Outcome prediction in pneumonia induced ALI/ARDS by clinical features and peptide patterns of BALF determined by mass spectrometry

Abstract

Background: Peptide patterns of bronchoalveolar lavage fluid (BALF) were assumed to reflect the complex pathology of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) better than clinical and inflammatory parameters and may be superior for outcome prediction.

Methodology/principal findings: A training group of patients suffering from ALI/ARDS was compiled from equal numbers of survivors and nonsurvivors. Clinical history, ventilation parameters, Murray's lung injury severity score (Murray's LISS) and interleukins in BALF were gathered. In addition, samples of bronchoalveolar lavage fluid were analyzed by means of hydrophobic chromatography and MALDI-ToF mass spectrometry (MALDI-ToF MS). Receiver operating characteristic (ROC) analysis for each clinical and cytokine parameter revealed interleukin-6>interleukin-8>diabetes mellitus>Murray's LISS as the best outcome predictors. Outcome predicted on the basis of BALF levels of interleukin-6 resulted in 79.4% accuracy, 82.7% sensitivity and 76.1% specificity (area under the ROC curve, AUC, 0.853). Both clinical parameters and cytokines as well as peptide patterns determined by MALDI-ToF MS were analyzed by classification and regression tree (CART) analysis and support vector machine (SVM) algorithms. CART analysis including Murray's LISS, interleukin-6 and interleukin-8 in combination was correct in 78.0%. MALDI-ToF MS of BALF peptides did not reveal a single identifiable biomarker for ARDS. However, classification of patients was successfully achieved based on the entire peptide pattern analyzed using SVM. This method resulted in 90% accuracy, 93.3% sensitivity and 86.7% specificity following a 10-fold cross validation (AUC = 0.953). Subsequent validation of the optimized SVM algorithm with a test group of patients with unknown prognosis yielded 87.5% accuracy, 83.3% sensitivity and 90.0% specificity.

Conclusions/significance: MALDI-ToF MS peptide patterns of BALF, evaluated by appropriate mathematical methods can be of value in predicting outcome in pneumonia induced ALI/ARDS.

Figure 1. Data mining from the patients.

Processing of generally clinical data and interleukins and peptide pattern from BALF. The diagram illustrates sample preparation, data processing and analysis by statistics and different mathematical algorithms. Best results were obtained by applying a support vector machine (SVM) to peptide patterns. The SVM classifier was optimized with the patterns from the patients of the training group. The performance of the classifier was then validated by patterns of the patients from a subsequently recruited test group. BAL, bronchoalveolar lavage; BALF, bronchoalveolar lavage fluid; ROC, receiver operating characteristic curve.

Figure 2. Receiver operating characteristic curve for interleukin-6.

The receiver operating characteristic (ROC) curve for IL-6 as an important clinical parameter of outcome prognosis was computed from the raw data of the 30 patients of the training group. The closed symbol indicates the point at IL-6, 62.4 pg/ml which classifies best. Insert: ROC curves for accuracy (solid line), sensitivity (dashed line) and specificity (dotted line) in dependence on the IL-6 concentration in the BALF. The vertical dashed line indicates best separation at optimum discrimination value of IL-6 with an accuracy of 83.3%. Accuracy defines the percentage of true positives and true negatives related to all patients.

Figure 3. Presentation of typical mass spectra of BALF from patients of the training group.

(A) Three examples of nonsurvivors and three examples of survivors are depicted. Peaks indicated by arrows at m/z, 2740.0 and 10049.9 are the most typical spectral features (cluster masses plus intensities) for survivors while m/z, 4121.6/4135.6 are most typical spectral features for nonsurvivors. (B) All spectral features in the mass spectra of the training group calculated by the clustering procedure and the spider algorithm. The lines running downwards (blue) are representative for the class nonsurvivors (NS), whereas the lines running upwards (red) are characteristic for the class survivors (S).

Figure 4. Best classification tree for the training group using MALDI-ToF MS data from BALF.

Four cluster masses (mass peaks) were used to construct the tree (m/z, 4468.6, 2719.8, 2052.1 and 2334.9). The nodes were sequentially labelled on the basis of the branching level and show splitting criteria. As an example, m/z 4468.6<0.230 means that BALF with peak intensities lower than 0.230 at m/z, 4468.6 are allocated to the left branch and all other BALF to the right branch. BALF continue down the tree until they reach a terminal node depicted as ellipses. Ellipses with full lines denote terminal nodes of nonsurvivors. The number of BALF at each node are given for both survivors (S) and nonsurvivors (NS). The tree classifies all patients correctly, however, accuracy decreased to 76.7% after 10-fold cross validation.

Figure 5. Effect of the spectral features on the accuracy of prognosis.

Dependence of accuracy, sensitivity and specificity on the number of spectral features used by the SVM algorithm for classification of the individual patients of the training group on the basis of the BALF mass spectra. Accuracy, sensitivity and specificity have been obtained after 10-fold cross validation.

Figure 6. BALF mass spectra from two patients and calculated spectral features for outcome prediction.

(A, E) Mass spectra of the BALF from a nonsurvivor and from a survivor. (B, D) The 20 most important spectral features found in (A) and (E). The lines running downwards (blue) are representative for the class nonsurvivors (NS), whereas the lines running upwards (red) are characteristic for the class survivors (S). (C) For comparison, all spectral features in the mass spectra of the training group calculated by the clustering procedure and the spider algorithm. Downward lines (blue) are representative for nonsurvivors (NS), upward lines (red) for survivors (S). The SVM algorithm considers both the occurrence and the absence of a spectral feature.

Figure 7. Prognosis by MALDI-ToF MS approach and clinical features for the training group.

Accuracy, sensitivity and specificity of outcome prediction by pattern analysis of MALDI-ToF mass spectra of BALF in comparison to the results obtained on the basis of the interleukin-6 concentration and a classification tree of clinical features (IL-6, IL-8 and Murray's LISS). In addition, the accuracy (*) of outcome prediction of the test group is given. The error bars indicate SD after 10-fold cross validation.