Michelson Kenneth A, Dart Arianna H, Finkelstein Jonathan A, Bachur Richard G
Divisions of Emergency Medicine
Divisions of Emergency Medicine.
Hosp Pediatr. 2021 Aug;11(8):864-878. doi: 10.1542/hpeds.2020-005792.
Illness complications are condition-specific adverse outcomes. Detecting complications of pediatric illness in administrative data would facilitate widespread quality measurement, however the accuracy of such detection is unclear.
We conducted a cross-sectional study of patients visiting a large pediatric emergency department. We analyzed those <22 years old from 2012 to 2019 with 1 of 14 serious conditions: appendicitis, bacterial meningitis, diabetic ketoacidosis (DKA), empyema, encephalitis, intussusception, mastoiditis, myocarditis, orbital cellulitis, ovarian torsion, sepsis, septic arthritis, stroke, and testicular torsion. We applied a method using disposition, diagnosis codes, and procedure codes to identify complications. The automated determination was compared with the criterion standard of manual health record review by using positive predictive values (PPVs) and negative predictive values (NPVs). Interrater reliability of manual reviews used a κ.
We analyzed 1534 encounters. PPVs and NPVs for complications were >80% for 8 of 14 conditions: appendicitis, bacterial meningitis, intussusception, mastoiditis, myocarditis, orbital cellulitis, sepsis, and testicular torsion. Lower PPVs for complications were observed for DKA (57%), empyema (53%), encephalitis (78%), ovarian torsion (21%), and septic arthritis (64%). A lower NPV was observed in stroke (68%). The κ between reviewers was 0.88.
An automated method to measure complications by using administrative data can detect complications in appendicitis, bacterial meningitis, intussusception, mastoiditis, myocarditis, orbital cellulitis, sepsis, and testicular torsion. For DKA, empyema, encephalitis, ovarian torsion, septic arthritis, and stroke, the tool may be used to screen for complicated cases that may subsequently undergo manual review.
疾病并发症是特定病情的不良后果。在行政数据中检测儿科疾病的并发症将有助于广泛的质量测量，然而这种检测的准确性尚不清楚。
我们对一家大型儿科急诊科的就诊患者进行了横断面研究。我们分析了2012年至2019年间年龄小于22岁且患有14种严重疾病之一的患者：阑尾炎、细菌性脑膜炎、糖尿病酮症酸中毒（DKA）、脓胸、脑炎、肠套叠、乳突炎、心肌炎、眼眶蜂窝织炎、卵巢扭转、败血症、化脓性关节炎、中风和睾丸扭转。我们应用一种使用出院情况、诊断代码和手术代码的方法来识别并发症。通过使用阳性预测值（PPV）和阴性预测值（NPV），将自动判定结果与人工健康记录审查的标准进行比较。人工审查的评分者间信度使用κ值。
我们分析了1534次就诊情况。14种疾病中有8种疾病并发症的PPV和NPV大于80%：阑尾炎、细菌性脑膜炎、肠套叠、乳突炎、心肌炎、眼眶蜂窝织炎、败血症和睾丸扭转。DKA（57%）、脓胸（53%）、脑炎（78%）、卵巢扭转（21%）和化脓性关节炎（64%）的并发症PPV较低。中风的NPV较低（68%）。审查者之间的κ值为0.88。
一种使用行政数据测量并发症的自动化方法可以检测阑尾炎、细菌性脑膜炎、肠套叠、乳突炎、心肌炎、眼眶蜂窝织炎、败血症和睾丸扭转的并发症。对于DKA、脓胸、脑炎、卵巢扭转、化脓性关节炎和中风，该工具可用于筛查可能随后进行人工审查的复杂病例。
