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

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.