A Nomogram For Predicting Prolonged Length Of Stay After Valve Surgery Via Mini-thoracotomy
Vito Domenico Bruno, MD, PhD, Bleri Celmeta, MD, Tommaso Viva, MD, Antonio Miceli, MD, PhD, Mattia Glauber, MD.
IRCCS Galeazzi - Sant'Ambrogio Hospital, Milano, Italy.
BACKGROUND: It is generally acknowledged that minimal access incisions are associated with reduced postoperative length of stay (LOS). Previous evidence showed that mini-thoracotomy positively impacted postoperative LOS even when compared to mini-sternotomy: however contrasting results have been reported and defining potential factors affecting LOS would be beneficial. We aimed to identify predictors for prolonged LOS in valve surgery conducted via mini-thoracotomy.
METHODS: We retrospectively reviewed 189 patients operated for aortic or mitral or tricuspid valve surgery conducted via a mini-thoracotomy at our institution. The most common preoperative characteristics were collected and analysed to predict prolonged LOS, defined as more than 7 postoperative days. Univariable and multivariable logistic regression analyses were used to screen the predictors and develop a risk nomogram.
RESULTS: The mean postoperative LOS was 9.13 days (median: 7 days). 64 patients (33.9%) had prolonged LOS. These patients were older, more frequently in NYHA Class III or IV and with worst left ventricular function and with higher incidence of reoperation and chronic kidney disease (CKD). A multivariable logistic regression model showed that the most significant preoperative factors predicting prolonged LOS were age (OR 1.03, 95% CI 1.01-1.06, p = 0.02) and redo surgery (OR 3.33, 95% CI 1.29-8.9, p = 0.01). A prognostic nomogram based on seven preoperative variables was developed (Fig.1).
CONCLUSIONS: Minimal invasive valve surgery has a positive impact in reducing LOS. The most important factors impacting on this outcome are represented by age and redo surgery, although several other preoperative characteristics have a potential role in delaying recovery. Further larger multicenter analyses are required to better define a prognostic model. 
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