Structural Properties of MHC Class II Ligands, Implications for the Prediction of MHC Class II Epitopes
2010

K W Jørgensen
S Buus
M Nielsen
Structural Properties of MHC Class II Ligands, Implications for the Prediction of MHC Class II Epitopes

PLoS ONE
512158772010Received September 13, 2010; Accepted November 25, 2010;Editor: Bostjan Kobe, University of Queensland, Australia The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. *
Major Histocompatibility class II (MHC-II) molecules sample peptides from the extracellular space allowing the immune system to detect the presence of foreign microbes from this compartment. Prediction of MHC class II ligands is complicated by the open binding cleft of the MHC class II molecule, allowing binding of peptides extending out of the binding groove. Furthermore, only a few HLA-DR alleles have been characterized with a sufficient number of peptides (100-200 peptides per allele) to derive accurate description of their binding motif. Little work has been performed characterizing structural properties of MHC class II ligands. Here, we perform one such large-scale analysis. A large set of SYFPEITHI MHC class II ligands covering more than 20 different HLA-DR molecules was analyzed in terms of their secondary structure and surface exposure characteristics in the context of the native structure of the corresponding source protein. We demonstrated that MHC class II ligands are significantly more exposed and have significantly more coil content than other peptides in the same protein with similar predicted binding affinity. We next exploited this observation to derive an improved prediction method for MHC class II ligands by integrating prediction of MHC-peptide binding with prediction of surface exposure and protein secondary structure. This combined prediction method was shown to significantly outperform the state-of-the-art MHC class II peptide binding prediction method when used to identify MHC class II ligands. We also tried to integrate N-and Oglycosylation in our prediction methods but this additional information was found not to improve prediction performance. In summary, these findings strongly suggest that local structural properties influence antigen processing and/or the accessibility of peptides to the MHC class II molecule.

Introduction

Major histocompatibility complex (MHC) class II molecules orchestra essential parts of the immune system defining the onset of for instance cytotoxic T cell induced apoptosis and B cell proliferation. Identification of which peptides will bind a given MHC class II molecule is hence of pivotal interest for the understanding of a host immune response to any given pathogen. To guide this identification, several prediction methods have been developed over the last decade (see [1] and references herein). Prediction of naturally processed MHC class II binding peptides (MHC class II ligands) is not an easy task. The open binding cleft for MHC class II molecules allows peptides to extend the nonamer binding core. This makes prediction of peptide binding more challenging for MHC class II compared to MHC class I due to the need to simultaneous predict the binding register and binding motif.

Antibodies have been demonstrated to be able to affect antigen processing either positively or negatively depending upon the specificity of the antibody and the CD4+T cell [2,3], and the three-dimensional structure of antigens has been suggested to influence the processing and presentation of helper T-cell epitopes [4]. It therefore seems plausible that local structural properties of the source protein, even though not directly impacting the MHC class II binding, could impose a differential bias in the likelihood of a given peptide being processed and presented on the MHC class II molecule.

In this work, we seek to investigate this assumption and analyze if properties of peptides defined by the native local structure of the source protein influence their likelihood of being made available for binding to MHC class II molecules. The aspect of glycosylation is also included in the analysis. The vast majority of studies investigating the effect of glycosylation on T cell recognition is based on very limited amount of data and is hence highly anecdotal. Glycosylation of ligands in the MHC-II binding core region has been found to disfavour MHC class II binding [5]. When present in the binding core, some evidence indicates that these carbohydrate moieties play an important role in T-cell recognition [6]. Glycosylations of the flanking amino acids are found more frequently since these often will allow the T-cell receptor to get in contact with the MHC:peptide complex [7]. Here, we investigate using a large benchmark data set, whether ligands are glycosylated or not and if the glycosylation is overrepresented in MHC class II ligands compared to the background.

Two large-scale benchmark data sets were used for the analysis consisting of MHC class II ligands obtained from the SYFPEITHI database [8]. Native local protein structure properties were predicted using NetSurfP [9]. Binding affinities to the MHC class II molecules were predicted using NetMHCIIpan version 1.0 [10], and N-and O-glycosylation sites were predicted using NetNglyc [11] and NetOglyc [12]. Using these predicted features, we seek to analyze if structural properties for MHC class II ligands differ from other non-ligand peptides with equal binding affinity to the relevant MHC-II molecule. Demonstrating such a structural differential bias, we next attempt to combine the local structural information with the binding strength to the MHC-II molecule in a model with improved accuracy of prediction for MHC class II ligands.

Results

Ligands versus non-ligand binders

We first compared the local structural properties of the MHC-II ligands to that of the corresponding non-ligand peptides. We define a non-ligand binder as an affinity matched peptide within the ligand source protein not overlapping with the ligand peptide, where affinity matched is a predicted binding affinity (in logtransformed units) in the range 65% of the binding affinity of the MHC-II ligand. For 87 out of 644 ligands, the criterion (65%) did not result in any selected peptides because these ligands were predicted by NetMHCIIpan to be very strong binders, and no other peptides were found within the source protein with a binding affinity within the range of -5% of the reference ligand binding affinity. Requiring that peptides in the non-ligand data set were found both with stronger and weaker binding affinities compared to the reference ligand further reduced the set of ligand:non-ligand binder pairs to 459. When multiple peptides in the source protein fulfilled the above criteria for non-ligand binder, a random peptide from the non-ligand pool was selected.

Performing a pairwise comparison of local structural properties between these 459 ligands and their non-ligand affinity matched counter-part revealed that MHC class II ligands were significantly more exposed and had significantly less secondary structure element (a-helix and b-strand) compared to the non-ligands (see Table 1). The difference in binding affinity for ligands and nonligands was found not significant (p,0.895, paired t-test).

To investigate if this difference in local structural properties could be contributed to a bias in the NetsurfP method used to predict the local structural properties, a comparative analysis was performed using the Real-SPINE 3.0 method for prediction of residue solvent accessibility [13]. Real-SPINE 3.0 was able to return residue solvent accessibility predictions for the source-proteins of 457 of the 459 ligand included in table 1. Using these predicted exposure values, the mean RSA for the ligand and nonligands peptides was found to be 0.214+/2 0.048, and 0.205+/2 0.058, respectively. These values are different from the values obtained using the NetSurfP method (see table 1). However, the difference between the two peptides sets is statistically significant (p,0.05, paired T test). This result thus strongly suggests that the results obtained in this work are robust and not influenced by the method applied to obtain local structure predictions, as long as the method is indeed state-of-the art. Through out the remaining part of the manuscript only NetSurfP predictions were used.

