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* From the Mallinckrodt Institute of Radiology (Drs. Gierada, Pilgram, Slone, and Mr. Villanueva), and the Divisions of Pulmonary and Critical Care Medicine (Drs. Lefrak and Yusen), and Cardiothoracic Surgery (Dr. Cooper), Washington University School of Medicine, St. Louis, MO 63110.
Correspondence to: David S. Gierada, MD, Mallinckrodt Institute of Radiology, Barnes-Jewish Hospital, 216 South Kingshighway Blvd, St. Louis, MO 63110; e-mail: gieradad{at}mir.wustl.edu
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Abstract |
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 TOP Abstract IntroductionMaterials and MethodsResultsDiscussionReferences |
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Design: Logistic regression analysis using QCT indexes of emphysema and preoperative physiologic test results as the independent variables, and the decision to operate as the dependent variable.
Setting: University hospital.
Patients: Seventy patients selected for bilateral lung volume reduction surgery and 32 otherwise operable patients excluded from surgery based on subjective assessment of emphysema morphology on chest radiography, CT, and perfusion scintigraphy.
Intervention: Bilateral lung volume reduction surgery in the selected group.
Measurements and results: Emphysema in patients selected for surgery was more severe overall and in the upper lungs by multiple QCT indexes (p < 0.01, unpaired two-tailed t test). Physiologic abnormalities were slightly more severe in selected patients (p < 0.05, unpaired two-tailed t test). The range of many QCT and physiologic values overlapped considerably between the selected and excluded groups. The percent severe emphysema (<- 960 Hounsfield units [HU]), upper/lower lung emphysema ratio (- 900 HU threshold), and residual volume were the key variables in the model predicting selection decisions (model r2 = 0.48; p < 0.0001). The model correctly predicted selection decisions in 87% of all cases, 91% of the selected group, and 78% of the excluded group. Surgical patients with a higher model-derived probability of selection had greater postoperative improvement in FEV1 and 6-min walk distance.
Conclusions: Radiologic selection criteria are applied consistently to the majority of patients. QCT features are strongly associated with selection decisions, are related to outcome, and may help improve consistency and confidence in patient selection.
Key Words: CT • emphysema • lung volume reduction surgery
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Introduction |
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TOPAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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The importance of anatomic features has been demonstrated in studies showing better clinical outcome after LVRS in patients with a heterogeneous distribution of emphysema that predominates in the upper lobes.6 7 8 9 A large percentage of patients evaluated for LVRS are excluded because they lack this pattern,3 9 and instead have a uniform distribution of emphysema throughout the lungs, or emphysema that is only mild or moderate in severity on imaging studies. However, these anatomic features are quite variable and frequently difficult to categorize, and individual selection decisions are accompanied by varying degrees of uncertainty and confidence.
In clinical practice, anatomic features are assessed subjectively by visual inspection of the imaging studies. Objective assessment is also possible, using whole-lung quantitative CT analysis (QCT). QCT measurements of lung density accurately reflect macroscopic emphysema.10 11 This technique has been used to assess the anatomic effects of LVRS12 13 and to identify preoperative anatomic features related to outcome after LVRS.14 Specific QCT guidelines for prospective use in selection have not yet been defined.
The objectives of this study were to use QCT to see whether subjective assessments of radiologic features are applied consistently, and to derive objective selection criteria based on QCT measurements, physiologic data, and the selection decisions that resulted from our standard preoperative evaluation. To do this, we compiled the numerous QCT and physiologic variables considered in the preoperative assessment, and compared them in selected and excluded patients. Using multiple regression analysis to identify the variables having the greatest impact on selection decisions, we developed a model for patient selection based only on objective, quantifiable parameters. We then assessed the relevance of the model to postoperative outcome.
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Materials and Methods |
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TOPAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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The selected group consisted of 70 patients selected for LVRS using criteria previously described,1 including review of radiologic studies as noted above, who had undergone LVRS between December 1993 and May 1995. These patients are from the first 142 patients to undergo LVRS at our institution, for whom CTs were available for QCT analysis. The total study group thus consisted of 102 patients: 51 women and 51 men, between the ages of 33 and 77 years (mean, 62 ± 8 years).
