From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system

Abstract

Objective: To develop a decision support system to identify patients at high risk for hyperlactatemia based upon routinely measured vital signs and laboratory studies.

Materials and methods: Electronic health records of 741 adult patients at the University of California Davis Health System who met at least two systemic inflammatory response syndrome criteria were used to associate patients' vital signs, white blood cell count (WBC), with sepsis occurrence and mortality. Generative and discriminative classification (naïve Bayes, support vector machines, Gaussian mixture models, hidden Markov models) were used to integrate heterogeneous patient data and form a predictive tool for the inference of lactate level and mortality risk.

Results: An accuracy of 0.99 and discriminability of 1.00 area under the receiver operating characteristic curve (AUC) for lactate level prediction was obtained when the vital signs and WBC measurements were analysed in a 24 h time bin. An accuracy of 0.73 and discriminability of 0.73 AUC for mortality prediction in patients with sepsis was achieved with only three features: median of lactate levels, mean arterial pressure, and median absolute deviation of the respiratory rate.

Discussion: This study introduces a new scheme for the prediction of lactate levels and mortality risk from patient vital signs and WBC. Accurate prediction of both these variables can drive the appropriate response by clinical staff and thus may have important implications for patient health and treatment outcome.

Conclusions: Effective predictions of lactate levels and mortality risk can be provided with a few clinical variables when the temporal aspect and variability of patient data are considered.

Keywords: Clinical Decision Support; Electronic Health Records; Lactate Level Prediction; Machine Learning; Mortality Prediction; Sepsis.

Figure 1

Processing pipeline of electronic health record (EHR) sepsis database. The processing pipeline has four main components, (1) database preprocessing, (2) Bayes network structure learning, (3) mortality prediction with support vector machine (SVM) and naïve Bayes (NB), and (4) lactate level prediction with NB, Gaussian mixture model (GMM), and hidden Markov model (HMM). The variable number of patients used in lactate level prediction is given in tables 2 and 3. MAP, mean arterial pressure; WBC, white blood cell count.

Figure 2

Bayesian networks from the five clinical measurements and two outcomes. (A) The Bayesian network structure determined from the mean of the five clinical measurements. (B) The Bayesian network structure from the median of five clinical measurements. On each directed arc, the strength of the arc is indicated, ranging from 0 to 1, with 1 being the strongest. MAP, mean arterial pressure; WBC, white blood cell count.

Figure 3

The receiver operating characteristic (ROC) curve showing the discriminability of sepsis-only patients using the support vector machine (SVM) with only three features. The ROC curve showing the discriminability of the best prediction of mortality (area under the ROC curve (AUC)=0.726±0.045, accuracy=0.728, F=0.821, sensitivity=0.949, specificity=0.308) was provided by SVM (C=4 and γ=0.25) using the top three features of (1) median lactate levels, (2) the mean absolute deviation of respiratory rate, and (3) median mean arterial pressure. RBF, radial basis function.

Figure 4

Cluster membership scores for clustering using Gaussian mixture model (GMM) in the second 24 h time bin. Cluster membership scores of patients’ lactate levels indicate the posterior probability of assigning high or low lactate levels for each patient. A sharp delineation (the presence of few 0.5 probabilities) between the high and low lactate levels indicates good separation. The cluster membership score of the second 24 h time bin with the vital signs and white blood cell count measurements thresholded at the 1st and 99th centiles is shown (area under the receiver operating characteristic curve (AUC)=1.000±0, accuracy=0.990, F=0.994, sensitivity=0.988, specificity=1.000). This is the best performance of lactate level prediction.