INTRODUCTION: There currently exists a wide variation in anesthesia perioperative management for pediatric patients undergoing laparoscopic appendectomy. The purpose of this retrospective chart review is to compare outcome indicators by using patient demographics. This study aims to establish evidence based guidelines for safe, efficient and effective anesthetic management for patients undergoing laparoscopic appendectomies by analyzing selected outcome indicators and metrics in relation to Surgical-End-to-Transport (SET) time: defined as the time from the end of surgical time until the patient is ready to exit the operating room. METHODS: After institutional review board approval, all laparoscopic appendectomies performed from 2012 through 2014 (n=790) were queried. Using the median SET time of 14 minutes, two groups were established as follows: Group A (n=431), SET time between 0 and 14 minutes, and Group B (n=338), SET time of 14 minutes and longer. Bivariate and multivariate logistic regression models were used to compare readmissions by American Society of Anesthesiologists (ASA) status and reports of high pain with PACU (Post-Anesthesia Care Unit) duration, gender, age, and surgical duration using IBM SPSS Statistics (version 21.0, IBM, Armonk, NY). RESULTS: To limit confounding variables, patients over the age of 21 and those assigned an ASA Physical Status Classification 3 or 4 were excluded. Remaining cases (n=769) were then used to calculate readmission incidence. The median SET time for the study population was 14 minutes, while the median surgical and PACU durations were 58 minutes and 59 minutes, respectively. The readmission incidence rate was 300 per 10,000 (n=23, 3%). The study population consisted of 56% males and 44% females. Females had a higher incidence of readmission (n=13, 3.8%) than males (n=10, 2.3%), while males had longer SET times than females (Group A Males 52.33% vs. Group B Males 60.30%, p=0.0276). There was no difference in readmission incidence rates between ASA I (n=473) and ASA II (n=296) patients (ASA I readmits 3.2 % vs. ASA II readmits 2.7%, p=.711). Patients who reported high postoperative pain (n=75) were more than twice as likely to be readmitted than patients who did not report high pain (p=.071). Ethnicity frequencies were collected as follows: 60.3% White, 6.8% Black or African American, 3.6% Asian, and 29.1% Other. DISCUSSION: Males had significantly longer durations in SET times, and they experienced fewer readmissions than females. There were no significant findings related to the ethnic demographics. Further analysis identifying intraoperative and postoperative anesthesia management for both groups will be performed. This study was subject to the following limitations: retrospective design, incomplete data acquisition, and inconsistent EMR documentation. The correlations and results are preliminary in nature and will serve as a framework for future analyses.

Évaluer la sécurité des patients hospitalisés constitue un enjeu majeur de la gestion des risques pour les services de santé. Le développement d’indicateurs destinés à mesurer les événements indésirables liés aux soins (EIS) est une étape cruciale dont le défi principal repose sur la performance des données utilisées. Le développement d’indicateurs de la sécurité des patients – les Patient Safety Indicators (PSIs) – par l’Agency for Healthcare Research and Quality (AHRQ) aux Etats Unis, utilisant des codes de la 9ème révision (cliniquement modifiée) de la Classification Internationale des Maladies (CIM) présente des perspectives intéressantes. Nos travaux ont abordé cinq questions fondamentales liées au développement de ces indicateurs : la définition du cadre nosologique, la faisabilité de calcul des algorithmes et leur validité, la qualité des données pour coder les diagnostics médicaux à partir de la CIM et leur performance pour comparer plusieurs pays, et la possibilité d’établir une valeur de référence pour comparer ces indicateurs. Certaines questions demeurent cependant et nous proposons des pistes de recherche pour améliorer les PSIs : une meilleure définition des algorithmes et l’utilisation d’autres sources de données pour les valider (i.e., données de registre), ainsi que l’utilisation de modèles d’ajustement utilisant l’index de Charlson, le nombre moyen de diagnostics codés et une variable de la valeur prédictive positive, afin de contrôler les variations du case-mix et les différences de qualité du codage entre hôpitaux et pays. / Assessing safety among hospitalized patients is a major issue for health services. The development of indicators to measure adverse events related to health care (HAE) is a crucial step, for which the main challenge lies on the performance of the data used for this approach. Based on the limitations of the measurement in terms of reproducibility and on the high cost of studies conducted using medical records audit, the development of Patient Safety Indicators (PSI) by the Agency for Healthcare Research and Quality (AHRQ) in the United States, using codes from the clinically modified 9th revision of the International Classification of Diseases (ICD) shows interesting prospects. Our work addressed five key issues related to the development of these indicators: nosological definition; feasibility and validity of codes based algorithms; quality of medical diagnoses coding using ICD codes, comparability across countries; and possibility of establishing a benchmark to compare these indicators. Some questions remain, and we suggest several research pathways regarding possible improvements of PSI based on a better definition of PSI algorithms and the use of other data sources to validate PSI (i.e., registry data). Thus, the use of adjustment models including the Charlson index, the average number of diagnoses coded and a variable of the positive predictive value should be considered to control the case-mix variations and differences of quality of coding for comparisons between hospitals or countries.