Karolinska Institutet, Department of Clinical Science and Education, Söderssjukhuset, Sjukhusbacken 10, 118 83, Stockholm, Sweden.
Fisksätra Vårdcentral (Primary Health Care Center), Fisksätra torg 20, 133 41, Saltsjöbaden, Sweden.
Scand J Trauma Resusc Emerg Med. 2020 Jun 25;28(1):59. doi: 10.1186/s13049-020-00745-6.
Despite sepsis being a time critical condition with a high mortality, it is often not identified in a timely fashion. The aim of the current study was to create a screening tool based on bedside measurable variables predictive of sepsis among ambulance patients with infection according to clinical judgment by ambulance personnel.
Prospective cohort study of 551 adult patients presenting with suspected infection, performed in the ambulance setting of Stockholm during 2017-2018. 18 variables were measured in the ambulance (8 keywords related to medical history, 6 vital signs, 4 point-of-care blood tests, in addition to age, gender, and comorbidity. Logistic regression, area under the curve (AUC) and classification trees were used to study the association with sepsis. The AUC, sensitivity, specificity, predictive values and likelihood ratios were used to evaluate the predictive ability of sepsis screening models.
The six variables with the strongest association with sepsis were: systolic blood pressure ≤ 100 mmHg, temperature > 38.5 °C, GCS < 15, lactate > 4 mmol/L, gastrointestinal symptoms, and a history of acute altered mental status. These were combined into the Predict Sepsis screening tool 1, with a sensitivity of 0.90, specificity 0.41, AUC 0.77; 95% confidence interval [CI] 0.73-0.81, PPV 0.52, and NPV 0.86. Combining a history of acute altered mental status with GCS < 15 and excluding lactate in the Predict Sepsis screening tool 2 did not noticeably affect the AUC. In addition, the AUCs of these models did not differ noticeably when compared to a model including vital signs alone, with novel calculated cut-offs; the Predict Sepsis screening tool 3.
Systolic blood pressure ≤ 100 mmHg, temperature > 38.5 °C, GCS < 15, lactate > 4 mmol/L, gastrointestinal symptoms, and a history of acute altered mental status demonstrated the strongest association with sepsis. We present three screening tools to predict sepsis with similar sensitivity. The results indicated no noticeable increase of predictive ability by including symptom-variables and blood tests to a sepsis screening tool in the current study population.
NCT03249597.
尽管脓毒症是一种具有高死亡率的时间关键疾病,但通常不能及时识别。本研究的目的是根据急救人员的临床判断,创建一个基于床边可测量变量的预测工具,以预测救护车感染患者中的脓毒症。
这是一项前瞻性队列研究,纳入了 2017 年至 2018 年在斯德哥尔摩救护车环境中就诊的 551 名疑似感染的成年患者。在救护车内测量了 18 个变量(8 个与医疗史相关的关键词,6 个生命体征,4 个即时检验血液检查,以及年龄、性别和合并症。使用逻辑回归、曲线下面积(AUC)和分类树来研究与脓毒症的关联。使用 AUC、敏感性、特异性、预测值和似然比来评估脓毒症筛查模型的预测能力。
与脓毒症相关性最强的六个变量是:收缩压≤100mmHg、体温>38.5°C、GCS<15、乳酸>4mmol/L、胃肠道症状和急性意识状态改变史。这些变量组合成预测脓毒症筛查工具 1,其敏感性为 0.90,特异性为 0.41,AUC 为 0.77;95%置信区间[CI]为 0.73-0.81,PPV 为 0.52,NPV 为 0.86。在预测脓毒症筛查工具 2 中,将急性意识状态改变史与 GCS<15 相结合,并排除乳酸,对 AUC 没有明显影响。此外,与仅包括生命体征的模型相比,这些模型的 AUC 没有明显差异,使用新的计算切点;预测脓毒症筛查工具 3。
收缩压≤100mmHg、体温>38.5°C、GCS<15、乳酸>4mmol/L、胃肠道症状和急性意识状态改变史与脓毒症相关性最强。我们提出了三种具有相似敏感性的预测脓毒症的筛查工具。结果表明,在当前研究人群中,通过将症状变量和血液检查纳入脓毒症筛查工具,并没有明显提高预测能力。
NCT03249597。