APACHE IV评分在大流行时期预测Dr. Sardjito医院ICU死亡率的验证

Rayhandika, A. Y. Jufan, Y. Widyastuti, J. Kurniawaty
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The findings can be utilized for future use and risk stratification, and ICU quality benchmarking. Objectives: This study aims to assess the validity of the APACHE IV score in ICU Mortality prediction in Dr. Sardjito Hospital for medical patients, surgical patients, and patients with both cases during the pandemic. Materials and Method: This study used retrospective data from 336 patients at Dr. Sardjito Hospital Yogyakarta from the 1st of January 2020 to the 31st of December 2021. All data required for calculating the APACHE IV score was collected, and the patient’s observed ICU Mortality was used. The model’s predictive validity is measured by finding the discrimination and calibration of the APACHE IV score and comparing it to the observed ICU mortality. Validation was also conducted separately for medical and surgical cases. 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引用次数: 0

摘要

导读:ICU服务质量必须不断提高,才能为患者提供更好的服务。其中一项改进工作是使用风险预测系统通过利用危险因素来预测ICU的死亡率。该系统可帮助医疗服务机构对密集服务进行评估和比较审计,这也有助于制定更有针对性的计划。APACHE IV被认为具有良好的有效性。然而,它的预测能力可能会随着时间的推移而改变,因为各种因素,例如大流行,病例组合的变化可能会影响其预测能力。因此,本研究通过Dr. Sardjito医院的患者来检验APACHE IV对印尼人群的有效性。研究结果可用于未来的使用和风险分层,以及ICU质量基准。目的:本研究旨在评估APACHE IV评分在大流行期间Sardjito博士医院内科患者、外科患者和两种病例患者的ICU死亡率预测中的有效性。材料和方法:本研究使用了2020年1月1日至2021年12月31日在日惹Dr. Sardjito医院的336名患者的回顾性数据。收集计算APACHE IV评分所需的所有数据,并使用患者观察到的ICU死亡率。该模型的预测效度是通过发现APACHE IV评分的鉴别性和校准来衡量的,并将其与观察到的ICU死亡率进行比较。还分别对内科病例和外科病例进行了验证。结果:APACHE IV在所有病例中均具有良好的鉴别能力(AUC-ROC 95% CI: 0.819[0.772-0.866]),但在预测ICU死亡率方面校准较差(p = 0.023)。对于内科病例,识别能力较差,但仍可接受(AUC-ROC 95% CI: 0.698[0.614-0.782]),对于外科病例,识别能力较好(AUC-ROC 95% CI: 0.848[0.776-0.921])。这两种情况在预测大流行期间的死亡率方面都显示出良好的校准(p:医学= 0.569,外科= 0.579)。结论:APACHE IV对大流行时期所有ICU患者的死亡率预测具有较好的判别能力,但校准能力较差。手术病例死亡率预测具有良好的判别性和校准性。但在医学案例中,辨别性差,校正性好。
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Validation of the APACHE IV Score for ICU Mortality Prediction in Dr. Sardjito Hospital During the Pandemic Era
Introduction: ICU service quality must continuously improve to provide better patient service. One of these improvement efforts is the use of a risk prediction system to predict mortality rates in the ICU by utilizing risk factors. This system helps healthcare services perform evaluations and comparative audits of intensive services, which can also aid with more targeted planning. APACHE IV is considered to have good validity. However, its predictive capabilities may change over time due to various factors, such as the pandemic, where changes in the case mix may affect its predictive abilities. Therefore, this research tests the validity of APACHE IV on the Indonesian population through Dr. Sardjito Hospital patients. The findings can be utilized for future use and risk stratification, and ICU quality benchmarking. Objectives: This study aims to assess the validity of the APACHE IV score in ICU Mortality prediction in Dr. Sardjito Hospital for medical patients, surgical patients, and patients with both cases during the pandemic. Materials and Method: This study used retrospective data from 336 patients at Dr. Sardjito Hospital Yogyakarta from the 1st of January 2020 to the 31st of December 2021. All data required for calculating the APACHE IV score was collected, and the patient’s observed ICU Mortality was used. The model’s predictive validity is measured by finding the discrimination and calibration of the APACHE IV score and comparing it to the observed ICU mortality. Validation was also conducted separately for medical and surgical cases. Results: APACHE IV shows good discrimination ability in all cases (AUC-ROC 95% CI: 0.819 [0.772-0.866]) but poor calibration (p = 0.023) for mortality prediction in the ICU. For medical cases, the discrimination ability is poor but still acceptable (AUC-ROC 95% CI: 0.698 [0.614-0.782]), and in surgical cases, the discrimination ability is good (AUC-ROC 95% CI: 0.848 [0.776-0.921]). Both cases showed good calibration (p: medical = 0.569, surgical = 0.579) in predicting mortality during the pandemic. Conclusion: APACHE IV showed good discrimination but poor calibration ability for predicting mortality for all ICU patients during the pandemic era. Mortality prediction for surgical cases showed good discrimination and calibration. However, medical cases showed poor discrimination but good calibration.
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