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Ann Thorac Surg 1999;67:329-331
© 1999 The Society of Thoracic Surgeons

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Original Articles

POSSUM scoring system as an instrument of audit in lung resection surgery

^a Department of Thoracic Surgery, University of Ancona, Ancona, Italy
^b Department of Epidemiology, Biostatistics and Medical Information Technology, University of Ancona, Ancona, Italy
^c Department of Biomedical Sciences, University of Ancona, and Fon dazione Anziano Operato "Biancalana-Mastera," Ancona, Italy

Accepted for publication June 28, 1998.

Address reprint requests to Dr Brunelli, Via S. Margherita 23, 60129 Ancona, Italy

	Abstract

Top Abstract Introduction Patients and methods Results Comment References

 
Background. The physiological and operative severity score for the enumeration of mortality and morbidity (POSSUM) is a scoring system that was validated in general surgery with the aim of being used as an instrument to evaluate surgical outcome. We applied POSSUM to a population of lung resection candidates to assess its capability to predict postoperative complications.

Methods. Two hundred fifty lung resection candidates were prospectively evaluated from 1993 through 1996. The POSSUM value was entered along with other variables (sex, smoking history, type of resection, pulmonary function tests, arterial carbon dioxide, serum albumin level, total lymphocyte count, neoadjuvant chemotherapy and radiotherapy, and diabetes) in a multivariate analysis to identify independent predictors of postoperative morbidity.

Results. Logistic regression analysis showed POSSUM was predictive of postoperative complications, showing no significant difference between predicted and observed morbidity (² test, p > 0.05).

Conclusions. We think POSSUM can be appropriately used as a tool of surgical audit in lung resection operations.

	Introduction

Top Abstract Introduction Patients and methods Results Comment References

 
In the present era of medical cost analysis, the individualization of instruments of evaluation of the surgical outcome in relationship to the physiologic state of the patient at the time of the operation and to the type of procedure performed is recommendable. A physiological and operative severity score for the enumeration of mortality and morbidity (POSSUM) was validated for this purpose in a number of general and vascular surgery populations [1–4], but never tested in a thoracic surgery setting.

	Patients and methods

Top Abstract Introduction Patients and methods Results Comment References

 
We prospectively applied POSSUM to 250 consecutive lung resections (36 minor resections, 160 lobectomies, 54 pneumonectomies) for cancer, from 1993 through 1996, to evaluate its ability to predict postoperative complications. These were limited to those occurring within 30 days of the operation or during a longer period if the patient was still in the hospital, and included respiratory failure requiring mechanical ventilation for more than 48 hours, pneumonia, atelectasis requiring bronchoscopy, bronchopleural fistula, adult respiratory distress syndrome (ARDS), hemothorax requiring reoperation, pulmonary edema, pulmonary embolism, myocardial infarction, arrhythmia requiring medical therapy, and death.

The physiologic score (PS) is a 12-factor, four-grade score including age; cardiac status; pulse rate; systolic blood pressure; respiratory status; Glasgow Coma Score; serum concentration of urea, potassium, and sodium; hemoglobin concentration; white blood cell count; and findings on the electrocardiogram. The operative severity score (OSS) is a six-factor, four-grade score including type and number of procedures, total blood loss, peritoneal contamination, presence and extent of malignancy, and timing of operation. These scores were calculated according to Copeland and associates [1], substituting the peritoneal soiling factor with a pleural soiling factor, and assigning 8 points to extended resection (associated with chest wall resection, lymph nodes dissection, and resection of other mediastinal structures), 4 to lobectomy and pneumonectomy, and 2 to atypical resection and segmentectomy in the severity factor.

Two models were analyzed by means of logistic regression, in which the dependent variable was the postoperative outcome and the independent variables (predictors) were, respectively, PS and OSS in model 1; and in model 2, PS, OSS, and a number of preoperative predictive factors including sex, smoking history (smoker, nonsmoker, former smoker of at least 3 months’ cessation), type of resection (extended, nonextended), pulmonary function tests expressed as percentage of predicted values for age, sex, and body surface area (forced expiratory volume in 1 second [FEV₁], forced vital capacity [FVC], FEV₁/FVC, predicted postoperative FEV₁ [ppoFEV₁]—the latter was calculated on the basis of functioning segments removed and estimated by computed tomographic scan and bronchoscopy as stated in a previous report [5]), arterial carbon dioxide, serum albumin level, total lymphocyte count, neoadjuvant chemotherapy and radiotherapy, and diabetes. The statistical significance of each independent variable in each model was tested with the Wald test statistics [6] with a level of significance of p less than 0.05. The statistical significance of each model was assessed by means of the likelihood ratio test, or G statistic [6].

