Dataset Information

Development and validation of a predictive model for American Society of Anesthesiologists Physical Status.

ABSTRACT: BACKGROUND:The American Society of Anesthesiologists Physical Status (ASA-PS) classification system was developed to categorize the fitness of patients before surgery. Increasingly, the ASA-PS has been applied to other uses including justification of inpatient admission. Our objectives were to develop and cross-validate a statistical model for predicting ASA-PS; and 2) assess the concurrent and predictive validity of the model by assessing associations between model-derived ASA-PS, observed ASA-PS, and a diverse set of 30-day outcomes. METHODS:Using the 2014 American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Participant Use Data File, we developed and internally cross-validated multinomial regression models to predict ASA-PS using preoperative NSQIP data. Accuracy was assessed with C-Statistics and calibration plots. We assessed both concurrent and predictive validity of model-derived ASA-PS relative to observed ASA-PS and 30-day outcomes. To aid further research and use of the ASA-PS model, we implemented it into an online calculator. RESULTS:Of the 566,797 elective procedures in the final analytic dataset, 8.9% were ASA-PS 1, 48.9% were ASA-PS 2, 39.1% were ASA-PS 3, and 3.2% were ASA-PS 4. The accuracy of the 21-variable model to predict ASA-PS was C?=?0.77 +/-?0.0025. The model-derived ASA-PS had stronger association with key indicators of preoperative status including comorbidities and higher BMI (concurrent validity) compared to observed ASA-PS, but less strong associations with postoperative complications (predictive validity). The online ASA-PS calculator may be accessed at https://s-spire-clintools.shinyapps.io/ASA_PS_Estimator/ CONCLUSIONS: Model-derived ASA-PS better tracked key indicators of preoperative status compared to observed ASA-PS. The ability to have an electronically derived measure of ASA-PS can potentially be useful in research, quality measurement, and clinical applications.

SUBMITTER: Mudumbai SC

PROVIDER: S-EPMC6868867 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and validation of a predictive model for American Society of Anesthesiologists Physical Status.

Mudumbai Seshadri C SC Pershing Suzann S Bowe Thomas T Kamal Robin N RN Sears Erika D ED Finlay Andrea K AK Eisenberg Dan D Hawn Mary T MT Weng Yingjie Y Trickey Amber W AW Mariano Edward R ER Harris Alex H S AHS

BMC health services research 20191121 1

<h4>Background</h4>The American Society of Anesthesiologists Physical Status (ASA-PS) classification system was developed to categorize the fitness of patients before surgery. Increasingly, the ASA-PS has been applied to other uses including justification of inpatient admission. Our objectives were to develop and cross-validate a statistical model for predicting ASA-PS; and 2) assess the concurrent and predictive validity of the model by assessing associations between model-derived ASA-PS, obser ...[more]

PMID: 31752856

Similar Datasets

Project description:BackgroundElectronic health records (EHR) contain large volumes of unstructured free-form text notes that richly describe a patient's health and medical comorbidities. It is unclear if perioperative risk stratification can be performed directly from these notes without manual data extraction. We conduct a feasibility study using natural language processing (NLP) to predict the American Society of Anesthesiologists Physical Status Classification (ASA-PS) as a surrogate measure for perioperative risk. We explore prediction performance using four different model types and compare the use of different note sections versus the whole note. We use Shapley values to explain model predictions and analyze disagreement between model and human anesthesiologist predictions.MethodsSingle-center retrospective cohort analysis of EHR notes from patients undergoing procedures with anesthesia care spanning all procedural specialties during a 5 year period who were not assigned ASA VI and also had a preoperative evaluation note filed within 90 days prior to the procedure. NLP models were trained for each combination of 4 models and 8 text snippets from notes. Model performance was compared using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC). Shapley values were used to explain model predictions. Error analysis and model explanation using Shapley values was conducted for the best performing model.ResultsFinal dataset includes 38,566 patients undergoing 61,503 procedures with anesthesia care. Prevalence of ASA-PS was 8.81% for ASA I, 31.4% for ASA II, 43.25% for ASA III, and 16.54% for ASA IV-V. The best performing models were the BioClinicalBERT model on the truncated note task (macro-average AUROC 0.845) and the fastText model on the full note task (macro-average AUROC 0.865). Shapley values reveal human-interpretable model predictions. Error analysis reveals that some original ASA-PS assignments may be incorrect and the model is making a reasonable prediction in these cases.ConclusionsText classification models can accurately predict a patient's illness severity using only free-form text descriptions of patients without any manual data extraction. They can be an additional patient safety tool in the perioperative setting and reduce manual chart review for medical billing. Shapley feature attributions produce explanations that logically support model predictions and are understandable to clinicians.

