Development and validation of an instrument to predict prolonged length of stay in the postanesthesia care unit following ambulatory surgery.

PURPOSE: We sought to develop and validate an Anticipated Surveillance Requirement Prediction Instrument (ASRI) for prediction of prolonged postanesthesia care unit length of stay (PACU-LOS, more than four hours) after ambulatory surgery. METHODS: We analyzed hospital registry data from patients who received anesthesia care in ambulatory surgery centres (ASCs) of university-affiliated hospital networks in New York, USA (development and internal validation cohort [n = 183,711]) and Massachusetts, USA (validation cohort [n = 148,105]). We used stepwise backwards elimination to create ASRI. RESULTS: The model showed discriminatory ability in the development, internal, and external validation cohorts with areas under the receiver operating characteristic curve of 0.82 (95% confidence interval [CI], 0.82 to 0.83), 0.82 (95% CI, 0.81 to 0.83), and 0.80 (95% CI, 0.79 to 0.80), respectively. In cases started in the afternoon, ASRI scores ≥ 43 had a total predicted risk for PACU stay past 8 p.m. of 32% (95% CI, 31.1 to 33.3) vs 8% (95% CI, 7.9 to 8.5) compared with low score values (P-for-interaction < 0.001), which translated to a higher direct PACU cost of care of USD 207 (95% CI, 194 to 2,019; model estimate, 1.68; 95% CI, 1.64 to 1.73; P < 0.001) The effects of using the ASRI score on PACU use efficiency were greater in a free-standing ASC with no limitations on PACU bed availability. CONCLUSION: We developed and validated a preoperative prediction tool for prolonged PACU-LOS after ambulatory surgery that can be used to guide scheduling in ambulatory surgery to optimize PACU use during normal work hours, particularly in settings without limitation of PACU bed availability.

RéSUMé: OBJECTIF: Nous avons cherché à mettre au point et à valider un Instrument de prédiction anticipée des besoins de surveillance pour anticiper toute prolongation de la durée de séjour en salle de réveil (plus de quatre heures) après chirurgie ambulatoire. MéTHODE: Nous avons analysé les données enregistrées dans le registre de l'hôpital des patient·es qui ont reçu des soins d'anesthésie dans des centres de chirurgie ambulatoire (CCA) des réseaux hospitaliers affiliés à une université à New York, aux États-Unis (cohorte de développement et de validation interne [n = 183 711]) et au Massachusetts, États-Unis (cohorte de validation [n = 148 105]). Nous avons utilisé un procédé d'élimination progressive régressive pour créer notre instrument de prédiction. RéSULTATS: Le modèle a montré une capacité discriminatoire dans les cohortes de développement, de validation interne et de validation externe, avec des surfaces sous la courbe de fonction d'efficacité de l'opérateur (ROC) de 0,82 (intervalle de confiance [IC] à 95 %, 0,82 à 0,83), 0,82 (IC 95 %, 0,81 à 0,83), et 0,80 (IC 95 %, 0,79 à 0,80), respectivement. Dans les cas commencés en après-midi, les scores sur notre instrument de prédiction ≥ 43 montraient un risque total prédit de séjour en salle de réveil après 20 h de 32 % (IC 95 %, 31,1 à 33,3) vs 8 % (IC 95 %, 7,9 à 8,5) comparativement aux valeurs de score faibles (P-pour-interaction < 0,001), ce qui s'est traduit par une augmentation de 207 USD du coût direct des soins en salle de réveil (IC 95 %, 194 à 2019; estimation du modèle, 1,68; IC 95 %, 1,64 à 1,73; P < 0,001). Les effets de l'utilisation du score de notre instrument de prédiction sur l'efficacité d'utilisation de la salle de réveil étaient plus importants dans un CCA autonome sans limitation dans la disponibilité des lits en salle de réveil. CONCLUSION: Nous avons mis au point et validé un outil de prédiction préopératoire de la prolongation de la durée de séjour en salle de réveil après une chirurgie ambulatoire qui peut être utilisé pour guider la planification en chirurgie ambulatoire afin d'optimiser l'utilisation de la salle de réveil pendant les heures normales de travail, en particulier dans les milieux sans limitation de disponibilité des lits en salle de réveil.

Development and validation of a machine learning ASA-score to identify candidates for comprehensive preoperative screening and risk stratification.

