Purpose

Early accurate diagnosis of infection ± organ dysfunction (sepsis) remains a major challenge in clinical practice. Utilizing effective biomarkers to identify infection and impending organ dysfunction before the onset of clinical signs and symptoms would enable earlier investigation and intervention. To our knowledge, no prior study has specifically examined the possibility of pre-symptomatic detection of sepsis.

Methods

Blood samples and clinical/laboratory data were collected daily from 4385 patients undergoing elective surgery. An adjudication panel identified 154 patients with definite postoperative infection, of whom 98 developed sepsis. Transcriptomic profiling and subsequent RT-qPCR were undertaken on sequential blood samples taken postoperatively from these patients in the three days prior to the onset of symptoms. Comparison was made against postoperative day-, age-, sex- and procedure- matched patients who had an uncomplicated recovery (n =151) or postoperative inflammation without infection (n =148).

Results

Specific gene signatures optimized to predict infection or sepsis in the three days prior to clinical presentation were identified in initial discovery cohorts. Subsequent classification using machine learning with cross-validation with separate patient cohorts and their matched controls gave high Area Under the Receiver Operator Curve (AUC) values. These allowed discrimination of infection from uncomplicated recovery (AUC 0.871), infectious from non-infectious systemic inflammation (0.897), sepsis from other postoperative presentations (0.843), and sepsis from uncomplicated infection (0.703).

Conclusion

Host biomarker signatures may be able to identify postoperative infection or sepsis up to three days in advance of clinical recognition. If validated in future studies, these signatures offer potential diagnostic utility for postoperative management of deteriorating or high-risk surgical patients and, potentially, other patient populations.

中文翻译：

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使用基因表达特征对术后感染和败血症进行症状前诊断

目的

感染±器官功能障碍（脓毒症）的早期准确诊断仍然是临床实践中的主要挑战。利用有效的生物标志物在临床体征和症状出现之前识别感染和即将发生的器官功能障碍，将使早期调查和干预成为可能。据我们所知，之前没有研究专门检查过症状前检测脓毒症的可能性。

方法

每天从 4385 名接受择期手术的患者中收集血样和临床/实验室数据。裁决小组确定了 154 名明确术后感染的患者，其中 98 名发展为败血症。对症状出现前三天从这些患者术后采集的连续血样进行转录组学分析和随后的 RT-qPCR。与术后天数、年龄、性别和手术匹配的患者进行了比较，这些患者恢复简单（n = 151）或术后炎症无感染（n = 148）。

结果

在初始发现队列中确定了经过优化以预测临床表现前三天内感染或败血症的特定基因特征。随后使用机器学习进行分类，并与单独的患者队列及其匹配的对照进行交叉验证，给出了较高的接受者操作曲线下面积 (AUC) 值。这些允许区分感染与无并发症恢复 (AUC 0.871)、感染与非感染性全身炎症 (0.897)、脓毒症与其他术后表现 (0.843) 和脓毒症与无并发症感染 (0.703)。

结论

宿主生物标志物签名可能能够在临床识别之前最多提前三天识别术后感染或败血症。如果在未来的研究中得到验证，这些特征将为病情恶化或高风险手术患者以及其他潜在患者群体的术后管理提供潜在的诊断效用。