Trial reportPloS one2018
Prediction of clinically relevant adverse drug events in surgical patients.
Trial report in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 21 citations in OpenAlex.
- Impact of medication therapy management (MTM) service model on multi-morbidity (MMD) patients with hypertension: a pilot RCT.BMC geriatrics · 2023Trial
- Prediction tools to prioritise hospitalised adult patients at risk of drug related problems: An umbrella review.PLOS digital health · 2026Article
- Medication Discrepancy-Related Medication Safety Incidents in Surgical Patients: A Register Study Based on Medication Safety Incident Reports.Health science reports · 2026Article
- Article
- Healthcare risk stratification model for emergency departments based on drugs, income and comorbidities: the DICER-score.BMC emergency medicine · 2024Article
- Systematic Review of Risk Factors Assessed in Predictive Scoring Tools for Drug-Related Problems in Inpatients.Journal of clinical medicine · 2022Review
- Development of an Emergency Revisit Score for Patients With Drug-Related Problems.The Journal of pharmacy technology : jPT : official publication of the Association of Pharmacy Technicians · 2021Article
- Development and validation of a clinical instrument to predict risk of an adverse drug reactions in hospitalized patients.PloS one · 2020Observational
- Development of a multivariable prediction model for identification of patients at risk for medication transfer errors at ICU discharge.PloS one · 2019Observational
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRisk stratification of hospital patients for adverse drug events would enable targeting patients who may benefit from interventions aimed at reducing drug-related morbidity. It would support clinicians and hospital pharmacists in selecting patients to deliver a more efficient health care service. This study aimed to develop a prediction model that helps to identify patients on the day of hospital admission who are at increased risk of developing a clinically relevant, preventable adverse drug event during their stay on a surgical ward.
methodsData of the pre-intervention measurement period of the P-REVIEW study were used. This study was designed to assess the impact of a multifaceted educational intervention on clinically relevant, preventable adverse drug events in surgical patients. Thirty-nine variables were evaluated in a univariate and multivariate logistic regression analysis, respectively. Model performance was expressed in the Area Under the Receiver Operating Characteristics. Bootstrapping was used for model validation.
results6780 admissions of patients at surgical wards were included during the pre-intervention period of the PREVIEW trial. 102 patients experienced a clinically relevant, adverse drug event during their hospital stay. The prediction model comprised five variables: age, number of biochemical tests ordered, heparin/LMWH in therapeutic dose, use of opioids, and use of cardiovascular drugs. The AUROC was 0.86 (95% CI 0.83-0.88). The model had a sensitivity of 80.4% and a specificity of 73.4%. The positive and negative predictive values were 4.5% and 99.6%, respectively. Bootstrapping generated parameters in the same boundaries.
conclusionsThe combined use of a limited set of easily ascertainable patient characteristics can help physicians and pharmacists to identify, at the time of admission, surgical patients who are at increased risk of developing ADEs during their hospital stay. This may serve as a basis for taking extra precautions to ensure medication safety in those patients.
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