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Yeates EO, Nahmias J, Gabriel V, Luo X, Ogunnaike B, Ahmed MI, Melikman E, Moon T, Shoultz T, Feeler A, Dudaryk R, Navas-Blanco J, Vasileiou G, Yeh DD, Matsushima K, Forestiere M, Lian T, Dominguez OH, Ricks-Oddie JL, Kuza CM. A Prospective Multicenter Comparison of Trauma and Injury Severity Score, American Society of Anesthesiologists Physical Status, and National Surgical Quality Improvement Program Calculator's Ability to Predict Operative Trauma Outcomes. Anesth Analg 2024; 138:1260-1266. [PMID: 38091502 DOI: 10.1213/ane.0000000000006802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
BACKGROUND Trauma outcome prediction models have traditionally relied upon patient injury and physiologic data (eg, Trauma and Injury Severity Score [TRISS]) without accounting for comorbidities. We sought to prospectively evaluate the role of the American Society of Anesthesiologists physical status (ASA-PS) score and the National Surgical Quality Improvement Program Surgical Risk-Calculator (NSQIP-SRC), which are measurements of comorbidities, in the prediction of trauma outcomes, hypothesizing that they will improve the predictive ability for mortality, hospital length of stay (LOS), and complications compared to TRISS alone in trauma patients undergoing surgery within 24 hours. METHODS A prospective, observational multicenter study (9/2018-2/2020) of trauma patients ≥18 years undergoing operation within 24 hours of admission was performed. Multiple logistic regression was used to create models predicting mortality utilizing the variables within TRISS, ASA-PS, and NSQIP-SRC, respectively. Linear regression was used to create models predicting LOS and negative binomial regression to create models predicting complications. RESULTS From 4 level I trauma centers, 1213 patients were included. The Brier Score for each model predicting mortality was found to improve accuracy in the following order: 0.0370 for ASA-PS, 0.0355 for NSQIP-SRC, 0.0301 for TRISS, 0.0291 for TRISS+ASA-PS, and 0.0234 for TRISS+NSQIP-SRC. However, when comparing TRISS alone to TRISS+ASA-PS ( P = .082) and TRISS+NSQIP-SRC ( P = .394), there was no significant improvement in mortality prediction. NSQIP-SRC more accurately predicted both LOS and complications compared to TRISS and ASA-PS. CONCLUSIONS TRISS predicts mortality better than ASA-PS and NSQIP-SRC in trauma patients undergoing surgery within 24 hours. The TRISS mortality predictive ability is not improved when combined with ASA-PS or NSQIP-SRC. However, NSQIP-SRC was the most accurate predictor of LOS and complications.
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Affiliation(s)
- Eric Owen Yeates
- From the Division of Trauma, Burns and Surgical Critical Care, Department of Surgery, University of California, Irvine, Orange, California
| | - Jeffry Nahmias
- From the Division of Trauma, Burns and Surgical Critical Care, Department of Surgery, University of California, Irvine, Orange, California
| | - Viktor Gabriel
- From the Division of Trauma, Burns and Surgical Critical Care, Department of Surgery, University of California, Irvine, Orange, California
| | - Xi Luo
- Department of Anesthesiology, University of Texas Southwestern, Dallas, Texas
| | - Babatunde Ogunnaike
- Department of Anesthesiology, University of Texas Southwestern, Dallas, Texas
| | - M Iqbal Ahmed
- Department of Anesthesiology, University of Texas Southwestern, Dallas, Texas
| | - Emily Melikman
- Department of Anesthesiology, University of Texas Southwestern, Dallas, Texas
| | - Tiffany Moon
- Department of Anesthesiology, University of Texas Southwestern, Dallas, Texas
| | - Thomas Shoultz
- Division of Burns, Trauma and Critical Care, Department of Surgery, University of Texas Southwestern, Dallas, Texas
