Personalized Predictive Hemodynamic Management for Gynecologic Oncologic Surgery: Feasibility of Cost-Benefit Derivatives of Digital Medical Devices.

BACKGROUND

Intraoperative hypotension is associated with increased perioperative complications, hospital length of stay (LOS) and healthcare expenditure in gynecologic surgery. We tested the hypothesis that the adoption of a machine learning-based warning algorithm (hypotension prediction index-HPI) might yield an economic advantage, with a reduction in adverse outcomes that outweighs the costs for its implementation as a medical device.

METHODS

A retrospective-matched cohort cost-benefit Italian study in gynecologic surgery was conducted. Sixty-six female patients treated with standard goal-directed therapy (GDT) were matched in a 2:1 ratio with thirty-three patients treated with HPI based on ASA status, diagnosis, procedure, surgical duration and age.

RESULTS

The most relevant contributor to medical costs was operating room occupation (46%), followed by hospital stay (30%) and medical devices (15%). Patients in the HPI group had EURO 300 greater outlay for medical devices without major differences in total costs (GDT 5425 (3505, 8127), HPI 5227 (4201, 7023) p = 0.697). A pre-specified subgroup analysis of 50% of patients undergoing laparotomic surgery showed similar medical device costs and total costs, with a non-significant saving of EUR 1000 in the HPI group (GDT 8005 (5961, 9679), HPI 7023 (5227, 11,438), p = 0.945). The hospital LOS and intensive care unit stay were similar in the cohorts and subgroups.

CONCLUSIONS

Implementation of HPI is associated with a scenario of cost neutrality, with possible economic advantage in high-risk settings.