Training set

To investigate if these findings could be applied to improve the in silico identification of MHC class II ligands, a simple model was created which integrated the MHC-II binding affinity with a structural feature or surface exposure as described by Eq.(1).
x~(1{a) : MHC pep za : struc pep ,ð1Þ
where x is an MHC class II ligand likelihood score, MHC pep represents the MHC binding affinity and struc pep is the mean value of a structural class (i.e. a-helix, b-strand, coil or RSA) for the peptide. Brute force grid search was used to identify the value of a that gave the optimal AUC0.1 (see material and methods) performance measured on the balanced training set. The average predicted binding affinity for ligands restricted to different HLA alleles is often very different. For instance is the average 1-log50k predicted binding value for the ligands in the training data set restricted to the alleles HLA-DRB1*0101 and HLA-DRB1*0301 0.4960.19 and 0.1960.14, respectively. This difference is highly statistically significant (p,0.005, t-test). Even though these differences are based purely on predicted binding affinities, the findings correspond to what has been observed for MHC class I binding, where evidence is merging suggesting that MHC class I molecules present peptide on the cell surface at different binding thresholds [14,15]. Thus, to identify an optimal and HLA universal a-value across all the different HLA-DR alleles, a re-scaling was made for all predicted binding affinities. For every allele, the 1-log50k(IC50 nM) binding affinities were rescaled with a given percentile score. This percentile score was calculated for each specific allele using a set of 200.000 random natural 15-mer peptides. The binding affinities were hereby rescaled according to the percentile chosen. Rescaling at percentiles from 1-5 gave very similar results (data not shown). Rescaling was decided to be based on the 1-percentile (Rescaled01) since this gave a slightly improved performance compared to other percentile values when evaluated on the balanced training data.

We now use the balanced training data set to define the optimal value of a for the model defined using Eq. (1). All details of this calculation are found in Table 2. The AUC0.1 for the raw NetMHCIIpan method was found to be 0.293. When combined with relative surface exposure an optimal value of a RSA was found to be 0.30 with an AUC0.1 performance of 0.312. This difference between the model and the NetMHCIIpan method is highly statistically significant (p,3.9?10 24 ). When combining the rescaled NetMHCIIpan predicted binding affinity with coil predictions an optimal a coil value was found to be 0.20 with an AUC0.1 value of 0.317. Also, this increase in performance between the model and the raw NetMHCIIpan method is statistically significant (p,6.3?10 24 ). For the remaining part of the training set, the two models with a RSA = 0.30, and a coil = 0.20, respectively, significantly outperformed the NetMHCIIpan method (p,10 27 , and p,3.5?10 23 ). To estimate the robustness of the model parameter a, 5-fold cross validation on the balanced training set was performed. In the cross-validation 4/5 of the data were applied to estimate the optimal model parameter a, and the remaining 1/5 of the data was next used as test set and predicted using this optimal value of a. This procedure was repeated five times ensuring that all data points form part of the test set exactly one time. For RSA, the AUC0.1 value obtained using five-fold cross-validation was 0.309, and the average optimal a for the 5 cross-validations was 0.33060.045, and similarly was the AUC0.1 value 0.303 for the model using coil with an average optimal a for the 5 crossvalidations 0.22060.027. These low values on the standard deviation of the a values indicate that the model is robust and that is does not suffer from noticeable overfitting. Next, a model combining MHC binding, RSA and coil was investigated. The model was defined as described by Eq. (2).
y~Rescale01 pep za : coilzb : RSA,ð2Þ
where Rescale01 pep is the rescaled binding affinity of the peptide to the given HLA allele, coil is the average predicted coil score of the peptide, RSA is the average predicted relative exposure score of the peptide, and a and b are relative adjustable weights. Brute force grid search was used again to define weights on RSA and coil that optimized the AUC0.1 value on the balanced training data. The best performing model (2) was achieved with a = 0.1 and b = 0.5 given an AUC0.1 of 0.351. This value is significantly higher than the AUC0.1 value of 0.315 obtained by the NetMHCIIpan (p,2.0?10 29 ). However, the improvement was not significant for the model combining RSA and coil compared the model with RSA alone (p,0.345). This more complex model was therefore not investigated further.

Test set

Next, the model define by Eq. (1) was investigated using the 697 ligands in the test set. The optimal a-value of 0.30 for RSA was chosen based on the previous results. The performance measured by AUC0.1 was increased from 0.318 (NetMHCIIpan) to 0.329 (p,0.018) for MHC binding combined with RSA. For MHC binding combined with coil, the a-value of 0.20 gave a slight decrease in the performance from 0.318 to 0.318 (p,0.978). For details see Table 2.

The model combining MHC affinity with RSA thus consistently and significantly improved the predictive performance compared to MHC binding alone on all benchmark data sets, thus supporting the consistency of the model. In contrast to this, did the model with coil combined with MHC affinity not improve the predictive performance above what is obtained using MHC binding alone when evaluated on the test set.

Glycosylation

The previous analysis demonstrated significant differences in local structural properties between ligands and affinity matched non-ligands. Here, we apply a similar approach to investigate if differences existed between ligands and affinity matched nonligands with respect to glycosylation. The comparison between the 459 ligands and non-ligands was used again to identify trends regarding glycosylation. For all the corresponding source proteins N-and O-glycosylation were predicted, and the number of predicted glycosylation within ligands/non-ligands was calculated. Out of the 459 ligands:non-ligand pairs, 27 ligands were predicted to be glycosylated. For the non-ligands this number was 50. The ligands were thus predicted to be significantly (p,0.012, binomial test) less glycosylated than the non-ligands (see Table 3).

The full training set and test set were also analyzed according to the glycosylation sites for both N-and O-glycosylation. Out of the 644 ligands in the training set, 6.2% were predicted glycosylated (25 N-and 15 O-glycosylated). Out of the 697 ligands in the test set, 9.6% were predicted glycosylated (36, N-and 31 O-glycosylated).  No ligands from the training and the test set were predicted both Nand O-glycosylated. Taking all peptides within the source proteins as the background, it was found that the predicted background frequency for glycosylation for the training set was 9.11% (29654 peptides out of 325276) and for the test set 10.7% (47958 peptides out of 447453). For both data sets, the background frequency of glycosylation was thus slightly higher than what was found to the corresponding ligands, suggesting the MHC class II ligand presentation could be interfered by glycosylations. An attempt was made to improve the prediction of MHC class II ligands combining NetMHCIIpan binding affinity and glycosylation. Several models were investigated. Even though we showed above that the ligands are less glycosylated than affinity matched non-ligands, no model was found that consistently improved the predictive performance on all three data sets.

