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

Konda, Sanjit R.; Parola, Rown; Perskin, Cody R.; Egol, Kenneth A.

doi:10.1177/2151459321989534

Cited by 32 publications

(32 citation statements)

References 23 publications

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“…16 Furthermore, the model's predictive ability has also been validated in several other patient populations such as hip, femur, and humerus fracture patients. [10][11][12][13][14] In the clinical setting, the STTGMA tool has the potential to aid clinicians in providing more value-based care. 10,14,15,17 Healthcare providers can use the score early in a patient's admission to gain insight into what the trajectory of the patient's hospital course may look like.…”

Section: Discussionmentioning

confidence: 99%

“…16 Furthermore, the model’s predictive ability has also been validated in several other patient populations such as hip, femur, and humerus fracture patients. 10-14…”

Section: Discussionmentioning

confidence: 99%

“…Since its development, studies have also shown STTGMA to successfully risk stratify additional outcomes such as need for ICU, length of stay, readmissions, and hospital costs in hip, ankle, tibia, and humerus fracture patients. [10][11][12][13][14][15][16][17] Clinicians can use these scores to identify high risk patients who may benefit from a higher level of care.…”

Section: Introductionmentioning

confidence: 99%

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Trauma Risk Score Also Predicts Blood Transfusion Requirements in Hip Fracture Patients

Konda

Perskin

Parola

et al. 2021

Geriatr Orthop Surg Rehabil

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Introduction The purpose of this study is to determine if the risk of receiving a blood transfusion during hip fracture hospitalization can be predicted by a validated risk profiling score (Score for Trauma Triage in Geriatric and Middle Aged (STTGMA)). Materials and Methods A consecutive series of 1449 patients 55 years and older admitted for a hip fracture at one academic medical center were identified from a trauma database. The STTGMA risk score was calculated for each patient. Patients were stratified into risk groups based on their STTGMA score quantile: minimal risk (0–50%), low risk (50–80%), moderate risk (80–95%), and high risk (95–100%). Incidence and volume of blood transfusions were compared between risk groups. Results There were 562 (38.8%) patients who received a transfusion during their admission. 58.3% of patients in the high risk group received a transfusion during admission compared to 31.2% of minimal risk group patients, 42.6% of low risk group patients, and 50.0% of moderate risk group patients ( p < 0.001). STTGMA was predictive of first transfusion incidence in both the preoperative and postoperative periods. There was no difference in mean total transfusion volume between the four risk groups. Conclusion The STTGMA model is capable of risk stratifying hip fracture patients more likely to receive blood transfusions during hospitalization. Surgeons can use this tool to anticipate transfusion requirements.

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Section: Discussionmentioning

confidence: 99%

“…16 Furthermore, the model’s predictive ability has also been validated in several other patient populations such as hip, femur, and humerus fracture patients. 10-14…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Trauma Risk Score Also Predicts Blood Transfusion Requirements in Hip Fracture Patients

Konda

Perskin

Parola

et al. 2021

Geriatr Orthop Surg Rehabil

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“…Due to the increase in mortality associated with older age, and increase in strain on hospital resources and medical personnel [28], the American College of Surgeons recommend that patients older than 55 years of age who sustained a traumatic injury should have special considerations for triage [25], and be transported to a designated trauma center for higher level of care [29 ▪ ]. Geriatric trauma patients are more likely to have worse outcomes, longer hospital stays, and increased in-hospital mortality compared with younger patients [34].…”

Section: Trauma Patterns In Elderly and Associated Mortalitymentioning

confidence: 99%

Predicting mortality in elderly trauma patients: a review of the current literature

Chow

Kuza

2022

Current Opinion in Anaesthesiology

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Purpose of reviewAdvances in medical care allow patients to live longer, translating into a larger geriatric patient population. Adverse outcomes increase with older age, regardless of injury severity. Age, comorbidities, and physiologic deterioration have been associated with the increased mortality seen in geriatric trauma patients. As such, outcome prediction models are critical to guide clinical decision making and goals of care discussions for this population. The purpose of this review was to evaluate the various outcome prediction models for geriatric trauma patients.Recent findingsThere are several prediction models used for predicting mortality in elderly trauma patients. The Geriatric Trauma Outcome Score (GTOS) is a validated and accurate predictor of mortality in geriatric trauma patients and performs equally if not better to traditional scores such as the Trauma and Injury Severity Score. However, studies recommend medical comorbidities be included in outcome prediction models for geriatric patients to further improve performance.SummaryThe ideal outcome prediction model for geriatric trauma patients has not been identified. The GTOS demonstrates accurate predictive ability in elderly trauma patients. The addition of medical comorbidities as a variable in outcome prediction tools may result in superior performance; however, additional research is warranted.

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“…This commented file analyses the data in the STTGMA Matching DB.xlsx file. Matching variables used in the experiment include Charlson Comorbidity Index (CCI) [1] in column Q, sex in column H, Age in column G, body mass index (BMI) in column AB, and the appropriate Score for Trauma Triage in Geriatric and Middle-Aged (STTGMA) score [2] , [3] , [4] in column BM based on injury mechanism energy in column L.…”

Section: Data Descriptionmentioning

confidence: 99%

Trauma risk score matching for observational studies in orthopedic trauma dataset and code

et al. 2022

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The dataset presented was collected via retrospective review from an orthopedic trauma database approved by the institutional review board at the author's institution from patients treated at any of the four hospitals serviced by the academic orthopedic surgery department. Femoral neck and intertrochanteric hip fracture patients from low energy mechanisms admitted between October 2014 and February 2020, were selected if they were age 55 or older and had recorded sex, body mass index (BMI), Charlson Comorbidity Index (CCI), American Society of Anaesthesiologists (ASA) physical status classification, Glasgow Coma Score, Abbreviated Injury Severity score for the chest, head and neck, and extremities, and ambulation status prior to injury. The resultant 1,590 subject dataset may be analysed via the supplied R statistical code to determine the frequency of equipoise in baseline and outcome variables from propensity matching via three matching schemes. The code implements three matching schemes including matching by (1) The Score for Trauma Triage in Geriatric and Middle-Aged (STTGMA) (2) CCI alone, or (3) a combination of sex, age, CCI and BMI. The code selects a subset of ten percent of hip fracture patients by a pseudorandom number generator (PRNG). The code matches the remaining patients 1:1 to the selected patients by propensity score generated by logistic regression of STTGMA, CCI, or a combination of sex, age, CCI and BMI using greedy nearest neighbor matching without replacement by the MatchIt package for R software. The code then compares matched cohorts by Chi-square, Fisher, or Mann-Whitney U test with significance level of 0.05 representing a 5% chance of significant differences due to random sampling of subjects. The supplied code repeats the random selection, matching and testing process 100,000 times for each matching method. The resultant code output is the frequency of significantly different demographic or outcome parameters among matched cohorts by matching method. This data and statistical code have reuse potential to explore alternative matching schemes. The supplied baseline variables should be robust enough to derive alternative risk scores for each patient which may be included as a matching variable for comparison. The authors also look forward to unexpected ways that this data may be used by readers.

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

Cited by 32 publications

References 23 publications

Trauma Risk Score Also Predicts Blood Transfusion Requirements in Hip Fracture Patients

Trauma Risk Score Also Predicts Blood Transfusion Requirements in Hip Fracture Patients

Predicting mortality in elderly trauma patients: a review of the current literature

Trauma risk score matching for observational studies in orthopedic trauma dataset and code

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