| | - Anne Feeler
- Division of Burns, Trauma and Critical Care, Department of Surgery, University of Texas Southwestern, Dallas, Texas
| | - Roman Dudaryk
- Department of Anesthesiology and Pain Management, University of Miami, Miami, Florida
| | - Jose Navas-Blanco
- Department of Anesthesiology and Pain Management, University of Miami, Miami, Florida
| | | | - D Dante Yeh
- Department of Surgery, University of Miami, Miami, Florida
| | - Kazuhide Matsushima
- Department of Surgery, University of Southern California, Los Angeles, California
| | - Matthew Forestiere
- Department of Surgery, University of Southern California, Los Angeles, California
| | - Tiffany Lian
- Department of Surgery, University of Southern California, Los Angeles, California
| | - Oscar Hernandez Dominguez
- From the Division of Trauma, Burns and Surgical Critical Care, Department of Surgery, University of California, Irvine, Orange, California
- Department of General Surgery, Cleveland Clinic, Digestive Disease and Surgery Institute, Cleveland, Ohio
| | - Joni Ladawn Ricks-Oddie
- Center for Statistical Counseling, University of California, Irvine, Irvine, California
- Institute for Clinical and Translation Sciences, Biostatistics, Epidemiology, and Research Design Unit, University of California, Irvine, Irvine, California
| | - Catherine M Kuza
- Department of Anesthesiology, Keck School of Medicine of the University of Southern California, Los Angeles, California
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Santos J, Kuza CM, Luo X, Ogunnaike B, Ahmed MI, Melikman E, Moon T, Shoultz T, Feeler A, Dudaryk R, Navas J, Vasileiou G, Yeh DD, Matsushima K, Forestiere M, Lian T, Grigorian A, Ricks-Oddie J, Nahmias J. Comparison of National Surgical Quality Improvement Program Surgical Risk Calculator and Trauma and Injury Severity Score Risk Assessment Tools in Predicting Outcomes in High-Risk Operative Trauma Patients. Am Surg 2023; 89:4038-4044. [PMID: 37173283 DOI: 10.1177/00031348231175488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
BACKGROUND The Trauma and Injury Severity Score (TRISS) uses anatomic/physiologic variables to predict outcomes. The National Surgical Quality Improvement Program Surgical Risk Calculator (NSQIP-SRC) includes functional status and comorbidities. It is unclear which of these tools is superior for high-risk trauma patients (American Society of Anesthesiologists Physical Status (ASA-PS) class IV or V). This study compares risk prediction of TRISS and NSQIP-SRC for mortality, length of stay (LOS), and complications for high-risk operative trauma patients. METHODS This is a prospective study of high-risk (ASA-PS IV or V) trauma patients (≥18 years-old) undergoing surgery at 4 trauma centers. We compared TRISS vs NSQIP-SRC vs NSQIP-SRC + TRISS for ability to predict mortality, LOS, and complications using linear, logistic, and negative binomial regression. RESULTS Of 284 patients, 48 (16.9%) died. The median LOS was 16 days and number of complications was 1. TRISS + NSQIP-SRC best predicted mortality (AUROC: .877 vs .723 vs .843, P = .0018) and number of complications (pseudo-R2/median error (ME) 5.26%/1.15 vs 3.39%/1.33 vs 2.07%/1.41, P < .001) compared to NSQIP-SRC or TRISS, but there was no difference between TRISS + NSQIP-SRC and NSQIP-SRC with LOS prediction (P = .43). DISCUSSION For high-risk operative trauma patients, TRISS + NSQIP-SRC performed better at predicting mortality and number of complications compared to NSQIP-SRC or TRISS alone but similar to NSQIP-SRC alone for LOS. Thus, future risk prediction and comparisons across trauma centers for high-risk operative trauma patients should include a combination of anatomic/physiologic data, comorbidities, and functional status.
