Using latent class analysis to identify clinical features of patients with occlusive myocardial infarction: Preangiogram prediction remains difficult.

BACKGROUND: Treatment decisions in myocardial infarction (MI) are currently stratified by ST elevation (ST-elevation myocardial infarction [STEMI]) or lack of ST elevation (non-ST elevation myocardial infarction [NSTEMI]) on the electrocardiogram. This arose from the assumption that ST elevation indicated acute coronary artery occlusion (OMI). However, one-quarter of all NSTEMI cases are an OMI, and have a higher mortality. The purpose of this study was to identify features that could help identify OMI. METHODS: Prospectively collected data from patients undergoing percutaneous coronary intervention (PCI) was analyzed. Data included presentation characteristics, comorbidities, treatments, and outcomes. Latent class analysis was undertaken, to determine patterns of presentation and history associated with OMI. RESULTS: A total of 1412 patients underwent PCI for acute MI, and 263 were diagnosed as OMI. Compared to nonocclusive MI, OMI patients are more likely to have fewer comorbidities but no difference in cerebrovascular disease and increased acute mortality (4.2% vs. 1.1%; p < .001). Of OMI, 29.5% had delays to their treatment such as immediate reperfusion therapy. With latent class analysis, while clusters of similar patients are observed in the data set, the data available did not usefully identify patients with OMI compared to non-OMI. CONCLUSION: Features between OMI and STEMI are broadly very similar. However, there was no difference in age and risk of cerebrovascular disease in the OMI/non-OMI group. There are no reliable characteristics therefore for identifying OMI versus non-OMI. Delays to treatment also suggest that OMI patients are still missing out on optimal treatment. An alternative strategy is required to improve the identification of OMI patients.

Identifying gaps in consumer health library collections: a retrospective review.

BACKGROUND: The objective of this study was to determine if search request forms, which are used when a patron's request for information cannot be fulfilled at the time of contact with the library team, can be used to identify gaps in consumer health library collections. CASE PRESENTATION: Search request forms were collected from 2013 to 2020 and analyzed independently by two reviewers. Search request forms were included if they were complete and contained a record of how the request was fulfilled. Descriptive statistics were used to summarize patron characteristics. Search request forms were iteratively coded to identify themes in the data and determine if resources provided to patrons could be found within the library collection. The study team subsequently reviewed search request forms to determine reasons for identified gaps. Two hundred and forty-nine search request forms were analyzed. Six main content themes were identified: 1) understanding the cancer diagnosis, 2) cancer treatments, 3) understanding disease prognosis, 4) support during and after treatment, 5) natural health products and therapeutic effects in oncology, and 6) research literature. The majority of patrons were patients (53%). Over half (60%) of the submitted search request forms reflected collection gaps, and many (16%) contained queries for information about rare cancer diagnoses. The main reason that queries could not be satisfied was that there was limited consumer health information on the requested topics (53%). CONCLUSIONS: Search request forms are a useful resource for assessing gaps in consumer health library collections.