The purpose of this study is to compare the efficacy and cost difference of using a parenchymal stapling device versus hand sewing for a pulmonary lobectomy in patients with lung disease (mass or others).
There are many lung diseases that need surgical treatment including malignancy lesion or benign lesions such as lung bleb or bullae, lung cyst, benign tumor, infection (necrotizing pneumonia, lung abscess, aspergilloma) etc. One of the most common procedure is pulmonary lobectomy. The surgical method for dividing parenchyma when performing lobectomy was divided in two methods. In the past, the investigators used hand-sewn technique but this procedure was time-consuming, high risk of air leakage, infection and re-operation, long length of hospital stay and high total cost of treatment. In the present time, the investigators use stapling device which has been used worldwide in various field of surgery since 1995. Many studies proved that using stapling devices can reduce post-operative complication, length of hospital stay and total cost of treatment and also became the standard instrument for lung surgery. In Thailand, these devices have been used for at least 5 years but they were not included in all of Health Insurance of Thai Government. Many patients had to pay for these devices by themselves, approximately 323-484.5 USD. Therefore, the investigators try to prove the hypothesis that using stapling devices for lung surgery would reduce the cost of treatment, post-operative air leakage, re-operative rate, length of hospital stay and time of surgery. The result of this study may effect the decision for including these devices in the Health Insurance of Thai Government.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
DOUBLE
Enrollment
53
Stapling device include GIA 80, TA 45, endo GIA 60, endo GIA 45
Department of surgery, Faculty of medicine, Chiang Mai University Hospital
Amphoe Meung, Chaing Mai, Thailand
Post-operative air leak, operative time, and duration of air leak
To compare post-operative air leakage between two groups. This data will be analyzed by fisher exact probability test. We will start observation of the air leakage at postoperative day 1. The stoping role is when statistically significant difference occur. We will measure and report in the number of patients who have post-operative air leakage.
Time frame: In the operative day, after surgery, until patient can be discharged.
cost of treatment
The cost measurements for each treatment arm focused on the following: 1) direct medical cost (costs of goods and services that are directly provided by the health care system including surgical equipment, drugs and nursing care), 2) direct non-medical cost (costs of goods and services used for health care not directly provided by the heath care system such as transportation, additional meals for patient or their relatives and residence for their relatives),and 3) indirect cost (costs of health care consumption gained as a result of a health care intervention and value of production loss due to illness or treatment such as income lost from sick-leave).
Time frame: within the time that patient admit in the hospital
Re-operation due to postoperative air leakage
To compare the re-operation due to air leakage between two groups. This data will be analyzed by fisher exact probability test. We will record the re-operation due to air leakage. The stopping role is when statistically significant difference occur. In general, we accepted re-operation for air leakage especially patients who have chronic lung disease (COPD), the rate of re-operation that we can accept approximately 20-30 percent We will measure and report in the number of patients who have to perform re-operation due to post-operative air leakage.
Time frame: 8 month after recording data
Length of Hospital stay
To compare the length of hospital stay between two groups. Counting of Length of hospital stay (days) will start at 1st post-operative day until discharge. This data will be analyzed by t-test. We will measure and report in the unit of time (days).
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Time frame: 8 month after recording data