Project description:BackgroundSeventy percent of surgical procedures are currently performed in the outpatient setting. Although the American Society of Anesthesiologists (ASA) Physical Classification ability to predict risk has been evaluated for in-patient surgeries, an evaluation in outpatient surgeries has yet to be performed. The major goal of the current study is to determine if the ASA classification is an independent predictor for morbidity and mortality for outpatient surgeries.MethodsThe 2005 through 2016 NSQIP Participant Use Data Files were queried to extract all patients scheduled for outpatient surgery. ASA PS class was the primary independent variable of interest. The primary outcome was 30-day medical complications, defined as having one or more of the following postoperative outcomes: (1) deep vein thrombosis, (2) pulmonary embolism, (3) reintubation, (4) failure to wean from ventilator, (5) renal insufficiency, (6) renal failure, (7) stroke, (8) cardiac arrest, (9) myocardial infarction, (10) pneumonia, (11) urinary tract infection, (12) systemic sepsis or septic shock. Mortality was also evaluated as a separate outcome.ResultsA total of 2,089,830 cases were included in the study. 24,777 (1.19%) patients had medical complications and 1,701 (0.08%) died within 30 days. ASA PS IV patients had a much greater chance of dying when compared to healthy patients, OR (95%CI) of 89 (55 to 143), P < 0.001. Nonetheless, over 30,000 ASA PS IV patients had surgery in the outpatient setting. Multivariable analysis demonstrated a stepwise independent association between ASA PS class and medical complications (C statistic = 0.70), mortality (C statistic = 0.74) and readmissions (C statistic = 0.67). Risk stratifying ability was maintained across surgical procedures and anesthesia techniques.ConclusionsASA PS class is a simple risk stratification tool for surgeries in the outpatient setting. Patients with higher ASA PS classes subsequently developed medical complications or mortality at a greater frequency than patients with lower ASA PS class after outpatient surgery. Our results suggest that the ambulatory setting may not be able to match the needs of high-risk patients.

Project description:BackgroundOver the past 2 decades, the use of intravenous ketamine infusions as a treatment for chronic pain has increased dramatically, with wide variation in patient selection, dosing, and monitoring. This has led to a chorus of calls from various sources for the development of consensus guidelines.MethodsIn November 2016, the charge for developing consensus guidelines was approved by the boards of directors of the American Society of Regional Anesthesia and Pain Medicine and, shortly thereafter, the American Academy of Pain Medicine. In late 2017, the completed document was sent to the American Society of Anesthesiologists' Committees on Pain Medicine and Standards and Practice Parameters, after which additional modifications were made. Panel members were selected by the committee chair and both boards of directors based on their expertise in evaluating clinical trials, past research experience, and clinical experience in developing protocols and treating patients with ketamine. Questions were developed and refined by the committee, and the groups responsible for addressing each question consisted of modules composed of 3 to 5 panel members in addition to the committee chair. Once a preliminary consensus was achieved, sections were sent to the entire panel, and further revisions were made. In addition to consensus guidelines, a comprehensive narrative review was performed, which formed part of the basis for guidelines.ResultsGuidelines were prepared for the following areas: indications; contraindications; whether there was evidence for a dose-response relationship, or a minimum or therapeutic dose range; whether oral ketamine or another N-methyl-D-aspartate receptor antagonist was a reasonable treatment option as a follow-up to infusions; preinfusion testing requirements; settings and personnel necessary to administer and monitor treatment; the use of preemptive and rescue medications to address adverse effects; and what constitutes a positive treatment response. The group was able to reach consensus on all questions.ConclusionsEvidence supports the use of ketamine for chronic pain, but the level of evidence varies by condition and dose range. Most studies evaluating the efficacy of ketamine were small and uncontrolled and were either unblinded or ineffectively blinded. Adverse effects were few and the rate of serious adverse effects was similar to placebo in most studies, with higher dosages and more frequent infusions associated with greater risks. Larger studies, evaluating a wider variety of conditions, are needed to better quantify efficacy, improve patient selection, refine the therapeutic dose range, determine the effectiveness of nonintravenous ketamine alternatives, and develop a greater understanding of the long-term risks of repeated treatments.

Project description:BackgroundThe American Society of Anesthesiologists (ASA) classification is the most used system to assess patient health status before surgery, ranging from I to V levels. This study aims to explore the impact of different ASA risk classes (ASA II [mild risk] and ASA III [severe risk]) on clinical outcomes following hip fracture surgery, including all-cause mortality and postoperative complications.MethodsA retrospective analysis from 2019 to 2021 across three Jordanian centers was conducted. The study included patients aged 65 and above who underwent hip fracture repair surgeries. Preoperative measures, intraoperative management protocols, and postoperative care were collected. Clinical data were extracted from electronic medical records, including demographics, fracture type, intraoperative data, and postoperative outcomes.ResultsThe analysis included 1033 patients, with 501 (48.5%) in the mild anesthetic risk group (ASA I-II) and 532 (51.5%) in the severe anesthetic risk group (ASA III-V). The mean age was 73 years, with a higher prevalence of males in the severe risk group. Patients in the severe risk group had more comorbidities, higher ICU admissions (15.23% vs. 6.18%), longer hospital stays (median 7 vs. 6 days), and higher rates of postoperative thromboembolic complications (3.39% vs. 1.39%) compared to the mild risk group. Additionally, the severe risk group showed higher mortality rates both in-hospital mortality (3.38% vs. 1.39%) and all-cause mortality (16.92% vs. 10.36%). Multivariate analysis identified higher ASA score as independent risk factors for increased all-cause mortality (HR = 1.64 95%CI 1.51-2.34) and thromboembolic complications (OR = 2.85 95%CI 1.16-7). Length of hospital stay was significantly associated with higher ASA score (OR = 1.04 95%CI 0.96-1.11).ConclusionThe study underscores the significant impact of anesthetic risk on the outcomes of hip fracture surgeries. Patients with higher ASA scores associated with severe systemic diseases may have at increased risk of adverse outcomes.

Dataset Information

Development and validation of a predictive model for American Society of Anesthesiologists Physical Status.

Publications

Development and validation of a predictive model for American Society of Anesthesiologists Physical Status.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets