Delphi consensus on the American Society of Anesthesiologists' physical status classification in an Asian tertiary women's hospital

• Main Authors: Osman, Tarig, Lew, Eileen, Sng, Ban L., Dabas, Rajive, Griva, Konstadina, Car, Josip
• Other Authors: Lee Kong Chian School of Medicine (LKCMedicine)
• Format: Article
• Language: English
• Published: 2022
• Subjects: Science::Medicine; Clinical Decision-Making; Preoperative Care
• Online Access: https://hdl.handle.net/10356/163210
• Institution: Nanyang Technological University

Background: The American Society of Anesthesiologists (ASA) score is generated based on patients’ clinical status. Accurate ASA classification is essential for the communication of perioperative risks and resource planning. Literature suggests that ASA classification can be automated for consistency and time-efficiency. To develop a rule-based algorithm for automated ASA classification, this study seeks to establish consensus in ASA classification for clinical conditions encountered at a tertiary women’s hospital. Methods: Thirty-seven anesthesia providers rated their agreement on a 4-point Likert scale to ASA scores assigned to items via the Delphi technique. After Round 1, the group’s collective responses and individual item scores were shared with participants to improve their responses for Round 2. For each item, the percentage agreement (‘agree’ and ‘strongly agree’ responses combined), median (interquartile range/IQR), and SD were calculated. Consensus for each item was defined as a percentage agreement ≥ 70%, IQR ≤ 1.0, and SD < 1.0. Results: All participants completed the study and none had missing data. The number of items that reached consensus increased from 25 (51.0%) to 37 (75.5%) in the second Delphi round, particularly for items assigned ASA scores of III and IV. Nine items, which pertained to alcohol intake, asthma, thyroid disease, limited exercise tolerance, and stable angina, did not reach consensus even after two Delphi rounds. Conclusions: Delphi consensus was attained for 37 of the 49 study items (75.5%), facilitating their incorporation into a rule-based clinical support system designed to automate the prediction of ASA classification.