Timbrook Tristan T, Garner Cherilyn D, Hueth Kyle D, Capraro Gerald A, Zimmer Louise, Dwivedi Hari P
BioMérieux, Salt Lake City, UT 84104, USA.
Department of Pharmacotherapy, University of Utah College of Pharmacy, Salt Lake City, UT 84112, USA.
Diagnostics (Basel). 2023 Oct 11;13(20):3174. doi: 10.3390/diagnostics13203174.
Bacteremia is associated with significant morbidity and mortality. Timely, appropriate therapy may improve clinical outcomes, and therefore, determining which patients benefit from more comprehensive diagnostic strategies (i.e., direct specimen testing) could be of value. We performed an assessment of procalcitonin (PCT) and clinical characteristics in the discrimination of bacteremic hospitalizations. We analyzed 71,105 encounters and 14,846 visits of patients with bacteremia alongside 56,259 without an admission. The area under the receiver-operating characteristic (AUROC) curve for the prediction of bacteremia via procalcitonin was 0.782 (95% CI 0.779-0.787). The prediction modeling of clinical factors with or without PCT resulted in a similar performance to PCT alone. However, the clinically predicted risk of bacteremia stratified by PCT thresholds allowed the targeting of high-incidence bacteremia groups (e.g., ≥50% positivity). The combined use of PCT and clinical characteristics could be useful in diagnostic stewardship by targeting further advanced diagnostic testing in patients with a high predicted probability of bacteremia.
菌血症与显著的发病率和死亡率相关。及时、恰当的治疗可能改善临床结局,因此,确定哪些患者能从更全面的诊断策略(即直接标本检测)中获益可能具有重要意义。我们对降钙素原(PCT)和临床特征在鉴别菌血症住院患者中的作用进行了评估。我们分析了71105例患者的诊疗情况以及14846例菌血症患者的就诊情况,同时纳入了56259例未住院患者。通过降钙素原预测菌血症的受试者工作特征曲线(AUROC)下面积为0.782(95%CI 0.779 - 0.787)。有无PCT的临床因素预测模型表现与单独使用PCT相似。然而,根据PCT阈值分层的菌血症临床预测风险能够针对高发病率菌血症组(例如,阳性率≥50%)。PCT与临床特征联合应用,通过针对菌血症预测概率高的患者进行进一步的高级诊断检测,可能有助于诊断管理。