Prospective Validation of an Artificial Intelligence Tool for Pre-Anesthetic Assessment
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 270
- 试验地点
- 1
- 主要终点
- Preoperative Risk Assessment
研究概览
简要总结
This prospective observational cohort study aims to validate an artificial intelligence (AI) tool designed for pre-anesthetic assessment in Portuguese, tailored to the Brazilian healthcare context. Conducted at a single tertiary hospital, the study will enroll 270 adult patients (aged >18 years) scheduled for elective non-cardiac surgeries. Participants will use the AI tool to complete a self-assessment, generating general patient guidance and a detailed medical evaluation (the latter withheld from the anesthesiologist). A standard pre-anesthetic evaluation will then be performed by an anesthesiologist blinded to the AI results. A third blinded anesthesiologist will compare the assessments for accuracy, consistency, and risk identification (e.g., ASA classification and perioperative risk models). Primary outcome is concordance between AI and human assessments using Cohen's Kappa. Secondary outcomes include anesthesiologist perceptions of the tool's utility, impact on assessment quality, and patient usability challenges. The study poses minimal risks, with data collected over 24 months, and aims to enhance perioperative safety and efficiency in Brazil.
详细描述
Background:
The pre-anesthetic assessment is a critical process preceding surgical procedures, playing a fundamental role in ensuring perioperative safety and quality of care. Over recent decades, anesthesiologists have contributed significantly to improving perioperative safety and quality, as highlighted in the Institute of Medicine's report on quality of health care. This evaluation has been continually refined to enhance clinical outcomes and reduce costs associated with unnecessary preoperative laboratory tests and examinations.
The benefits of pre-anesthetic assessment are well-documented, including increased operational efficiency through early identification of potential complications, optimization of preoperative management for patients with underlying conditions such as diabetes, cardiopulmonary diseases, and renal insufficiency, and minimization of postoperative complications. In Brazil, the Federal Council of Medicine's Resolution No. 1.802/2006 mandates pre-anesthetic evaluation as an essential component for patient safety, recommending it be performed before hospital admission for elective procedures.
Recently, Brazil has experienced a surge in demand for surgical procedures, exacerbated by the COVID-19 pandemic. A 2022 survey by the Oswaldo Cruz Foundation (Fiocruz) revealed a backlog of 910,621 surgeries in the Unified Health System (SUS), with significant deficits in digestive system surgeries (374,475), genitourinary procedures (241,752), circulatory system interventions (104,925), and upper airway, face, head, and neck surgeries (102,352). This pressure on the public health system led to the launch of the National Program for Reducing Waiting Lists in 2024 by the Brazilian federal government. Consequently, there is a need to rethink the flow and process of pre-anesthetic assessments to ensure safe and adequate care for this pent-up demand.
With technological advancements and the increasing volume of available medical data, clinical evaluations have become more complex, requiring faster and more precise decisions. In this context, artificial intelligence (AI) emerges as a promising tool to transform anesthesiology, particularly in pre-anesthetic assessment. AI tools, including Natural Language Processing (NLP) and Large Language Models (LLMs), have shown potential to improve accuracy, efficiency, and personalization of medical care.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients aged 18 years or older
- •Patients scheduled for elective non-cardiac surgeries
排除标准
- •Patients undergoing diagnostic procedures with isolated sedation or local anesthesia
- •If a patient undergoes more than one surgical intervention during the same hospitalization, only the major procedure will be considered (i.e., additional procedures during the same admission are not eligible for separate inclusion).
结局指标
主要结局
Preoperative Risk Assessment
时间窗: The assessments are conducted preoperatively for each participant, with data collected during their preoperative evaluation phase. The comparison is performed post-assessment, during the data analysis phase, which occurs after the 24-mo data collection.
The primary outcome is the level of concordance between the preoperative risk assessments performed by the artificial intelligence (AI) tool, based on a Large Language Model (LLM) in Portuguese, and those conducted by a human anesthesiologist. This concordance is evaluated in terms of the quality of information collected (e.g., completeness and relevance of patient data) and the precision of clinical judgment (e.g., identification of perioperative risks, American Society of Anesthesiologists (ASA) classification, and alignment with validated surgical risk models such as Ex-Care). Measurement Method: A third anesthesiologist, blinded to both the AI and human evaluations, will compare the assessments. The concordance will be quantified using the Cohen's Kappa coefficient for categorical variables (e.g., quality of information and clinical judgment).
次要结局
未报告次要终点
研究者
Andre Prato Schmidt
MD, PhD (Anesthesiologist - Department Chair).
Hospital Nossa Senhora da Conceicao