Discussion

Characterizing and identifying peptides that bind MHC class II molecules and elicit an immunogenic response is critical for the understanding of host-pathogen immune system interactions and in the selection of candidate peptides in vaccine research. The process of identifying such peptides is however a highly resource intensive and difficult task.

During the recent decade several in silico prediction algorithms have therefore been developed aiming at guiding and costreducing the task of identifying T cell epitopes. While the accuracy for prediction of MHC class I restricted epitopes/ligands has reached a level where only few percent of the predictions turn out to be false [13], the situation for MHC class II is different. Here the algorithms, even though recently achieving improved predictive performances, maintain a relative low accuracy when it comes to MHC epitope/ligand discovery [1].

The majority of the methods developed for prediction of MHC class I restricted ligands and to our knowledge all methods developed for prediction of MHC class II restricted ligands focus on prediction of the peptide:MHC binding event alone. The classical pathway for MHC class II ligand presentation involves uptake of protein or protein fragments through endocytosis or phagocytosis by antigen presenting cells (APC). Antibodies can enhance specific source antigen uptake and presentation to CD4+T cells by orders of magnitude (reviewed in [16,17,18]). Once the source antigen has been internalized and processed, one would a priori expect that any peptide derived from this source antigen would be offered to MHC class II and could be presented. However, antibodies have been demonstrated to be able to affect antigen processing either positively or negatively depending upon the specificity of the antibody and the CD4+T cell. This T-B cell reciprocity has been suggested to be the result of antibodies affecting the outcome of antigen processing in ways that are not easy to predict. The effect of an antibody that binds to and protects the bound epitope itself could be to protect the determinant (enhanced presentation), whereas the effect of an antibody that binds to and protects a site that needs to be processed in order to liberate the epitope could lead to inhibition of processing (reduced presentation). Both effects have been demonstrated in the literature [2,3].

Since the majority of B cell epitopes are characterized by a structural signature in that they tend to protrude at the protein surface and be highly exposed [19,20], we postulated that upon uptake by the antigen presenting cell, the local structural properties of an epitope, in the context of the native of the structure source protein, could impose a differential bias in the likelihood of a given peptide epitope being appropriately processed and presented. Moreover has earlier work on antigen processing and presentation suggested that antigen three-dimensional structure might influence the processing and presentation of helper Tcell epitopes [2,3,4].

Here, we further investigated this hypothesis, and analyze to what extent the local structural properties of an epitope, in the context of the native structure of the source protein, can impose a differential bias in the likelihood of a given peptide epitope being appropriately processed and presented. We investigated this hypothesis on a large set of MHC class II ligands from the SYFPEITHI database. Using the state-of-the-art MHC class II binding predictions, NetMHCIIpan, combined with prediction of local protein structure by NetSurfP, our analysis revealed that HLA class II ligands are significantly more exposed and have significantly less local secondary structure elements compared to affinity matched non-ligand peptides within the same source protein. Since the source protein is internalized as a unit and then processed, our comparison neutralizes the effect upon antigen uptake, which otherwise would confound the issue of the effect upon antigen processing. We suggest that our observation could be the result of antibody-mediated determinant protection and that this effect dominates over antibody-mediated inhibition of processing. This interpretation of our results would agree with recent finding by Sette et al. [21], that in a vaccinia model demonstrated that CD4+T cells and neutralizing antibodies target the same protein antigen. Alternatively, the observed structural bias in MHC ligands could stem from the digestion of the antigen in the endosomes, since it is possible that surface exposed peptide fragments are more effectively be released from the antigen protein structure compared to fragments in the more structurally stable hydrophobic protein core.

Next, we proposed a method for prediction of MHC ligands combining MHC class II binding predictions with local structure prediction, and demonstrated that this method consistently in three benchmark studies significantly improved the prediction accuracy of a method based on MHC binding alone.

It is important to stress that our observations should not be taken as an indication for MHC class II ligands being absent form the protein core. Our observations merely demonstrate that a differential bias exists and that exposed peptide fragments are more likely presented by MHC class II molecules compared to affinity matched buried peptides.

We also investigated if glycosylations could have a potential influence on the likelihood of peptides being presented on MHC class II molecules, and demonstrated that the MHC class II ligands are significantly less glycosylated compared to affinity matched non-ligands. All the ligands used in this work were obtained from the SYFPEITHI database. The ligand data in this database might have a certain bias with respect to glycosylation due to the experimental procedure in which they are defined (Stefan Stevanovic, personal communication). Thus the conclusions regarding glycosylation should be read with some caution.

In conclusion, this work has shown strong evidence that local structural properties of proteins will significantly bias processing and presentation of the corresponding peptides, and that highly exposed peptides will have a higher likelihood of being presented on the cell surface in complex with MHC class II molecules compared to other affinity matches but less exposed peptides. We further demonstrate how this finding in a simple way can be applied to significantly improve the predictions of MHC class II ligands by combining predicted surface exposure with state-of-the-art prediction methods for MHC class II binding.

Materials and Methods

Data

The analysis was run on two data sets; a training set and a test set. The training data set contained 644 unique ligands covering 22 HLA-DR molecules and the test set contained 697 unique ligands covering 28 HLA-DR molecules. All ligands were obtained from the SYFPEITHI database [8] and no ligands were common to both data sets. The source protein for every ligand was searched in the Uniprot database [22]. If more than one hit existed, the longest protein was chosen. The two data sets are summarized in Table 4.

The training set was analyzed thoroughly according to the distribution of alleles. The allele HLA-DRB1*0401 constituted more than half of the data set alone -365 ligands out of 644 ligands. To reduce the allelic bias imposed by the uneven distribution of ligands per allele in the training set, a balanced subset of the training set consisting of no more than 30 ligands per allele was created. This balanced training data set consisted of 290 ligands.

MHC-II ligands and non-ligand MHC-II binding peptides

In the data set of MHC-II ligands, we only have access to information on which peptides in the given source-protein are MHC-II ligands. To define a negative set of non-ligand peptide, we take an approach described earlier in for validation of prediction methods for both MHC class I and class II (see for instance [15,23]), and construct the negative set from all other peptides in the source-protein not equal to a known MHC ligand. The rational behind this approach is that the MHC molecule is very specific and will only bind and present a very small fraction of the source protein peptides [24,25]. Assuming that only one peptide is presented from each source-protein is clearly an oversimplification, and it is very likely that some of the non-ligands might be falsely classified. However, such erroneous classifications will not invalidate the approach but merely introduce noise lowering the overall predictive performance of the methods. If we despite this potential noise observe a significant signal in our analysis, then the signal would become even stronger in the ideal (but impossible) situation where also the negative data set was experimentally validated.

