A novel aGAPSS-based nomogram for the prediction of ischemic stroke in patients with antiphospholipid syndrome

Developing a nomogram model for 3-month prognosis in patients who had an acute ischaemic stroke after intravenous thrombolysis: a multifactor logistic regression model approach

OBJECTIVES

This study is to establish a nomination graph model for individualised early prediction of the 3-month prognosis of patients who had an acute ischaemic stroke (AIS) receiving intravenous thrombolysis with recombinant tissue plasminogen activator.

DESIGN

For the period from January 2016 through August 2022, 991 patients who had an acute stroke eligible for intravenous thrombolysis were included in the retrospective analysis study. The study was based on multifactor logistic regression.

PARTICIPANTS

Patients who received treatment from January 2016 to February 2021 were included in the training cohort, and those who received treatment from March 2021 to August 2022 were included in the testing cohort.

INTERVENTIONS

Each patient received intravenous thrombolysis within 4.5 hours of onset, with treatment doses divided into standard doses (0.9 mg/kg).

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome measure was a 3-month adverse outcome (modified Rankin Scale 3-6).

RESULTS

The National Institutes of Health Stroke Scale Score after thrombolysis (OR=1.18; 95% CI: 1.04 to 1.36; p = 0.015), door-to-needle time (OR=1.01; 95% CI: 1.00 to 1.02; p = 0.003), baseline blood glucose (OR=1.08; 95% CI: 1.00 to 1.16; p=0.042), blood homocysteine (OR=7.14; 95% CI: 4.12 to 12.71; p<0.001), monocytes (OR=0.05; 95% CI: 0.01 to 0.043; p=0.005) and monocytes/high-density lipoprotein (OR=62.93; 95% CI: 16.51 to 283.08; p<0.001) were independent predictors of adverse outcomes 3 months after intravenous thrombolysis, and the above six factors were included in the nominated DGHM2N nomogram. The area under the receiver operating characteristic curve value of the training cohort was 0.870 (95% CI: 0.841 to 0.899) and in the testing cohort was 0.822 (95% CI: 0.769 to 0.875).

CONCLUSIONS

A reliable nomogram model (DGHM2N model) was developed and validated in this study. This nomogram could individually predict the adverse outcome of patients who had an AIS receiving intravenous thrombolysis with alteplase for 3 months.

Predictors of outcome in large vessel occlusion stroke patients with intravenous tirofiban treatment: a post hoc analysis of the RESCUE BT clinical trial

OBJECTIVE

The aim of this study was to investigate the factors influencing good outcomes in patients receiving only intravenous tirofiban with endovascular thrombectomy for large vessel occlusion stroke.

METHODS

Post hoc exploratory analysis using the RESCUE BT trial identified consecutive patients who received intravenous tirofiban with endovascular thrombectomy for large vessel occlusion stroke in 55 comprehensive stroke centers from October 2018 to January 2022 in China.

RESULTS

A total of 521 patients received intravenous tirofiban, 253 of whom achieved a good 90-day outcome (modified Rankin Scale [mRS] 0-2). Younger age (adjusted odds ratio [aOR]: 0.965, 95% confidence interval [CI]: 0.947-0.982; p < 0.001), lower serum glucose (aOR: 0.865, 95%CI: 0.807-0.928; p < 0.001), lower baseline National Institutes of Health Stroke Scale (NIHSS) score (aOR: 0.907, 95%CI: 0.869-0.947; p < 0.001), fewer total passes (aOR: 0.791, 95%CI: 0.665-0.939; p = 0.008), shorter punctures to recanalization time (aOR: 0.995, 95%CI:0.991-0.999; p = 0.017), and modified Thrombolysis in Cerebral Infarction (mTICI) score 2b to 3 (aOR: 8.330, 95%CI: 2.705-25.653; p < 0.001) were independent predictors of good outcomes after intravenous tirofiban with endovascular thrombectomy for large vessel occlusion stroke.

CONCLUSION

Younger age, lower serum glucose level, lower baseline NIHSS score, fewer total passes, shorter punctures to recanalization time, and mTICI scores of 2b to 3 were independent predictors of good outcomes after intravenous tirofiban with endovascular thrombectomy for large vessel occlusion stroke.

CHINESE CLINICAL TRIAL REGISTRY IDENTIFIER

ChiCTR-IOR-17014167.

