Prognosis of Patients With Ischemic Stroke With Prior Anticoagulant Therapy: Direct Oral Anticoagulants Versus Warfarin.

BACKGROUND: Direct oral anticoagulants (DOACs) have been the drug of choice for preventing ischemic stroke in patients with atrial fibrillation since 2014. In previous studies, the stroke risk while taking warfarin was 2 per 100 patient-years and 1.5% per year while taking DOACs. We hypothesized that even if ischemic stroke occurred during anticoagulation therapy with DOACs, the prognosis was likely to be better than that with warfarin. METHODS AND RESULTS: Data from 2002 to 2019, sourced from a nationwide claims database, were used to identify atrial fibrillation patients using International Classification of Diseases codes. Patients who experienced an ischemic stroke during anticoagulation were categorized by the drugs used (warfarin, dabigatran, apixaban, rivaroxaban, and edoxaban). The primary outcome was mortality within 3 months and 1 year after the ischemic stroke. Among the 9578 patients with ischemic stroke during anticoagulation, 3343 received warfarin, and 6235 received DOACs (965 dabigatran, 2320 apixaban, 1702 rivaroxaban, 1248 edoxaban). The DOACs group demonstrated lower risks of 3-month (adjusted hazard ratio [HR], 0.550, [95% CI, 0.473-0.639]; P<0.0001) and 1-year mortality (adjusted HR, 0.596 [95% CI, 0.536-0.663]; P<0.0001) than the warfarin group. Apixaban and edoxaban within the DOAC group exhibited particularly reduced 1-year mortality risk compared with other DOACs (P<0.0001). CONCLUSIONS: Our study confirmed that DOACs have a better prognosis than warfarin after ischemic stroke. The apixaban and edoxaban groups had a lower risk of death after ischemic stroke than the other DOAC groups.

Estimation of the Three Phases by Direct Cost of Care for Non-surviving Patients with Cancer: A National Population-based Patient-level Study.

Background: Determining the cost structure of medical care from diagnosis to the death of patients with cancer is crucial for establishing budgets to support patients with cancer. The breakdown of the cost estimation in distinct phases of survival is essential for optimizing the allocation of limited funds. Therefore, this study aims to examine the patterns of direct medical costs of cancer care associated with seven major cancer types and estimate cost thresholds to distinguish each phase based on the incurred cost. Methods: In this nationwide, population-based study, we used claims data from the National Health Insurance Service, Korea. Patients newly diagnosed with cancer since 2006 and who died in 2016-2017 were enrolled, and their use of medical services during cancer survival from at least 6 months up to 12 years was observed. The monthly cost exhibited a non-linear function with two unknown thresholds resembling a U-shape; therefore, we fitted three linear segment models. Individual costs were assessed by dividing the survival time into the initial, continuing, and terminal phases by estimated thresholds, and the average medical cost for each phase was calculated. Results: Based on survival durations of 12 years or less, the initial phase occurred within 1.1-4.8 months after diagnosis, while the terminal phase was observed in 1.4-4.7 months before death. The length of these two phases increased with the increased survival time of the patients. Medical costs in these phases ranged from $4067-7431 and $3127-6114 (US dollars), respectively, regardless of the variations in survival time. However, the average costs in the continuing phase were higher for patients with a short survival time. Conclusions: This study highlights the cost dynamics in cancer care through a breakdown of the phases of survival. It suggests that through a more refined definition of the initial and terminal phases, the average cost in these stages increases, indicating the significant implications of the findings for resource allocation and tailored financial support strategies for patients with cancer with varying prognoses.

Use of machine learning techniques for identifying ischemic stroke instead of the rule-based methods: a nationwide population-based study.

BACKGROUND: Many studies have evaluated stroke using claims data; most of these studies have defined ischemic stroke using an operational definition following the rule-based method. Rule-based methods tend to overestimate the number of patients with ischemic stroke. OBJECTIVES: We aimed to identify an appropriate algorithm for identifying stroke by applying machine learning (ML) techniques to analyze the claims data. METHODS: We obtained the data from the Korean National Health Insurance Service database, which is linked to the Ilsan Hospital database (n = 30,897). The performance of prediction models (extreme gradient boosting [XGBoost] or gated recurrent unit [GRU]) was evaluated using the area under the receiver operating characteristic curve (AUROC), the area under precision-recall curve (AUPRC), and calibration curve. RESULTS: In total, 30,897 patients were enrolled in this study, 3145 of whom (10.18%) had ischemic stroke. XGBoost, a tree-based ML technique, had the AUROC was 94.46% and AUPRC was 92.80%. GRU showed the highest accuracy (99.81%), precision (99.92%) and recall (99.69%). CONCLUSIONS: We proposed recurrent neural network-based deep learning techniques to improve stroke phenotyping. This can be expected to produce rapid and more accurate results than the rule-based methods.

Risk Factors and Characteristics of In-Hospital Falls After Spine Surgery: A Retrospective, Single-Center Cohort Study in the Republic of Korea.

Background: Falls after orthopaedic surgery can cause serious injuries, which lengthen hospital stays and increase medical expenses. This has prompted hospitals to implement various fall-prevention protocols. The aims of this study were to determine the incidence of in-hospital falls after spine surgery, to analyze the overall risk factors, to discern factors that have a major influence on falls, and to evaluate the effectiveness of the fall-prevention protocol that we implemented. Methods: This was a retrospective, single-center study including patients who underwent spine surgery from January 2011 to November 2021 at the National Health Insurance Service Ilsan Hospital (NHISIH) in Goyang, Republic of Korea. Reported falls among these patients were examined. Patient demographics; surgery type, date, and diagnosis; and fall date and time were evaluated. Results: Overall, 5,317 spine surgeries were performed, and 128 in-hospital falls were reported (overall incidence: 2.31%). From the multivariable analyses, older age and American Society of Anesthesiologists (ASA) score were identified as independent risk factors for in-hospital patient falls (multivariable adjusted hazard ratio [aHR] for age 70 to 79 years, 1.021 [95% confidence interval (CI), 1.01 to 1.031]; for age ≥80 years, 1.035 [1.01 to 1.06]; and for ASA score of 3, 1.02 [1.01 to 1.031]). Similar results were seen in the subgroup who underwent primary surgery. Within 2 weeks following surgery, the highest frequency of falls occurred at 3 to 7 days postoperatively. The lowest fall rate was observed in the evening (6 to 10 p.m.). Morbidities, including rib, spine, and extremity fractures, were recorded for 14 patients, but none of these patients underwent operative treatment related to the fall. The NHISIH implemented a comprehensive nursing care service in May 2015 and a fall protocol in May 2017, but the annual incidence rate did not improve. The fall rate was higher after thoracolumbar surgeries (2.47%) than after cervical surgeries (1.20%). Moreover, a higher fall rate was observed in thoracolumbar cases with a greater number of fusion levels and revision spine surgeries. Conclusions: Patients with advanced age, more comorbidities, a greater number of fusion levels, and revision surgeries and who are female are more vulnerable to in-hospital falls after spine surgery. Novel strategies that target these risk factors are warranted. Level of Evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.