Methods

The source protein was cut into overlapping peptides of a length equal to the corresponding MHC-II ligand, and the pan-specific prediction server, NetMHCIIpan version 1.0, which computes the binding affinity in nM units and reports both this value and a log50k transformed (i.e. 1-log50k(IC50 nM)) binding value [10], was applied to predict for each peptide the binding affinity to the MHC-II molecule in question. Local protein structural features for each of the source protein peptides were predicted using the NetSurfP method [9]. This method predicts for each amino acid the relative surface accessibility (RSA), as well as probabilities the residue being in an a-helix, b-stand, or coil secondary structure element. The output from NetSurfP was processed to generate mean values for the secondary structure elements: a-helix, bstrand, coil and RSA for each peptide in the source protein. Thus every peptide was coupled to a predicted MHC class II binding affinity as well as mean values for a-helix, b-strand, coil and RSA. The glycosylation sites were predicted with NetNglyc and NetOglyc with default parameter settings [11,12].

Performance measures

The predictive performance was measured in terms of the area under the ROC curve (AUC). For each ligand the corresponding source protein was split into overlapping peptide sequences of the length of the ligand. All peptides except the annotated HLA ligand were taken as negatives. This is a very stringent assumption since for instance suboptimal peptides sharing the ligand binding-core are counted as negatives even though they could be presented on the HLA molecule. Thus, this setup is likely to underestimate the predictive performance, but the effect should be equal for all methods compared in the benchmark. AUC values were calculated for each protein-HLA ligand pair and the overall predictive performance was next measured as the average AUC value per protein-HLA ligand pair over the data set. Since the balance between positive and negative peptides in the data sets is highly skewed with the majority of the peptide being negative, the AUC measure might not be optimal if a prediction method is required to have a high specificity in order to lower the false positive rate for subsequent experimental validation. In such situations, it is beneficial to use only the high specificity part of the ROC curve to calculate a fractional AUC value [15]. Here, the AUC0.1 (AUC integrated up to a specificity of 0.9) value was used.

Statistical tests

Paired t-tests and binomial tests were used to access differences between ligands and non-ligands as well as differences between NetMHCIIpan and the new method. P-values less than 0.05 were considered statistical significant.

Table 1 .
1Mean and standard deviation value for ligands and non-ligands compared for the different groups.P-values are obtained from a paired t-test. Class indicates the different classes/ methods used in the analysis. The first groups are self-explanatory (i.e. a-helix, b-strand, a+b, and coil). RSA is the relative surface accessibility. All these values are obtained using NetSurfP. 1-log50k is the binding affinity in log-transformed units obtained from NetMHCIIpan. doi:10.1371/journal.pone.0015877.t001Class
Ligand
Non-ligand
P-value

a-helix
0.23160.276
0.28560.322
P,0.002

b-strand
0.28960.207
0.27960.232
P,0.396

a+b
0.52060.171
0.56460.192
P,0.0002

Coil
0.48060.076
0.43760.192
P,0.0002

RSA
0.29860.076
0.27360.099
P,0.0013

1-log50k
0.40460.173
0.40460.173
P,0.895

Table 2 .
2Predictive performance of the model compared to NetMHCIIpan as measured by AUC0.1 and AUC.RSA
NetMHCIIpan
Model -Rescaled01

AUC
AUC0.1
a
AUC
AUC0.1
P-value

Balanced training set
0.781
0.293
0.3
0.784
0.312
,0.0004

Rest of training sset
0.823
0.334
0.3
0.834
0.371
,10 27

Test set
0.796
0.318
0.3
0.792
0.329
,0.02

Coil
NetMHCIIpan
Model -Rescaled01

AUC
AUC0.1
a
AUC
AUC0.1
P-value

Balanced training set
0.781
0.293
0.2
0.782
0.317
,0.0005

Rest of training set
0.823
0.334
0.2
0.829
0.351
,0.004

Test set
0.796
0.318
0.2
0.795
0.318
,0.98

The balanced set was used to identify the optimal weights for RSA and coil combined with rescaled binding affinities (Rescaled01) as define by Eq. (1). The optimal a-
values for each model are given in the table. P-values are given by paired t-tests when comparing AUC0.1 of the model to the NetMHCIIpan method. Rest of training set
refers to the training set, and test to the 697 ligands in the test set.
doi:10.1371/journal.pone.0015877.t002

Table 3 .
3Comparison of glycosylation between ligand and non-ligands.Class
Ligand
Non-ligand
P-value

l-log50k
0.40460.173
0.40460.173
,0.895

N-glyc
20
40
,0.015

O-glyc
7
10
,0.63

All the 459 ligands with corresponding non-ligands were analyzed in respect to
N-and O-glycosylation. Ligands and non-ligands were defined as described in
the text. P-values are based on binomial tests, with a hypothesized proportion
of 0.5.
doi:10.1371/journal.pone.0015877.t003

Table 4 .
4The MHC class II ligands distribution across alleles for the two data sets. doi:10.1371/journal.pone.0015877.t004Allele

Training
set

Test
set
Allele

Training
set

Test
set

DRB1*0101
13
47
DRB1*1104
7
2

DRB1*0102
5
1
DRB1*1201
8
6

DRB1*0301
20
89
DRB1*1301
14
12

DRB1*0401
365
154
DRB1*1302
14
9

DRB1*0402
33
4
DRB1*1401
3
7

DRB1*0403
-
1
DRB1*1501
2
21

DRB1*0404
43
4
DRB1*1502
-
3

DRB1*0405
26
10
DRB1*1601
-
2

DRB1*0701
23
27
DRB3*0101
-
3

DRB1*0801
33
7
DRB3*0202
3
-

DRB1*0802
-
1
DRB3*0301
3
2

DRB1*0803
-
1
DRB4*0101
1
5

DRB1*0901
4
2
DRB4*0103
-
2

DRB1*1001
1
241
DRB5*0101
7
14

DRB1*1101
16
20
Total
644
697

PLoS ONE | www.plosone.org
December 2010 | Volume 5 | Issue 12 | e15877
AcknowledgmentsThe authors thank B Petersen and TN Petersen for guidance and help in carrying out the REAL-SPINE predictions.Author Contributions
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10.1109/TNNLS.2019.2921515
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"Anterior convergent" chest probing in rapid ultrasound transducer positioning versus formal chest ultrasonography to detect pneumothorax during the primary survey of hospital trauma patients: a diagnostic accuracy study

