Predictors of elderly mortality after trauma: A novel outcome score

Morris, Rachel; Milia, David; Glover, James W.; Napolitano, Lena M.; Chen, Benjamin; Lindemann, Elizabeth; Hemmila, Mark R.; Stein, Deborah M.; Kummerfeld, Erich; Chipman, Jeffrey G.; Tignanelli, Christopher J.

doi:10.1097/ta.0000000000002569

Cited by 27 publications

(11 citation statements)

References 36 publications

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“…We identified 59 studies for the topic of in-hospital mortality. 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 While the majority of studies used a retrospective cohort design, 11 used a prospective approach, 120 122 129 140 143 166 169 170 172 174 175 and two were meta-analysis studies. 154 …”

Section: Resultsmentioning

confidence: 99%

“…120 123 128 131 140 142 143 146 169 Study populations primarily comprised adults (sometimes limited to subpopulations such as those with chronic illnesses [e.g., Takada et al 164 and Sukmark et al 163 ]), but three studies included pediatric populations, 138 154 161 one study included newborns, 150 and two studies included elder patients aged over 65 years. 121 155 Sample sizes ranged from 15 to 9,000,000 observations.…”

Section: Resultsmentioning

confidence: 99%

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Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

et al. 2022

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Background The term “data science” encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications. Objectives This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature. Methods We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care–acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture. Results Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing. Conclusion This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

et al. 2022

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“…To date, the Geriatric Trauma Outcome Score and the Elderly Mortality after Trauma Score are the only mortality models specific for elderly trauma patients. 24,25 However, Geriatric Trauma Outcome Score does not include functional status or significant comorbidities and thus is somewhat outdated because it focuses more on age as a number instead of frailty, which is associated with increased likelihood of mortality, complications, and discharge to a skilled nursing facility after a trauma admission in elderly patients. 26 Hence, this study sought to use existing clinical tools such as ASA-PS and NSQIP-SRC to see if they could better prognosticate risk for elderly trauma patients.…”

Section: Discussionmentioning

confidence: 99%

Comparison of National Surgical Quality Improvement Program Surgical Risk Calculator, Trauma and Injury Severity Score, and American Society of Anesthesiologists Physical Status to predict operative trauma mortality in elderly patients

Stopenski¹,

Kuza²,

Luo³

et al. 2021

J Trauma Acute Care Surg

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BACKGROUND:The Trauma and Injury Severity Score (TRISS) uses anatomical and physiologic variables to predict mortality. Elderly (65 years or older) trauma patients have increased mortality and morbidity for a given TRISS, in part because of functional status and comorbidities. These factors are incorporated into the American Society of Anesthesiologists Physical Status (ASA-PS) and National Surgical Quality Improvement Program Surgical Risk Calculator (NSQIP-SRC). We hypothesized scoring tools using comorbidities and functional status to be superior at predicting mortality, hospital length of stay (LOS), and complications in elderly trauma patients undergoing operation. METHODS:Four level I trauma centers prospectively collected data on elderly trauma patients undergoing surgery within 24 hours of admission. Using logistic regression, five scoring models were compared: ASA-PS, NSQIP-SRC, TRISS, TRISS-ASA-PS, and TRISS-NSQIP-SRC.Brier scores and area under the receiver operator characteristics curve were calculated to compare mortality prediction. Adjusted R 2 and root mean squared error were used to compare LOS and predictive ability for number of complications. RESULTS:From 122 subjects, 9 (7.4%) died, and the average LOS was 12.9 days (range, 1-110 days). National Surgical Quality Improvement Program Surgical Risk Calculator was superior to ASA-PS and TRISS at predicting mortality (area under the receiver operator characteristics curve, 0.978 vs. 0.768 vs. 0.903; p = 0.007). Furthermore, NSQIP-SRC was more accurate predicting LOS (R 2 , 25.9% vs. 13.3% vs. 20.5%) and complications (R 2 , 34.0% vs. 22.6% vs. 29.4%) compared with TRISS and ASA-PS. Adding TRISS to NSQIP-SRC improved predictive ability compared with NSQIP-SRC alone for complications (R 2 , 35.5% vs. 34.0%; p = 0.046). However, adding ASA-PS or TRISS to NSQIP-SRC did not improve the predictive ability for mortality or LOS. CONCLUSION:The NSQIP-SRC, which includes comorbidities and functional status, had superior ability to predict mortality, LOS, and complications compared with TRISS alone in elderly trauma patients undergoing surgery.

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“…However, extrapolation to the geriatric population has limitations and, therefore, to date, only a few feasible scores for the prediction of mortality in geriatric trauma patients exist. To our knowledge, only two scoring systems were explicitly developed for mortality prediction after trauma in the geriatric population: namely, the new Geriatric Trauma Outcome Score (GTOS) and the very recently published Elderly Mortality after Trauma Score (EMAT) [21]. Unfortunately, we were not able to evaluate the EMAT Score because the registry data do not include all parameters used in the scoring system.…”

Section: Discussionmentioning

confidence: 99%

The GERtality Score: The Development of a Simple Tool to Help Predict in-Hospital Mortality in Geriatric Trauma Patients

Scherer

Kalbas

Ziegenhain

et al. 2021

JCM

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Feasible and predictive scoring systems for severely injured geriatric patients are lacking. Therefore, the aim of this study was to develop a scoring system for the prediction of in-hospital mortality in severely injured geriatric trauma patients. The TraumaRegister DGU® (TR-DGU) was utilized. European geriatric patients (≥65 years) admitted between 2008 and 2017 were included. Relevant patient variables were implemented in the GERtality score. By conducting a receiver operating characteristic (ROC) analysis, a comparison with the Geriatric Trauma Outcome Score (GTOS) and the Revised Injury Severity Classification II (RISC-II) Score was performed. A total of 58,055 geriatric trauma patients (mean age: 77 years) were included. Univariable analysis led to the following variables: age ≥ 80 years, need for packed red blood cells (PRBC) transfusion prior to intensive care unit (ICU), American Society of Anesthesiologists (ASA) score ≥ 3, Glasgow Coma Scale (GCS) ≤ 13, Abbreviated Injury Scale (AIS) in any body region ≥ 4. The maximum GERtality score was 5 points. A mortality rate of 72.4% was calculated in patients with the maximum GERtality score. Mortality rates of 65.1 and 47.5% were encountered in patients with GERtality scores of 4 and 3 points, respectively. The area under the curve (AUC) of the novel GERtality score was 0.803 (GTOS: 0.784; RISC-II: 0.879). The novel GERtality score is a simple and feasible score that enables an adequate prediction of the probability of mortality in polytraumatized geriatric patients by using only five specific parameters.

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Predictors of elderly mortality after trauma: A novel outcome score

Cited by 27 publications

References 36 publications

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

Comparison of National Surgical Quality Improvement Program Surgical Risk Calculator, Trauma and Injury Severity Score, and American Society of Anesthesiologists Physical Status to predict operative trauma mortality in elderly patients

The GERtality Score: The Development of a Simple Tool to Help Predict in-Hospital Mortality in Geriatric Trauma Patients

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