Discordant American Society of Anesthesiologists Physical Status Classification between anesthesiologists and surgeons and its correlation with adverse patient outcomes

Abstract

The American Society of Anesthesiologists Physical Status Classification (ASA) is used for communication of patient health status, risk scoring, benchmarking and financial claims. Prior studies using hypothetical scenarios have shown poor concordance of ASA classification among healthcare providers. There is a paucity of studies using clinical data, and of clinical factors or patient outcomes associated with discordant classification. The study aims to assess ASA classification concordance between surgeons and anesthesiologists, factors surrounding discordance and its impact on patient outcomes. This retrospective cohort study was conducted in a tertiary medical center on 46,284 consecutive patients undergoing elective surgery between January 2017 and December 2019. The ASA class showed moderate concordance (weighted Cohen’s κ 0.53) between surgeons and anesthesiologists. We found significant associations between discordant classification and patient comorbidities, age and race. Patients with discordant classification had a higher risk of 30-day mortality (odds ratio (OR) 2.00, 95% confidence interval (CI) = 1.52–2.62, p < 0.0001), 1-year mortality (OR 1.53, 95% CI = 1.38–1.69, p < 0.0001), and Intensive Care Unit admission > 24 h (OR 1.69, 95% CI = 1.47–1.94, p < 0.0001). Hence, there is a need for improved standardization of ASA scoring and cross-specialty review in ASA-discordant cases.

Short-Term Outcomes of Total Knee Arthroplasty Using a Conventional, Computer-Assisted, and Robotic Technique: A Pilot Clinical Trial

“…The present study contributes to these different opinions on the efficiency of RA-TKA and CAS. The analysis of mechanical axis correction outcomes reveals that Group A attained an “excellent” outcome 2.3 times more often than Group B and 1.5 times more than Group C. Additionally, the occurrence of a “good” outcome in Group A was 1.6 times greater than in Group B and 1.4 times greater than in Group C. These data underscored the superiority of RA-TKA in achieving more accurate implant placement compared to conventional manual techniques, with or without CAS navigation, corroborating findings from previous research [ 14 ]. This accuracy is linked to a higher implant survival rate observed over a three-year follow-up period.…”

“…Inclusion criteria: patients of both sexes, 40 to 80 years of age, with knee osteoarthritis of grade 3–4 Kellgren and Lawrence classification, i.e., with moderate and severe joint deterioration [ 13 ]. The patients with, at most, a non-life-threatening systemic disease, i.e., according to the American Society of Anesthesiologists Classification (ASA) grade ≤ 3 [ 14 ], were included.…”

Defining complexity in anaesthesia: description and validation of the Oxford Anaesthetic Complexity (OxAnCo) score

“…This means that end‐users of the score have directly influenced its creation, a process shown to be important in emergency medicine audit and measurement projects [ 24 ] and mental health interventions [ 25 ]. The small range of complexity scores seen, with a skew towards lower complexity, representing the reality of clinical practice [ 26 , 27 ], is a likely product of this end‐user design. In addition, the score has been validated against a large real‐world dataset of anaesthetic cases, covering multiple subspecialties, hospital and operating theatre environments.…”

Novel Preoperative Risk Stratification Using Digital Phenotyping Applying a Scalable Machine-Learning Approach

“…7,8 However, the ASA score has several significant weaknesses: it disregards the type of surgery as a risk factor, relies on the anesthesiologist's experience, and an ASA score of 3 (intermediate) is overutilized. [8][9][10] Recently, machine-learning (ML) algorithms have been applied to electronic health record (EHR) data and have demonstrated the potential to improve risk prediction. [11][12][13][14][15] However, to date, most studies have presented models with supervised learning trained to predict specific postoperative complications, including mortality, cardiorespiratory adverse events, allergic reaction, as well as the ASA score itself.…”