scholarly journals Developing trauma mortality prediction models to measure injury severity

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Angharad Walters ◽  
Alan Cook ◽  
Belinda Gabbe ◽  
Ronan Lyons
Keyword(s):  
Injury Severity ◽  
Prediction Models ◽  
Laboratory Data ◽  
Population Based ◽  
Multiple Organ ◽  
Mortality Prediction ◽  
Trauma Patients ◽  
Mortality Models ◽  
Mortality Prediction Models ◽  
Trauma Mortality

IntroductionInjury severity measurement is integral to meaningful benchmarking and injury prevention strategies. Numerous injury mortality prediction methods have been developed and advanced, however, no consensus on the best model has been reached. Objectives and ApproachThe International Collaborative Effort (ICE) on Injury Statistics propose to develop a set of trauma mortality models for use in diverse settings by deriving and validating models with data from member countries including Wales, Australia, New Zealand and USA. The ICE team will create a definitive list of injuries required to identify trauma patients using ICD-10 codes. Models will be developed using Welsh data then validated with data from other member nations. The outcomes of interest are in-hospital and 30-day mortality. Models will be used for country benchmarking by comparing the distribution of injury severity and outcomes between nations. ResultsInitial results from replicating the model published by Wada, et al., as closely as possible using 348,433 cases held in SAIL for the years 2000 to 2013 achieved an AUROC value of 0.908 (95% CI 0.905-0.911). This model included 38 injury indicator variables, age, sex and comorbidity score. From 2009 onwards, the estimated number of deaths exceeded the actual number of deaths indicating improving risk adjusted survival. We aim to further enhance these models with additional covariates by linking with critical care data to enable us to determine the level of support patients received during their hospital stay, linking with laboratory data to provide indications of multiple organ dysfunction, acute physiological response and infection, and with GP data to incorporate measures of frailty. Conclusion/ImplicationsUsing multi-sourced population based linked data allows us to develop a suite of enhanced mortality models for use in observational and interventional research. Applying these methods to data from different countries will allow comparisons to be made of trends in severity and outcomes and support collaborative research.

Abstract 13274: Frailty is an Independent Prognostic Factor when Added to Existing Acute Ischemic Stroke Mortality Models

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Mathew J Reeves ◽  
Heidi Sucharew ◽  
Jane Khoury ◽  
Kathleen Alwell ◽  
Charles Moomaw ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Frailty Index ◽  
Mortality Prediction ◽  
Stroke Mortality ◽  
Age Related ◽  
Mortality Models ◽  
Mortality Prediction Models ◽  
Frailty Score

Introduction: Frailty is a state of decreased reserve and cumulative decline in multiple physiologic systems. Several frailty measures have been developed but have not been considered in existing stroke mortality prediction models. We sought to determine whether frailty is an independent predictor when added to existing stroke mortality models. Methods: Clinical data from incident ischemic stroke admissions in the Cincinnati/ Northern Kentucky Stroke Study during 2005 were abstracted by research nurses. Using established methods, we developed a frailty index using 35 age-related deficits that included pre-stroke function, comorbidities, symptoms, and clinical and lab values at admission. The index was specified as the total number of deficits or frailty score (range 0 to 35). Two established stroke mortality prediction models - one for in-hospital mortality and one for 1-year mortality, were identified and applied to the data. The independent contribution of the frailty score (expressed as a 1 deficit increase) to these existing models was determined from the logistic regression models. Results: A total of 2092 ischemic strokes were included. The median age was 72 years, 22% black, 56% female, median NIHSS 4 (IQR 2, 7). In-hospital and 1-year mortality were 8.8% and 26%, respectively. The median frailty score was 6 (IQR 4, 9). Both existing models fit the data well; the model c-statistics were both high (0.877 and 0.808, respectively) (Table). The frailty score was independently associated with higher mortality in both models; a 1 unit increase in age-related deficits was associated with a 20% and 17% increase in the adjusted odds ratio of in-hospital and 1-year mortality, respectively. Conclusions: When added to existing mortality prediction models, a frailty score showed strong independent associations with mortality. These data suggest that adding frailty to stroke mortality prediction models could improve their predictive accuracy and clinical utility.

scholarly journals High mortality rate of obstetric critically ill women in Rwanda and its predictability

