Development and validation of a prognostic prediction model for antithrombotic-related chronic subdural hematoma in patients with recent acute myocardial infarction

Abstract

Background: Antithrombotic therapy is a cornerstone of acute myocardial infarction (AMI) treatment and is thought to be associated with an increased risk of chronic subdural hematoma (CSDH). However, no wellestablished model exists to predict subsequent antithrombotic treatment outcomes after CSDH in patients with recent AMI. We aimed to identify a prognostic model to predict the 6-month outcome of treatment with antithrombotic therapy. Methods: This multicenter retrospective analysis involved 553 patients with recent AMI with antithrombotic-related CSDH. Several candidate clinical variables and biomarkers were examined in the training cohort (Chengdu training cohort; n=368). Patients with unfavorable outcomes had experienced at least 1 of the following: major adverse cardiovascular events (MACE), recurrence, or a modified Rankin scale (mRS) score of 2 to 6. To develop a 6-month outcome prediction model, three approaches were used: (I) a demographic variable model, (II) a clinical marker model and (III) a decision-driven model. A clinical outcome prediction model based on the superior predictors was assessed by logistic regression analysis. The nomogram for the final model was internally validated using a bootstrap procedure and externally validated in an independent cohort (Anhui cohort; n=185). Results: Model A produced 7 predictors of unfavorable outcomes, while models B and C yielded 2 and 1 predictors, respectively. The areas under the curve (AUC) increased from 0.743 [model A; 95% confidence interval (CI): 0.680-0.782] to 0.889 (model A + B + C; 95% CI: 0.851-0.916). The final prediction model included age, systolic blood pressure (SBP), body mass index (BMI), the Glasgow Coma Scale (GCS), the estimated glomerular filtration rate (eGFR), the early resumption of antithrombotic therapy, hematoma thickness and the presence of abdominal obesity, frailty and previous bleeding. Internal and external validation of the selected final model revealed adequate C-statistics and calibration slope values (internal validation: 0.81 and 0.78; external validation: 0.80 and 0.76, respectively).Conclusions: This model provided a risk stratification tool to predict unfavorable outcomes in patients with recent AMI with antithrombotic-related CSDH. Because the study was based on ten readily practical and available variables, it may be widely applicable to guide management and complement clinical assessment.

Frailty as a predictor of poor outcomes in patients with chronic subdural hematoma (cSDH): A systematic review of literature

“…1 ). 18 , 19 , 20 , 21 , 22 , 23 Table 1 depicts key findings of the studies assessing impact of frailty on outcomes in cSDH patients.

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“… Retrospective single center n = 109 Follow up till discharge from hospital or in-hospital mortality mFI-5, mFI-11, and CCI Frail patients with higher mFI-11 and mFI-5 scores were found to have significantly lower Glasgow coma score (GCS) at discharge and significant reduction in rates of discharge home. Zhou et al, 2020 21 Evaluation of different factors, including frailty, to develop a prognostic prediction model for unfavorable outcomes in patients with antithrombotic-related cSDH in patients with recent AMI Retrospective multi-center n = 553 Follow up at least 6 months. CFS Patients with unfavorable outcomes were associated with frailty Sastry et al, 2021 22 Evaluation of pre-operative frailty as an index for outcomes following craniotomy for evacuation of atraumatic cSDH.…”

“…Three were single-center studies 18 , 19 , 20 and three were multicenter studies, 21 , 22 one of which was performed using patient data extracted from on the National Surgical Quality Improvement Program (NSQIP) database 22 and the other one was performed using the Dutch CBS database. 23 Two studies used clinical frailty scale (CFS) alone as a measure of frailty, 18 , 21 one study employed modified frailty index-5 (mFI-5) alone, 22 one study used both mFI-5 and modified frailty index-11 (mFI-11), 20 and another study used mFI-11 and CFS as frailty measures. 19 One study 23 employed Charlson Comorbidity Index (CCI) alone while two studies employed it in addition to other frailty scales/indices.…”

“… 19 One study 23 employed Charlson Comorbidity Index (CCI) alone while two studies employed it in addition to other frailty scales/indices. 18 , 20 The duration of outcomes assessment varied widely between studies including, 6 months, 21 3 months, 18 and 30 days 22 ; two studies used disposition i.e., discharge from hospital or in-hospital mortality as outcomes measurement time point. 19 , 20 Four studies used modified Rankin score (mRS) at follow-up endpoint as a measure of disability.…”

Associations of serum expressions of miR-499 and sex determining region Y-box 6 with prognosis of acute myocardial infarction patients

“…Based on the above factors, the nomogram model for MACCE after PCI was established, and its accuracy was validated using C-index. C-index refers to the ability to accurately screen the risk of MACCE after operation, ranging from 0.5 (no predictive ability) to 1 (highest predictive ability) (20,21). In this study, the C-index of the nomogram model was 0.742 (95%CI=0.684-0.845), indicating that the model had high discrimination power and better predictive value, and it can be used as an auxiliary prediction tool after PCI in AMI patients to a certain extent.…”