scholarly journals ASA Physical Status Classification Improves Predictive Ability of a Validated Trauma Risk Score

2021 ◽  
Vol 12 ◽  
pp. 215145932198953
Author(s):  
Sanjit R. Konda ◽  
Rown Parola ◽  
Cody Perskin ◽  
Kenneth A. Egol
Keyword(s):  
Hip Fracture ◽  
Risk Score ◽  
Mortality Risk ◽  
Risk Scores ◽  
Inpatient Mortality ◽  
Physical Status ◽  
Middle Aged ◽  
Physical Status Classification ◽  
Significant Difference ◽  
Status Classification

Introduction: The Score for Trauma Triage in the Geriatric and Middle-Aged (STTGMA) is a validated mortality risk score that evaluates 4 major physiologic criteria: age, comorbidities, vital signs, and anatomic injuries. The aim of this study was to investigate whether the addition of ASA physical status classification system to the STTGMA tool would improve risk stratification of a middle-aged and elderly trauma population. Methods: A total of 1332 patients aged 55 years and older who sustained a hip fracture through a low-energy mechanism between October 2014 and February 2020 were included. The STTGMA and STTGMAASA mortality risk scores were calculated. The ability of the models to predict inpatient mortality was compared using area under the receiver operating characteristic curves (AUROCs) by DeLong’s test. Patients were stratified into minimal, low, moderate, and high risk cohorts based on their risk scores. Comparative analyses between risk score stratification distribution of mortality, complications, length of stay, ICU admission, and readmission were performed using Fisher’s exact test. Total cost of admission was fitted by univariate linear regression with STTGMA and STTGMAASA. Results: There were 27 inpatient mortalities (2.0%). When STTGMA was used, the AUROC was 0.742. When STTGMAASA was used, the AUROC was 0.823. DeLong’s test resulted in significant difference in predictive capacity for inpatient mortality between STTGMA and STTGMAASA (p = 0.04). Risk score stratification yielded significantly different distribution of all outcomes between risk cohorts (p < 0.01). STTGMAASA stratification produced a larger percentage of all negative outcomes with increasing risk cohort. Total hospital cost was statistically correlated with both STTGMAASA (p < 0.01) and STTGMA (p = 0.02). Conclusion: Including ASA physical status as a variable in STTGMA improves the model’s ability to predict inpatient mortality and risk stratify middle-aged and geriatric hip fracture patients.

scholarly journals How Does Frailty Factor Into Mortality Risk Assessment of a Middle-Aged and Geriatric Trauma Population?

2017 ◽  
Vol 8 (4) ◽  
pp. 225-230 ◽  
Author(s):  
Sanjit R. Konda ◽  
Ariana Lott ◽  
Hesham Saleh ◽  
Sebastian Schubl ◽  
Jeffrey Chan ◽  
...  
Keyword(s):  
Mortality Risk ◽  
Functional Independence ◽  
High Energy ◽  
Low Energy ◽  
Geriatric Trauma ◽  
Inpatient Mortality ◽  
Middle Aged ◽  
Trauma Population ◽  
Increased Risk ◽  
Significant Difference

Introduction: Frailty in elderly trauma populations has been correlated with an increased risk of morbidity and mortality. The Score for Trauma Triage in the Geriatric and Middle-Aged (STTGMA) is a validated mortality risk score that evaluates 4 major physiologic criteria: age, comorbidities, vital signs, and anatomic injuries. The aim of this study was to investigate whether the addition of additional frailty variables to the STTGMA tool would improve risk stratification of a middle-aged and elderly trauma population. Methods: A total of 1486 patients aged 55 years and older who met the American College of Surgeons Tier 1 to 3 criteria and/or who had orthopedic or neurosurgical traumatic consultations in the emergency department between September 2014 and September 2016 were included. The STTGMAORIGINAL and STTGMAFRAILTY scores were calculated. Additional “frailty variables” included preinjury assistive device use (disability), independent ambulatory status (functional independence), and albumin level (nutrition). The ability of the STTGMAORIGINAL and the STTGMAFRAILTY models to predict inpatient mortality was compared using area under the receiver operating characteristic curves (AUROCs). Results: There were 23 high-energy inpatient mortalities (4.7%) and 20 low-energy inpatient mortalities (2.0%). When the STTGMAORIGINAL model was used, the AUROC in the high-energy and low-energy cohorts was 0.926 and 0.896, respectively. The AUROC for STTGMAFRAILTY for the high-energy and low-energy cohorts was 0.905 and 0.937, respectively. There was no significant difference in predictive capacity for inpatient mortality between STTGMAORIGINAL and STTGMAFRAILTY for both the high-energy and low-energy cohorts. Conclusion: The original STTGMA tool accounts for important frailty factors including cognition and general health status. These variables combined with other major physiologic variables such as age and anatomic injuries appear to be sufficient to adequately and accurately quantify inpatient mortality risk. The addition of other common frailty factors that account for does not enhance the STTGMA tool’s predictive capabilities.

