Rates and Anticoagulation Treatment of Known Atrial Fibrillation in Patients with Acute Ischemic Stroke: A Real-World Study

Risk Factors for Massive Cerebral Infarction in Pediatric Patients With Moyamoya Disease

BACKGROUND

To explore the risk factors for preoperative massive cerebral infarction (MCI) in pediatric patients with moyamoya disease (MMD).

METHODS

Pediatric patients with MMD treated between 2017 and 2022 were enrolled. Logistic regression analysis was performed to identify risk factors for MCI among the patients, and a nomogram was constructed to identify potential predictors of MCI. Receiver operating characteristic (ROC) curves and areas under the curves were calculated to determine the effects of different risk factors.

RESULTS

This study included 308 pediatric patients with MMD, including 36 with MCI. The MCI group exhibited an earlier age of onset than the non-MCI group. Significant intergroup differences were observed in familial MMD history, postcirculation involvement, duration from diagnosis to initiation of treatment, Suzuki stage, magnetic resonance angiography (MRA) score, collateral circulation score, and RNF213 p.R4810K variations. Family history, higher MRA score, lower collateral circulation score, and RNF213 p.R4810K variations were substantial risk factors for MCI in pediatric patients with MMD. The nomogram demonstrated excellent discrimination and calibration capabilities. The integrated ROC model, which included all the abovementioned four variables, showed superior diagnostic precision with a sensitivity of 67.86%, specificity of 87.01%, and accuracy of 85.11%.

CONCLUSIONS

This study showed that family history, elevated MRA score, reduced collateral circulation score, and RNF213 p.R4810K variations are risk factors for MCI in pediatric patients with MMD. The synthesized model including these variables demonstrated superior predictive efficacy; thus, it can facilitate early identification of at-risk patients and timely initiation of appropriate interventions.

Oral anticoagulants status after acute ischemic stroke and prognosis in patients with atrial fibrillation

OBJECTIVES

To investigate the oral anticoagulants (OACs) use after acute ischemic stroke (AIS) and prognosis of patients with atrial fibrillation (AF).

METHODS

This was a real-world follow-up research of AIS patients with AF admitted to 5 hospitals in northwestern China. We visited these individuals every 6 months to check the type, dosage of OACs, and to record IS recurrence, bleeding, and death events and modified Rankin Scale (mRS) scores until December 2022. When one of the following occurring first was endpoint: IS recurrence, death or study end. Patients were divided into continuous anticoagulation group and non-continuous anticoagulation group based on whether they continued to take OACs from the moment they were discharged until the endpoint. We further analyzed the association between anticoagulation persistence and outcomes.

RESULTS

Among all 250 patients with OACs indication, 147 patients (58.8 %) received OACs at discharge. Only 37.9 % of patients (39/103) started OACs after discharge. Of the 147 patients treated with OACs, 21.8 % (32/147) discontinued anticoagulation after discharge. 239 of the 250 patients had completed the median 40-month follow-up with 91 patients in continuous anticoagulation group and 148 patients in non-continuous anticoagulation group. In the multivariate COX regression, non-continuous anticoagulation was an independent risk factor for poor prognosis (mRS>2) in AIS patients with AF (1.452[1.011, 2.086], p = 0.043).

CONCLUSIONS

This study revealed an upward trend in the use rate of OACs, but low OACs rates that meet guideline-based criteria and low anticoagulation persistence in AF patients after AIS in the northwestern China. Discontinuous anticoagulation was associated with an increased risk of poor prognosis in these patients.

Identifying stroke-related quantified evidence from electronic health records in real-world studies

BACKGROUND

Stroke is one of the leading causes of death and disability worldwide. The National Institutes of Health Stroke Scale (NIHSS) scores in electronic health records (EHRs), which quantitatively describe patients' neurological deficits in evidence-based treatment, are crucial in stroke-related clinical investigations. However, the free-text format and lack of standardization inhibit their effective use. Automatically extracting the scale scores from the clinical free text so that its potential value in real-world studies is realized has become an important goal.

OBJECTIVE

This study aims to develop an automated method to extract scale scores from the free text of EHRs.

METHODS

We propose a two-step pipeline method to identify NIHSS items and numerical scores and validate its feasibility using a freely accessible critical care database: MIMIC-III (Medical Information Mart for Intensive Care III). First, we utilize MIMIC-III to create an annotated corpus. Then, we investigate possible machine learning methods for two subtasks, NIHSS item and score recognition and item-score relation extraction. In the evaluation, we conduct both task-specific and end-to-end evaluations and compare our method with the rule-based method using precision, recall and F1 scores as evaluation metrics.

RESULTS

We use all available discharge summaries of stroke cases in MIMIC-III. The annotated NIHSS corpus contains 312 cases, 2929 scale items, 2774 scores and 2733 relations. The results show that the best F1-score of our method was 0.9006, which was attained by combining BERT-BiLSTM-CRF and Random Forest, and it outperformed the rule-based method (F1-score = 0.8098). In the end-to-end task, our method could successfully recognize the item "1b level of consciousness questions", the score "1" and their relation "('1b level of consciousness questions', '1', 'has value')" from the sentence "1b level of consciousness questions: said name = 1", while the rule-based method could not.

CONCLUSIONS

The two-step pipeline method we propose is an effective approach to identify NIHSS items, scores and their relations. With its help, clinical investigators can easily retrieve and access structured scale data, thereby supporting stroke-related real-world studies.