TMPM-ICD9 A Trauma Mortality Prediction Model Based on ICD-9-CM Codes

被引:126
作者
Glance, Laurent G. [1 ]
Osler, Turner M. [2 ]
Mukamel, Dana B. [3 ]
Meredith, Wayne [4 ]
Wagner, Jacob [2 ]
Dick, Andrew W.
机构
[1] Univ Rochester, Dept Anesthesiol, Med Ctr, Sch Med, Rochester, NY 14642 USA
[2] Univ Vermont, Dept Surg, Coll Med, Burlington, VT 05405 USA
[3] Univ Calif Irvine, Dept Med, Ctr Hlth Policy Res, Irvine, CA 92717 USA
[4] Wake Forest Univ, Dept Surg, Sch Med, Winston Salem, NC 27109 USA
关键词
INJURY SEVERITY SCORE; NEW-YORK-STATE; OF-VETERANS-AFFAIRS; INTERNATIONAL CLASSIFICATION; QUALITY IMPROVEMENT; ANATOMIC INJURY; SURGICAL CARE; TRAUMA; MORTALITY; OUTCOMES;
D O I
10.1097/SLA.0b013e3181a38f28
中图分类号
R61 [外科手术学];
学科分类号
摘要
Objective: To develop and validate a new ICD-9 injury model that uses regression modeling, as opposed to a simple ratio measurement, to estimate empiric injury severities for each of the injuries in the ICD-9-CM lexicon. Background: The American College of Surgeons now requires International Classification of diseases ninth Edition (ICD-9-CM) codes for injury coding in the National Trauma Databank. International Classification of diseases ninth Edition Injury Severity Score (ICISS) is the best-known risk-adjustment model when injuries are recorded using ICD-9-CM coding, and would likely be used to risk-adjust outcome measures for hospital trauma report cards. ICISS, however, has been criticized for its poor calibration. Methods: We developed and validated a new ICD-9 injury model using data on 749,374 patients admitted to 359 hospitals in the National Trauma Databank (version 7.0). Empiric measures of injury severity for each of the trauma ICD-9-CM codes were estimated using a regression-based approach, and then used as the basis for a new Trauma Mortality Prediction Model (TMPM-ICD9). ICISS and the Single-Worst Injury (SWI) model were also re-estimated. The performance of each of these models was compared using the area under the receiver operating characteristic (ROC), the Hosmer-Lemeshow statistic, and the Akaike information criterion statistic. Results: TMPM-ICD9 exhibits significantly better discrimination (ROCTMPM = 0.880 [0.876-0.883]; ROCICISS = 0.850 [0.846-0.855]; ROCSWI = 0.862 [0.858-0.867]) and calibration (HLTMPM = 29.3 [12.1-44.1]; HLICISS = 231 [176-279]; HLSWI = 462 [380-548]) compared with both ICISS and the Single Worst Injury model. All models were improved with the addition of age, gender, and mechanism of injury, but TMTM-ICD9 continued to demonstrate superior model performance. Conclusions: Because TMPM-ICD9 uniformly out-performs ICISS and the SWI model, it should be used in preference to ICISS for risk-adjusting trauma outcomes when injuries are recorded using ICD9-CM codes.
引用
收藏
页码:1032 / 1039
页数:8
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