SPARTAN-HN Algorithm Shows Promise for Surgical Prioritization of Head and Neck Cancer

A study of a surgical prioritization and ranking tool and navigation aid for head and neck cancer (SPARTAN-HN) found the surgical prioritization algorithm was able to consistently stratify patients requiring head and neck cancer surgical care in the coronavirus disease 2019 (COVID-19) era.

Importantly though, further evaluation of the implementation of the algorithm in various practice settings will still be necessary to validate these findings.

“In the setting of the COVID-19 pandemic, in which the availability of operating room time as well as hospital and intensive care unit beds is limited, the prioritization of surgical oncology cases is imperative to mitigate downstream adverse outcomes. The current methodology was adopted based on expert consensus,” the authors wrote. “In the current study, we have proposed the SPARTAN-HN, with the objective of providing transparency and facilitating surgical prioritization for treatment providers.”

Two separate expert panels consisting of a consensus panel made up of 11 participants and a validation panel made up of 15 participants were constructed among international head and neck surgeons. The consensus panel participated in a Delphi consensus process and a modification of the RAND/University of California at Los Angeles (UCLA) method was used to achieve consensus and develop the point-based scoring algorithm, SPARTAN-HN.

“Use of the RAND/UCLA consensus process avoids the need for multiple pairwise comparison and allows for consideration of each factor in isolation,” the authors noted. “The goal of the consensus rounds was not to establish a rank order for all indications, but mainly to understand which indications result in high, intermediate, or low priority.”

Following consensus rounds, 12 clinical vignettes were constructed to validate the SPARTAN-HN. The vignettes described a variety of clinical scenarios incorporating multiple prioritization indications and additional clinical information, and experts were asked to consider only the patient-level information provided to them and not their own unique clinical and community practice environments.

Overall, a total of 62 indications for surgical priority were rated. Weights for each indication ranged from -4 to +4 (scale range; -17 to 20). The response rate for the validation exercise was 100%.

Ultimately, agreement between the 5 coders for the SPARTAN-HN was excellent (K-alpha, .91) and agreement between the 15 expert raters was only moderate (K-alpha, .63). Moreover, convergent validity was demonstrated by a strong correlation between the rank orders generated by the SPARTAN-HN and external experts (rho, 0.81; 95% CI, 0.45-0.95; P = .0007).

“With established validity, this algorithm may be ready for preliminary clinical use, although further testing against real-world data to validate it with other cancer outcomes, such as survival, is needed,” the authors wrote.

Of note, the researchers indicated that the SPARTAN-HN algorithm is intended to assist in making difficult prioritization decisions and is not intended to make recommendations for the time frame in which patients should receive treatment. Instead, established guidelines should be adhered to for treatment targets and patient wait times as they relate to those targets should be considered when using the algorithm.

“Further evaluation of [the SPARTAN-HN] implementation in various practice settings will be obligatory,” the authors concluded. “However, the results of the current study have provided data with which to inform real-world use, as the current pandemic has obviated our ability to more rigorously study the instrument prior to making necessary and difficult real-time allocation decisions.”

Reference:

De Almeida JR, Noel CW, Forner D, et al. Development and Validation of a Surgical Prioritization and Ranking Tool and Navigation Aid for Head and Neck Cancer (SPARTAN-HN) in a Scarce Resource Setting: Response to the COVID-19 Pandemic. Cancer. doi: 10.1002/cncr.33114.