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Affiliation(s)
- Jeffrey Santos
- Division of Trauma, Burns, Critical Care & Acute Care Surgery, Department of Surgery, University of California, Irvine, Orange, CA, USA
| | - Catherine M Kuza
- Department of Anesthesiology, University of Southern California, Los Angeles, CA, USA
| | - Xi Luo
- Department of Anesthesiology, University of Texas Southwestern, Dallas, TX, USA
| | - Babatunde Ogunnaike
- Department of Anesthesiology, University of Texas Southwestern, Dallas, TX, USA
| | - M Iqbal Ahmed
- Department of Anesthesiology, University of Texas Southwestern, Dallas, TX, USA
| | - Emily Melikman
- Department of Anesthesiology, University of Texas Southwestern, Dallas, TX, USA
| | - Tiffany Moon
- Department of Anesthesiology, University of Texas Southwestern, Dallas, TX, USA
| | - Thomas Shoultz
- Division of Burns, Trauma and Critical Care, University of Texas Southwestern, Dallas, TX, USA
| | - Anne Feeler
- Division of Burns, Trauma and Critical Care, University of Texas Southwestern, Dallas, TX, USA
| | - Roman Dudaryk
- Department of Anesthesiology and Pain Management, University of Miami, Miami, FL, USA
| | - Jose Navas
- Department of Anesthesiology and Pain Management, University of Miami, Miami, FL, USA
| | | | - D Dante Yeh
- Department of Surgery, University of Miami, Miami, FL, USA
| | - Kazuhide Matsushima
- Department of Surgery, University of Southern California, Los Angeles, CA, USA
| | - Matthew Forestiere
- Department of Surgery, University of Southern California, Los Angeles, CA, USA
| | - Tiffany Lian
- Department of Surgery, University of Southern California, Los Angeles, CA, USA
| | - Areg Grigorian
- Division of Trauma, Burns, Critical Care & Acute Care Surgery, Department of Surgery, University of California, Irvine, Orange, CA, USA
| | - Joni Ricks-Oddie
- Institute for Clinical and Translation Sciences and Center for Statistical Consulting, University of California, Irvine, Orange, CA, USA
| | - Jeffry Nahmias
- Division of Trauma, Burns, Critical Care & Acute Care Surgery, Department of Surgery, University of California, Irvine, Orange, CA, USA
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Modification of the TRISS: simple and practical mortality prediction after trauma in an all-inclusive registry. Eur J Trauma Emerg Surg 2022; 48:3949-3959. [PMID: 35182160 DOI: 10.1007/s00068-022-01913-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 02/03/2022] [Indexed: 12/23/2022]
Abstract
PURPOSE Numerous studies have modified the Trauma Injury and Severity Score (TRISS) to improve its predictive accuracy for specific trauma populations. The aim of this study was to develop and validate a simple and practical prediction model that accurately predicts mortality for all acute trauma admissions. METHODS This retrospective study used Dutch National Trauma Registry data recorded between 2015 and 2018. New models were developed based on nonlinear transformations of TRISS variables (age, systolic blood pressure (SBP), Glasgow Coma Score (GCS) and Injury Severity Score (ISS)), the New Injury Severity Score (NISS), the sex-age interaction, the best motor response (BMR) and the American Society of Anesthesiologists (ASA) physical status classification. The models were validated in 2018 data and for specific patient subgroups. The models' performance was assessed based on discrimination (areas under the curve (AUCs)) and by calibration plots. Multiple imputation was applied to account for missing values. RESULTS The mortality rates in the development and validation datasets were 2.3% (5709/245363) and 2.5% (1959/77343), respectively. A model with sex, ASA class, and nonlinear transformations of age, SBP, the ISS and the BMR showed significantly better discrimination than the TRISS (AUC 0.915 vs. 0.861). This model was well calibrated and demonstrated good discrimination in different subsets of patients, including isolated hip fractures patients (AUC: 0.796), elderly (AUC: 0.835), less severely injured (ISS16) (AUC: 878), severely injured (ISS ≥ 16) (AUC: 0.889), traumatic brain injury (AUC: 0.910). Moreover, discrimination for patients admitted to the intensive care (AUC: s0.846), and for both non-major and major trauma center patients was excellent, with AUCs of 0.940 and 0.895, respectively. CONCLUSION This study presents a simple and practical mortality prediction model that performed well for important subgroups of patients as well as for the heterogeneous population of all acute trauma admissions in the Netherlands. Because this model includes widely available predictors, it can also be used for international evaluations of trauma care within institutions and trauma systems.