OBJECTIVE: The ASA physical status (ASA-PS) is determined by an anesthesia provider or surgeon to communicate co-morbidities relevant to perioperative risk. Assigning an ASA-PS is a clinical decision and there is substantial provider-dependent variability. We developed and externally validated a machine learning-derived algorithm to determine ASA-PS (ML-PS) based on data available in the medical record. DESIGN: Retrospective multicenter hospital registry study. SETTING: University-affiliated hospital networks. PATIENTS: Patients who received anesthesia at Beth Israel Deaconess Medical Center (Boston, MA, training [n = 361,602] and internal validation cohorts [n = 90,400]) and Montefiore Medical Center (Bronx, NY, external validation cohort [n = 254,412]). MEASUREMENTS: The ML-PS was created using a supervised random forest model with 35 preoperatively available variables. Its predictive ability for 30-day mortality, postoperative ICU admission, and adverse discharge were determined by logistic regression. MAIN RESULTS: The anesthesiologist ASA-PS and ML-PS were in agreement in 57.2% of the cases (moderate inter-rater agreement). Compared with anesthesiologist rating, ML-PS assigned more patients into extreme ASA-PS (I and IV), (p < 0.01), and less patients in ASA II and III (p < 0.01). ML-PS and anesthesiologist ASA-PS had excellent predictive values for 30-day mortality, and good predictive values for postoperative ICU admission and adverse discharge. Among the 3594 patients who died within 30 days after surgery, net reclassification improvement analysis revealed that using the ML-PS, 1281 (35.6%) patients were reclassified into the higher clinical risk category compared with anesthesiologist rating. However, in a subgroup of multiple co-morbidity patients, anesthesiologist ASA-PS had a better predictive accuracy than ML-PS. CONCLUSIONS: We created and validated a machine learning physical status based on preoperatively available data. The ability to identify patients at high risk early in the preoperative process independent of the provider's decision is a part of the process we use to standardize the stratified preoperative evaluation of patients scheduled for ambulatory surgery.

Incidence and predictors of case cancellation within 24 h in patients scheduled for elective surgical procedures.

OBJECTIVE: Avoidable case cancellations within 24 h reduce operating room (OR) efficiency, add unnecessary costs, and may have physical and emotional consequences for patients and their families. We developed and validated a prediction tool that can be used to guide same day case cancellation reduction initiatives. DESIGN: Retrospective hospital registry study. SETTING: University-affiliated hospitals network (NY, USA). PATIENTS: 246,612 (1/2016-6/2021) and 58,662 (7/2021-6/2022) scheduled elective procedures were included in the development and validation cohort. MEASUREMENTS: Case cancellation within 24 h was defined as cancelling a surgical procedure within 24 h of the scheduled date and time. Our candidate predictors were defined a priori and included patient-, procedural-, and appointment-related factors. We created a prediction tool using backward stepwise logistic regression to predict case cancellation within 24 h. The model was subsequently recalibrated and validated in a cohort of patients who were recently scheduled for surgery. MAIN RESULTS: 8.6% and 8.7% scheduled procedures were cancelled within 24 h of the intended procedure in the development and validation cohort, respectively. The final weighted score contains 29 predictors. A cutoff value of 15 score points predicted a 10.3% case cancellation rate with a negative predictive value of 0.96, and a positive predictive value of 0.21. The prediction model showed good discrimination in the development and validation cohort with an area under the receiver operating characteristic curve (AUC) of 0.79 (95% confidence interval 0.79-0. 80) and an AUC of 0.73 (95% confidence interval 0.72-0.73), respectively. CONCLUSIONS: We present a validated preoperative prediction tool for case cancellation within 24 h of surgery. We utilize the instrument in our institution to identify patients with high risk of case cancellation. We describe a process for recalibration such that other institutions can also use the score to guide same day case cancellation reduction initiatives.

International teleconsultation on conjoined twins leading to a successful separation: a case report.

Conjoined twins are identical twins that have incompletely separated in utero. The prognosis for conjoined twins is poor and management in a skilled tertiary care centre is paramount for definitive care. We describe our experience with a telemedical consultation on conjoined twins in The Dominican Republic from our eHealth centre in Valhalla, NY. The patients were two month old, female, pygopagus conjoined twins. A multidisciplinary teleconference was initiated with the patients, their family, the referring paediatrician and our team. Based on this teleconsultation, the team felt as though the twins may be amenable to a surgical separation. They presented to our centre in Valhalla, NY, for a detailed physical examination and series of imaging studies. Soon after, the patients underwent a successful 21 h separation procedure and were discharged 12 weeks later. To our knowledge, this is one of the first reports of an international teleconsultation leading to a successful conjoined twin separation procedure.