Surgeon’s Mortality Outcomes Unrelated to Experience Level

There is no statistical difference between the patient mortality rates of new and experienced surgeons, according to a new study.

Researchers at the University of Pennsylvania (Philadelphia, USA) reviewed Medicare data regarding 130,106 patients who were operated on in 498 hospitals. The data were used to create two matched groups of surgical patients consisting of 6,260 surgical patients each, with similar procedure categories. Additionally, among patients who received a particular procedure, the numbers of patients who had a particular preexisting condition (such as diabetes, past stroke, and high blood pressure) were also matched as close as possible across the two groups.

The researcher used a new statistical method that matched patients of each new surgeon to patients of an otherwise similar experienced surgeon at the same hospital, perfectly balancing 176 surgical procedures and closely balancing a total of 2.9 million categories of patients. Following the analysis of the data for each patient group, the researchers found that the mortality rate for patients of experienced surgeons was 3.59% (225 deaths out of 6,260 surgeries), while the mortality rate of new surgeons was 3.71% (232 deaths out of 6,260 surgeries). The study was published online on January 23, 2015, in the Journal of the American Statistical Association.

“It is reassuring that new surgeons were able to achieve similar mortality rates to experienced surgeons when caring for similar patients. However, mortality is a relatively rare event that may not expose the benefits of experience,” said study coauthor Associate Professor of Surgery Rachel Kelz, MD. “Therefore, future studies focused on additional outcomes are needed to ensure that new surgeon training and transition to independent practice are appropriately structured to meet the surgical needs of the public.”

The new statistical methodology used in the study, called "large, sparse optimal matching," uses an algorithm that creates multiple paths to a match in a network, wherein paths that introduce imbalances are penalized, and hence avoided to the extent possible. The algorithm exploits a sparse network to quickly optimize a match that is about two orders of magnitude larger than is typical in statistical matching problems, thereby permitting much more extensive use of fine and near-fine balance constraints.

The new methodology was developed because patients typically are assigned to surgeons in a nonrandom manner. As a result, there may be systematic differences between the patients of recent surgical graduates and experienced surgeons, making measurement of surgeon effects difficult. For example, newly trained surgeons tended to operate on a different mix of patients than experienced surgeons. The patients of new surgeons were also more often admitted via the emergency room (ER), and had higher risk factors on average. Other differences include the severity of a patient's illness and risk of mortality.

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