RESUMEN
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.
