PDF(Pubmed)
Abstract：
Surgical mortality indicators should be risk-adjusted when evaluating the performance of organisations. This study evaluated the performance of risk-adjustment models that used English hospital administrative data for 30-day mortality after neurosurgery.
This retrospective cohort study used Hospital Episode Statistics (HES) data from 1 April 2013 to 31 March 2018. Organisational-level 30-day mortality was calculated for selected subspecialties (neuro-oncology, neurovascular and trauma neurosurgery) and the overall cohort. Risk adjustment models were developed using multivariable logistic regression and incorporated various patient variables: age, sex, admission method, social deprivation, comorbidity and frailty indices. Performance was assessed in terms of discrimination and calibration.
The cohort included 49,044 patients. Overall, 30-day mortality rate was 4.9%, with unadjusted organisational rates ranging from 3.2 to 9.3%. The variables in the best performing models varied for the subspecialties; for trauma neurosurgery, a model that included deprivation and frailty had the best calibration, while for neuro-oncology a model with these variables plus comorbidity performed best. For neurovascular surgery, a simple model of age, sex and admission method performed best. Levels of discrimination varied for the subspecialties (range: 0.583 for trauma and 0.740 for neurovascular). The models were generally well calibrated. Application of the models to the organisation figures produced an average (median) absolute change in mortality of 0.33% (interquartile range (IQR) 0.15-0.72) for the overall cohort model. Median changes for the subspecialty models were 0.29% (neuro-oncology, IQR 0.15-0.42), 0.40% (neurovascular, IQR 0.24-0.78) and 0.49% (trauma neurosurgery, IQR 0.23-1.68).
Reasonable risk-adjustment models for 30-day mortality after neurosurgery procedures were possible using variables from HES, although the models for trauma neurosurgery performed less well. Including a measure of frailty often improved model performance.
This retrospective cohort study used Hospital Episode Statistics (HES) data from 1 April 2013 to 31 March 2018. Organisational-level 30-day mortality was calculated for selected subspecialties (neuro-oncology, neurovascular and trauma neurosurgery) and the overall cohort. Risk adjustment models were developed using multivariable logistic regression and incorporated various patient variables: age, sex, admission method, social deprivation, comorbidity and frailty indices. Performance was assessed in terms of discrimination and calibration.
The cohort included 49,044 patients. Overall, 30-day mortality rate was 4.9%, with unadjusted organisational rates ranging from 3.2 to 9.3%. The variables in the best performing models varied for the subspecialties; for trauma neurosurgery, a model that included deprivation and frailty had the best calibration, while for neuro-oncology a model with these variables plus comorbidity performed best. For neurovascular surgery, a simple model of age, sex and admission method performed best. Levels of discrimination varied for the subspecialties (range: 0.583 for trauma and 0.740 for neurovascular). The models were generally well calibrated. Application of the models to the organisation figures produced an average (median) absolute change in mortality of 0.33% (interquartile range (IQR) 0.15-0.72) for the overall cohort model. Median changes for the subspecialty models were 0.29% (neuro-oncology, IQR 0.15-0.42), 0.40% (neurovascular, IQR 0.24-0.78) and 0.49% (trauma neurosurgery, IQR 0.23-1.68).
Reasonable risk-adjustment models for 30-day mortality after neurosurgery procedures were possible using variables from HES, although the models for trauma neurosurgery performed less well. Including a measure of frailty often improved model performance.
摘要：
背景：在评估组织绩效时，手术死亡率指标应进行风险调整。这项研究评估了使用英国医院管理数据评估神经外科术后30天死亡率的风险调整模型的性能。
方法：本回顾性队列研究使用2013年4月1日至2018年3月31日的医院事件统计（HES）数据。计算了选定亚专科的组织水平30天死亡率(神经肿瘤学，神经血管和创伤神经外科）和整体队列。使用多变量逻辑回归开发了风险调整模型，并结合了各种患者变量：年龄，性别,录取方法,社会剥夺,合并症和虚弱指数。根据辨别和校准来评估性能。
结果：该队列包括49,044例患者。总的来说,30天死亡率为4.9%,未经调整的组织率从3.2%到9.3%不等。最佳性能模型中的变量因亚专业而异；对于创伤神经外科，一个包含剥夺和虚弱的模型有最好的校准，而对于神经肿瘤学，具有这些变量加合并症的模型表现最好。对于神经血管手术，一个简单的年龄模型，性别和入院方法表现最好。亚专业的歧视水平各不相同（范围：创伤为0.583，神经血管为0.740）。这些模型通常被很好地校准。将模型应用于组织数字，对于整个队列模型，死亡率的平均（中位数）绝对变化为0.33％（四分位数间距（IQR）0.15-0.72）。亚专科模型的中位数变化为0.29%(神经肿瘤学，IQR0.15-0.42），0.40%(神经血管，IQR0.24-0.78)和0.49%(创伤神经外科，IQR0.23-1.68）。
结论：使用HES的变量，可以建立神经外科手术后30天死亡率的合理风险调整模型，尽管创伤神经外科的模型表现不佳。包括弱点的度量通常会改善模型性能。
方法：本回顾性队列研究使用2013年4月1日至2018年3月31日的医院事件统计（HES）数据。计算了选定亚专科的组织水平30天死亡率(神经肿瘤学，神经血管和创伤神经外科）和整体队列。使用多变量逻辑回归开发了风险调整模型，并结合了各种患者变量：年龄，性别,录取方法,社会剥夺,合并症和虚弱指数。根据辨别和校准来评估性能。
结果：该队列包括49,044例患者。总的来说,30天死亡率为4.9%,未经调整的组织率从3.2%到9.3%不等。最佳性能模型中的变量因亚专业而异；对于创伤神经外科，一个包含剥夺和虚弱的模型有最好的校准，而对于神经肿瘤学，具有这些变量加合并症的模型表现最好。对于神经血管手术，一个简单的年龄模型，性别和入院方法表现最好。亚专业的歧视水平各不相同（范围：创伤为0.583，神经血管为0.740）。这些模型通常被很好地校准。将模型应用于组织数字，对于整个队列模型，死亡率的平均（中位数）绝对变化为0.33％（四分位数间距（IQR）0.15-0.72）。亚专科模型的中位数变化为0.29%(神经肿瘤学，IQR0.15-0.42），0.40%(神经血管，IQR0.24-0.78)和0.49%(创伤神经外科，IQR0.23-1.68）。
结论：使用HES的变量，可以建立神经外科手术后30天死亡率的合理风险调整模型，尽管创伤神经外科的模型表现不佳。包括弱点的度量通常会改善模型性能。
