The role of the American Society of anesthesiologists physical status classification in predicting trauma mortality and outcomes.
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ABSTRACT: Background
Trauma prediction scores such as Revised Trauma Score (RTS) and Trauma and Injury Severity Score (TRISS)) are used to predict mortality, but do not include comorbidities. We analyzed the American Society of Anesthesiologists physical status (ASA PS) for predicting mortality in trauma patients undergoing surgery.Methods
This multicenter, retrospective study compared the mortality predictive ability of ASA PS, RTS, Injury Severity Score (ISS), and TRISS using a complete case analysis with mixed effects logistic regression. Associations with mortality and AROC were calculated for each measure alone and tested for differences using chi-square.Results
Of 3,042 patients, 230 (8%) died. The AROC for mortality for TRISS was 0.938 (95%CI 0.921, 0.954), RTS 0.845 (95%CI 0.815, 0.875), and ASA PS 0.886 (95%CI 0.864, 0.908). ASA PS + TRISS did not improve mortality predictive ability (p = 0.18).Conclusions
ASA PS was a good predictor of mortality in trauma patients, although combined with TRISS it did not improve predictive ability.
SUBMITTER: Kuza CM
PROVIDER: S-EPMC8421011 | biostudies-literature | 2019 Dec
REPOSITORIES: biostudies-literature
Publications
The role of the American Society of anesthesiologists physical status classification in predicting trauma mortality and outcomes.
Kuza Catherine M CM Matsushima Kazuhide K Mack Wendy J WJ Pham Christopher C Hourany Talia T Lee Jessica J Tran Thang D TD Dudaryk Roman R Mulder Michelle B MB Escanelle Miguel A MA Ogunnaike Babatunde B Ahmed M Iqbal MI Luo Xi X Eastman Alexander A Imran Jonathan B JB Melikman Emily E Minhajuddin Abu A Feeler Anne A Urman Richard D RD Salim Ali A Spencer Dean D Gabriel Viktor V Ramakrishnan Divya D Nahmias Jeffry T JT
American journal of surgery 20190924 6
<h4>Background</h4>Trauma prediction scores such as Revised Trauma Score (RTS) and Trauma and Injury Severity Score (TRISS)) are used to predict mortality, but do not include comorbidities. We analyzed the American Society of Anesthesiologists physical status (ASA PS) for predicting mortality in trauma patients undergoing surgery.<h4>Methods</h4>This multicenter, retrospective study compared the mortality predictive ability of ASA PS, RTS, Injury Severity Score (ISS), and TRISS using a complete ...[more]
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Project description:The American Society of Anesthesiologists Physical Status Classification (ASA) is used for communication of patient health status, risk scoring, benchmarking and financial claims. Prior studies using hypothetical scenarios have shown poor concordance of ASA classification among healthcare providers. There is a paucity of studies using clinical data, and of clinical factors or patient outcomes associated with discordant classification. The study aims to assess ASA classification concordance between surgeons and anesthesiologists, factors surrounding discordance and its impact on patient outcomes. This retrospective cohort study was conducted in a tertiary medical center on 46,284 consecutive patients undergoing elective surgery between January 2017 and December 2019. The ASA class showed moderate concordance (weighted Cohen's κ 0.53) between surgeons and anesthesiologists. We found significant associations between discordant classification and patient comorbidities, age and race. Patients with discordant classification had a higher risk of 30-day mortality (odds ratio (OR) 2.00, 95% confidence interval (CI) = 1.52-2.62, p < 0.0001), 1-year mortality (OR 1.53, 95% CI = 1.38-1.69, p < 0.0001), and Intensive Care Unit admission > 24 h (OR 1.69, 95% CI = 1.47-1.94, p < 0.0001). Hence, there is a need for improved standardization of ASA scoring and cross-specialty review in ASA-discordant cases.
| S-EPMC9061797 | biostudies-literature
Project description:Background Electronic 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. Methods Single-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. Results Final 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. Conclusions Text 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. Supplementary Information The online version contains supplementary material available at 10.1186/s12871-023-02248-0.
