Brotherton B Jason, Joshi Mugdha, Otieno George, Wandia Sarah, Gitura Hannah, Mueller Ariel, Nguyen Tony, Letchford Steve, Riviello Elisabeth D, Karanja Evelyn, Rudd Kristina E
Department of Internal Medicine, AIC Kijabe Hospital, Kijabe, Kenya.
The Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
Front Med (Lausanne). 2023 Apr 5;10:1127672. doi: 10.3389/fmed.2023.1127672. eCollection 2023.
Mortality prediction among critically ill patients in resource limited settings is difficult. Identifying the best mortality prediction tool is important for counseling patients and families, benchmarking quality improvement efforts, and defining severity of illness for clinical research studies.
Compare predictive capacity of the Modified Early Warning Score (MEWS), Universal Vital Assessment (UVA), Tropical Intensive Care Score (TropICS), Rwanda Mortality Probability Model (R-MPM), and quick Sequential Organ Failure Assessment (qSOFA) for hospital mortality among adults admitted to a medical-surgical intensive care unit (ICU) in rural Kenya. We performed a pre-planned subgroup analysis among ICU patients with suspected infection.
Prospective single-center cohort study at a tertiary care, academic hospital in Kenya. All adults 18 years and older admitted to the ICU January 2018-June 2019 were included.
The primary outcome was association of clinical prediction tool score with hospital mortality, as defined by area under the receiver operating characteristic curve (AUROC). Demographic, physiologic, laboratory, therapeutic, and mortality data were collected. 338 patients were included, none were excluded. Median age was 42 years (IQR 33-62) and 61% ( = 207) were male. Fifty-nine percent ( = 199) required mechanical ventilation and 35% ( = 118) received vasopressors upon ICU admission. Overall hospital mortality was 31% ( = 104). 323 patients had all component variables recorded for R-MPM, 261 for MEWS, and 253 for UVA. The AUROC was highest for MEWS (0.76), followed by R-MPM (0.75), qSOFA (0.70), and UVA (0.69) ( < 0.001). Predictive capacity was similar among patients with suspected infection.
All tools had acceptable predictive capacity for hospital mortality, with variable observed availability of the component data. R-MPM and MEWS had high rates of variable availability as well as good AUROC, suggesting these tools may prove useful in low resource ICUs.
在资源有限的环境中，对重症患者进行死亡率预测很困难。确定最佳的死亡率预测工具对于为患者及其家属提供咨询、衡量质量改进工作以及为临床研究确定疾病严重程度至关重要。
比较改良早期预警评分（MEWS）、通用生命评估（UVA）、热带重症监护评分（TropICS）、卢旺达死亡率概率模型（R-MPM）和快速序贯器官衰竭评估（qSOFA）对肯尼亚农村地区一所内科-外科重症监护病房（ICU）收治的成年患者医院死亡率的预测能力。我们对疑似感染的ICU患者进行了预先计划的亚组分析。
设计、地点和参与者：在肯尼亚一家三级医疗学术医院进行的前瞻性单中心队列研究。纳入了2018年1月至2019年6月期间入住ICU的所有18岁及以上的成年人。
主要结局是临床预测工具评分与医院死亡率之间的关联，以受试者工作特征曲线下面积（AUROC）来定义。收集了人口统计学、生理学、实验室、治疗和死亡率数据。共纳入338例患者，无患者被排除。中位年龄为42岁（四分位间距33 - 62岁），61%（n = 207）为男性。59%（n = 199）的患者需要机械通气，35%（n = 118）的患者在入住ICU时接受了血管活性药物治疗。总体医院死亡率为31%（n = 104）。323例患者记录了R-MPM的所有组成变量，261例记录了MEWS的变量，253例记录了UVA的变量。MEWS的AUROC最高（0.76），其次是R-MPM（0.75）、qSOFA（0.70）和UVA（0.69）（P < 0.001）。疑似感染患者的预测能力相似。
所有工具对医院死亡率都有可接受的预测能力，但各组成数据的可获取性存在差异。R-MPM和MEWS的变量可获取率较高且AUROC良好，表明这些工具可能在资源匮乏的ICU中有用。