Behrad Ziapour
Houman Seyedjavady
Haji
"Anterior convergent" chest probing in rapid ultrasound transducer positioning versus formal chest ultrasonography to detect pneumothorax during the primary survey of hospital trauma patients: a diagnostic accuracy study
10.1186/s13032-015-0030-5R E S E A R C H Open Access
Background: Occult pneumothorax represents a diagnostic pitfall during the primary survey of trauma patients, particularly if these patients require early positive pressure ventilation. This study investigated the accuracy of our proposed rapid model of ultrasound transducer positioning during the primary survey of trauma patients after their arrival at the hospital.Methods: This diagnostic trial was conducted over 12 months and was based on the results of 84 ultrasound (US) exams performed on patients with severe multiple trauma. Our index test (US) was used to detect pneumothorax in four pre-defined locations on the anterior of each hemi-thorax using the "Anterior Convergent" approach, and its performance was limited to the primary survey. Consecutively, patients underwent chest-computed tomography (CT) with or without chest radiography. The diagnostic findings of both chest radiography and chest ultrasounds were compared to the gold-standard test (CT).Results: The diagnostic sensitivity was 78 % for US and 36.4 % for chest radiography (p < 0.001); the specificity was 92 % for US and 98 % for chest radiography (not significant); the positive predictive values were 74 % for US and 80 % for chest radiography (not significant); the negative predictive values were 94 % for US and 87 % for chest radiography (not significant); the positive likelihood ratio was 10 for US and 18 for chest radiography (p = 0.007); and the negative likelihood ratio was 0.25 for US and 0.65 for chest radiography (p = 0.001). The mean required time for performing the new method was 64 ± 10 s. An absence of the expected diffused dynamic view among ultrasound images obtained from patients with pneumothorax was also observed. We designated this phenomenon "Gestalt Lung Recession."Conclusions: "Anterior convergent" chest US probing represents a brief but efficient model that provides clinicians a safe and accurate exam and adequate resuscitation during critical minutes of the primary survey without interrupting other medical staff activities taking place around the trauma patient. The use of the new concept of "Gestalt Lung Recession" instead of the absence of "lung sliding" might improve the specificity of US in detecting pneumothorax.

Background

Introduction

Despite the continued development of imaging technologies, pneumothorax (PTX) still represents a major concern among those who care for patients in emergency trauma settings or critical care systems. This condition can easily deteriorate into a life-threatening ailment if it is not diagnosed at the beginning of its course. The prevalence of PTX is greater than 20 % among severe trauma patients and up to 50 % among severe chest trauma patients [1,2].

Applying a chest tube is a conventionally accepted therapeutic and diagnostic approach in patients whose clinical manifestations confirm a large or likely complicated PTX. Symptoms that indicate the diagnosis include shortness of breath and chest pain, and patients might appear ill or cyanotic and exhibit tachypnea; the signs include hypotension, decreased unilateral respiratory sounds and hyperresonance over the involved side, asymmetric chest expansion and subcutaneous emphysema or tachycardia. Nonetheless, a patient with a large PTX can appear misleadingly to be healthy [3].

Chest radiography has become the most popular diagnostic alternative in trauma settings. Most trauma guidelines, including ATLS (Advanced Trauma Life Support), recommend chest radiography in combination with anteroposterior pelvic radiography as adjuncts to the primary survey of trauma patients. However, several studies have noted the increased diagnostic value of ultrasonography compared to chest radiography for PTX [4][5][6][7][8][9][10][11][12].

Similar to our current model, in an unpublished online observation of American College of Emergency Physicians (ACEP) from December 2008, Arun Nagdev MM, explained the potential of ultrasound for detecting anterior traumatic PTX.

Importance

Chest ultrasonography is safe, fast and noninvasive. Emergency physicians can perform the procedure before or in combination with the Focused Abdominal Sonography for Trauma (FAST) examination [7,13,14]. The combination is known and widely used today as EFAST (Extended Focused Abdominal Sonography for Trauma).

However, because of the relatively long performance time, neither EFAST nor its thoracic component is recommended or practiced during the primary survey. In addition, to avoid overlooking occult PTXs, many physicians perform a precipitate primary survey and resuscitation to enable early chest imaging incorporated with Advanced Trauma Life Support as adjuncts to the primary survey and resuscitation.

Developing a chest US protocol that is appropriate for the primary survey could ensure the efficient use of time for an accurate primary survey rather than introducing unnecessary interruptions in the breathing reassessment (B) or performing a hurried resuscitation with even less accurate imaging chest radiography (CXR). Moreover, an ideal protocol would not force other medical staff to interrupt their duties to protect themselves from radiation exposure.

Goals of this investigation

Despite the advantages of chest US, few experiments have ever attempted or presented a chest US protocol appropriate for emergent moments in the primary survey. This time-directed performance study had two goals. First, we tried to develop a rapid chest US method, and second, we evaluated its performance accuracy and its time efficiency using an analytical approach.

Methods

Theoretical model of the problem

The physics concept that air accumulates at the most superior level in a closed space prompted the establishment of our relatively new and original but rapid method of transducer positioning. We used this principle in association with knowledge of the anatomical alignment of the ribs. Figure 1 outlines the concept of how the transducer positioning approach that we call "Anterior Convergent" enables the operator to detect even small pockets (<20 %) of likely entrapped air under the anterior wall of each hemi-thorax. In fact, the same reasons for selecting a needle decompression location provide the basis for the placement of the "Anterior Convergent" transducer [15]. Briefly, "Anterior convergent" is the transducer positioning protocol that obliges the operator to respectively locate the transducer on the anterior chest wall, over four convergent lines meeting near the intersection point of the midclavicular line and the third intercostal space Fig. 2.

Study design, ethics and consents

This diagnostic trial was an experimental prospective study. The ethics committee of Tabriz University of Medical Sciences approved the study protocol without mandating any informed consent because the trial did not interfere with conventional diagnostic or therapeutic processes or with the patients' autonomy in the emergency setting. No report of individual patient data was intended as well.

Setting

The study was performed over 12 months from November 2008 to November 2009 in the resuscitation room of the emergency department. This crowded department has an average of 65,000 presentations per year, including both medical and trauma patients.

Selection of participants

The study initially involved adult moderate or severe multiple trauma patients who had already been triaged and labeled as level 1, 2 or 3 by the Emergency Severity Index (ESI) Triage Algorithm Version 4. According to the ESI-4, patients who require an immediate life-saving intervention are labeled as level 1. The system labels patients as level 2 based on the following criteria: participation in high-risk situations, confusion, lethargy, disorientation, severe pain or distress or vital signs in  In practice, the anatomical locations for probing provide sufficient views to detect even anterior apical or anterior basal entrapped air the danger zone. Level 3, which accounts for the majority of moderate injuries, is applied to other patients than level 1 or 2. In this level, patients need more than one resource in their medical approach and do not have any vital sign in the danger zone.