| S-EPMC10476287 | biostudies-literature
Project description:Study objectiveTo explore how American Society of Anesthesiologists (ASA) physical status classification affects different machine learning models in hypotension prediction and whether the prediction uncertainty could be quantified.DesignObservational Studies SETTING: UofL health hospital PATIENTS: This study involved 562 hysterectomy surgeries performed on patients (≥ 18 years) between June 2020 and July 2021.InterventionsNone MEASUREMENTS: Preoperative and intraoperative data is collected. Three parametric machine learning models, including Bayesian generalized linear model (BGLM), Bayesian neural network (BNN), a newly proposed BNN with multivariate mixed responses (BNNMR), and one nonparametric model, Gaussian Process (GP), were explored to predict patients' diastolic and systolic blood pressures (continuous responses) and patients' hypotensive event (binary response) for the next five minutes. Data was separated into American Society of Anesthesiologists (ASA) physical status class 1- 4 before being read in by four machine learning models. Statistical analysis and models' constructions are performed in Python. Sensitivity, specificity, and the confidence/credible intervals were used to evaluate the prediction performance of each model for each ASA physical status class.Main resultsASA physical status classes require distinct models to accurately predict intraoperative blood pressures and hypotensive events. Overall, high sensitivity (above 0.85) and low uncertainty can be achieved by all models for ASA class 4 patients. In contrast, models trained without controlling ASA classes yielded lower sensitivity (below 0.5) and larger uncertainty. Particularly, in terms of predicting binary hypotensive event, for ASA physical status class 1, BNNMR yields the highest sensitivity of 1. For classes 2 and 3, BNN has the highest sensitivity of 0.429 and 0.415, respectively. For class 4, BNNMR and GP are tied with the highest sensitivity of 0.857. On the other hand, the sensitivity is just 0.031, 0.429, 0.165 and 0.305 for BNNMR, BNN, GBLM and GP models respectively, when training data is not divided by ASA physical status classes. In terms of predicting systolic blood pressure, the GP regression yields the lowest root mean squared errors (RMSE) of 2.072, 7.539, 9.214 and 0.295 for ASA physical status classes 1, 2, 3 and 4, respectively, but a RMSE of 126.894 if model is trained without controlling the ASA physical status class. The RMSEs for other models are far higher. RMSEs are 2.175, 13.861, 17.560 and 22.426 for classes 1, 2, 3 and 4 respectively for the BGLM. In terms of predicting diastolic blood pressure, the GP regression yields the lowest RMSEs of 2.152, 6.573, 5.371 and 0.831 for ASA physical status classes 1, 2, 3 and 4, respectively; RMSE of 8.084 if model is trained without controlling the ASA physical status class. The RMSEs for other models are far higher. Finally, in terms of the width of the 95% confidence interval of the mean prediction for systolic and diastolic blood pressures, GP regression gives narrower confidence interval with much smaller margin of error across all four ASA physical status classes.ConclusionsDifferent ASA physical status classes present different data distributions, and thus calls for distinct machine learning models to improve prediction accuracy and reduce predictive uncertainty. Uncertainty quantification enabled by Bayesian inference provides valuable information for clinicians as an additional metric to evaluate performance of machine learning models for medical decision making.
| S-EPMC10873053 | biostudies-literature
Project description:BackgroundPrevious studies, which relied on hypothetical cases and chart reviews, have questioned the inter-rater reliability of the ASA physical status (ASA-PS) scale. We therefore conducted a retrospective cohort study to evaluate its inter-rater reliability and validity in clinical practice.MethodsThe cohort included all adult patients (≥18 yr) who underwent elective non-cardiac surgery at a quaternary-care teaching institution in Toronto, Ontario, Canada, from March 2010 to December 2011. We assessed inter-rater reliability by comparing ASA-PS scores assigned at the preoperative assessment clinic vs the operating theatre. We also assessed the validity of the ASA-PS scale by measuring its association with patients' preoperative characteristics and postoperative outcomes.ResultsThe cohort included 10 864 patients, of whom 5.5% were classified as ASA I, 42.0% as ASA II, 46.7% as ASA III, and 5.8% as ASA IV. The ASA-PS score had moderate inter-rater reliability (κ 0.61), with 67.0% of patients (n=7279) being assigned to the same ASA-PS class in the clinic and operating theatre, and 98.6% (n=10 712) of paired assessments being within one class of each other. The ASA-PS scale was correlated with patients' age (Spearman's ρ, 0.23), Charlson comorbidity index (ρ=0.24), revised cardiac risk index (ρ=0.40), and hospital length of stay (ρ=0.16). It had moderate ability to predict in-hospital mortality (receiver-operating characteristic curve area 0.69) and cardiac complications (receiver-operating characteristic curve area 0.70).ConclusionsConsistent with its inherent subjectivity, the ASA-PS scale has moderate inter-rater reliability in clinical practice. It also demonstrates validity as a marker of patients' preoperative health status.
| S-EPMC4136425 | biostudies-literature
Project description: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.
| S-EPMC6868867 | biostudies-literature
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.
