Background and purpose The goal of this study was to investigate the rate and associated factors of Transient Ischemic Attack (TIA) misdiagnosis. Methods We retrospectively analyzed consecutive patients with an initial diagnosis of TIA in the emergency department (ED) in a 9-month period. All hospitalized TIA patients were evaluated by a neurologist within 24 h and had at least one hospital discharge follow-up visit within three months. Patients' clinical data and neuroimaging were reviewed. The final diagnosis was independently verified by two stroke neurologists. Results Out of 276 patients with the initial diagnosis of TIA, 254 patients (mean age 68.7 ± 15.4 years, 40.9% male, 25.2% final diagnosis of TIA) were included in the analysis. Twenty-four patients (9.4%) were referred to our rapid-access TIA clinic. The rate of TIA misdiagnosis among TIA clinic referred patients was 45.8%. Among the 230 patients in inpatient setting, the rate of TIA misdiagnosis was 60.0%. A hospital discharge diagnosis of TIA was observed in 54.3% of hospitalized patients; however, only 24.8% had the final diagnosis of TIA. Among hospitalized patients, the univariate analysis suggests a significant difference ( P < .05) between the two groups (correctly versus misdiagnosed patients) in terms of hospital discharge diagnosis, final diagnosis, history of diabetes mellitus, and coronary artery disease. In regression model hospital discharge diagnosis ( P < .001), final diagnosis ( P < .001), and diabetes mellitus ( P = .018) retained independent association with TIA misdiagnosis. Conclusion Our study indicates a high rate of TIA misdiagnosis in the emergency department, hospital, and outpatient clinics.

Background and Objective: Although the risk of recurrent cerebral ischemia is higher after a transient ischemic attack (TIA), there is limited data on the outcome of TIA mimics. The goal of this study is to compare the 6-month outcome of patients with negative and positive diffusion-weighted imaging (DWI) TIAs (DWI-neg TIA vs. DWI-pos TIA) and also TIA mimics. Methods: We prospectively studied consecutive patients with an initial diagnosis of TIA in our tertiary stroke centers in a 2-year period. Every included patient had an initial magnetic resonance (MR) with DWI and one-, three-, and six-month follow-up visits. The primary outcome was defined as the composition of intracerebral hemorrhage, ischemic stroke, TIA, coronary artery disease, and death. Results: Out of 269 patients with the initial diagnosis of TIA, 259 patients (mean age 70.5 ± 15.0 [30–100] years old, 56.8% men) were included in the final analysis. Twenty-one (8.1%, 95% confidence interval [CI] 5.1-12.1%) patients had a composite outcome event within the six-month follow-up. Five (23.8%) and 13 (61.9%) composite outcome events occurred in the first 30 and 90 days, respectively. Among patients with DWI-neg TIA, the one- and six-month ischemic stroke rate was 1.5 and 4.6%, respectively. The incidence proportion of composite outcome event was significantly higher among patients who had the diagnosis of DWI-neg TIA compared with those who had the diagnosis of TIA mimics (12.2 vs. 2.1%—relative risk 5.9; 95% CI, 1.4–25.2). In our univariable analysis among patients with DWI-neg TIA and DWI-pos TIA, age ( P = 0.017) was the only factor that was significantly associated with the occurrence of the composite outcome. Conclusion: Our study indicated that the overall six-month rate of the composite outcome among patients DWI-neg TIA, DWI-pos TIA, and TIA mimics were 12.2, 9.7, and 2.1%, respectively. Age was the only factor that was significantly associated with the occurrence of the composite outcome.

