Lee Young Seok, Han Seungbong, Lee Ye Eun, Cho Jaehwa, Choi Young Kyun, Yoon Sun-Young, Oh Dong Kyu, Lee Su Yeon, Park Mi Hyeon, Lim Chae-Man, Moon Jae Young
Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, Korea University Guro Hospital, Seoul, Republic of Korea.
Department of Biostatistics, Korea University College of Medicine, Seoul, Republic of Korea.
Sci Rep. 2024 Jun 13;14(1):13637. doi: 10.1038/s41598-024-64463-0.
There are numerous prognostic predictive models for evaluating mortality risk, but current scoring models might not fully cater to sepsis patients' needs. This study developed and validated a new model for sepsis patients that is suitable for any care setting and accurately forecasts 28-day mortality. The derivation dataset, gathered from 20 hospitals between September 2019 and December 2021, contrasted with the validation dataset, collected from 15 hospitals from January 2022 to December 2022. In this study, 7436 patients were classified as members of the derivation dataset, and 2284 patients were classified as members of the validation dataset. The point system model emerged as the optimal model among the tested predictive models for foreseeing sepsis mortality. For community-acquired sepsis, the model's performance was satisfactory (derivation dataset AUC: 0.779, 95% CI 0.765-0.792; validation dataset AUC: 0.787, 95% CI 0.765-0.810). Similarly, for hospital-acquired sepsis, it performed well (derivation dataset AUC: 0.768, 95% CI 0.748-0.788; validation dataset AUC: 0.729, 95% CI 0.687-0.770). The calculator, accessible at https://avonlea76.shinyapps.io/shiny_app_up/ , is user-friendly and compatible. The new predictive model of sepsis mortality is user-friendly and satisfactorily forecasts 28-day mortality. Its versatility lies in its applicability to all patients, encompassing both community-acquired and hospital-acquired sepsis.
有许多用于评估死亡风险的预后预测模型,但目前的评分模型可能无法完全满足脓毒症患者的需求。本研究开发并验证了一种适用于任何护理环境的脓毒症患者新模型,该模型能准确预测28天死亡率。推导数据集收集于2019年9月至2021年12月期间的20家医院,与2022年1月至2022年12月期间从15家医院收集的验证数据集形成对比。在本研究中,7436名患者被归类为推导数据集的成员,2284名患者被归类为验证数据集的成员。在测试的预测脓毒症死亡率的模型中,评分系统模型成为最优模型。对于社区获得性脓毒症,该模型的表现令人满意(推导数据集AUC:0.779,95%CI 0.765 - 0.792;验证数据集AUC:0.787,95%CI 0.765 - 0.810)。同样,对于医院获得性脓毒症,该模型表现良好(推导数据集AUC:0.768,95%CI 0.748 - 0.788;验证数据集AUC:0.729,95%CI 0.687 - 0.770)。可通过https://avonlea76.shinyapps.io/shiny_app_up/访问的计算器用户友好且兼容性良好。新的脓毒症死亡率预测模型用户友好,能令人满意地预测28天死亡率。其通用性在于它适用于所有患者,包括社区获得性和医院获得性脓毒症。