Models 1 and 2 were compared by means of receiver operating characteristic (ROC) analysis. This analysis generates a curve and the area under the ROC curve represents the probability of concordance between the predicted and the observed postoperative morbidity. This test has been described as the best index of detectability [7]. Finally, patients were grouped in deciles of predicted morbidity, and for each decile the number of predicted complications was compared with that observed by means of the ² test. If the test was significant at the 5% level it indicated no agreement.

	Results

Top Abstract Introduction Patients and methods Results Comment References

 
The morbidity rate in our series was 22% (55 cases) with a mortality rate of 2% (5 cases). One third of the patients with complications had more than one complication. Complications in order of frequency were arrhythmia (21 cases), respiratory failure (11), hemothorax (8), atelectasis (7), bronchopleural fistula (7), pneumonia (7), pulmonary edema (3), ARDS (2), pulmonary embolism (1), and myocardial infarction (1). Table 1 shows the results of logistic regression analysis. Both models had a significant G statistic value, and no difference was noted between them (p = 0.3).

View larger version (27K): [in this window] [in a new window]   Fig 1. Receiver operating characteristic curves for the two predictive models. The diagonal line indicates a predictive ability of the variables no better than chance (true positive rate equal to false positive rate). The area under the curve for model 1 is 0.66 and for model 2 is 0.67.

View larger version (23K): [in this window] [in a new window]   Fig 2. Comparison between observed and predicted postoperative morbidity by model 1 (A) and model 2 (B). No differences were found between predicted and observed complications (2 test, p > 0.05).

	Comment

Top Abstract Introduction Patients and methods Results Comment References

 
We think POSSUM is suitable for audit in lung cancer surgery and may be a valuable tool in comparing performances of different units, different surgeons, and even different periods of activity of the same unit. The POSSUM scoring system is quick and easy to use in all type of hospitals, is constituted by validated and weighted variables, and, at variance with other scoring systems such as APACHE II [8], takes into account morbidity, which is in our view at least as important as mortality in the evaluation of the quality of surgical care and costs.

	References

Top Abstract Introduction Patients and methods Results Comment References

 

1. Copeland G.P., Jones D., Walters M. POSSUM: a scoring system for surgical audit. Br J Surg 1991;78:356-370.
2. Sagar P.M., Hartley M.N., Mancey-Jones B., Sedman P.C., May J., Macfie J. Comparative audit of colorectal resection with the POSSUM scoring system. Br J Surg 1994;81:1492-1494.[Medline]
3. Copeland G.P., Jones D., Wilcox A., Harris P.L. Comparative vascular audit using the POSSUM scoring system. Ann R Coll Surg Engl 1993;75:175-177.[Medline]
4. Whiteley M.S., Prytherch D.R., Higgins B., Weaver P.C., Prout W.G. An evaluation of the POSSUM surgical scoring system. Br J Surg 1996;83:812-815.[Medline]
5. Brunelli A., Fianchini A. Predicted postoperative FEV₁ and complications in lung resection candidates. Chest 1997;111:1145-1146.[Free Full Text]
6. Hosmer D.W., Lemeshow S. The multiple logistic regression model. In: Hosmer D.W., Lemeshow S., eds. Applied logistic regression. New York: J Wiley & Sons, 1989:25-37.
7. Metz C.E., Wang P.L., Kronman H.B. A new approach for testing the significance of differences between ROC curves measured from correlated data. In: Deconik F., ed. Information processing in medical imaging VIII. The Hague: Nijhof, 1984:432-445.
8. Knaus W.A., Draper E.A., Wagner D.P., Zimmerman J.E. APACHE II: a severity of disease classification system. Crit Care Med 1985;13:818-829.[Medline]

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This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Add to Personal Folders Download to citation manager Author home page(s): Alessandro Brunelli Permission Requests Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Brunelli, A. Articles by Carle, F. Search for Related Content PubMed PubMed Citation Articles by Brunelli, A. Articles by Carle, F.