对 ICD-10-CA 至 AIS-2005 更新 2008 算法的内部和外部验证。
Internal and external validation of an updated ICD-10-CA to AIS-2005 update 2008 algorithm.
机构信息
From the Interdepartmental Division of Critical Care (B.W.T., D.C.S., B.H.), University of Toronto; Department of Critical Care Medicine (B.W.T., D.C.S., B.H.), Sunnybrook Health Sciences Centre; Institute of Health Policy, Management, and Evaluation (B.W.T., M.P.G., A.B.N., D.C.S., P.P., B.H.), Department of Surgery (M.P.G., A.B.N., B.H.), University of Toronto, Toronto, Ontario; Trauma Services (J.T., J.M.M., R.G.), Provincial Health Services Authority; Division of General Surgery, Department of Surgery, (D.C.E.), University of British Columbia, Vancouver, British Columbia; ICES (A.B.N., P.P., D.C.S., P.P., B.H.); Sunnybrook Research Institute (A.B.N., D.C.S., B.H.); Tory Trauma Program (A.P.), Sunnybrook Health Sciences Centre, Toronto, Ontario; Department of Surgery (N.L.Y.), University of Calgary, Calgary, Alberta; Department of Medicine (D.C.S.), University of Toronto; Toronto Health Economic and Technology Assessment Collaborative (P.P.); and The Hospital for Sick Children (P.P.), Toronto, Ontario, Canada.
出版信息
J Trauma Acute Care Surg. 2024 Feb 1;96(2):297-304. doi: 10.1097/TA.0000000000004052. Epub 2023 Jul 5.
BACKGROUND
Administrative data are a powerful tool for population-level trauma research but lack the trauma-specific diagnostic and injury severity codes needed for risk-adjusted comparative analyses. The objective of this study was to validate an algorithm to derive Abbreviated Injury Scale (AIS-2005 update 2008) severity scores from Canadian International Classification of Diseases (ICD-10-CA) diagnostic codes in administrative data.
METHODS
This was a retrospective cohort study using data from the 2009 to 2017 Ontario Trauma Registry for the internal validation of the algorithm. This registry includes all patients treated at a trauma center who sustained a moderate or severe injury or were assessed by a trauma team. It contains both ICD-10-CA codes and injury scores assigned by expert abstractors. We used Cohen's kappa (𝜅) coefficient to compare AIS-2005 Update 2008 scores assigned by expert abstractors to those derived using the algorithm and the intraclass correlation coefficient to compare assigned and derived Injury Severity Scores. Sensitivity and specificity for detection of a severe injury (AIS score, ≥ 3) were then calculated. For the external validation of the algorithm, we used administration data to identify adults who either died in an emergency department or were admitted to hospital in Ontario secondary to a traumatic injury (2009-2017). Logistic regression was used to evaluate the discriminative ability and calibration of the algorithm.
RESULTS
Of 41,869 patients in the Ontario Trauma Registry, 41,793 (99.8%) had at least one diagnosis matched to the algorithm. Evaluation of AIS scores assigned by expert abstractors and those derived using the algorithm demonstrated a high degree of agreement in identification of patients with at least one severe injury (𝜅 = 0.75; 95% confidence interval [CI], 0.74-0.76). Likewise, algorithm-derived scores had a strong ability to rule in or out injury with AIS ≥ 3 (specificity, 78.5%; 95% CI, 77.7-79.4; sensitivity, 95.1; 95% CI, 94.8-95.3). There was strong correlation between expert abstractor-assigned and crosswalk-derived Injury Severity Score (intraclass correlation coefficient, 0.80; 95% CI, 0.80-0.81). Among the 130,542 patients identified using administrative data, the algorithm retained its discriminative properties.
CONCLUSION
Our ICD-10-CA to AIS-2005 update 2008 algorithm produces reliable estimates of injury severity and retains its discriminative properties with administrative data. Our findings suggest that this algorithm can be used for risk adjustment of injury outcomes when using population-based administrative data.
LEVEL OF EVIDENCE
Diagnostic Tests/Criteria; Level II.
背景
行政数据是进行人群创伤研究的有力工具，但缺乏用于风险调整比较分析的特定于创伤的诊断和损伤严重程度代码。本研究的目的是验证一种从加拿大国际疾病分类（ICD-10-CA）诊断代码中提取损伤严重程度评分（AIS-2005 更新版 2008）的算法。
方法
这是一项回顾性队列研究，使用了 2009 年至 2017 年安大略省创伤登记处的数据对内验证算法。该登记处包括在创伤中心接受治疗的所有中度或重度损伤或由创伤小组评估的患者。它包含 ICD-10-CA 代码和由专家摘要员分配的损伤评分。我们使用 Cohen 的 kappa（𝜅）系数比较专家摘要员分配的 AIS-2005 更新版 2008 评分与使用算法得出的评分，并使用组内相关系数比较分配和推导的损伤严重程度评分。然后计算检测严重损伤（AIS 评分≥3）的敏感性和特异性。为了对算法进行外部验证，我们使用行政数据识别安大略省因创伤而在急诊室死亡或住院的成年人（2009-2017 年）。使用逻辑回归评估算法的判别能力和校准。
结果
在安大略省创伤登记处的 41869 名患者中，有 41793 名（99.8%）至少有一个与算法匹配的诊断。对专家摘要员分配的 AIS 评分和使用算法推导的评分进行评估，表明在确定至少有一个严重损伤的患者方面具有高度一致性（𝜅=0.75；95%置信区间[CI]，0.74-0.76）。同样，算法推导的评分具有很好的能力来确定或排除 AIS≥3 的损伤（特异性，78.5%；95%CI，77.7-79.4；敏感性，95.1%；95%CI，94.8-95.3）。专家摘要员分配的损伤严重程度评分与交叉验证推导的评分之间具有很强的相关性（组内相关系数，0.80；95%CI，0.80-0.81）。在使用行政数据识别的 130542 名患者中，该算法保留了其判别特性。
结论
我们的 ICD-10-CA 至 AIS-2005 更新版 2008 算法可产生可靠的损伤严重程度估计值，并保留其在行政数据中的判别特性。我们的研究结果表明，当使用基于人群的行政数据时，该算法可用于调整损伤结局的风险。
证据水平
诊断测试/标准；二级。
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