A nomographic model integrating preoperative and intraoperative factors can accurately predict complications after minimally invasive esophagectomy to enhance patient outcomes.
Can nomographic models combined with AI accurately identify patients at higher risk of morbidity after minimally invasive esophagectomy?
Nomographic models combined with AI may help accurately identify patients at high risk of morbidity after minimally invasive esophagectomy, allowing for preoperative optimization and early identification of complications.
Absolute Event Rate: 0% vs 0%
Perioperative morbidity of esophagectomy significantly affects the surgical outcome, like any major gastrointestinal procedure. Despite introduction of minimally invasive esophagectomy, the morbidity is still close to 30%-40%. The common complications following esophagectomy are pulmonary infections, cardiac events, anastomotic leakage, bleeding, chylous leak, and recurrent laryngeal nerve palsy which in turn lead to longer hospital stay, increased treatment cost and poor quality of life. A nomographic model comprising preoperative (patient, disease and treatment related) and intraoperative factors in combination with Artificial Intelligence may accurately identify the patients at higher risk of morbidity. This will aid in optimizing the modifiable risk factors preoperatively, and closely monitor these patients post operatively for early identification of complications and to initiate early corrective measures to improve the surgical outcome.
Parikh et al. (Thu,) reported a other. A nomographic model integrating preoperative and intraoperative factors can accurately predict complications after minimally invasive esophagectomy to enhance patient outcomes.