As discussed below, we included hemi-thoraces in the study on a case-by-case basis. Ultrasound (US) results of either side were recorded independently from the contra-lateral side. Hemi-thoraces were excluded if a patient's respiratory distress or clinical status mandated a PTX needle decompression or early chest tube placement before the computed tomography (CT) imaging. We had not yet proved the time efficiency of this first time trial. Thus, we could not ethically use this suggested adjunct to decide on such early interventions prior to the conventional clinical assessments. Hemithoraces in patients with severe chest wall pain who could not tolerate the placement of the transducer on their chest were also excluded. As presented in Fig. 3, all eligible hemi-thoraces were extracted from 130 patients who were sent to the trauma resuscitation room for primary exams.

In addition to one patient who left the ED against medical advice after the primary survey, the clinical status of three other patients required bilateral decompression of their hemothorax and/or PTX before the end of the primary survey.

A total of 81 patients who were primarily examined using US were considered to be moderate multiple trauma patients. Although they were excluded from the study, we believe that a comparison of their US results with their CXRs contributed to the optimization of the US operator's level of expertise.

All 45 patients with severe chest injuries underwent ultrasonography followed by CXR and CT scanning. Because of the direct rapid disposition of 15 patients with substantial chest injuries from the ED radiology room to the surgery service, we did not have access to the radiography results of those patients. Nevertheless, a routine transfer of CT plots through the ED enabled the physicians in charge to report the results of the chest CT scans in these patients. Of these 15 patients, six underwent placement of a unilateral chest tube before CT imaging, due to their respiratory distress. Thus, hemithoraces with inserted chest tubes were excluded, and the contralateral hemi-thorax results were independently incorporated into the analysis.

To estimate the required procedure time, we used a pilot time study based on the time records from the previous 15 patients. Limiting the pilot sampling to the last 15 cases favored a more reliable time estimate when the operator had reached a constant and sufficient level of expertise based on his past chest ultrasonographies.

We determined how to collect the data before performing our index test US and the reference test (CT scan).

Assignments

To avoid any inter-reviewer error, a single operator (corresponding author, emergency medicine resident PGY2-3) performed all the chest ultrasonographies. According to current standards, this operator had general emergency US competency derived from residency-based experience. Requirements to satisfy such standards include the performance of at least 150 US examinations in "critical" scenarios, rotation experience and supervised quality assurance with timely feedback during training [16]. After performing ultrasonographies on eight hemithoraces under the supervision of an attending physician, the operator then performed them independently. CTs of these training cases, with either normal or abnormal findings, were not included in the study, nor were their US results.

Based on the previously explained advantages of ultrasounds, all 130 patients who were sent to the resuscitation room underwent the anterior convergent chest US exam. The operator was required to perform the chest ultrasound exams as an adjunct to step B in the primary survey (Breathing; Ventilation and Oxygenation).

Chest CT scanning is generally considered to be the gold standard, particularly when the clinical status does not mandate an early chest tube insertion [17,18].

Because exposing all patients to CT radiation is not ethically or medically accepted, the gold-standard application was limited to patients with severe multiple trauma who were routinely included in our hospital's whole body scan protocol. This protocol included spiral CT scans of the head, neck, thorax, abdomen and pelvis along with scanning of the skull, vertebral and pelvic bone windows for all multiple trauma patients with substantial injury mechanisms. Abdominopelvic IV contrast images were obtained for substantial injury mechanisms, including falls from > 20 ft; automobile crash with extrusion > 12 in. on occupant site or > 18 in. on other sites; ejection from the automobile; death in same passenger compartment; auto-pedestrian /bicyclist thrown, run over or with substantial impact; and motorcycle accident > 20 mph. This selection strategy allowed us to assess the results of each hemi-thorax, independent of the contra-lateral hemi-thorax status. For example, of six patients with unilateral chest tubes inserted during the primary survey, only six hemi-thorax CT scan results were included in the analysis. Finally, patients underwent CT scans if they were sufficiently hemodynamically stable to be transferred out of the emergency department for imaging.

Consequently, moderate chest injuries were solely imaged using CXR after the primary survey during the phase known as Adjuncts to the Primary Survey and Resuscitation.

Chest US, radiography and CTs were all performed with the patient in a supine position. The US operator reported his results before learning of the radiography and CT results. CXRs and CTs were reported by physicians who were responsible for performing the secondary surveys. All these physicians were PGY2 emergency medicine residents, and they were all blinded to the chest US findings. The overall independent results of the imaging interpretations were recorded as positive or negative for PTX based on primary check lists. CT reports were noted in the checklist if the scan results were recorded within a one-hour interval after the US exam.

The recording times of the procedures were determined by the operator who used the US machine screen timer. The time between the first contact of the transducer over the first hemi-thorax until the transducer was removed from its last location on the second hemithorax was defined as the procedure time.

Methods of measurement

All present PTX ultrasound findings were based on features reflecting images from superficial pleural components. Therefore, to achieve our goal of a rapid exam, we modified the conventional US exam to include only B-mode images obtained by a 9-MHz linear transducer. The transducer was positioned over four locations above the anterior wall of each hemi-thorax. Each location was capable of providing the operator with a lung view through the window of at least one intercostal space.

These four linear positions generally created a radiation appearance around a point where the midclavicular line intersected with the third intercostal space. The shortest depth to the pleura was expected at these locations (Fig. 1).

Our chest US operator utilized the currently available US indicators that suggest a PTX, namely, the "pleural line", which is defined as a horizontal hyper-echoic line between the ribs [4,19]; the "sliding sign" (or gliding sign), which is visualized as a back-and-forth movement during the respiratory cycle and is caused by the visceral and parietal layers moving against each other [20][21][22][23]; and the "comet-tail artifact," which is known as a hyperechoic reverberation artifact that reflects the pleural line and is thought to rule out PTX [23][24][25][26] (Fig. 4).

Outcome measures

The primary focus of the study was to determine the sensitivity of our newly established method for chest US probing. Other diagnostic characteristics will be described below according to the following formulas [27,28]:
Sensitivity ¼ True Positive Rate ¼ TP TP þ FN Specificity ¼ True Negative Rate ¼ TN TN þ FP Positive Predictive Value ¼ probability that the disease is present when the test is positive ¼ TP TP þ FP Negative Predictive Value ¼ probability that the disease is not present when the test is negative ¼ TN TN þ FN
Moreover, the evaluation of all previously explained ultrasound diagnostic features, including the sliding sign, during one respiratory cycle was expected. A complete respiratory cycle lasts approximately five to ten seconds in a normal adult (with six to twelve respirations per minute). Thus, with regard to the use of eight locations, the entire US procedure on a single patient theoretically could be completed within 40 to 80 s. The mean required time for a single US application was considered our secondary outcome measure.

