Rutgers New Jersey Medical School, Newark, NJ.
Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.
Pediatr Crit Care Med. 2024 Nov 1;25(11):998-1004. doi: 10.1097/PCC.0000000000003582. Epub 2024 Jul 19.
Previously, we implemented a comprehensive decision support tool, a "New Fever Algorithm," to support the evaluation of PICU patients with new fever or instability. This tool was associated with a decline in culture rates without safety concerns. We assessed the impact of the algorithm on testing practices by identifying the proportion of cultures pre- vs. post-implementation that were discordant with algorithm guidance and may have been avoidable.
Retrospective evaluation 12 months pre- vs. post-quality improvement intervention.
Single-center academic PICU and pediatric cardiac ICU.
All admitted patients.
Implementing the "New Fever Algorithm" in July 2020.
Patient medical records were reviewed to categorize indications for all blood, respiratory, and urine cultures. Among cultures obtained for new fever or new clinical instability, we assessed specific testing patterns that were discordant from the algorithm's guidance such as blood cultures obtained without documented concern for sepsis without initiation of antibiotics, respiratory cultures without respiratory symptoms, urine cultures without a urinalysis or pyuria, and pan-cultures (concurrent blood, respiratory, and urine cultures). Among 2827 cultures, 1950 (69%) were obtained for new fever or instability. The proportion of peripheral blood cultures obtained without clinical concern for sepsis declined from 18.6% to 10.4% ( p < 0.0007). Respiratory cultures without respiratory symptoms declined from 41.5% to 27.4% ( p = 0.01). Urine cultures without a urinalysis did not decline (from 27.6% to 25.1%). Urine cultures without pyuria declined from 83.0% to 73.7% ( p = 0.04). Pan-cultures declined from 22.4% to 10.6% ( p < 0.0001). Overall, algorithm-discordant testing declined from 39% to 30% ( p < 0.0001).
The majority of cultures obtained were for new fever or instability and introduction of the "New Fever Algorithm" was associated with reductions in algorithm-discordant testing practices and pan-cultures. There remain opportunities for improvement and additional strategies are warranted to optimize testing practices for in this complex patient population.
我们之前实施了一种全面的决策支持工具，即“新发热算法”，以支持评估 ICU 中新发热或不稳定的患者。该工具与培养率下降有关，且不存在安全性问题。我们通过确定实施前后与算法指导不一致且可能可避免的培养比例，评估该算法对检测实践的影响。
回顾性评价 12 个月内质量改进干预前后的情况。
单中心学术儿科 ICU 和小儿心脏 ICU。
所有入院患者。
2020 年 7 月实施“新发热算法”。
回顾患者病历，对所有血液、呼吸道和尿液培养的指征进行分类。在因新发热或新临床不稳定而获得的培养中，我们评估了与算法指导不一致的特定检测模式，例如未明确关注败血症而未使用抗生素时进行血培养、无呼吸道症状时进行呼吸道培养、无尿液分析或脓尿时进行尿液培养、以及同时进行血液、呼吸道和尿液培养（泛培养）。在 2827 次培养中，1950 次（69%）是因新发热或不稳定而进行的。无临床关注败血症时外周血培养的比例从 18.6%降至 10.4%（p<0.0007）。无呼吸道症状时进行呼吸道培养的比例从 41.5%降至 27.4%（p=0.01）。无尿液分析的尿液培养没有下降（从 27.6%降至 25.1%）。无脓尿的尿液培养从 83.0%降至 73.7%（p=0.04）。泛培养从 22.4%降至 10.6%（p<0.0001）。总体而言，与算法不一致的检测从 39%降至 30%（p<0.0001）。
大多数培养是因新发热或不稳定而进行的，引入“新发热算法”与算法不一致的检测实践和泛培养减少有关。在这个复杂的患者群体中，仍有改进的空间，需要采取额外的策略来优化检测实践。