CT Scanning and Analysis
The preoperative CTs were performed without IV contrast during
full inspiration. Thirty were performed on a Somatom Plus 4 CT scanner
(Siemens Medical Systems; Iselin, NJ) using spiral technique with
0.75-s scan time, 8-mm section thickness, and 8-mm/s table
incrementation; 72 were performed on a Somatom Plus S scanner (Siemens
Medical Systems) using incremental technique, 1-s scan time, and 8-mm
(n = 10) or 10-mm (n = 62) section thickness.
Whole-lung QCT analysis was performed at the scanner operator’s console using the Pulmo software option (Siemens Medical Systems). This provides semiautomated segmentation of the lung in each image and automated calculation and display of pixel density statistics. The QCT variables in this study were defined to be analogous to the anatomic features considered important in visual assessment,6 15 and are listed in Table 1 . We used three indexes of global emphysema severity, three indexes of regional emphysema severity, two indexes of the heterogeneity in emphysema severity, and one index of the amount of reserve lung tissue (volume of lung minimally affected by emphysema).
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Interventions
Patients selected for surgery had bilateral LVRS to reduce the
volume of each lung by 20 to 30%, as previously
described.15
Lung was resected in areas that showed the
most severe emphysema on imaging studies and on direct inspection
during surgery.
Statistical Analysis
QCT and physiologic test values in the selected and excluded
patients were compared with two-tailed, unpaired t tests and
dot plots, using Excel 4.0 software (Microsoft; Redmond, WA).
Preoperative and postoperative physiologic test values were compared
using two-tailed, paired t tests. Logistic regression was
performed using JMP software (SAS Institute; Cary, NC). QCT and
physiologic parameters were entered as individual independent
variables, and the decision to operate as the dependent variable.
Backwards stepwise multivariate logistic regression was performed using
those parameters having the strongest association with the decision to
operate. The resulting model was internally validated by testing it on
the patients from whom it was derived. The effects of potentially
significant variables that the model rejected were tested by forcing
them into the model.
The relevance of the model to postoperative clinical outcome was assessed in two ways. First, the outcome of surgical patients for whom the model predicted exclusion from LVRS was compared with the outcome of surgical patients for whom the model predicted selection. Second, outcome of surgical patients having the highest model-calculated probability of selection was compared with the outcome of the other surgical patients. Postoperative improvements in FEV1, PaO2, and 6MW, 6 months after surgery, were used as measures of outcome. If 6-month data were unavailable, 3-month data were used (eight patients). One-month data were used in one patient lacking 3- and 6-month data. Patients who died prior to postoperative pulmonary function testing were excluded from this outcome analysis.
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Results |
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TOPAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Univariate logistic regression revealed statistically significant
associations (p < 0.05) between the decision to operate and the
variables for which mean differences between the two groups were
statistically significant, but in most cases the association was weak.
The five variables having 
2 > 12
(p < 0.001) and r2 > 0.10 were used in
multivariate logistic backwards stepwise regression. These included the
percent severe emphysema (- 960 HU threshold;
r2 = 0.35), the upper-lung percent
emphysema (- 900 HU threshold;
r2 = 0.15), the upper/lower-lung
emphysema ratio (- 900 HU threshold;
r2 = 0.11), the standard deviation of the
mean lung density ( r2 = 0.17), and the
percent predicted RV ( r2 = 0.13). This
produced a model in which the combination of percent severe emphysema
(- 960 HU threshold), upper/lower-lung emphysema (- 900 HU
threshold), and percent predicted RV explained nearly half of the
variability in the selection decisions ( r2
= 0.48; Table 3
). The standard deviation of the mean lung density and the upper-lung
percent emphysema did not make a significant contribution in the
multivariate model.
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0.5 as predictive of selection.
Predictions corresponded to actual decisions in 87% of all patients.