Data collection and processing

The chest US, CXR and chest CT scan results from 84 hemi-thoraces were recorded and saved in SPSS 15.0 / Win . The PTX results included in the obtained crosstabulations (Tables 1 and 2) were the initially processed results, and the chest ultrasound and chest radiography with chest CT scan findings were compared. The results from each hemi-thorax were independently incorporated into the calculations and estimations. To avoid correlated data between hemi-thoraces, the US diagnostic values were determined independently of the PTX prevalence of any particular chest trauma level of severity. P-values less than 0.05 were considered to be statistically significant for all results.

The procedure times were saved in a Word file for subsequent calculations using relevant software.

Primary data analysis

We used the findings of Zhang et al. [4] to estimate the sample size, which was computed using the Pearson chi square test for two proportions and with the elements fixed as Alpha = 0.05 and nominal power = 80 %.

Because our study evaluated the screening capabilities of a test, the sample size with a total number of 76 cases was calculated using the SAS system for a sensitivity of 0.86 (for proportion 1; US) and 0.27 (for proportion 2; CXR) and an actual power of 0.81, which were all extracted from the study by Mao Zhang et al. An ideal screening test is closely dependent on its sensitivity, as will be addressed in the "Discussion". However, to increase the validity of the study and further exclude exams throughout the trial, we continued collecting samples until the overall number of 84 exams was reached.

Results and discussion

Main results

According to the contents of Table 1, of 84 results obtained using chest US exams for detecting PTX, 14 were true positives (TPs), 61 were true negatives (TNs), five were false positives (FPs), and four were false negatives (FNs).

Similarly, of 60 CXR results as presented in Table 2, four TP, 48 TN, one FP and seven FN results were extracted.

A mean required time of 64 s for performing the US exam on each patient was calculated with a standard error of the mean (SEM) of 10 s.

As explained above, the "sliding sign" represents a linear back-and-forth movement that disappears in the presence of PTX. Nonetheless, in practice, the   immobilized PTX US views appeared to exhibit more than a simple absence of linear movement. Instead, they appeared to present a diffuse static background. Consequently, as the inductive part of our research, we called this phenomenon the "Gestalt Lung Recession."

Sensitivity analysis

Diagnostic values were calculated when the aboveobtained quantities were entered into MedCalc software (Ver. 10. 4.; Frank Schoonjans). The same software produced predictive values based on the use of Fisher's exact test (Table 3). The kappa agreement coefficient indicated a very acceptable inter-reviewer validity of > 0.8 for the reports (p < 0.001) [29].

Limitations

Assuming that a PTX diagnosis is not a core emergency US application [17], our inability to comply with the completion of the US training over 25-50 cases was a limitation. Fortunately, as the study progressed, we found previous research that suggested that one didactic one-hour session was sufficient for improving PTX recognition skills [30]. A spontaneous PTX case during our short supervised training schedule was a valuable opportunity for our operator to evaluate the presentation of PTX, which occurs infrequently. We offer US training using artificial PTXs to practitioners who intend to reproduce our model, although we do not have the appropriate setting for the training [31,32]. The "emergency ultrasound guideline" we used and offered during our study is the minimal requirement and basic standard for performing an US examination. However, because an US examination is an experience-dependent skill, an operator with more experience is expected to exhibit better practice.

Estimating the PTX volume requires determining the borders at which the sonographic PTX features disappear [33]. To determine this point at which a normal lung pattern replaces the PTX pattern, known as the "lung point," each examination needs to be extended to the lateral regions of the chest wall [19]. Without extension to the lateral chest walls, an "Anterior Convergent" chest US is unable to provide an estimation of the PTX volume. However, because the volume estimation could be time consuming, it contradicted the main purposes of the study.

US diagnostic findings also have inherent limitations. The comet-tail artifact is usually known as a pathologic finding [34], and its absence might be expected in completely normal lung parenchyma. "Lung sliding" might disappear when an adhesion of parietal and visceral pleura exist [14] as well as in conditions such as bullous emphysema and advanced chronic obstructive pulmonary disease [5]. Such limitations might be able to explain how five false-positive results occurred among our 84 exams. Conversely, chest wall contractions should be distinct from "lung sliding." Finally, subcutaneous emphysema can cause a poor view, and unusual locations of entrapped air might theoretically be masked from being detected in our model. All such mechanisms could have contributed to our four false-negative results.

Moreover, it should be acknowledged that a larger sample size could have increased the power of our study.

Discussion

The CXR specificity of 98 % and the positive predictive value of 80 % were similar to the findings of our chest US exam, which were 93 % and 74 %, respectively, and the differences were not significant (p = 0.1). The "Anterior Convergent" chest US demonstrated a sensitivity that was significantly superior to that of CXR (78 % versus 36 %, respectively, with p < 0.001). Although they are not values, the diagnostic characteristics of "Anterior Convergent" chest probing exhibited the same accuracy pattern based on the available studies that investigated the conventional models of detecting PTX using US.

The clinically valuable high NPV of 93 % indicates that a negative US result can strongly rule out PTX. Of course, the NPV of CXR was acceptable and did not indicate a significant difference from the US NPV (p = 0.14). However, the US diagnostic values are related to the clinical importance of both "sensitivity" and "specificity." Tests with high specificity are required when the risky consequences of a false-positive diagnosis are a primary concern. Chemotherapy following a falsely diagnosed malignancy is an example. In fact, highly specific tests are optimal for ruling in diseases, and we believe that CXR is still a valuable test in such instances. However, a test with a high sensitivity is obviously required when a patient is exposed to a high risk by a potential false-negative diagnosis. Patients with occult PTX who require either bag mask ventilation during the primary survey or  [35,36]. As a result, until diagnostic alternatives with higher sensitivity are developed, chest US remains the superior bedside screening test for detecting PTX in supine trauma patients.

We believe that chest US using the "Anterior Convergent" probing method constitutes a feasible model for clinical practice, particularly during critical moments of the primary survey. The pilot result of 64 s for the mean required time was considerably lower than the estimations of two to four minutes to perform formal chest US [5]. We particularly recommend optimization of the related sensitivity. Serial US exams during the secondary survey using the "Anterior Convergent" model appear to be a viable option [37]. Adding a single thoracic US using conventional whole-chest probing and integrating it into FAST of the secondary survey might be another strategy.

