Department of Emergency Medicine & Critical Care, National Center for Global Health and Medicine, Hospital, Shinjuku, Tokyo, 162-8655, Japan.
Scand J Trauma Resusc Emerg Med. 2012 Feb 2;20:9. doi: 10.1186/1757-7241-20-9.
For real-time assessment of the probability of survival (Ps) of blunt trauma victims at emergency centers, this study aimed to establish regression models for estimating Ps using simplified coefficients.
The data of 10,210 blunt trauma patients not missing both the binary outcome data about survival and the data necessary for Ps calculation by The Trauma and Injury Severity Score (TRISS) method were extracted from the Japan Trauma Data Bank (2004-2007) and analyzed. Half (5,113) of the data was allocated to a derivation data set, with the other half (5,097) allocated to a validation data set. The data of 6,407 blunt trauma victims from the trauma registry of Khon Kaen Regional Hospital in Thailand were analyzed for validation. The logistic regression models included age, the Injury Severity Score (ISS), the Glasgow Coma Scale score (GCS), systolic blood pressure (SBP), respiratory rate (RR), and their coded values (cAGE, 0-1; cISS, 0-4; cSBP, 0-4; cGCS, 0-4; cRR, 0-4) as predictor variables. The coefficients were simplified by rounding off after the decimal point or choosing 0.5 if the coefficients varied across 0.5. The area under the receiver-operating characteristic curve (AUROCC) was calculated for each model to measure discriminant ability.
A group of formulas (log (Ps/1-Ps) = logit (Ps) = -9 + cISS - cAGE + cSBP + cGCS + cRR/2, where -9 becomes -7 if the predictor variable of cRR or cISS is missing) was developed. Using these formulas, the AUROCCs were between 0.950 and 0.964. When these models were applied to the Khon Kean data, their AUROCCs were greater than 0.91.
These equations allow physicians to perform real-time assessments of survival by easy mental calculations at Asian emergency centers, which are overcrowded with blunt injury victims of traffic accidents.
为了在急救中心实时评估钝器创伤患者的生存率（Ps），本研究旨在建立使用简化系数估算 Ps 的回归模型。
从日本创伤数据库（2004-2007 年）中提取了 10210 例钝器创伤患者的资料，这些患者的二项生存结局数据和使用创伤和损伤严重程度评分（TRISS）方法计算 Ps 所需的数据均无缺失。将数据的一半（5113 例）分配给推导数据集，另一半（5097 例）分配给验证数据集。还分析了来自泰国孔敬地区医院创伤登记处的 6407 例钝器创伤患者的数据以进行验证。逻辑回归模型纳入了年龄、损伤严重程度评分（ISS）、格拉斯哥昏迷评分（GCS）、收缩压（SBP）、呼吸频率（RR）及其编码值（cAGE，0-1；cISS，0-4；cSBP，0-4；cGCS，0-4；cRR，0-4）作为预测变量。通过四舍五入或选择 0.5 将系数简化到小数点后一位，或者如果系数在 0.5 之间变化。计算每个模型的受试者工作特征曲线下面积（AUROCC）以衡量判别能力。
得出了一组公式（log（Ps/1-Ps）=logit（Ps）=-9+cISS-cAGE+cSBP+cGCS+cRR/2，其中如果预测变量 cRR 或 cISS 缺失，则-9 变为-7）。使用这些公式，AUROCC 介于 0.950 和 0.964 之间。当将这些模型应用于孔敬数据时，其 AUROCC 大于 0.91。
这些方程允许医生在亚洲急救中心通过简单的心理计算实时评估患者的生存情况，这些中心挤满了因交通事故而遭受钝器伤的患者。
