Trauma survival prediction in Asian population: a modification of TRISS to improve accuracy

Chan, Canon King On; Yau, Kelvin K. W.; Cheung, Moon‐Tong

doi:10.1136/emermed-2012-201831

Cited by 17 publications

(26 citation statements)

References 62 publications

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“…In recent years, based on reports discussing the possibilities that changes to various coefficients, such as use of actual age and changes to the 55‐year age classification, have on the accuracy of the regression model and the background of the remarkable improvement in the survival rate of patients with unstable vital signs on admission, reports have acknowledged that there is less need for RR and BP as variables, particularly at the Level 1 trauma center . These results also support the verification results attained herein.…”

Section: Discussionsupporting

confidence: 83%

A survival prediction logistic regression models for blunt trauma victims in Japan

Suzuki

Kimura

Sasaki

et al. 2016

Acute Medicine & Surgery

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AimThis research aimed to propose a logistic regression model for Japanese blunt trauma victims.MethodsWe tested whether the logistic regression model previously created from data registered in the Japan Trauma Data Bank between 2005 and 2008 is still valid for the data from the same data bank between 2009 and 2013. Additionally, we analyzed whether the model would be highly accurate even when its coefficients were rounded off to two decimal places.ResultsThe model was proved to be highly accurate (94.56%) in the recent data (2009–2013). We also showed that the model remains valid without respiratory rate data and the simplified model would maintain high accuracy.ConclusionWe propose the equation of survival prediction of blunt trauma victims in Japan to be Ps = 1/(1+e−b), where b = −0.76 + 1.03 × Revised Trauma Score − 0.07 × Injury Severity Score − 0.04 × age.

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Section: Discussionsupporting

confidence: 83%

A survival prediction logistic regression models for blunt trauma victims in Japan

Suzuki

Kimura

Sasaki

et al. 2016

Acute Medicine & Surgery

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“…Comparing ISS and RTS means in deceased and recovered patients showed a statistically significant difference between the two groups, with RTS mean significantly lower in deceased patients than in recovered patients and ISS mean higher in deceased patients than in recovered patients. The results of all studies in this field showed a direct relationship between ISS and injury severity and an inverse relationship between RTS and injury severity (31)(32)(33) Norouzi et al (11) conducted a study on traffic-injured patients in Ardebil in which RTS mean for deceased patients was 5.29 and 7.62 for recovered patients. Also, ISS mean was 29.65 in deceased patients and 13.98 in recovered patients.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Survival and Hospital Care Quality in Patients with Traffic Injury in East Azerbaijan

Esmaeili¹,

Sadeghi‐Bazargani²,

Vahdati³

et al. 2016

Trauma Mon

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Background: Traffic events are the second most common cause of mortality and the first cause of years of life lost (YLL) in Iran. Objectives: The aim of this study was to determine the survival of patients with traffic injury and evaluate hospital care quality using the trauma and injury severity score (TRISS) method. Methods: This cross-sectional study was conducted on 1000 patients aged 1 to 89 years old who were hospitalized in two university hospitals in East Azerbaijan, Iran. Patients were selected by using stratified sampling. Data were extracted from medical records and analyzed by STATA11 software. Injury severity score (ISS), revised trauma score (RTS), and expected survival were calculated using the TRISS software package. W-scores and Z-scores were calculated to evaluate the performance of hospitals. Finally, results were compared to those of the major trauma outcome study (MTOS). Results: Among 1000 patients, 246 (24.6%) were female. The mean age was 32.6 years (SD, 18.6). Mean ISS for living patients was 12.4 (SD, 4.3) while it was 36.9 (SD, 8.9) for fatal cases. The average RTS was reported to be 7.1 and 4.35 for alive and fatal cases, respectively (P < 0.001). Based on TRISS, 58 cases were expected to result in death; however, 65 fatalities were observed (65/1000). The W-score was -7 per 1000 and Z-score was + 0.02. The TRISS misclassification rate for survival status of patients with traffic injury was reported as 2.6%. Conclusions: Our findings indicate that the quality of care at the hospitals under study using the TRISS method was lower than the quality of care in the MTOS study. Further, survival of traffic injury patients was clearly influenced by the quality of hospital services provided.

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“…The TRISS based on Multiple Trauma Outcome Study (MTOS) is the largest database of descriptive contemporary injury information; the survival probability norms use the RTS, ISS, patient age and injury mechanisms 37. Modification may provide better prediction 38. The lower ISS and higher TRISS during Phase 3 than Phases 2 and 1 may lead to the suspicion that the improvement in survival was due to the lesser severity of injury in Phase 3.…”

Section: Discussionmentioning

confidence: 99%

Improvement in institutional protocols leads to decreased mortality in patients with haemodynamically unstable pelvic fractures

et al. 2013

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Trauma survival prediction in Asian population: a modification of TRISS to improve accuracy

Cited by 17 publications

References 62 publications

A survival prediction logistic regression models for blunt trauma victims in Japan

A survival prediction logistic regression models for blunt trauma victims in Japan

Assessment of Survival and Hospital Care Quality in Patients with Traffic Injury in East Azerbaijan

Improvement in institutional protocols leads to decreased mortality in patients with haemodynamically unstable pelvic fractures

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