与抗生素和灌注支持不同,脓毒症源控制指南缺乏高质量证据,且未分级.需要内部有效的管理数据方法来识别代表来源控制程序的案例,以评估结果。
经过五次修改的Delphi回合,两名独立的审阅者确定了与源代码控制相关的当前程序术语(CPT)代码。在每一轮中,具有完美协议的代码被保留或排除,而意见分歧由小组成员审查。符合脓毒症-3标准(2010-2017年)的400例患者记录的手动审查临床裁定,其遇到包括来源控制程序(金标准)。将共识代码的性能与黄金标准进行比较,以评估灵敏度,特异性,预测值,和似然比。
5752个CPT代码中,609个共识代码表示源代码控制过程。400例败血症住院,39个(9.8%;95%置信区间[CI]7.0%-13.1%)接受了金标准源控制程序,29个(7.3%;95%CI4.9-10.3%)共识代码定义的源控制程序。确定了30个共识代码(20.0%胃肠道/腹内,10.0%泌尿生殖系统,13.3%肝胰胆管,23.3%骨科/颅骨,23.3%软组织,和10.0%胸内),其灵敏度为61.5%(95%CI44.6%-76.6%),98.6%(95%CI96.8%-99.6%)特异性,83.2%(95%CI66.6%-92.4%)阳性,阴性预测值为95.9%(95%CI93.9%-97.2%)。在样本患病率的预测试概率下,确定的共识代码的后测概率为83.0%(95%CI66.0%-92.0%),而接受来源控制程序的共识代码缺失的概率为4.0%(95%CI3.0~6.0).
使用改进的Delphi方法,我们创建并验证了识别源代码控制程序的CPT代码,提供一个评估脓毒症患者手术护理的框架。
Unlike antibiotic and perfusion support,
guidelines for sepsis source control lack high-quality evidence and are ungraded. Internally valid administrative data methods are needed to identify cases representing source control procedures to evaluate outcomes.
Over five modified Delphi rounds, two independent reviewers identified Current Procedural Terminology (CPT) codes pertinent to source control. In each round, codes with perfect agreement were retained or excluded, whereas disagreements were reviewed by the panelists. Manual review of 400 patient records meeting Sepsis-3 criteria (2010-2017) clinically adjudicated which encounters included source control procedures (gold standard). The performance of
consensus codes was compared with the gold standard to assess sensitivity, specificity, predictive values, and likelihood ratios.
Of 5752 CPT codes, 609
consensus codes represented source control procedures. Of 400 hospitalizations for sepsis, 39 (9.8%; 95% confidence interval [CI] 7.0%-13.1%) underwent gold standard source control procedures and 29 (7.3%; 95% CI 4.9-10.3%) consensus code-defined source control procedures. Thirty consensus codes were identified (20.0% gastrointestinal/intraabdominal, 10.0% genitourinary, 13.3% hepatopancreatobiliary, 23.3% orthopedic/cranial, 23.3% soft tissue, and 10.0% intrathoracic), which had 61.5% (95% CI 44.6%-76.6%) sensitivity, 98.6% (95% CI 96.8%-99.6%) specificity, 83.2% (95% CI 66.6%-92.4%) positive, and 95.9% (95% CI 93.9%-97.2%) negative predictive values. With pretest probability at sample prevalence, an identified
consensus code had a posttest probability of 83.0% (95% CI 66.0%-92.0%), whereas
consensus code absence had a probability of 4.0% (95% CI 3.0-6.0) for undergoing a source control procedure.
Using modified Delphi methodology, we created and validated CPT codes identifying source control procedures, providing a framework for evaluation of the surgical care of patients with sepsis.