Jensen Signe S, Rønnegaard Anders B, Gundtoft Per H, Kold Søren, Viberg Bjarke
Department of Orthopedic Surgery and Traumatology, Kolding Hospital; Department of Clinical Research, University of Southern Denmark, Denmark.
Department of Orthopedic Surgery and Traumatology, Aarhus University Hospital. Denmark.
Acta Orthop. 2025 Jan 13;96:66-72. doi: 10.2340/17453674.2024.42633.
Disease- or procedure-specific registers offer valuable information but are costly and often inaccurate regarding outcome measures. Alternatively, automatically collected data from administrative systems could be a solution, given their high completeness. Our primary aim was to validate a method for identifying secondary surgical procedures (reoperations) in the Danish National Patient Register (DNPR) within the first year following primary fracture surgery. The secondary aim was to evaluate the accuracy of the diagnosis and procedure codes used to determine the causes of these reoperations. Finally, we developed algorithms to enhance precision in identifying the reasons for reoperations.
In a national cohort of 11,551 patients with primary fracture surgery, reoperations were identified through subsequent surgical procedure codes in the DNPR. Each patient record was reviewed to confirm the reoperations and causes. To improve accuracy, a stepwise algorithm was developed for each cause.
We identified 2,347 possible reoperations; 2,212 were validated as true reoperations by review of patient record, i.e., a 94% positive predictive value (PPV). However, the coding for the causes of these reoperations was inaccurate. Our algorithm identified major reoperations with a sensitivity/PPV of 89/77%, minor reoperations 99%/89%, infections 77/85%, nonunion 82/56%, early re-osteosynthesis 90/75%, and secondary arthroplasties 95/87%.
While the overall reported reoperations in the DNPR had a high PPV, the predefined diagnosis and procedure codes alone were not sufficient to accurately determine the causes of these reoperations. An algorithm was developed for this purpose, yielding acceptable results for all causes except nonunion.
疾病或特定手术登记册可提供有价值的信息，但成本高昂且在结局测量方面往往不准确。另外，鉴于行政系统自动收集的数据具有高度完整性，或许可以作为一种解决方案。我们的主要目的是验证一种在初次骨折手术后第一年内识别丹麦国家患者登记册（DNPR）中二次外科手术（再次手术）的方法。次要目的是评估用于确定这些再次手术原因的诊断和手术编码的准确性。最后，我们开发了算法以提高识别再次手术原因的精确性。
在一个包含11551例初次骨折手术患者的全国队列中，通过DNPR中的后续手术编码识别再次手术。对每个患者记录进行审查以确认再次手术及其原因。为提高准确性，针对每个原因开发了逐步算法。
我们识别出2347例可能的再次手术；经患者记录审查，2212例被确认为真正的再次手术，即阳性预测值（PPV）为94%。然而，这些再次手术原因的编码不准确。我们的算法识别主要再次手术的灵敏度/PPV为89/77%，次要再次手术为99%/89%，感染为77/85%，骨不连为82/56%，早期再次骨固定为90/75%，二次关节成形术为95/87%。
虽然DNPR中报告的总体再次手术具有较高的PPV，但仅靠预定义的诊断和手术编码不足以准确确定这些再次手术的原因。为此开发了一种算法，除骨不连外，对所有原因均产生了可接受的结果。