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Validation of the artificial intelligence-based trauma outcomes predictor (TOP) in patients 65 years and older. Surgery 2021; 171:1687-1694. [PMID: 34955288 PMCID: PMC9131296 DOI: 10.1016/j.surg.2021.11.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 10/22/2021] [Accepted: 11/16/2021] [Indexed: 12/03/2022]
Abstract
Background The Trauma Outcomes Predictor tool was recently derived using a machine learning methodology called optimal classification trees and validated for prediction of outcomes in trauma patients. The Trauma Outcomes Predictor is available as an interactive smartphone application. In this study, we sought to assess the performance of the Trauma Outcomes Predictor in the elderly trauma patient. Methods All patients aged 65 years and older in the American College of Surgeons–Trauma Quality Improvement Program 2017 database were included. The performance of the Trauma Outcomes Predictor in predicting in-hospital mortality and combined and specific morbidity based on incidence of 9 specific in-hospital complications was assessed using the c-statistic methodology, with planned subanalyses for patients 65 to 74, 75 to 84, and 85+ years. Results A total of 260,505 patients were included. Median age was 77 (71–84) years, 57% were women, and 98.8% had a blunt mechanism of injury. The Trauma Outcomes Predictor accurately predicted mortality in all patients, with excellent performance for penetrating trauma (c-statistic: 0.92) and good performance for blunt trauma (c-statistic: 0.83). Its best performance was in patients 65 to 74 years (c-statistic: blunt 0.86, penetrating 0.93). Among blunt trauma patients, the Trauma Outcomes Predictor had the best discrimination for predicting acute respiratory distress syndrome (c-statistic 0.75) and cardiac arrest requiring cardiopulmonary resuscitation (c-statistic 0.75). Among penetrating trauma patients, the Trauma Outcomes Predictor had the best discrimination for deep and organ space surgical site infections (c-statistics 0.95 and 0.84, respectively). Conclusion The Trauma Outcomes Predictor is a novel, interpretable, and highly accurate predictor of in-hospital mortality in the elderly trauma patient up to age 85 years. The Trauma Outcomes Predictor could prove useful for bedside counseling of elderly patients and their families and for benchmarking the quality of geriatric trauma care.
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Hung KCK, Lai CY, Yeung JHH, Maegele M, Chan PSL, Leung M, Wong HT, Wong JKS, Leung LY, Chong M, Cheng CH, Cheung NK, Graham CA. RISC II is superior to TRISS in predicting 30-day mortality in blunt major trauma patients in Hong Kong. Eur J Trauma Emerg Surg 2021; 48:1093-1100. [PMID: 33900416 DOI: 10.1007/s00068-021-01667-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 04/07/2021] [Indexed: 01/18/2023]
Abstract
PURPOSE Hong Kong (HK) trauma registries have been using the Trauma and Injury Severity Score (TRISS) for audit and benchmarking since their introduction in 2000. We compare the mortality prediction model using TRISS and Revised Injury Severity Classification, version II (RISC II) for trauma centre patients in HK. METHODS This was a retrospective cohort study with all five trauma centres in HK. Adult trauma patients with Injury Severity Score (ISS) > 15 suffering from blunt injuries from January 2013 to December 2015 were included. TRISS models using the US and local coefficients were compared with the RISC II model. The primary outcome was 30-day mortality and the area under the receiver operating characteristic curve (AUC) for tested models. RESULTS 1840 patients were included, of whom 1236/1840 (67%) were male. Median age was 59 years and median ISS was 25. Low falls were the most common mechanism of injury. The 30-day mortality was 23%. RISC II yielded a superior AUC of 0.896, compared with the TRISS models (MTOS: 0.848; PATOS: 0.839; HK: 0.858). Prespecified subgroup analyses showed that all the models performed worse for age ≥ 70, ASA ≥ III, and low falls. RISC II had a higher AUC compared with the TRISS models in all subgroups, although not statistically significant. CONCLUSION RISC II was superior to TRISS in predicting the 30-day mortality for Hong Kong adult blunt major trauma patients. RISC II may be useful when performing future audit or benchmarking exercises for trauma in Hong Kong.