The model predicted acceptance for 7 of the 32 patients who were
excluded (22%), and predicted exclusion for 6 of the 70 patients who
underwent LVRS (9%). Physiologic changes in the patients who underwent LVRS are listed in Table 4 . Five patients died, for a hospital mortality rate of 7%, and these patients were excluded from the outcome comparison. Comparison of physiologic outcome after LVRS in operated patients for whom the model predicted exclusion vs outcome of those for whom the model predicted selection revealed a trend toward greater physiologic improvement in the patients for whom the model predicted selection (Table 5 ). Significantly greater mean improvement in FEV1 and 6MW was found for patients having model-derived selection probabilities higher than thresholds from 0.85 to 0.98 (outcome for patients with a probability of selection < 0.9 and > 0.9 is shown in Table 5 ). Correlations between the probability of selection and postoperative improvement in FEV1 (r = 0.34; p = 0.0048) and 6MW (r = 0.25; p = 0.048) were not high. However, when patients were stratified into quartiles according to the model-derived probability of selection, greater improvement in FEV1 and 6MW was seen in patients with a higher probability (Table 6 ).
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2 = 0; p = 0.98), and when removed, the
three other added variables became significant
(2 = 4 to 6; p = 0.01 to 0.04), and the
overall model containing six variables improved
(2 = 75; p < 0.0001;
r2 = 0.59). Internal validation revealed
minimal improvement in overall accuracy (88%) compared to the
three-variable model, with exclusion predicted for four of the patients
who were accepted, and acceptance predicted for eight of the patients
who were excluded. |
Discussion |
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TOPAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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The QCT comparison of selected and excluded patients confirms that overall, the subjective visual assessment of radiologic studies resulted in selection of the patients with more severe emphysema, and greater upper-lobe predominance of emphysema. The differences between the two groups in functional impairment shown by physiologic testing likely relate to these differences in emphysema severity found by QCT. Although the excluded group also had substantial emphysema by QCT, it was not quite as severe as in the selected patients, and the upper-lobe predominance was not as pronounced. However, the moderate amount of overlap between the selected and excluded groups suggests that radiologic criteria may not be applied consistently in some cases. This is understandable, considering that visual assessment is subjective.
By multiple regression modeling, the individual selection decisions in operable patients were accurately predicted from objective, quantitative data. An encouraging aspect of the model is that each component is analogous to a feature considered important in selection, according to criteria derived in part from studies assessing the relationship between preoperative variables and outcome.7 9 This suggests that QCT data could be a valuable adjunct for clinical use, potentially improving consistency and confidence in patient selection.
In the simplest model containing three variables (r2 = 0.48), the combination of the percent severe emphysema (-960 HU threshold) and the upper/lower lung emphysema distribution (-900 HU threshold) accounted for most of the variability in selection decisions (r2 = 0.42). Of these, the percent severe emphysema had the greatest influence (r2 = 0.35). RV was also a significant independent factor in the model, suggesting that the degree of hyperinflation was important in selection. The smaller influence of this variable may be due to a lack of variability among the candidates for LVRS, reflecting our approach of restricting evaluations to patients that have at least modest hyperinflation. The more complex six-variable model accounted for a higher proportion of the variability in selection decisions (r2 = 0.59), but resulted in only marginal improvement in the number of selection decisions correctly predicted.
The relationship between the model and postoperative physiologic
outcome helps to validate both the subjective selection criteria used
in LVRS and the quantitative model derived here. In stratified groups,
patients with the highest probability of selection calculated by the
model had substantially and significantly greater postoperative
physiologic improvement. There was also a trend toward lesser
physiologic improvement in the small number of patients operated on for
whom the model predicted exclusion. The lack of a high correlation
between the probability of selection and postoperative physiologic
outcome is likely due to the small range of probability values seen
among the selected patients, as nearly two-thirds of those selected had
a selection probability of 0.90.