The minimal probing and transducer positioning were required in practice as we expected from the "Anterior Convergent" model. Nevertheless, we intend to explain some other aspects of this experience to assist clinicians who intend to use our model in their practice.

A pre-set depth of five to six centimeters on the US machine provided a convenient view in most patients. Performing an accurate US exam generally appeared to be easier among males and small patients than on females or obese patients. The breast tissue was implicated in the poor view in the female patients. Different layers of the chest wall could easily resemble the "pleural line," particularly for a non-expert operator. Our overall concept of "lung sliding" was the indicator with the lowest specificity but the highest sensitivity [38]. The presence of this sign contributed to the quickest ability to rule out the presence of PTX, whereas the absence of this sign is unreliable for ruling in the diagnosis.

Furthermore, we investigated "lung sliding" beyond the traditional definition of the sole pleural movement. Shadows and artifacts associated with the pleura induce a back-and-forth movement of the entire image background. In the presence of PTX, the absence of such a diffuse dynamic appearance creates a static view that we named the "Gestalt Lung Recession." The "Gestalt Lung Recession" appeared to be more specific for ruling out PTX than the absence of "lung sliding."

The performance time appeared to be shorter among patients with tachypnea and relatively prolonged in patients with bradypnea or a poor view, including those with subcutaneous emphysema.

Despite the growing body of evidence confirming the advantages of chest US in the diagnosis of PTX, a general hesitation is still apparent in global guidelines. The American College of Emergency Physicians (ACEP) has recommended the use of ultrasound for the detection of PTX but did not provide the details of such methods [16], and the American College of Surgeons (ACS) has not included thoracic US in the 9 th edition of ATLS [39]. Therefore, future studies should review the literature for all unidentified aspects of chest US, either independently or in conjunction with meta-analyses. Such reviews should include the available models of chest US and comparisons of the models and the diagnostic presentation of PTX for all available model types. Such a comparison should include all aspects of accuracy and time effectiveness as well as the performance feasibility of each method or the recognition indicators for different conditions. If such studies are capable of explaining the above-mentioned uncharted issues, they will break through the current trauma guidelines and ATLS.

Our study would have provided much more raw data for future analysis if it had been primarily designed to repeat the "Anterior Convergent" protocol during the secondary survey. If that strategy had been employed, the sensitivity of both single-practiced and duelpracticed "Anterior Convergent" chest probing could have been compared. Any higher level of sensitivity for the dual-practiced model would have emphasized the "Anterior Convergent" model as the ideal method for detecting PTX during the primary survey.

Conclusion

Ultimately, we believe that "Anterior Convergent" chest probing is a practical method for detecting PTX. This approach is a more appropriate screening test than both supine conventional CXR and lung auscultation. We particularly recommend using this brief but efficient technique during the emergent minutes of the primary survey in trauma patients who present to the emergency department, and we currently use this application in our center.

It is reasonable to predict that clinicians will one day consider US machines with the same familiarity they currently feel for their stethoscopes, although US machines are more sophisticated.

Fig. 2
2Anatomic guide to "anterior convergent" chest ultrasonography. The operator locates the transducer on the anterior chest wall over four convergent lines meeting near the intersection point of the midclavicular line and the third intercostal space

Fig. 1 "
1Anterior convergent" chest ultrasonography for pneumothorax.

Fig. 3
3Case selection. The diagram depicts the case selection process and the number of patients involved in each preferred diagnostic imaging

Fig. 4
4Ultrasound pattern of the normal lung in B-mode with a linear transducer. The pleural line is depicted as the hyper echoic line limited between two sequential ribs. The backward and forward movement of pleural layers against each other occurs on the same level as the pleural results in lung sliding. The comet-tail artifact is a reverberation artifact arising from the pleural line and appears as a hyper echoic line parallel to the pleural line and at the depth of the parenchyma

Abbreviations ACEP: American College of Emergency Physicians; ACS: American College of Surgeons; ATLS: Advanced Trauma Life Support; CT: computed tomography; CXR: chest radiography; ED: Emergency Department; EFAST: Extended Focused Abdominal Sonography for Trauma; FAST: Focused Abdominal Sonography for Trauma; FN: false negative; FP: false positive; LR: Likelihood ratio; NPV: negative predictive value; PPV: positive predictive value; PTX: pneumothorax:; SEM: standard error of the mean; TN: true negative; TP: true positive; US: ultrasound.

Table 1
1Cross-tabulation of the ultrasound results by CT scan for pneumothoraxResults for pneumothorax
Total

Negative CT
scans

Positive CT
scans

Results for
pneumothorax

Negative
ultrasounds

61
4
65

Positive
ultrasounds

5
1 4
1 9

Total
66
18
84

Table 2
2Cross-tabulation of chest radiography results by CT scan for pneumothoraxResult for pneumothorax
Total

Negative CT
scans

Negative CT
scans

Results for
pneumothorax

Negative chest
radiographies

48
7
55

Positive chest
radiographies

1
4
5

Total
49
11
60

Table 3
3Diagnostic characteristics of index tests for pneumothoraxChest ultrasound
Chest radiography
Comparison

Parameter
Value
95 % CI
Value
95 % CI
P-Value

Sensitivity
78
52-93
36.4
11-69
<0.001

Specificity
92
83-97
98
90-100
0.1

LR+
10
4.3-25
18
2-144
0.007

LR-
0.25
0.1-0.6
0.65
0.4-1
<0.001

PPV
74
49-91
80
28-99
0.4

NPV
94
85-98
87
75-95
0.14

LR+: positive likelihood ratio; LR-: negative likelihood ratio; CI: confidence
interval. All values are expressed as percentages, except for LR+ and LR-

AcknowledgmentsWe sincerely appreciate Dr. Morteza Ghojazadeh (MD, MPH), Assistant Professor of Physiology, Tabriz University of Medical Sciences, Iran, for the statistical advice and data analysis. We also thank all Emergency Medicine Residents of Imam Reza Hospital, Tabriz who helped us in reporting and recording CXRs and chest CTs. No remuneration was provided during the study. The authors would also like to acknowledge the professional manuscript services of American Journal Experts.Competing interestsThe authors declare that they have no competing interest.Authors' contributions BZ conceived the solution, proposed the theoretical model of "Anterior Convergent" probing, performed ultrasound exams and explained the "Gestalt Lung Recession" concept. He has depicted the concept in the article. HSH stated the problem and trained the operator for chest ultrasonography. He supervised the data collection and conduct of the study and managed quality control during the project. Both authors read and approved the final manuscript.• We accept pre-submission inquiries • Our selector tool helps you to find the most relevant journal Submit your next manuscript to BioMed Central and we will help you at every step:
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