Academic Division of Neurosurgery, Addenbrookes' Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Academic Division of Neurosurgery, Addenbrookes' Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Surgeon. 2023 Oct;21(5):e271-e278. doi: 10.1016/j.surge.2023.02.001. Epub 2023 Mar 24.
Chronic subdural hematoma (CSDH) is one of the commonest neurosurgical pathologies with an increasing incidence. Observational studies of routine care have demonstrated high perioperative morbidity and approximately 10% mortality at one year. The development, implementation, and evaluation of a potential care framework relies on an accurate and reproducible method of case identification and case ascertainment. With this manuscript, we report on the accuracy of diagnostic ICD codes for identifying patients with CSDH from retrospective electronic data and explore whether basic demographic data could improve the identification of CSDH.
Data were collected retrospectively from the hospital administrative system between 2014 and 2018 of all patients coded with either S065 or I620. Analysis of the ICD codes in identifying patients with CSDH diagnosis was calculated using the caret package in RStudio.and stepwise logistic regression analysis was performed to evaluate the best predictive model for CSDH.
A total of 1861 patients were identified. Of these, 189 (10.2%) had a diagnosis of non-traumatic SDH (I620) and 1672 (89.8%) traumatic subdural haematomas (S065). Variables that identified CSDH as a diagnosis on univariate logistic regression included male sex (Odds Ratios (OR) - 1.606 (1.197-2.161), elderly age (OR) - 1.023 (1.015-1.032) per year for age (p < 0.001) and shorter length of hospital stay. Using stepwise regression against AIC the best model to predict CSDH included male sex, older age, and shorter LOS. The calculated sensitivity for identifying CSDH with the model is 88.4% with a specificity of 84.5% and PPV of 87.9%.
CSDH is a common neurosurgical pathology with increasing incidence and ongoing unmet clinical need. We demonstrate that case ascertainment for research purposes can be improved with the incorporation of additional demographic data but at the expense of significant case exclusion.
慢性硬脑膜下血肿（CSDH）是最常见的神经外科疾病之一，其发病率不断上升。常规护理的观察性研究表明，围手术期发病率高，一年死亡率约为 10%。潜在护理框架的开发、实施和评估依赖于准确和可重复的病例识别和病例确定方法。本文报告了从回顾性电子数据中使用诊断 ICD 代码识别 CSDH 患者的准确性，并探讨了基本人口统计学数据是否可以提高 CSDH 的识别率。
我们从 2014 年至 2018 年期间从医院管理系统中回顾性收集了所有编码为 S065 或 I620 的患者的数据。使用 RStudio 中的 caret 包计算 ICD 代码在识别 CSDH 诊断中的准确性，并进行逐步逻辑回归分析，以评估 CSDH 的最佳预测模型。
共确定了 1861 名患者。其中，189 名（10.2%）患有非创伤性硬脑膜下血肿（I620），1672 名（89.8%）患有创伤性硬脑膜下血肿（S065）。单变量逻辑回归分析确定 CSDH 为诊断的变量包括男性（优势比（OR）为 1.606（1.197-2.161））、年龄较大（OR）为 1.023（1.015-1.032），每增加 1 岁（p<0.001）和较短的住院时间。使用逐步回归对抗 AIC，预测 CSDH 的最佳模型包括男性、年龄较大和较短的 LOS。该模型识别 CSDH 的敏感性为 88.4%，特异性为 84.5%，阳性预测值为 87.9%。
CSDH 是一种常见的神经外科病理，发病率不断上升，临床需求未得到满足。我们证明，通过纳入额外的人口统计学数据可以提高研究目的的病例确定，但代价是大量病例被排除在外。
