Department of Paediatric Critical Care, Children's Health Ireland, Dublin, Ireland; and.
Section of Critical Care Medicine, Department of Pediatrics, School of Medicine, University of Colorado and Children's Hospital Colorado, Aurora, Colorado
Hosp Pediatr. 2021 Feb;11(2):179-182. doi: 10.1542/hpeds.2020-001263.
To determine the validity of palliative care (PC) administrative codes (V66.7 and Z51.5) among critically ill pediatric patients.
In this single-center retrospective cohort study, we included all hospitalizations with a PICU admission between March 2016 and March 2018. Sensitivity, specificity, and positive and negative predictive values of the relevant codes were estimated by using a gold standard of a local PC registry.
During the study period, 4670 hospitalizations were included. The median admission age was 5 years (interquartile range 1.5-12.9) with 55% having at least 1 complex chronic condition. The median length of PICU stay was 1.8 days (interquartile range 1-3.4) and mortality was low (1.3%). A total 182 (3.9%) hospitalizations had evidence of a PC consultation. Administrative codes for PC had a sensitivity of 11% (95% confidence interval [CI] 6.8%-16.5%) and a specificity of 99.8% (95% CI 99.6%-99.9%). The positive and negative predictive values were 66.7% (95% CI 47.2%-82.7%) and 96.5% (95% CI 95.9%-97.0%), respectively.
Among critically ill children, PC administrative codes had high specificity but poor sensitivity. The potential for underascertainment of this resource should be considered in future research using administrative data.
确定重症儿科患者姑息治疗(PC)管理代码(V66.7 和 Z51.5)的有效性。
在这项单中心回顾性队列研究中,我们纳入了 2016 年 3 月至 2018 年 3 月期间所有有儿科重症监护病房(PICU)入院的住院患者。使用当地姑息治疗登记处作为金标准来估计相关代码的敏感性、特异性、阳性预测值和阴性预测值。
在研究期间,共纳入了 4670 例住院患者。中位入院年龄为 5 岁(四分位距 1.5-12.9),55%的患者至少有一种复杂的慢性疾病。PICU 住院时间的中位数为 1.8 天(四分位距 1-3.4),死亡率较低(1.3%)。共有 182 例(3.9%)住院患者有姑息治疗咨询的证据。姑息治疗管理代码的敏感性为 11%(95%置信区间 [CI] 6.8%-16.5%),特异性为 99.8%(95% CI 99.6%-99.9%)。阳性预测值和阴性预测值分别为 66.7%(95% CI 47.2%-82.7%)和 96.5%(95% CI 95.9%-97.0%)。
在重症儿童中,PC 管理代码具有高特异性但敏感性较低。在使用行政数据进行未来研究时,应考虑这种资源的潜在漏报问题。