Jawaid Tabinda, Gangat Naseema, Weister Timothy, Kashyap Rahul
Pathology, Mayo Clinic, Rochester, USA.
Hematology & Oncology, Mayo Clinic, Rochester, USA.
Cureus. 2020 Oct 15;12(10):e10972. doi: 10.7759/cureus.10972.
We aim to create and validate an electronic search algorithm for accurate detection of disseminated intravascular coagulopathy (DIC) from medical records.
Patients with DIC in Mayo Clinic's intensive care units (ICUs) from Jan 1, 2007, to May 4, 2018, were included in the study. An algorithm was developed based on clinical notes and ICD diagnosis codes. A cohort of 50 patients was included with DIC diagnosis, its variations, and no diagnosis of DIC. Then, the next set of 50 patients was used to refine the algorithm. Results were compared with a manual reviewer and the disagreements were resolved by the third reviewer. The same process was repeated with 'revised clinical note search' for the first and second derivation cohort with additional exclusion terms. The obtained sensitivity and specificity were reported. The generated algorithm was applied to another set of 50 patients for validation.
In the first derivation cohort- DIC search by clinical notes and diagnosis codes had 92% sensitivity and 100% specificity. Sensitivity dropped to 71% in the second cohort although specificity remains the same. Therefore, the algorithm was refined to clinical notes search only. The revised search was reapplied to first and second derivation cohorts and results obtained for the first derivation were the same but 91.3% sensitive and 100% specific for the second derivation. The search was locked and applied in the validation cohort with 95.8% sensitivity and 100% specificity, respectively.
The revised clinical note based electronic search algorithm was found to be highly sensitive and specific for DIC during the corresponding ICU duration.
我们旨在创建并验证一种电子检索算法,用于从医疗记录中准确检测弥散性血管内凝血(DIC)。
纳入2007年1月1日至2018年5月4日在梅奥诊所重症监护病房(ICU)的DIC患者进行研究。基于临床记录和ICD诊断编码开发了一种算法。纳入一组50例患者,包括DIC诊断、其变异情况以及未诊断为DIC的患者。然后,使用接下来的50例患者来完善算法。将结果与人工审核员进行比较,分歧由第三位审核员解决。对第一组和第二组推导队列采用“修订临床记录搜索”并添加额外排除项,重复相同过程。报告获得的敏感性和特异性。将生成的算法应用于另一组50例患者进行验证。
在第一组推导队列中,通过临床记录和诊断编码进行的DIC搜索敏感性为92%,特异性为100%。在第二组队列中,敏感性降至71%,尽管特异性保持不变。因此,算法优化为仅进行临床记录搜索。将修订后的搜索重新应用于第一组和第二组推导队列,第一组推导队列获得的结果相同,但第二组推导队列的敏感性为91.3%,特异性为100%。该搜索被锁定并应用于验证队列,敏感性和特异性分别为95.8%和100%。
在相应的ICU期间,基于修订临床记录的电子搜索算法对DIC具有高度敏感性和特异性。