Clinical performance of an interactive clinical decision support system for assessment of plasma lactate in hospitalized patients with organ dysfunction

• Main Authors: Raschke RA, Khurana H, Owen-Reece H, Groves RH Jr, Curry SC, Martin M, Stoffer B
• Format: Article
• Language: English
• Published: Arizona Thoracic Society 2017-05-01
• Series: Southwest Journal of Pulmonary and Critical Care
• Subjects: sepsis; EMR; electronic medical record; decision support system; lactate; mortality; receiver operating curve; early warning; organ dysfunction; survival
• Online Access: http://www.swjpcc.com/critical-care/2017/5/29/clinical-performance-of-an-interactive-clinical-decision-sup.html

Purpose: Elevated plasma lactate concentration can be a useful measure of tissue hypo-perfusion in acutely deteriorating patients, focusing attention on the need for urgent resuscitation. But lactate is not always assessed in a timely fashion in patients who have deteriorating vital signs. We hypothesized that an electronic medical record (EMR)-based decision support system could interact with clinicians to prompt assessment of plasma lactate in appropriate clinical situations in order to risk stratify a population of inpatients and identify those who are acutely deteriorating in real-time. Methods: All adult patients admitted to our hospital over a three month period were monitored by an EMR-based lactate decision support system (lactate DSS) programmed to detect patients exhibiting acute organ dysfunction and engage the clinician in the decision to order a plasma lactate concentration. Inpatient mortality was determined for the five risk categories that this system generated, and chart review was performed on a high-risk subgroup to describe the spectrum of bedside events that triggered the system logic. Results: The lactate DSS segregated inpatients into five strata with mortality rates of 0.8% (95%CI:0.6-1.0%); 2.7% (95%CI:1.0-4.4%); 7.9% (95%CI: 6.0-10.1%), 13.0% (95%CI: 9.0-17.8%) and 42.1% (95%CI: 32.0-52.4%), achieving a discriminant accuracy of 80% (95%CI:76-84%) by AUROC for predicting inpatient mortality. Classification into the two highest risk strata had a positive predictive value for detecting acute life-threatening clinical events of 54% (95%CI: 41.5-66.5%). Conclusions: Our lactate decision support system is different than previously-described computerized “early warning systems”, because it engages the clinician in decision-making and incorporates clinical judgment in risk stratification. Our system has favorable operating characteristics for the prediction of inpatient mortality and real-time detection of acute life-threatening deterioration.