title: Patient-Centered Anesthesia Triage System Predicts ASA Physical Status.
abstract: The purpose of this study was to validate a patient-centered anesthesia triage system (PCATS) by examining its association with, and predictive value of, ASA physical status (PS) classification. ASA PS classification is a widely used indicator of health status and the predictor of risk of perioperative complications. Thus, ASA PS is a good triage point such that healthy surgical patients (ASA PS I and II) undergoing low-complexity surgery are assessed by telephone, whereas less-healthy patients (ASA PS III and IV) or those patients undergoing highly complex surgery are seen in person at a presurgical clinic. However, ASA PS is not commonly available in electronic health records or easily determined by nonanesthesiologists. PCATS criteria, including the number of prescription medications used daily, body mass index (BMI), age, and surgical complexity, are readily available in electronic health records. Nonclinical scheduling personnel can use PCATS to make appropriate preassessment appointments for elective surgical patients before surgery.
  
  
  
  After getting approval from the University of Florida IRB for an exempt study, 300 consecutive patients scheduled in the presurgical clinic over a 1-week span were retrospectively enrolled. Each of the records was reviewed and collated for study identification number, number of prescription medications, BMI, and ASA PS classification assigned on the day of surgery. In addition, a surgical complexity score was assigned to each procedure (high, moderate, minimal).The association between PCATS and individual PCATS criteria and ASA PS was assessed by χ test. The utility of PCATS to discriminate between ASA PS classifications was assessed using receiver operating characteristic (ROC) curves as well as other indicators of clinical validity: sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and positive clinical utility index ([CIU+] = sensitivity × PPV) and negative CIU ([CIU-] = specificity × PPV).
  
  
  
  BMI (P = .002), age (P = .01), surgical complexity (P < .0001), and number of prescriptions (P < .001) were significantly associated with ASA PS. Definitions included as PCATS criteria were BMI > 35, age > 80 years, 5 or more prescriptions, and high surgical complexity. Eighty-seven percent of patients with any PCATS criterion were ASA PS classification III or IV. From ROC curve analysis, PCATS emerged as a significant, and moderately good, predictor of ASA PS class (area under the curve = 0.75, 95% confidence interval [CI], 0.69-0.83). PCATS was highly sensitive (0.88, 95% CI, 0.84-0.92) and specific (0.74; 95% CI, 0.61-0.86), and had excellent utility in confirmation/case finding (CUI+ = 0.83, 95% CI, 0.82-0.84) and moderate utility in screening out cases (CUI- = 0.43, 95% CI, 0.41-0.44).
  
  
  
  PCATS serves as a useful, and valid, predictor of ASA PS classification. Thus, it may also serve as a tool to triage patients to an appropriate venue for preoperative assessment that can be utilized by nonclinical schedulers. Using a simple tool such as PCATS may help streamline the presurgical patient experience and improve clinic staff utilization.
authors.affiliation: From the *Department of Anesthesiology, University of Florida College of Medicine, Gainesville, Florida; †Division of Hospital Medicine, Department of Medicine, University of Florida College of Medicine, Gainesville, Florida; and ‡University of Florida College of Medicine, Gainesville, Florida.
authors.forename: F Kayser
authors.initials: FK
authors.lastname: Enneking
authors.forename: Nila S
authors.initials: NS
authors.lastname: Radhakrishnan
authors.forename: Kent
authors.initials: K
authors.lastname: Berg
authors.forename: Saharsh
authors.initials: S
authors.lastname: Patel
authors.forename: Judith M
authors.initials: JM
authors.lastname: Wishin
authors.forename: Terrie
authors.initials: T
authors.lastname: Vasilopoulos
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D015992:Body Mass Index; D016009:Chi-Square Distribution; D002648:Child; D002675:Child, Preschool; D000066491:Clinical Decision-Making; D003661:Decision Support Techniques; D017558:Elective Surgical Procedures; D005260:Female; D005431:Florida; D006304:Health Status; D006305:Health Status Indicators; D006801:Humans; D007223:Infant; D008297:Male; D008875:Middle Aged; D018579:Patient Selection; D018802:Patient-Centered Care / Q000379:methods*; D019338:Polypharmacy; D011237:Predictive Value of Tests; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D000068436:Surgical Clearance; D014218:Triage / Q000379:methods*; D055815:Young Adult