ASA Physical Status Classification and Complications Following Facial Fracture Repair

Background: To investigate the association between American Society of Anesthesiologists (ASA) physical status classification and rates of postoperative complications in patients undergoing facial fracture repair. Methods: Patients were divided into 2 cohorts based on the ASA classification system: Class I/II and Class III/IV. Chi-square and Fisher’s exact tests were used for univariate analyses. Multivariate logistic regressions were used to assess the independent associations of covariates on postoperative complication rates. Results: A total of 3575 patients who underwent facial fracture repair with known ASA classification were identified. Class III/IV patients had higher rates of deep surgical site infection ( P = .012) as well as bleeding, readmission, reoperation, surgical, medical, and overall postoperative complications ( P < .001). Multivariate regression analysis found that Class III/IV was significantly associated with increased length of stay ( P < .001) and risk of overall complications ( P = .032). Specifically, ASA Class III/IV was associated with increased rates of deep surgical site infection ( P = .049), postoperative bleeding ( P = .036), and failure to wean off ventilator ( P = .027). Conclusions: Higher ASA class is associated with increased length of hospital stay and odds of deep surgical site infection, bleeding, and failure to wean off of ventilator following facial fracture repair. Surgeons should be aware of the increased risk for postoperative complications when performing facial fracture repair in patients with high ASA classification.

Development of a Better Solid Waste Management Program for Sustainable Development in a Residential Subdivision

Proper implementation of Solid Waste Management (SWM) is an essential part for the protection of the residents’ health, safety and environmental quality. SWM methods have been adapted by many residential subdivisions into a more practical and effective option to establish sustainability based on the reduce, reuse, and recycle principles. This study aims to contribute a solution to the challenging operation of solid waste management in Modena Mactan subdivision (1) to comprehensively describe the homeowner’s status classification and demographic characteristics, (2) to evaluate volume of waste produced and recycled waste revenue collected, (3) to recognize homeowner’s perception on the current waste management status, and (4) to showcase feasible approaches for sustainable waste management program. The study applied the descriptive research design and was carried out to 93 homeowners who went through the (house-to-house) paper-pencil-questionnaire survey. Results showed that the subdivision produced an average of 33 tons or 16.974 kilograms of solid waste per household per month, and generated an amount of 1,369 PHP or 27.41 USD revenue from the segregated recyclable waste collected from August 1-28, 2021. Moreover, about 87% of the respondents found convenient and sought to change in paperless system. Conclusively, it was revealed that 74% of the respondents found the recycling incentive scheme more inclusive as a feasible approach for waste management strategy to sustain the solid waste management program in Modena Mactan subdivision, Basak, Lapu-Lapu City, Cebu, Philippines.

Health status classification model for medical adherence system in retirement township

Medical adherence and remote patient monitoring have gained huge attention from researchers recently, especially with the need to observe the patients’ health outside hospitals due to the ongoing pandemic. The main goal of this research work is to propose a health status classification model that provides a numerical indicator of the overall health condition of a patient via four major vital signs, which are body temperature, blood pressure, blood oxygen saturation level, and heart rate. A dataset has been prepared based on the data obtained from hospital records, with these four vital signs extracted for each patient. This dataset provides a label associating each patient to the number of medical diagnoses. Generally, the number of diagnoses correlates with the patient's medical condition, with no diagnoses indicating normal condition, one to two diagnoses suggest low risk, and more than that implies high risk. Thus, we propose a method to classify a patient’s health status into three classes, which are normal, low risk and high risk. This would provide guidance for healthcare workers on the patient's medical condition. By training the classification model using the prepared dataset, the seriousness of a patient's health condition can be predicted. This prediction is performed by classifying the patients based on their four vital signs. Our tests have yielded encouraging results using precision and recall as the evaluation metrics. The key outcome of this work is a trained classification model that quantifies a patient's health condition based on four vital signs. Nevertheless, the model can be further improved by considering more input features such as medical history. The results obtained from this research can assist medical personnel by providing a secondary advice regarding the health status for the patients who are located remotely from the medical facilities.

Machine learning algorithm to perform ASA Physical Status Classification

BackgroundThe American Society of Anesthesiologists (ASA) Physical Status Classification System defines peri-operative patient scores as 1 (healthy) thru 6 (brain dead). The scoring is used by the anesthesiologists to classify surgical patients based on co-morbidities and various clinical characteristics. The classification is always done by an anesthesiologist prior operation. There is a variability in scoring stemming from individual experiences / biases of the scoring anesthesiologists, which impacts prediction of operating times, length of stay in the hospital, necessity of blood transfusion, etc. In addition, the score affects anesthesia coding and billing. It is critical to remove subjectivity from the process to achieve reproducible generalizable scoring.MethodsA machine learning (ML) approach was used to associate assigned ASA scores with peri-operative patients’ clinical characteristics. More than ten ML algorithms were simultaneously trained, validated, and tested with retrospective records. The most accurate algorithm was chosen for a subsequent test on an independent dataset. DataRobot platform was used to run and select the ML algorithms. Manual scoring was also performed by one anesthesiologist. Intra-class correlation coefficient (ICC) was calculated to assess the consistency of scoringResultsRecords of 19,095 procedures corresponding to 12,064 patients with assigned ASA scores by 17 City of Hope anesthesiologists were used to train a number of ML algorithms (DataRobot platform). The most accurate algorithm was tested with independent records of 2325 procedures corresponding to 1999 patients. In addition, 86 patients from the same dataset were scored manually. The following ICC values were computed: COH anesthesiologists vs. ML – 0.427 (fair); manual vs. ML – 0.523 (fair-to-good); manual vs. COH anesthesiologists – 0.334 (poor).ConclusionsWe have shown the feasibility of using ML for assessing the ASA score. In principle, a group of experts (i.e. physicians, institutions, etc.) can train the ML algorithm such that individual experiences and biases would cancel each leaving the objective ASA score intact. As more data are being collected, a valid foundation for refinement to the ML will emerge.

The Evolution, Current Value, and Future of the American Society of Anesthesiologists Physical Status Classification System

The American Society of Anesthesiologists (ASA) Physical Status classification system celebrates its 80th anniversary in 2021. Its simplicity represents its greatest strength as well as a limitation in a world of comprehensive multisystem tools. It was developed for statistical purposes and not as a surgical risk predictor. However, since it correlates well with multiple outcomes, it is widely used—appropriately or not—for risk prediction and many other purposes. It is timely to review the history and development of the system. The authors describe the controversies surrounding the ASA Physical Status classification, including the problems of interrater reliability and its limitations as a risk predictor. Last, the authors reflect on the current status and potential future of the ASA Physical Status system.