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alcade Rudakemwa ◽  
Amyl Lucille Cassidy ◽  
Théogène Twagirumugabe
Keyword(s):  
Prediction Models ◽  
Average Length ◽  
Mortality Prediction ◽  
Routine Practice ◽  
Icu Mortality ◽  
Resource Limited ◽  
Obstetric Haemorrhage ◽  
Tertiary Hospitals ◽  
Mortality Prediction Models ◽  
Qsofa Score

Abstract Background Reasons for admission to intensive care units (ICUs) for obstetric patients vary from one setting to another. Outcomes from ICU and prediction models are not well explored in Rwanda owing to lack of appropriate scores. This study aimed to assess reasons for admission and accuracy of prediction models for mortality of obstetric patients admitted to ICUs of two public tertiary hospitals in Rwanda. Methods We prospectively collected data from all obstetric patients admitted to the ICUs of the two public tertiary hospitals in Rwanda from March 2017 to February 2018 to identify reasons for admission, demographic and clinical characteristics, outcome including death and its predictability by both the Modified Early Obstetric Warning Score (MEOWS) and quick Sequential Organ Failure Assessment (qSOFA). We analysed the accuracy of mortality prediction models by MEOWS or qSOFA by using logistic regression adjusting for factors associated with mortality. Area under the Receiver Operating characteristic (AUROC) curves is used to show the predicting capacity for each individual tool. Results Obstetric patients (n = 94) represented 12.8 % of all 747 ICU admissions which is 1.8 % of all 4.999 admitted women for pregnancy or labor. Sepsis (n = 30; 31.9 %) and obstetric haemorrhage (n = 24; 25.5 %) were the two commonest reasons for ICU admission. Overall ICU mortality for obstetric patients was 54.3 % (n = 51) with average length of stay of 6.6 ± 7.525 days. MEOWS score was an independent predictor of mortality (adjusted (a)OR 1.25; 95 % CI 1.07–1.46) and so was qSOFA score (aOR 2.81; 95 % CI 1.25–6.30) with an adjusted AUROC of 0.773 (95 % CI 0.67–0.88) and 0.764 (95 % CI 0.65–0.87), indicating fair accuracy for ICU mortality prediction in these settings of both MEOWS and qSOFA scores. Conclusions Sepsis and obstetric haemorrhage were the commonest reasons for obstetric admissions to ICU in Rwanda. MEOWS and qSOFA scores could accurately predict ICU mortality of obstetric patients in resource-limited settings, but larger studies are needed before a recommendation for their use in routine practice in similar settings.

Non-Selective Thoracic Computed Tomography in Trauma Patients Results in Injury Severity Score Inflation

2021 ◽  
pp. 000313482110249
Author(s):  
Leonardo Alaniz ◽  
Omaer Muttalib ◽  
Juan Hoyos ◽  
Cesar Figueroa ◽  
Cristobal Barrios
Keyword(s):  
Computed Tomography ◽  
Injury Severity Score ◽  
Severity Score ◽  
Injury Severity ◽  
Treatment Plan ◽  
Mortality Prediction ◽  
Trauma Patients ◽  
Mortality Benefit ◽  
Severity Scores ◽  
Score Inflation

Introduction Extensive research relying on Injury Severity Scores (ISS) reports a mortality benefit from routine non-selective thoracic CTs (an integral part of pan-computed tomography (pan-CT)s). Recent research suggests this mortality benefit may be artifact. We hypothesized that the use of pan-CTs inflates ISS categorization in patients, artificially affecting admission rates and apparent mortality benefit. Methods Eight hundred and eleven patients were identified with an ISS >15 with significant findings in the chest area. Patient charts were reviewed and scores were adjusted to exclude only occult injuries that did not affect treatment plan. Pearson chi-square tests and multivariable logistic regression were used to compare adjusted cases vs non-adjusted cases. Results After adjusting for inflation, 388 (47.8%) patients remained in the same ISS category, 378 (46.6%) were reclassified into 1 lower ISS category, and 45 (5.6%) patients were reclassified into 2 lower ISS categories. Patients reclassified by 1 category had a lower rate of mortality ( P < 0.001), lower median total hospital LOS ( P < .001), ICU days ( P < .001), and ventilator days ( P = 0.008), compared to those that remained in the same ISS category. Conclusion Injury Severity Score inflation artificially increases survival rate, perpetuating the increased use of pan-CTs. This artifact has been propagated by outdated mortality prediction calculation methods. Thus, prospective evaluations of algorithms for more selective CT scanning are warranted.

scholarly journals Equitably Allocating Resources During Crises: Racial Differences in Mortality Prediction Models

