Introduction Adverse Events (AE) are one of the main problems in healthcare. Therefore, many policies have been developed worldwide to mitigate their impact. The Patient Safety Incident Study in Hospitals in the Community of Madrid (ESHMAD) measures the results of them in the region. Methods Cross‐sectional study, conducted in May 2019, in hospitalised patients in 34 public hospitals using the Harvard Medical Practice Study methodology. A logistic regression model was carried out to study the association of the variables with the presence of AE, calibrated and adjusted by patient. Results A total of 9975 patients were included, estimating a prevalence of AE of 11.9%. A higher risk of AE was observed in patients with surgical procedures (OR[CI95%]: 2.15[1.79 to 2.57], vs. absence), in Intensive Care Units (OR[CI95%]: 1.60[1.17 to 2.17], vs. Medical) and in hospitals of medium complexity (OR[CI95%]: 1.45[1.12 to 1.87], vs. low complexity). A 62.6% of AE increased the length of the stay or it was the cause of admission, and 46.9% of AE were considered preventable. In 11.5% of patients with AE, they had contributed to their death. Conclusions The prevalence of AE remains similar to the previously estimated one in studies developed with the same methodology. AE keep leading to longer hospital stays, contributing to patient's death, showing that it is necessary to put focus on patient safety again. A detailed analysis of these events has enabled the detection of specific areas for improvement according to the type of care, centre and patient.

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Background In spite of the global implementation of standardized surgical safety checklists and evidence-based practices, general surgery remains associated with a high residual risk of preventable perioperative complications and adverse events. This study was designed to validate the hypothesis that a new “Trigger Tool” represents a sensitive predictor of adverse events in general surgery. Methods An observational multicenter validation study was performed among 31 hospitals in Spain. The previously described “Trigger Tool” based on 40 specific triggers was applied to validate the predictive power of predicting adverse events in the perioperative care of surgical patients. A prediction model was used by means of a binary logistic regression analysis. Results The prevalence of adverse events among a total of 1,132 surgical cases included in this study was 31.53%. The “Trigger Tool” had a sensitivity and specificity of 86.27% and 79.55% respectively for predicting these adverse events. A total of 12 selected triggers of overall 40 triggers were identified for optimizing the predictive power of the “Trigger Tool”. Conclusions The “Trigger Tool” has a high predictive capacity for predicting adverse events in surgical procedures. We recommend a revision of the original 40 triggers to 12 selected triggers to optimize the predictive power of this tool, which will have to be validated in future studies.

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