We set out to determine whether our newly developed scale could be useful as a predictor of PAL. Although there have been many studies to predict PAL, most of which have tried to determine predictive risk factors by comparing PAL groups with other groups and reported various risk factors such as poor pulmonary function [6, 8–10], poor nutrition [7], or specific operative findings [8, 10], but their results were not consistent with each other and therefore are of limited clinical use. Brunelli et al. [10] in 2010, unlike previous studies, reported a risk factor scoring system for prediction of PAL. He elicited 4 predictors from a study group of 658 lobectomy patients: age > 65 years, presence of pleural adhesion, FEV1% < 80, and BMI < 25.5 kg/m2, and different scores were given to the factors according to their weights. The scoring system was validated externally in 233 other hospital patients. However it also had a limitation because of its low positive predictive value of about 25%.
Various investigators have examined the concept that grading the amount of early postoperative air leak might be helpful in predicting PAL [13, 14]. In 2001, Cerfolio et al. [13] reported that PAL can be predicted by air leak grade on POD 1. In their study, a commercially available air-leak meter, scoring leaks from 1 to 7 with 7 being the highest, was used. However, originally his work aimed to evaluate the effectiveness of water seal for stopping air leaks. Thus he did not give much weight to air leak grading as a predictor of PAL. In addition, the air leak grading system devised by Cerfolio et al. requires special equipment, and thus the system is not widely used.
To the best of our knowledge, the present study is the first practical attempt to focus only on quantifying air leakage in the early postoperative period to predict PAL. The easy-to-use variable, SUM4to9, has the highest positive predictive value among reports until now. Our air leak grading system, to obtain SUM4to9, needs no special equipment, and yet it is very convenient to apply in the clinical field. Based on our results, we can now decide whether to wait or perform a reintervention (e.g. pleurodesis or redo surgery) for air leak cessation on POD 3.
In addition to proposing a practical and effective method to predict PAL, this study tangibly confirms our hypothesis that the amount of early postoperative air leak predicts air leak duration, by correlation analyses of SUM variables with air leak duration. Furthermore, it reveals that other preoperative or intraoperative variables do not increase the predictive power of SUM4to9. These findings can be integrated to mean that 1) grading postoperative air leak might be the only factor needed to predict PAL, and 2) the effects of various possible factors contributing to prolongation of air leak might combine to result in the grade of early postoperative air leak. Therefore, future studies aiming at providing more accurate prediction of PAL will have to focus on the evaluation of early postoperative air leak in terms of when, by what method, and how frequently it should be measured, not on other indirect factors.
However, this study has the following potential limitations. First of all, the retrospective nature of the study might have incurred some problems in defining and recording the variables. In particular, since air leak cessation was determined retrospectively from medical records, there might have been some discrepancy between the real and the defined cessations. Essentially, our air leak grading is based on subjective assessment, albeit by specially trained nurses, so some might question its reliability. However, over the past several years, we have empirically recognized that its simplicity enables strong interobserver agreement, as shown in the test to see the reliability of our air leak grade. Recently developed digital airflowmetry may be helpful in further increasing interobserver agreement in future studies.