2021 ◽  
Author(s):  
Deepshikha Charan Ashana ◽  
George L Anesi ◽  
Vincent X Liu ◽  
Gabriel J Escobar ◽  
Christopher Chesley ◽  
...  
Keyword(s):  
Racial Differences ◽  
Prediction Models ◽  
Mortality Prediction ◽  
Mortality Prediction Models

scholarly journals High mortality rate of obstetric critically ill patients in Rwanda and its predictability

2020 ◽  
Author(s):  
Alcade Rudakemwa ◽  
Amy Lucille Cassidy ◽  
Theogene Twagirumugabe
Keyword(s):  
Prediction Models ◽  
Average Length ◽  
Roc Curves ◽  
Mortality Prediction ◽  
Icu Mortality ◽  
Average Length Of Stay ◽  
Referral Hospitals ◽  
Tertiary Hospitals ◽  
Mortality Prediction Models ◽  
Qsofa Score

Abstract Background Reasons for admission at the intensive care units (ICU) for obstetric patients vary from a setting to another. Outcomes from ICU and its prediction models are not well explored in Rwanda because of lack of appropriate scores. This study intended to assess profile and accuracy of predictive models for obstetric patients admitted in ICU in the two public tertiary hospitals in Rwanda.Methods We prospectively collected data from all obstetric patients admitted in the ICU of public referral hospitals in Rwanda from March 2017 to February 2018 to identify reasons for admissions and factors for prognosis. We analysed the accuracy of mortality prediction models including the quick Sequential Organ Failure Assessment (qSOFA) and Modified Early Obstetric Warning Score (MEOWS) by using the Logistic Regression and adjusted Receiver Operating characteristic (ROC) curves. Results Obstetric patients represented 12.8% of all ICU admissions and 1.8% of all deliveries. Sepsis (31.9%) and haemorrhage (25.5%) were the two commonest reasons for ICU admission in our study participants. The overall ICU mortality for our obstetric patients was 54.3% while the average length of stay was 6.6 days. MEOWS score was an independent predictor to mortality (adjusted OR=1.25[1.07-1.46]; p=0.005) and so was the qSOFA score (adjusted OR=2.81[1.25-6.30]; p=0.012). The adjusted Area Under the ROC (AUROC) for MEOWS was 0.773[0.666-0.880] and that of the qSOFA was 0.764[0.654-0.873] signing fair accuracies for ICU mortality prediction in these settings for both models.Conclusion Sepsis is the commonest reason for admissions to ICU for obstetric patients in Rwanda. Simple models comprising MEOWS and qSOFA could accurately predict the mortality for those patients but further larger studies are needed before generalization.

Abstract 183: Outcomes for Major Trauma Patients in Counties Without Trauma Centers Are Worse: Nationwide Population-Based Research in Taiwan

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Dorji Harnod ◽  
Chu Hui Chang ◽  
Ray E Chang
Keyword(s):  
Major Trauma ◽  
Injury Severity ◽  
Total Mortality ◽  
Data Bank ◽  
Claim Data ◽  
Population Based ◽  
Trauma Patients ◽  
Direct Transfer ◽  
Trauma Centers ◽  
Icu Admission

Background: Some articles proved indirect-transfer the major trauma patients to the trauma centers had non-significant different outcomes with the patients direct-transfer to the centers. But the outcomes for the major trauma patients in the counties without trauma centers still can be worse. So we did a population based research by using the NHIRD data for the results. Methods: From the claim data of one million beneficiaries of Taiwan National Health Insurance during the year of 2006 to 2008, all of the trauma patients were identified from the database by the ICD-9-CM system. ICDMAP-90 was used for calculating the Injury Severity Score (ISS) as the variable controlling the disease severity. The patients of major trauma were defined as ISS more than fifteen. We used the diagnosis one year before the trauma admission for calculating Charlson Comorbidity Index (CCI). The first hospitals and the second transferred hospitals that the major trauma patients admitted, and the areas of the first hospitals were recognized in our data bank. The condition of transfer, age, genders, intubation, ICU admission, ISS, CCI, and the triage classifications were adjusted in a logistic regression model for further analysis. Results: There were 2497 major trauma patients (ISS more then 15). The total mortality rate was 12.49%. The variables like age, intubation, ICU admission, ISS and CCI were significant for mortality, but the condition of transfer was not significant in our model. After controlling all the factors, the major trauma patients that first admitted in the areas with no trauma centers have a significant higher risk of mortality (OR=1.73, P=0.005). Conclusions: Our results hint that, although indirect-transfer for the major trauma patients have insignificant difference in mortality with the direct transfer patients, the counties with no trauma centers have significant higher mortality rates in major trauma patients. Further researches are needed for investigating the possible reasons.

