The Evaluation of a Preanaesthesia Assessment Clinic

Overview

International evidence highlights the preanaesthetic assessment clinics (PAC) as a system of benefits for both the patient and the anaesthesiologist. The system has gained global acceptance as a routine method of optimising patients' medical conditions prior to surgery and therefore minimising surgery cancellations and improving hospital efficiency. However, a systematic review found no evidence of the efficiency of (PAC) and therefore we wanted to investigate this with the use of hospitals journals.

The aim of this study is to evaluate the incidence, causes, and consequences of cancellations, and unanticipated intraoperative events after open or laparoscopic surgery of hemicolectomy patients before and after the implementation of a preoperative anaesthesia assessment clinic (PAC). The primary outcome is to evaluate the incidence of cancellations and unanticipated intraoperative events after open or laparoscopic surgery of hemicolectomy patients before and after implementation of the preoperative anaesthesia assessment clinic (PAC). The secondary outcome is to evaluate the patients' characteristics, the main events leading to cancellations and the consequences this brings to the patient and the hospital. Adverse events reported pre-and post-intervention will be analysed. Patient characteristics will be collected: age, gender, diagnosis, reason and type of surgery, patient admission, assessed in PAC or not, ASA classification (American Society of Anesthesiologist Classification system), who performed the assessment (anaesthetic nurse or anaesthesiologist), type of anaesthesia, former diagnosis/disease with impact on anaesthesia/surgery, present smoker, allergies, blood samples, other examinations, cancellation of surgery, reasons and consequences for cancellations, time of anaesthetic assessment before surgery, intraoperative events, mallampati, Intubation grade, BMI and type of medication. A specific data collection form will be developed and the data will be collected retrospectively from patient journals. The number is calculated to: n = 588, n = 294 before and after the implementation of PAC. We use a significance level of 5%. To achieve statistical strength of 80% (beta = 20%), we need 288 patients before and after PAC to detect a difference of at least 10%. Given that we have the opportunity to include many more then we assume that our study has adequate strength. Prevalences of cancellations are estimated as shares and presented with 95% confidence intervals (CIs) calculated using the exact method. Possible differences in background variables in patients included before and after the implementation of PAC will be estimated using kji-square tests or t-tests. The effect of the implementation of PAC will be estimated using logistic regression adjusted for possible confounding factors and results will be presented as adjusted odds ratio (OR) with 95% CI.