Platelets and Thrombotic Antiphospholipid Syndrome

Antiphospholipid antibody syndrome (APS) is an autoimmune disorder characterised by thrombosis and the presence of antiphospholipid antibodies (aPL): lupus anticoagulant and/or IgG/IgM anti-β2-glycoprotein I and anticardiolipin antibodies. APS carries significant morbidity for a relatively young patient population from recurrent thrombosis in any vascular bed (arterial, venous, or microvascular), often despite current standard of care, which is anticoagulation with vitamin K antagonists (VKA). Platelets have established roles in thrombosis at any site, and platelet hyperreactivity is clearly demonstrated in the pathophysiology of APS. Together with excess thrombin generation, platelet activation and aggregation are the common end result of all the pathophysiological pathways leading to thrombosis in APS. However, antiplatelet therapies play little role in APS, reserved as a possible option of low dose aspirin in addition to VKA in arterial or refractory thrombosis. This review outlines the current evidence and mechanisms for excessive platelet activation in APS, how it plays a central role in APS-related thrombosis, what evidence for antiplatelets is available in clinical outcomes studies, and potential future avenues to define how to target platelet hyperreactivity better with minimal impact on haemostasis.

Risk factors of first thrombosis in obstetric antiphospholipid syndrome

OBJECTIVE

There is limited evidence on long-term thrombosis risk in patients with obstetric antiphospholipid syndrome (OAPS). This study aimed to investigate the clinical features and risk factors associated with the first thrombosis in patients with isolated OAPS.

METHODS

Data from patients with isolated OAPS were collected. All patients were followed up until the first thrombotic event during or after delivery or until the end of the study. Logistic regression analysis identified independent risk factors associated with the first thrombosis in patients with isolated OAPS.

RESULTS

The study enrolled 186 patients with OAPS. During a mean 5.4-year follow-up, 11 (5.9%) patients experienced thrombotic events. Multivariate binary logistic regression analysis revealed that triple-positive antiphospholipid antibodies (aPLs, OR=11.662, 95% CI=2.117 to 64.243, p=0.005) and hypocomplementemia (OR=9.047, 95% CI=1.530 to 53.495, p=0.015) were identified as independent risk factors for the first thrombosis in OAPS, after adjustment for low-dose aspirin and hydroxychloroquine.

CONCLUSIONS

Triple-positive aPLs and hypocomplementemia are risk factors for the first thrombosis in patients with OAPS.

Establishment and verification of a nomogram and a preliminary study on predicting the clinical response of conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) in rheumatoid arthritis patients

Background

Rheumatoid arthritis (RA) is an autoinflammatory disease, its core treatment principle is to achieve remission as soon as possible. There is no good prediction model that can accurately predict the remission rate of patients to choose a good treatment scheme. Here, we aimed to verify the prognostic value of some inflammatory indicators in RA and establish a prediction model to predict the remission rate after treatment.

Methods

A total of 223 patients were enrolled at Qilu Hospital from June 2014 to June 2020. Baseline clinical data were collected and plasma was obtained to detect the inflammatory indicators. All patients were treated with conventional synthetic disease-modifying antirheumatic drugs (csDMARDs). All patients were followed up and were recorded the time to reach the disease activity score-28 with erythrocyte sedimentation rate (DAS28-ESR) of <2.6. A total of 156 patients were randomly assigned to the development cohort, and 67 patients were assigned to the validation cohort. Inflammatory indicators in plasma were detected by enzyme-linked immunosorbent assay (ELISA). The predictive factors were screeded by using least absolute shrinkage and selection operator (LASSO) and Cox regression. The model was created and verified by using the standard method. A total of 6 independent risk factors were analyzed to construct a nomogram to predict the remission rate in 3, 6 and 12 months.

Results

The remission rates after treatment in 3, 6 and 12 months were 38.76%, 58.91%, and 81.40%, respectively. Patient age, C-reactive protein (CRP), interleukin (IL)-6, galectin-9 (Gal-9), health assessment questionnaire (HAQ), and DAS28-ESR were included in the prognostic model to predict the remission rate. The resulting model had good discrimination ability in both the development cohort (C-index, 0.729) and the validation cohort (C-index, 0.710). Time-dependent receiver operating characteristic (ROC) curve, calibration analysis, and decision curve analysis (DCA) showed that the model has significant discriminant power and clinical practicability in predicting the remission rate.

Conclusions

We established a new predictive model and validated it. The model can predict the remission rate in 3, 6 and 12 months after receiving csDMARDs treatment. By using this model, we can facilitate the identification of high-risk patients early and intervene with them as soon as possible.