The Value of Decision Analytical Modeling in Surgical Research: An Example of Laparoscopic Versus Open Distal Pancreatectomy

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Abstract

Objective:

To illustrate how decision modeling may identify relevant uncertainty and can preclude or identify areas of future research in surgery.

Summary Background Data:

To optimize use of research resources, a tool is needed that assists in identifying relevant uncertainties and the added value of reducing these uncertainties.

Methods:

The clinical pathway for laparoscopic distal pancreatectomy (LDP) versus open (ODP) for nonmalignant lesions was modeled in a decision tree. Cost-effectiveness based on complications, hospital stay, costs, quality of life, and survival was analyzed. The effect of existing uncertainty on the cost-effectiveness was addressed, as well as the expected value of eliminating uncertainties.

Results:

Based on 29 nonrandomized studies (3.701 patients) the model shows that LDP is more cost-effective compared with ODP. Scenarios in which LDP does not outperform ODP for cost-effectiveness seem unrealistic, e.g., a 30-day mortality rate of 1.79 times higher after LDP as compared with ODP, conversion in 62.2%, surgically repair of incisional hernias in 21% after LDP, or an average 2.3 days longer hospital stay after LDP than after ODP. Taking all uncertainty into account, LDP remained more cost-effective. Minimizing these uncertainties did not change the outcome.

Conclusions:

The results show how decision analytical modeling can help to identify relevant uncertainty and guide decisions for future research in surgery. Based on the current available evidence, a randomized clinical trial on complications, hospital stay, costs, quality of life, and survival is highly unlikely to change the conclusion that LDP is more cost-effective than ODP.

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