Artificial intelligence to investigate predictors and prognostic impact of time to surgery in colon cancer.

Background and Objectives: The role of time to surgery (TTS) for long-term outcomes in colon cancer (CC) remains ill-defined. We sought to utilize artificial intelligence (AI) to characterize the drivers of TTS and its prognostic impact.
Methods: The National Cancer Database was utilized to identify patients diagnosed with non-metastatic CC between 2004 and 2018. AI models were employed to rank the importance of several sociodemographic, facility, and tumor characteristics in determining TTS, and postoperative survival.
Results: Among 518 983 patients, 137 902 (26.6%) received intraoperative diagnosis of CC (TTS = 0), while 381 081 (74.4%) underwent elective surgery (TTS > 0) with median TTS of 19.0 days (interquartile range [IQR]: 7.0-33.0). An AI model, identified tumor stage, receipt of adequate lymphadenectomy, histologic grade, lymphovascular invasion, and insurance status as the most important variables associated with TTS = 0. Conversely, the type and location of treating facility and receipt of adjuvant therapy were among the most important variables for TTS > 0. Notably, TTS was among the most important variables associated with survival, and TTS > 3 weeks was associated with an incremental increase in mortality risk.
Conclusions: The identification of factors associated with TTS can help stratify patients most likely to suffer poor outcomes due to prolonged TTS, as well as guide quality improvement initiatives related to timely surgical care.
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