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Internal validation of the Tampa Robotic Difficulty Scoring System: real-time assessment of the novel robotic scoring system in predicting clinical outcomes after hepatectomy

  • Tara M. Pattilachan,
  • Maria Christodoulou,
  • Sharona B. Ross,
  • Gowtham Lingamaneni,
  • Alexander Rosemurgy,
  • Iswanto Sucandy

摘要

Introduction

As the robotic approach in hepatectomy gains prominence, the need to establish a robotic-specific difficulty scoring system (DSS) is evident. The Tampa Difficulty Score was conceived to bridge this gap, offering a novel and dedicated robotic DSS aimed at improving preoperative surgical planning and predicting potential clinical challenges in robotic hepatectomies. In this study, we internally validated the recently published Tampa DSS by applying the scoring system to our most recent cohort of patients.

Methods

The Tampa Difficulty Score was applied to 170 recent patients who underwent robotic hepatectomy in our center. Patients were classified into: Group 1 (score 1–8, n = 23), Group 2 (score 9–24, n = 120), Group 3 (score 25–32, n = 20), and Group 4 (score 33–49, n = 7). Key variables for each of the groups were analyzed and compared. Statistical significance was accepted at p ≤ 0.05.

Results

Notable correlations were found between the Tampa Difficulty Score and key clinical parameters such as operative duration (p < 0.0001), estimated blood loss (p < 0.0001), and percentage of major resection (p = 0.00007), affirming the score’s predictive capacity for operative technical complexity. The Tampa Difficulty Score also correlated with major complications (Clavien–Dindo ≥ III) (p < 0.0001), length of stay (p = 0.011), and 30-day readmission (p = 0.046) after robotic hepatectomy.

Conclusions

The Tampa Difficulty Score, through the internal validation process, has confirmed its effectiveness in predicting intra- and postoperative outcomes in patients undergoing robotic hepatectomy. The predictive capacity of this system is useful in preoperative surgical planning and risk categorization. External validation is necessary to further explore the accuracy of this robotic DSS.