ISMICS 17 Annual Scientific Meeting, 7-10 June 2017, Rome Cavalieri, Rome, Italy
ISMICS 17 Annual Scientific Meeting, 7-10 June 2017, Rome Cavalieri, Rome, Italy
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Evaluation Of Factors Driving The Cost Of Pulmonary Resection
Hadi Toeg1, Daniel French2, James Villeneuve2, Andrew Seely2, Donna Maziak2, Farid Shamji2, Sudir Sundaresan2, Sebastien Gilbert2.
1University of Ottawa Heart Insitiute, Ottawa, ON, Canada, 2University of Ottawa, The Ottawa Hospital, Ottawa, ON, Canada.

Objectives:
Quality-based procedure funding, which involves evidence-based funding allocation to targeted clinical groups, is designed to lower cost by providing reimbursement incentives to providers who deliver high-quality, cost-effective surgical care. As a first step in addressing this challenge, our objective was to identify factors driving the cost of lung resection.
Methods:
A prospective database of consecutive patients(n=229) undergoing elective pulmonary resection from November 2012 to May 2014 was reviewed. All direct and indirect costs were tabulated and the severity of complications was graded according to our previously published modified Clavien-Dindo classification. Log-transformation of the total cost was performed due to a skewed distribution. Univariable analysis was performed and factors associated with variation in cost (p < 0.2) were selected for inclusion in a stepwise selection multivariable linear regression model to predict total procedure cost.
Result:
Most patients were female gender (59.4%;136/229), the mean age was 65±11 years, and 93.8%(215/229) were diagnosed with cancer. Lobectomy was performed in 70.7%(162/229) and 29.3%(67/229) had sublobar resection. The median total cost for sublobar resection ($9,157.94[IQR=$7,599.07, 13,049.92]) was significantly lower than lobar resection ($15,197.70[IQR=$12,755.66, 19,048.63]; p<0.0001). Univariable linear regression analyses demonstrated that complication grade, surgical approach, and length of stay were significantly associated with increasing costs (Table 1, expressed as mean increase in 1,000$ units) while age, sex, BMI, surgeon, or Charlson co-morbidity index did not (p>0.2). In multivariate analysis, complication severity grade, and length of hospital stay predicted the cost per patient (R2=0.75; p<0.0001) while surgeon, or Charlson co-morbidity index did not.
Conclusion:
Length of hospital stay, and severity of complications were associated with increased health care costs after lobectomy while patient demographics, comorbidity, and surgical approach had no significant impact. Efforts to improve quality and decrease cost should focus on: 1) prospectively documenting complications and their severity grade, 2) reducing length of stay by identifying strategies to promote early recovery and reduce postoperative complication severity.
LEGEND: Factors associated with total cost using univariable and multivariable linear regression analysis.


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