Background: Transient ischemic attack (TIA) is a brief episode of neurological dysfunction resulting from cerebral ischemia not associated with permanent cerebral infarction. TIA is associated with high diagnostic errors because of the subjective nature of findings and the lack of clinical and imaging biomarkers. The goal of this study was to design and evaluate a novel multinomial classification model, based on a combination of feature selection mechanisms coupled with logistic regression, to predict the likelihood of TIA, TIA mimics, and minor stroke. Methods: We conducted our modeling on consecutive patients who were evaluated in our health system with an initial diagnosis of TIA in a 9-month period. We established the final diagnoses after the clinical evaluation by independent verification from two stroke neurologists. We used Recursive Feature Elimination (RFE) and Least Absolute Shrinkage and Selection Operator (LASSO) for prediction modeling. Results: The RFE-based classifier correctly predicts 78% of the overall observations. In particular, the classifier correctly identifies 68% of the cases labeled as "TIA mimic" and 83% of the "TIA" discharge diagnosis. The LASSO classifier had an overall accuracy of 74%. Both the RFE and LASSO-based classifiers tied or outperformed the ABCD2 score and the Diagnosis of TIA (DOT) score. With respect to predicting TIA, the RFE-based classifier has 61.1% accuracy, the LASSO-based classifier has 79.5% accuracy, whereas the DOT score applied to the dataset yields an accuracy of 63.1%. Conclusion: The results of this pilot study indicate that a multinomial classification model, based on a combination of feature selection mechanisms coupled with logistic regression, can be used to effectively differentiate between TIA, TIA mimics, and minor stroke.

The advanced scheduling of patients for elective surgeries is challenging when the operating room capacity usage by these procedures is uncertain. We study the application of some revenue management concepts and techniques to operating rooms for several surgical procedures performed in a multi-tier reimbursement system. Our approach focuses on booking requests for elective procedures, under the assumption that each request uses a random amount of time. We create and use a modified version of Belobaba's well-known EMSRb algorithm (Belobaba 1989) to decide on near-optimal protection levels for various classes of patients. Under the random resource utilization assumption, we decide, for each planning horizon, how much time to reserve for satisfying the demand coming from each class of patients, based on the type of surgical procedure requested and the patient's reimbursement level.

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Patients are increasingly interested in becoming involved in the medical decisionmaking process. As a result, healthcare providers and researchers are concerned with finding new ways to integrate patients' preferences, by understanding their commitment to and the stability of those preferences. Preventive medicine, such as colorectal cancer screening, presents an opportunity for personalising the decision-making trajectory based on patients' preferences. In this paper, we propose a framework for a joint decision-making process, capable of integrating patients' changing preferences, as described by a stability analysis of those preferences and design scenarios for implementing the process in clinical practice. In this study, a secondary data analysis, we present scenarios that demonstrate how the stability analysis of an Analytic Network Process (ANP) model supports personalising the process of agreeing on an appropriate colorectal cancer screening option. We illustrate the framework using two patients whose preferences have different stabilities and for whom the healthcare provider has different rankings for the recommended alternatives. The results show the differences in additional medical information the healthcare provider might need to provide as part of the joint decision-making process in order to reach an agreement between the patient and the provider. A stability analysis of the patient's preferences provides the healthcare provider with a mapping of the preferred options. It also shows how the patient's most preferred alternative might change as the patient obtains additional relevant medical information. Knowing how the patient's priorities might change supports a personalisation of the medical decision-making process. We conclude that the healthcare provider can utilise the stability analysis of a patient's preferences to identify possible dialogue paths that would enable reaching a consensus about an appropriate screening option.

In this paper, we investigate the behavior of the expected revenue function generated from selling bundles with arbitrarily many components. A motivating example of such bundles includes the production and delivery of digital content, where variable costs are generally negligible. Specifically, we derive generic lower and upper bounds for the expected revenue function even when accounting for arbitrary, potentially complex, dependence structures among the bundle components. The expected revenue bounds in turn provide upper and lower bounds regarding the optimal pure bundle price. Our results reconcile the extant bundling literature involving expected revenue bounds, while sharpening some of these results even when relaxing the traditional assumption of independence among the valuations for the bundle components. We show how these bounds can be further tightened when the seller has additional information about the dependence relationship. Since these results effectively reduce the search space for the optimal bundle price, the pricing bounds provided by our framework have important managerial implications.