Of the two emphysema threshold density values evaluated, the - 960 HU threshold had greater relevance to patient selection. The amount of severe emphysema defined by this threshold was the parameter that best discriminated between the excluded and selected groups in the comparison of mean values, and had the greatest impact in the multivariate model. This parameter also had the strongest correlation with clinical outcome after LVRS among multiple QCT and physiologic parameters in a previous study.14 Although the difference between selected and excluded groups in percent emphysema based on the - 900 HU threshold was also statistically significant (p < 0.01), the difference was not very large. Thus, while thresholds closer to - 900 HU on scans obtained with conventional section thickness have been found to correlate well with macroscopic emphysema in pathologic specimens,18 the - 960 HU threshold appears more appropriate for evaluating global emphysema severity using conventional CT section thickness in LVRS candidates.
Of the two QCT variables used to indicate the variation in lung density values, only the standard deviation of the mean density had a significant association with selection decisions by univariate logistic regression; this variable and the percent upper-lung emphysema dropped out of the final model, suggesting that they are influenced by one or more of the other stronger variables. Neither the standard deviation of the mean density nor the full width at half maximum of the density histogram reflect the spatial distribution of emphysema, however, and therefore they cannot be considered directly analogous to the degree of emphysema heterogeneity, ie, the unevenness in the spatial distribution of emphysema, or presence of surgical target areas. Although likely related in part to the upper/lower lung emphysema distribution, a quantitative measure of spatial heterogeneity, such as through texture analysis,19 or quantitation of continuous low-attenuation areas,20 might improve the model.
A shortcoming of the patient selection model developed here is the lack of a "gold standard"; it is unknown whether the clinical decisions to exclude patients were appropriate. The model predicted selection for 7 of the 32 excluded patients, indicating that 22% of excluded patients had relevant features similar to those of selected patients, suggesting that at least some excluded patients might benefit from LVRS. This could only be evaluated by operating on these patients. Of additional interest is that the model predicted exclusion for six of the selected patients. Since most of these patients obtained at least mild physiologic benefit, some excluded patients for whom exclusion was predicted by the model also might conceivably obtain some physiologic benefit from LVRS. This is supported by studies showing a lesser degree of but definite clinical improvement after LVRS in patients with a homogeneous distribution of emphysema throughout the lungs.8 However, the likelihood of physiologic improvement appears greater for those with higher model-derived probabilities of selection.
This study did not include some of the variables that were used prospectively in patient selection decisions. Radiographic hyperinflation, perfusion scintigraphy, self-reported dyspnea scores, and even the physical appearance of the patient are all factors that were undoubtedly considered to some degree, but were not a part of the quantitative model. Despite this, the modeling process was relatively successful. Including those additional features that could be quantified objectively might improve the model. The retrospective identification of patients for the excluded group, which relied on review of written records, may have introduced bias into the study. However, the excluded patients were identified prior to the quantitative analysis of their CT studies.
In summary, QCT analysis of lung density patterns shows that subjective radiologic evaluation resulted in selection of patients for LVRS in whom emphysema was more severe and had a greater upper-lung distribution compared to those excluded. The percent severe emphysema (- 960 threshold), upper-lower-lung emphysema ratio (- 900 HU threshold), and RV (percent of predicted) were the variables having the greatest impact on selection decisions. An objective selection model based on these variables accurately predicted the outcome of the selection process, and relates to postoperative physiologic outcome. Overlap of characteristics among selected and excluded patients and model prediction of selection of some excluded patients suggests that additional potentially appropriate patients may be identified using QCT.
These results help support the validity of QCT as a potential tool for patient selection in LVRS, and suggest that QCT could help to improve consistency and confidence in this often difficult aspect of LVRS. Prospective application of this model to subsequent LVRS candidates would be helpful in further testing its validity. Identification of other preoperative variables that are associated with postoperative outcome in addition to those currently considered in the preoperative assessment, such as inspiratory pressure,21 may refocus patient selection strategies over time. Such potential improvements to patient selection could also be incorporated into the type of model presented here.
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Acknowledgements |
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Footnotes |
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Supported in part by the American Lung Association of Eastern Missouri.
Received for publication July 26, 1999. Accepted for publication October 5, 1999.
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References |
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TOPAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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