scholarly journals Preoperative predictions of in-hospital mortality using electronic medical record data

2018 ◽  
Author(s):  
Brian Hill ◽  
Robert Brown ◽  
Eilon Gabel ◽  
Christine Lee ◽  
Maxime Cannesson ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Hospital Mortality ◽  
Risk Score ◽  
Surgical Mortality ◽  
Physical Status ◽  
Preoperative Risk ◽  
Physical Status Classification ◽  
Postoperative Risk ◽  
Status Classification

AbstractBackgroundPredicting preoperative in-hospital mortality using readily-available electronic medical record (EMR) data can aid clinicians in accurately and rapidly determining surgical risk. While previous work has shown that the American Society of Anesthesiologists (ASA) Physical Status Classification is a useful, though subjective, feature for predicting surgical outcomes, obtaining this classification requires a clinician to review the patient’s medical records. Our goal here is to create an improved risk score using electronic medical records and demonstrate its utility in predicting in-hospital mortality without requiring clinician-derived ASA scores.MethodsData from 49,513 surgical patients were used to train logistic regression, random forest, and gradient boosted tree classifiers for predicting in-hospital mortality. The features used are readily available before surgery from EMR databases. A gradient boosted tree regression model was trained to impute the ASA Physical Status Classification, and this new, imputed score was included as an additional feature to preoperatively predict in-hospital post-surgical mortality. The preoperative risk prediction was then used as an input feature to a deep neural network (DNN), along with intraoperative features, to predict postoperative in-hospital mortality risk. Performance was measured using the area under the receiver operating characteristic (ROC) curve (AUC).ResultsWe found that the random forest classifier (AUC 0.921, 95%CI 0.908-0.934) outperforms logistic regression (AUC 0.871, 95%CI 0.841-0.900) and gradient boosted trees (AUC 0.897, 95%CI 0.881-0.912) in predicting in-hospital post-surgical mortality. Using logistic regression, the ASA Physical Status Classification score alone had an AUC of 0.865 (95%CI 0.848-0.882). Adding preoperative features to the ASA Physical Status Classification improved the random forest AUC to 0.929 (95%CI 0.915-0.943). Using only automatically obtained preoperative features with no clinician intervention, we found that the random forest model achieved an AUC of 0.921 (95%CI 0.908-0.934). Integrating the preoperative risk prediction into the DNN for postoperative risk prediction results in an AUC of 0.924 (95%CI 0.905-0.941), and with both a preoperative and postoperative risk score for each patient, we were able to show that the mortality risk changes over time.ConclusionsFeatures easily extracted from EMR data can be used to preoperatively predict the risk of in-hospital post-surgical mortality in a fully automated fashion, with accuracy comparable to models trained on features that require clinical expertise. This preoperative risk score can then be compared to the postoperative risk score to show that the risk changes, and therefore should be monitored longitudinally over time.Author summaryRapid, preoperative identification of those patients at highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores either lack specificity at the patient level, or utilize the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart. In this manuscript we report on using machine-learning algorithms, specifically random forest, to create a fully automated score that predicts preoperative in-hospital mortality based solely on structured data available at the time of surgery. This score has a higher AUC than both the ASA physical status score and the Charlson comorbidity score. Additionally, we integrate this score with a previously published postoperative score to demonstrate the extent to which patient risk changes during the perioperative period.

scholarly journals Safety and Efficacy of Nonanesthesiologist-Administrated Propofol during Endoscopic Submucosal Dissection of Gastric Epithelial Tumors

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Keiichiro Abe ◽  
Keiichi Tominaga ◽  
Akira Kanamori ◽  
Tsunehiro Suzuki ◽  
Hitoshi Kino ◽  
...  
Keyword(s):  
Adverse Events ◽  
Endoscopic Submucosal Dissection ◽  
University Hospital ◽  
Physical Status ◽  
Safety And Efficacy ◽  
Submucosal Dissection ◽  
Epithelial Tumors ◽  
Physical Status Classification ◽  
Significant Difference ◽  
Status Classification