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Affiliation(s)
- Kei Ching Kevin Hung
- Accident and Emergency Medicine Academic Unit, The Chinese University of Hong Kong, Shatin, Hong Kong.,Trauma and Emergency Centre, Prince of Wales Hospital, Shatin, Hong Kong
| | - Chun Yu Lai
- Accident and Emergency Medicine Academic Unit, The Chinese University of Hong Kong, Shatin, Hong Kong.,Trauma and Emergency Centre, Prince of Wales Hospital, Shatin, Hong Kong
| | - Janice Hiu Hung Yeung
- Accident and Emergency Medicine Academic Unit, The Chinese University of Hong Kong, Shatin, Hong Kong.,Trauma and Emergency Centre, Prince of Wales Hospital, Shatin, Hong Kong
| | - Marc Maegele
- Cologne-Merheim Medical Center (CMMC), Department of Trauma and Orthopedic Surgery, University Witten/Herdecke, Campus Cologne-Merheim, Cologne, Germany
| | - Po Shan Lily Chan
- Trauma Service, Queen Elizabeth Hospital, 30 Gascoigne Road, Kowloon, Hong Kong
| | - Ming Leung
- Department of Surgery, Princess Margaret Hospital, 2‑10 Princess Margaret Hospital Road, Lai Chi Kok, Kowloon, Hong Kong
| | - Hay Tai Wong
- Trauma Service, Queen Mary Hospital, 102 Pok Fu Lam Road, Hong Kong Island, Hong Kong
| | - John Kit Shing Wong
- Trauma Service, Tuen Mun Hospital, 23 Tsing Chung Koon Road, Tuen Mun, New Territories, Hong Kong
| | - Ling Yan Leung
- Accident and Emergency Medicine Academic Unit, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Marc Chong
- School of Public Health and Primary Care, Chinese University of Hong Kong, Shatin, Hong Kong
| | - Chi Hung Cheng
- Accident and Emergency Medicine Academic Unit, The Chinese University of Hong Kong, Shatin, Hong Kong.,Trauma and Emergency Centre, Prince of Wales Hospital, Shatin, Hong Kong
| | - Nai Kwong Cheung
- Accident and Emergency Medicine Academic Unit, The Chinese University of Hong Kong, Shatin, Hong Kong.,Trauma and Emergency Centre, Prince of Wales Hospital, Shatin, Hong Kong
| | - Colin Alexander Graham
- Accident and Emergency Medicine Academic Unit, The Chinese University of Hong Kong, Shatin, Hong Kong. .,Trauma and Emergency Centre, Prince of Wales Hospital, Shatin, Hong Kong.
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Schuijt HJ, Bos J, Smeeing DPJ, Geraghty O, van der Velde D. Predictors of 30-day mortality in orthogeriatric fracture patients aged 85 years or above admitted from the emergency department. Eur J Trauma Emerg Surg 2019; 47:817-823. [PMID: 31802136 PMCID: PMC8187176 DOI: 10.1007/s00068-019-01278-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 11/26/2019] [Indexed: 11/26/2022]
Abstract
Purpose Orthogeriatric trauma patients are at risk for functional decline and mortality. It is important to identify high-risk patients in an early stage, to improve outcomes and make better informed treatment decisions. The aim of this study was to identify independent risk factors for 30-day mortality in patients aged 85 years or above admitted from the emergency department with a fracture. Methods All orthopaedic trauma patients 85 years or above admitted from the emergency department were included. After a 30-day follow-up, mortality was determined by consulting the patient records. Multivariable logistics regression analysis generated odd ratios for mortality risk factors. A subgroup analysis was performed for patients undergoing hip fracture surgery. Results The 30-day mortality in geriatric fracture patients admitted to the hospital was 12%. Risk factors for 30-day mortality were: increased age, male sex, decreased hemoglobin levels, living in an institutional care facility and a decreased BMI. For geriatric patients undergoing hip fracture surgery 30-day mortality was 11%. Independent risk factors for this group were: increased age, male sex, and a decreased BMI. Conclusion Orthopaedic trauma patients aged 85 years or above who are admitted to the hospital with a fracture are at high risk for mortality. This study identified older age, male sex, and decreased BMI as predictors of 30-day mortality in admitted geriatric fracture patients and in geriatric hip fracture patients undergoing surgery.
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Affiliation(s)
- Henk Jan Schuijt
- Department of Surgery, Sint Antonius Hospital, Nieuwegein, Postbus 2500, 3430 EM, Utrecht, The Netherlands.
- Department of Surgery, University Medical Center, Utrecht, The Netherlands.
| | - Jelle Bos
- Department of Surgery, Sint Antonius Hospital, Nieuwegein, Postbus 2500, 3430 EM, Utrecht, The Netherlands
| | | | - Olivia Geraghty
- Department of Internal Medicine and Geriatrics, Sint Antonius Hospital, Utrecht, The Netherlands
| | - Detlef van der Velde
- Department of Surgery, Sint Antonius Hospital, Nieuwegein, Postbus 2500, 3430 EM, Utrecht, The Netherlands
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