| S-EPMC8140433 | biostudies-literature
Project description:Aim:The American Society of Anesthesiologists Physical Status (ASA-PS) classification system is used worldwide and has also been incorporated into various prediction rules. However, concerns have been raised regarding inter-rater agreement in various surgical fields. Although emergency gastrointestinal surgery is relatively common and associated with high postoperative mortality, a reliability study has not yet been undertaken in this field. The aim of the present study was to investigate the inter-rater reliability of ASA-PS for emergency gastrointestinal surgery. Methods:Three sets of scenarios were generated for each ASA-PS class (2E, 3E, and 4E) in emergency gastrointestinal surgery, resulting in nine scenarios. These scenarios described the preoperative profiles of patients in one hospital. Two or three anesthesiologists from 18 other hospitals provided scores for ASA-PS for each scenario. Results:Fifty anesthesiologists scored the ASA-PS class. Between 66% and 90% of these anesthesiologists assigned the same ratings as the reference ratings for the individual scenarios. Inter-rater reliability was assessed using Fleiss' kappa (95% confidence interval) of 0.55 (0.54-0.56, P < 0.001) and an intraclass correlation coefficient (95% confidence interval) of 0.79 (0.63-0.93, P < 0.001). Conclusion:The results of the present study revealed the consistency of ASA-PS ratings between anesthesiologists for emergency gastrointestinal surgery. The ASA-PS may serve as a reliable variable in the prediction rules for this field.
| S-EPMC5667267 | biostudies-literature
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.
| S-EPMC11299364 | biostudies-literature
Project description:ObjectiveTo test the hypothesis of associations of preoperative physical status system with major postoperative complications at hospital discharge in prostate cancer (PCa) patients treated with robot-assisted radical prostatectomy (RARP).Materials and methodsIn a period ranging from January 2013 to October 2020, 1143 patients were evaluated. The physical status was assessed by the American Society of Anesthesiologists' (ASA) system, which was computed trained anesthesiologists. The Clavien-Dindo system was used to classify postoperative complications, which were coded as major if greater than 1.ResultsASA physical status system included class I in 102 patients (8.9%), class II in 934 subjects (81.7%), and class III in 107 cases (9.4%). Clavien-Dindo complications were distributed as follows: grade 1: 141 cases (12.3%), grade 2: 108 patients (9.4%), grade 3a: 5 subjects (0.4%), grade 3b: 9 patients (0.8%), and grade 4a: 3 cases (0.3%). Overall, major complications were detected in 125 cases (10.9%). On multivariate analysis, major Clavien-Dindo complications were predicted by ASA score grade II (adjusted odds ratio, OR = 2.538; 95%CI 1.007-6.397; p = 0.048) and grade III (adjusted OR 3.468; 95%CI 1.215-9.896; p = 0.020) independently by pelvic lymph node dissection (PLND) and/or blood lost.ConclusionIn RARP surgery, the risk of major postoperative Clavien-Dindo complications increased as the physical status system deteriorated independently by performing or not a PLND and/or large intraoperative blood lost. The ASA score system was an effective predictor of major Clavien-Dindo complications, which delayed LOHS in RARP surgery. Confirmatory studies are required.Supplementary informationThe online version contains supplementary material available at 10.1007/s13193-022-01577-9.
| S-EPMC9845474 | biostudies-literature
Project description:BackgroundFor hip and knee arthroplasties, an American Society of Anesthesiologists (ASA) score greater than 2 is associated with an increased risk of medical and surgical complications. No study, to our knowledge, has evaluated this relationship for total shoulder arthroplasty (TSA) or reverse total shoulder arthroplasty (reverse TSA).Questions/purposesWe aimed to assess the relationship between the ASA score and (1) surgical complications, (2) medical complications, and (3) hospitalization length after TSA, reverse TSA, and revision arthroplasty.MethodsWe retrospectively analyzed all patients who had undergone TSAs, reverse TSAs, or revision arthroplasties by the senior author (EGM) from November 1999 through July 2011 who had at least 6 months' followup. Of the 485 procedures, 452 (93.2%) met the inclusion criteria. Data were collected on patient demographics, comorbidities, hospitalization length, and short-term (? 6 months) medical and surgical complications. Logistic regression analysis modeled the risk of having postoperative complications develop as a function of the ASA score.ResultsPatients with an ASA score greater than 2 had a greater risk of having a surgical complication develop (p < 0.001; OR, 2.27; 95% CI, 1.36-3.70) and three times the risk of prosthesis failure (ie, component dislocation, component loosening, and hardware failure) (p < 0.001; OR, 3.23; 95% CI, 1.54-6.67). Higher ASA scores were associated with prolonged length of hospitalization (effect size 0.46, p < 0.001), but not medical complications.ConclusionsASA score is associated with surgical, but not medical, complications after TSA and reverse TSA. The ASA score could be used for risk assessment and preoperative counseling.Level of evidenceLevel III, therapeutic study. See the Instructions for Authors for a complete description of levels of evidence.
| S-EPMC3971223 | biostudies-literature