Simplified Acute Physiology Score II as Predictor of Mortality in Intensive Care Units: A Decision Curve Analysis

<div><p>Background</p><p>End-of-life decision-making in Intensive care Units (ICUs) is difficult. The main problems encountered are the lack of a reliable prediction score for death and the fact that the opinion of patients is rarely taken into consideration. The Decision Curve Analysis (DCA) is a recent method developed to evaluate the prediction models and which takes into account the wishes of patients (or surrogates) to expose themselves to the risk of obtaining a false result. Our objective was to evaluate the clinical usefulness, with DCA, of the Simplified Acute Physiology Score II (SAPS II) to predict ICU mortality.</p><p>Methods</p><p>We conducted a retrospective cohort study from January 2011 to September 2015, in a medical-surgical 23-bed ICU at University Hospital. Performances of the SAPS II, a modified SAPS II (without AGE), and age to predict ICU mortality, were measured by a Receiver Operating Characteristic (ROC) analysis and DCA.</p><p>Results</p><p>Among the 4.370 patients admitted, 23.3% died in the ICU. Mean (standard deviation) age was 56.8 (16.7) years, and median (first-third quartile) SAPS II was 48 (34–65). Areas under ROC curves were 0.828 (0.813–0.843) for SAPS II, 0.814 (0.798–0.829) for modified SAPS II and of 0.627 (0.608–0.646) for age. DCA showed a net benefit whatever the probability threshold, especially under 0.5.</p><p>Conclusion</p><p>DCA shows the benefits of the SAPS II to predict ICU mortality, especially when the probability threshold is low. Complementary studies are needed to define the exact role that the SAPS II can play in end-of-life decision-making in ICUs.</p></div

Predicted ICU mortality and net benefit of SAPS II.

<p>Predicted ICU mortality and net benefit of SAPS II.</p

Characteristics of patients at admission and evolution in ICU.

<p>Characteristics of patients at admission and evolution in ICU.</p

Diagnostic performance of SAPS II in predicting ICU mortality.

<p>Diagnostic performance of SAPS II in predicting ICU mortality.</p

Decision curves showing the clinical usefulness of SAPS II, modified SAPS II and age to predict ICU mortality.

<p>Solid black line represents the net benefit of applying palliative care for no patients, assuming that all patients would be alive. Solid gray line represents the net benefit of applying palliative care for all patients, assuming that all would die. Long dashed line, medium dash line and short dash line represent the net benefit of applying palliative care to patients according to SAPS II, modified SAPS II and age, respectively.</p

Decision curves according to reason for ICU admission to predict ICU mortality.

<p>Decision curves showing the clinical usefulness of SAPS II to predict ICU mortality according to major reason for ICU admission: (a) cardiogenic shock, (b) hypovolemic shock, (c) septic shock, (d) coma and (e) respiratory distress syndrome. Solid black line represents the net benefit of applying palliative care for no patients, assuming that all patients would be alive. Solid gray line represents the net benefit of applying palliative care for all patients, assuming that all would die. Dashed line represents the net benefit of applying palliative care to patients according to SAPS II.</p

Characteristics of the patients with melioidosis in Indian Ocean region.

<p>Characteristics of the patients with melioidosis in Indian Ocean region.</p

A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis

<div><p>Background</p><p>The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models.</p><p>Methods and finding</p><p>We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755–0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691–0.783) and 0.742 (0.698–0.785), p < 0.0001). Decision Curve Analysis showed that the machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold.</p><p>Conclusions</p><p>According to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction.</p></div

Receiver operating characteristic curves showing the performance of EuroSCORE I, EuroSCORE II, and the ML model in predicting post-operative mortality.

<p>Areas under curves (95% CI) are 0.719 (0.674–0.763), 0.737 (0.691–0.783), and 0.795 (0.755–0.834), respectively.</p

Decision curves showing the clinical usefulness of EuroSCORE I, EuroSCORE II, and the ML model in predicting post-operative mortality.

<p>The blue line represents the net benefit of providing surgery for all patients, assuming that all patients would survive. The red line represents the net benefit of surgery to none patients, assuming that all would die after surgery. The green, purple and turquoise lines represent the net benefit of applying surgery to patients according to EuroSCORE I, EuroSCORE II, and ML model, respectively. The selected probability threshold (<i>i</i>.<i>e</i>., the degree of certitude of postoperative mortality over which the patient's decision is not to operate) is plotted on the abscissa.</p