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A model for predicting prolonged length of stay in patients undergoing anatomical lung resection: a National Surgical
Matthew R DeLuzio1, Hari B Keshava2, Zuoheng Wang3
1Department of Surgery, Yale School of Medicine, New Haven, CT, USA.
Objectives:
There are currently no studies that have specifically delineated the risk factors for a prolonged length of hospitalization in patients undergoing anatomical lung resection. Knowing these risk factors is important in terms of risk stratification and improving outcomes in the high-risk population. The goal of this study was to identify risk factors associated with a prolonged length of stay (≥14 days) in patients undergoing an anatomical lung resection and to further create a model for predicting the probability of a prolonged length of stay in these patients.
Methods:
The NSQIP database (2005-2013) was culled for data on 45 distinct preoperative, intraoperative and postoperative variables among patients undergoing anatomical pulmonary resections. Univariate and multivariate logistic regression analyses were used to determine variables that contributed to a prolonged length of stay. A scoring system was created based on these results and applied to internal and external (a single institution database) validation groups to test for the adequacy of the model through the comparison of receiver operating characteristic curves.
Results:
Fifteen factors were found to be significant for prolonged length of stay; six were preoperative (age >70 years [P < 0.0001], functional status-dependent [P = 0.0020], chronic obstructive pulmonary disease [P < 0.0001], serum sodium <135 mmol/l [P = 0.0200], ASA Class 3 [P = 0.0070] and ASA Class 4 or 5 [P = 0.0010]), one was intraoperative (open thoracotomy [P < 0.0001]) and eight were postoperative (pneumonia [P < 0.0001], unplanned reintubation [P < 0.0001], prolonged mechanical ventilation [P < 0.0001], urinary tract infection [P < 0.0001], stroke [P = 0.0020], transfusion [P = 0.0010], deep vein thrombosis/thrombophlebitis [P < 0.0001] and return to the operating room [P < 0.0001]).
Conclusions:
A simple model for predicting the probability of a prolonged length of stay in patients undergoing anatomical lung resection has been successfully created. This model can allow for better risk stratification of patients preoperatively based on certain existing comorbidities, and can help to predict the impact the development of various postoperative complications will have on overall patient outcomes.
