用于识别初级保健中存在伤害的患者安全事件的高性能触发因素的简化集的验证：TriggerPrim 项目。

Validation of a Reduced Set of High-Performance Triggers for Identifying Patient Safety Incidents with Harm in Primary Care: TriggerPrim Project.

机构信息

From the Quality and Safety Unit, Primary Care Management (Gerencia Asistencial de Atención Primaria), Madrid Health Service (SERMAS).

Information Systems Unit, Primary Care Management (Gerencia Asistencial de Atención Primaria), Madrid Health Service (SERMAS).

出版信息

J Patient Saf. 2023 Dec 1;19(8):508-516. doi: 10.1097/PTS.0000000000001161. Epub 2023 Sep 14.

DOI:10.1097/PTS.0000000000001161

PMID:37707868

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10662617/

Abstract

OBJECTIVE

The aim of the study was to construct and validate a reduced set of high-performance triggers for identifying adverse events (AEs) via electronic medical records (EMRs) review in primary care (PC).

METHODS

This was a cross-sectional descriptive study for validating a diagnostic test. The study included all 262 PC centers of Madrid region (Spain). Patients were older than 18 years who attended their PC center over the last quarter of 2018. The randomized sample was n = 1797. Main measurements were as follows: ( a ) presence of each of 19 specific computer-identified triggers in the EMR and ( b ) occurrence of an AE. To collect data, EMR review was conducted by 3 doctor-nurse teams. Triggers with statistically significant odds ratios for identifying AEs were selected for the final set after adjusting for age and sex using logistic regression.

RESULTS

The sensitivity (SS) and specificity (SP) for the selected triggers were: ≥3 appointments in a week at the PC center (SS = 32.3% [95% confidence interval {CI}, 22.8%-41.8%]; SP = 92.8% [95% CI, 91.6%-94.0%]); hospital admission (SS = 19.4% [95% CI, 11.4%-27.4%]; SP = 97.2% [95% CI, 96.4%-98.0%]); hospital emergency department visit (SS = 31.2% [95% CI, 21.8%-40.6%]; SP = 90.8% [95% CI, 89.4%-92.2%]); major opioids prescription (SS = 2.2% [95% CI, 0.0%-5.2%]; SP = 99.8% [95% CI, 99.6%-100%]); and chronic benzodiazepine treatment in patients 75 years or older (SS = 14.0% [95% CI, 6.9%-21.1%]; SP = 95.5% [95% CI, 94.5%-96.5%]).The following values were obtained in the validation of this trigger set (the occurrence of at least one of these triggers in the EMR): SS = 60.2% (95% CI, 50.2%-70.1%), SP = 80.8% (95% CI, 78.8%-82.6%), positive predictive value = 14.6% (95% CI, 11.0%-18.1%), negative predictive value = 97.4% (95% CI, 96.5%-98.2%), positive likelihood ratio = 3.13 (95% CI, 2.3-4.2), and negative likelihood ratio = 0.49 (95% CI, 0.3-0.7).

CONCLUSIONS

The set containing the 5 selected triggers almost triples the efficiency of EMR review in detecting AEs. This suggests that this set is easily implementable and of great utility in risk-management practice.

摘要

目的

本研究旨在构建和验证一套用于通过电子病历（EMR）审查识别初级保健（PC）中不良事件（AE）的高性能触发因素。

方法

这是一项用于验证诊断测试的横断面描述性研究。该研究包括马德里地区的 262 个 PC 中心（西班牙）。患者年龄大于 18 岁，在 2018 年最后一个季度到 PC 中心就诊。随机样本为 n = 1797。主要测量如下：（a）EMR 中存在 19 个特定计算机识别触发因素中的每一个，以及（b）发生 AE。为了收集数据，由 3 名医生-护士团队对 EMR 进行了审查。使用逻辑回归调整年龄和性别后，选择具有统计学显著比值比的触发因素，用于最终集。

结果

选定触发因素的敏感性（SS）和特异性（SP）如下：每周在 PC 中心就诊≥3 次（SS = 32.3% [95%置信区间 {CI}，22.8%-41.8%]；SP = 92.8% [95% CI，91.6%-94.0%]）；住院（SS = 19.4% [95% CI，11.4%-27.4%]；SP = 97.2% [95% CI，96.4%-98.0%]）；医院急诊部就诊（SS = 31.2% [95% CI，21.8%-40.6%]；SP = 90.8% [95% CI，89.4%-92.2%]）；主要阿片类药物处方（SS = 2.2% [95% CI，0.0%-5.2%]；SP = 99.8% [95% CI，99.6%-100%]）；以及 75 岁或以上患者的慢性苯二氮䓬类药物治疗（SS = 14.0% [95% CI，6.9%-21.1%]；SP = 95.5% [95% CI，94.5%-96.5%]）。在验证此触发集时获得了以下值（EMR 中至少出现其中一个触发因素）：SS = 60.2%（95% CI，50.2%-70.1%），SP = 80.8%（95% CI，78.8%-82.6%），阳性预测值 = 14.6%（95% CI，11.0%-18.1%），阴性预测值 = 97.4%（95% CI，96.5%-98.2%），阳性似然比 = 3.13（95% CI，2.3-4.2），阴性似然比 = 0.49（95% CI，0.3-0.7）。