Pilot Observational Cohort Study to Validate the Aiinane Preoperative and Preanesthesia Risk Assessment Tool in Real-world Conditions: Proof of Concept.
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 30
- 试验地点
- 1
- 主要终点
- The aiinane tool is able to create a final report/document with the clinical information required for preoperative assessment
研究概览
简要总结
BACKGROUND: Each year, over 13,000 patients in Catalonia and more than 4 million worldwide experience last-minute surgery cancellations (LMC) due to preoperative inefficiencies. As anaesthesia services struggle to meet surgical demands, thorough preoperative evaluations become challenging. Current resource-intensive pre-anaesthetic assessments are undermined by high demand, causing inefficiency. However, proper assessment identifies that most patients (>70%) are low-risk and ensures high-risk patients are adequately prepared by analyzing risk profiles, health status, medical history, treatment, and lab results.
RATIONALE: Previous attempts to improve preoperative risk assessment have mainly relied on self-administered questionnaires to detect at-risk patients. The investigators have identified a care model that enhances quality by adding value to preoperative risk assessment. By combining anaesthesiology expertise with AI techniques, the investigators developed an automated digital environment to detect risks, optimize visits, avoid cancellations, and reduce postoperative complications. This system uses parameterized medical knowledge to verify responses and objectively assess patient risk by integrating multiple data sources.
The investigators have developed a Class IIa active diagnostic and monitoring product, a Medical Device Software (MDSW, EMDN V92), to support clinical decision-making in an automated digital preoperative environment. It helps assess patients and flags low-risk from medium/high-risk individuals, reporting personalized needs to the medical team. The software was developed exclusively by independent researchers from Bellvitge University Hospital and the Bellvitge Biomedical Research Institute (IDIBELL).
HYPOTHESIS: The tool has been tested successfully on fictitious patients in controlled preclinical scenarios. The aim now is a proof-of-concept study to verify its performance with real patients in uncontrolled, real-world settings.
This is a Clinical Performance Research Study, aligned with MDSW Clinical Evaluation Guidelines (MDR, MDCG 2020-1) for (EU) 2017/745, and authorized by the Spanish Agency of Medicines and Medical Devices (AEMPS) and the local Ethics Committee.
MAIN OBJECTIVE: The clinical trial aims to verify that the software (aiinane), supporting preanesthetic assessment and preoperative risk estimation, is fit for its intended purpose and performs as expected under normal conditions of use.
This is an observational, non-interventional, single-center, prospective, longitudinal adult cohort study. The target population is any adult (>18 years) of any gender undergoing breast cancer surgery.
详细描述
The aiinane software has been developed by IDIBELL and HUB research staff. It is a software medical device (MDSW) with the intention of obtaining the CE marking under the modality 'class IIa active product'. The intention of this study is to take the first step towards obtaining the marking, as established by EU Regulation 2017 / 745.
The tool is in the preliminary development phase. An alpha version has been developed and executed within the hospital servers, in a simulated pre-production environment, with fictitious patients provided by the HUB.
Subsequently, a Beta version has been developed within the HUB servers, which has also been tested on simulated patients in the fictitious pre-production environment. The Beta phase is currently pending to be executed within the Hospital App 'ElMeuHUB', once it has been approved by the CEIm and the AEMPS to be tested in the real environment with real patients.
Intended purpose The aiinane tool is a computer programme designed to create a report where all the information collected and collated from different sources of information is shown in an aggregated manner. This report will include the results of applying the data collected to different risk scales/calculators already published in the scientific literature, preoperative risk assessment scales to assist the anaesthesiologist in decision making during the preoperative process.
It is expected that the aiinane software programme will be able to generate a final document for the healthcare professional with information on a patient, where a preoperative risk level is indicated and segmented, in order to support the healthcare professional (anaesthesiologist) in a pre-anaesthesia assessment.
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
- •Adult patients (age ≥18 years) of both sexes. Although breast neoplasia is a pathology with high prevalence in the female biological gender, we do not rule out recruiting patients of male biological gender.
- •Who have been indicated for breast surgery to resolve breast cancer pathology.
- •Who will undergo a pre-anesthetic evaluation during the inclusion period..
- •Subjects must understand the nature of the study procedures and provide written informed consent prior to any study-related procedures.
排除标准
- •Surgery scheduled for <7 days from enrollment
- •Inability to participate in the study, in the opinion of the investigator, due to, for example, severe brain damage, language barrier, dementia, or other clinically significant or unstable conditions.
- •Subject's participation in any other clinical study.
- •Subjects dependent (as employee or relative) of the promoter or researcher.
- •Subjects placed in an institution by virtue of an order issued by either judicial or administrative authorities.
- •Limited legal capacity or incapacity.
- •Pregnancy.
结局指标
主要结局
The aiinane tool is able to create a final report/document with the clinical information required for preoperative assessment
时间窗: From enrollment to 2 weeks after surgery has been performed.
The present clinical trial aims to verify that the software (hereinafter aiinane) for the support of pre-anesthetic assessment in the estimation of preoperative risk, is adequate for its established purposes and offers the expected performance under normal conditions of use. Therefore the outcome measure is the total number of complete anesthesia reports generated.
次要结局
- Patient Satisfaction(From enrollment to 2 weeks after surgery)
- Clinical usefulness(From enrollment to 2 weeks after surgery)
- clinical significance(From enrollment to 2 weeks after surgery)
- usefulness of risk stratification(From enrollment to 2 weeks after surgery)
研究者
Ancor Serrano
MD, PhD,. Principal Investigator, Senior Consultant in Anesthesiology.
Hospital Universitari de Bellvitge
