to estimate the discriminative accuracy of the students, nurses, and physicians. Results In 827 out of 1,010 patients, in-hospital mortality rates were predicted to be 11%, 15%, and 17% by medical students, nurses, and physicians, respectively. The estimations of students, nurses, and physicians were all associated with in-hospital mortality (OR 5.8, 95% CI [3.7, 9.2], OR 4.7, 95% CI [3.0, 7.3], and OR 7.7 95% CI [4.7, 12.8], respectively). Discriminative accuracy was moderate for all students, nurses, and physicians (between 0.58 and 0.68). When more estimations were of non-survival, the odds of non-survival increased (OR 2.4 95% CI [1.9, 3.1]) per additional estimate, AUROC 0.70 (0.65, 0.76). For 6-month mortality predictions, similar results were observed. Conclusions Based on the initial examination, students, nurses, and physicians can only moderately predict in-hospital and 6-month mortality in critically ill patients. Combined estimations led to more accurate predictions and may serve as an example of the benefit of multidisciplinary clinical care and future research efforts.

Accuracy of physician and nurse predictions for 28-day prognosis in ICU: a single center prospective study

“…Thus, only 15 nurses were involved in this study, and the proportion of correct responses was low. This result is similar to that reported by Eline M.’s study 13 . Nurses with less seniority attends to the patient at his bedside every day and is required to perform various treatments 18 .…”

“…This suggests that physicians can only moderately predict 28-day mortality in critically ill patients. This result is similar to that of a previous study 13 . Eline et al, showed that the estimations of all students, nurses, and physicians were correct in 71% of 481 cases.…”

“…Each time a patient is admitted, all medical staff actively rescues the patient. At the same time, everyone has their own opinion on the patient's outcome 13 . The patient's outcome is not only related to the patient's disease state and complications, but also to the expectation of the patient's family members and economic conditions 14 .…”

Risk factors prediction of 6-month mortality after noncardiac surgery of older patients in China: a multicentre retrospective cohort study

“…Third, they may not account for long-term outcomes beyond 30 days, such as quality of life, functional status, or the need for additional procedures or treatments 7 – 9 . To address these limitations, some studies have built excellent predictive models by focusing on 6-month mortality, but the sample size of these studies is mostly below 5000 patients and few studies can represent the situation in China 10 , 11 . In our study, we collected 11 894 patients’ medical history and various inspection indicators and followed them up for 6 months, hoping to construct a predictive model that can represent the medical situation in China.…”