Objective. There is no consensus regarding administration of propofol for performing endoscopic submucosal dissection (ESD) in patients with comorbidities. The aim of this study was to evaluate the safety and efficacy of propofol-induced sedation administered by nonanesthesiologists during ESD of gastric cancer in patients with comorbidities classified according to the American Society of Anesthesiologists (ASA) physical status. Methods. Five hundred and twenty-two patients who underwent ESD for gastric epithelial tumors under sedation by nonanesthesiologist-administrated propofol between April 2011 and October 2017 at Dokkyo Medical University Hospital were enrolled in this study. The patients were divided into 3 groups according to the ASA physical status classification. Hypotension, desaturation, and bradycardia were evaluated as the adverse events associated with propofol. The safety of sedation by nonanesthesiologist-administrated propofol was measured as the primary outcome. Results. The patients were classified according to the ASA physical status classification: 182 with no comorbidity (ASA 1), 273 with mild comorbidity (ASA 2), and 67 with severe comorbidity (ASA 3). The median age of the patients with ASA physical status of 2/3 was higher than the median age of those with ASA physical status of 1. There was no significant difference in tumor characteristics, total amount of propofol used, or ESD procedure time, among the 3 groups. Adverse events related to propofol in the 522 patients were as follows: hypotension (systolic blood pressure<90 mmHg) in 113 patients (21.6%), respiratory depression (SpO2<90%) in 265 patients (50.8%), and bradycardia (pulse rate<50 bpm) in 39 patients (7.47%). There was no significant difference in the incidences of adverse events among the 3 groups during induction, maintenance, or recovery. No severe adverse event was reported. ASA 3 patients had a significantly longer mean length of hospital stay (8 days for ASA 1, 9 days for ASA 2, and 9 days for ASA 3, P=0.003). However, the difference did not appear to be clinically significant. Conclusions. Sedation by nonanesthesiologist-administrated propofol during ESD is safe and effective, even for at-risk patients according to the ASA physical status classification.

Prognosis-Related Autophagy Genes in Female Lung Adenocarcinoma

2020 ◽  
Author(s):  
Zhongxiang Liu ◽  
Haicheng Tang ◽  
Yongqian Jiang ◽  
Xiaopeng Zhan ◽  
Sheng Kang ◽  
...  
Keyword(s):  
Survival Rate ◽  
Lung Adenocarcinoma ◽  
Regression Model ◽  
Clinical Data ◽  
Risk Score ◽  
Risk Group ◽  
Risk Scores ◽  
Clinicopathological Parameters ◽  
Significant Difference ◽  
Cox Analysis

Abstract Background: To screen the prognosis-related autophagy genes of female lung adenocarcinoma by the transcriptome data and clinical data from TCGA database.Methods: In this study, Screen meaningful female lung adenocarcinoma differential genes in TCGA, use univariate COX proportional regression model to select genes related to prognosis, and establish the best risk model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were applied for carrying out bioinformatics analysis of gene function.Results: The gene expression and clinical data of 260 female lung adenocarcinoma patient samples were downloaded from TCGA. 12 down-regulated genes: NRG3, DLC1, NLRC4, DAPK2, HSPB8, PPP1R15A, FOS, NRG1, PRKCQ, GRID1, MAP1LC3C, GABARAPL1. Up-regulated 15 genes: PARP1, BNIP3, P4HB, ATIC, IKBKE, ITGB4, VMP1, PTK6, EIF4EBP1, GAPDH, ATG9B, ERO1A, TMEM74, CDKN2A, BIRC5. GO and KEGG analysis showed that these genes were significantly associated with autophagy and mitochondria (animals). Multi-factor COX analysis of autophagy-related genes showed that ITGA6, ERO1A, FKBP1A, BAK1, CCR2, FADD, EDEM1, ATG10, ATG4A, DLC1, VAMP7, ST13 were identified as independent prognostic indicators. According to the multivariate COX proportional hazard regression model, there was a significant difference in the survival rate observed between the high-risk group (n=124) and the low-risk group (n=126) during the 10-year follow-up (p<0.05). Univariate COX analysis showed that tumor stage, T, M and N stages, and risk score were all related to the survival rate of female lung adenocarcinoma patients. Multivariate COX analysis found that autophagy-related risk scores were independent predictors, with an AUC value of 0.842. At last, there are autophagy genes differentially expressed among various clinicopathological parameters: ATG4A, BAK1, CCR2, DLC1, ERO1A, FKBP1A, ITGA6.Conclusion: The risk score can be used as an independent prognostic indicator for female patients with lung adenocarcinoma. The autophagy genes ITGA6, ERO1A, FKBP1A, BAK1, CCR2, FADD, EDEM1, ATG10, ATG4A, DLC1, VAMP7, ST13 were identified as prognostic genes in female lung adenocarcinoma, which may be the targets of treatment in the future.

Classification of comorbidity in trauma: The reliability of pre-injury ASA physical status classification

Injury ◽  
2013 ◽  
Vol 44 (1) ◽  
pp. 29-35 ◽  
Author(s):  
Kjetil G. Ringdal ◽  
Nils Oddvar Skaga ◽  
Petter Andreas Steen ◽  
Morten Hestnes ◽  
Petter Laake ◽  
...  
Keyword(s):  
Physical Status ◽  
Physical Status Classification ◽  
Status Classification
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