Clinical and Laboratory Prognosis of Patients at Risk for Traumatic ARDS

QJM ◽  
2021 ◽  
Vol 114 (Supplement_1) ◽  
Author(s):  
Mohamed Ahmed Mohamed Eshohady ◽  
Galal Adel El Kady ◽  
Milad Ragaey Zakry
Keyword(s):  
At Risk ◽  
Injury Severity ◽  
Data Extraction ◽  
Multiple Organ ◽  
Apache Ii Score ◽  
Trauma Patients ◽  
Eligibility Criteria ◽  
Laboratory Findings ◽  
Lung Protection ◽  
Modifiable Factors

Abstract Background Trauma victims who survive their initial injuries to hospitalization in the intensive care unit (ICU) face the possibility of life-threatening complications such as multiple organ failure (MOF), the leading cause of death in these patients. Acute respiratory distress syndrome (ARDS) is the most frequent manifestation of MOF after trauma. Objective Diagnosis of traumatic patients who are at risk of developing ARDS based on clinical and laboratory findings and their proper management. Data Sources Medline databases (PubMed, Medscape, ScienceDirect. EMF-Portal) and all materials available in the Internet till 2020. Data Extraction If the studies did not fulfill the inclusion criteria, they were excluded. Study quality assessment included whether ethical approval was gained, eligibility criteria specified, appropriate controls, and adequate information and defined assessment measures. Conclusion Identifying potentially causal and modifiable factors that could lead to the development and testing of preventative ARDS therapies has been slow in part because of an incomplete understanding of which patients are likely to develop ARDS after major trauma. There are several ARDS predictors including an injury severity score (ISS), Acute Physiology and Chronic Health Evaluation (APACHE) II Score and others which try to identify trauma patients at greatest risk for ARDS. However, despite the intense research, only few effective therapies for ARDS have been postulated, including the lung protection strategies.

scholarly journals Clinical and Laboratory Predictors of In-hospital Mortality in Patients With Coronavirus Disease-2019: A Cohort Study in Wuhan, China

2020 ◽  
Vol 71 (16) ◽  
pp. 2079-2088 ◽  
Author(s):  
Kun Wang ◽  
Peiyuan Zuo ◽  
Yuwei Liu ◽  
Meng Zhang ◽  
Xiaofang Zhao ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Mortality Prediction ◽  
Gradient Boosting ◽  
Laboratory Model ◽  
Training Cohort ◽  
Clinical Model ◽  
Extreme Gradient Boosting ◽  
Mortality Prediction Models

Abstract Background This study aimed to develop mortality-prediction models for patients with coronavirus disease-2019 (COVID-19). Methods The training cohort included consecutive COVID-19 patients at the First People’s Hospital of Jiangxia District in Wuhan, China, from 7 January 2020 to 11 February 2020. We selected baseline data through the stepwise Akaike information criterion and ensemble XGBoost (extreme gradient boosting) model to build mortality-prediction models. We then validated these models by randomly collected COVID-19 patients in Union Hospital, Wuhan, from 1 January 2020 to 20 February 2020. Results A total of 296 COVID-19 patients were enrolled in the training cohort; 19 died during hospitalization and 277 discharged from the hospital. The clinical model developed using age, history of hypertension, and coronary heart disease showed area under the curve (AUC), 0.88 (95% confidence interval [CI], .80–.95); threshold, −2.6551; sensitivity, 92.31%; specificity, 77.44%; and negative predictive value (NPV), 99.34%. The laboratory model developed using age, high-sensitivity C-reactive protein, peripheral capillary oxygen saturation, neutrophil and lymphocyte count, d-dimer, aspartate aminotransferase, and glomerular filtration rate had a significantly stronger discriminatory power than the clinical model (P = .0157), with AUC, 0.98 (95% CI, .92–.99); threshold, −2.998; sensitivity, 100.00%; specificity, 92.82%; and NPV, 100.00%. In the subsequent validation cohort (N = 44), the AUC (95% CI) was 0.83 (.68–.93) and 0.88 (.75–.96) for the clinical model and laboratory model, respectively. Conclusions We developed 2 predictive models for the in-hospital mortality of patients with COVID-19 in Wuhan that were validated in patients from another center.

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