A NAC nomogram to predict the probability of three-month unfavorable outcome in Chinese acute ischemic stroke patients treated with mechanical thrombectomy

Application of a nomogram model for the prediction of 90-day poor outcomes following mechanical thrombectomy in patients with acute anterior circulation large-vessel occlusion

Background

The past decade has witnessed advancements in mechanical thrombectomy (MT) for acute large-vessel occlusions (LVOs). However, only approximately half of the patients with LVO undergoing MT show the best/independent 90-day favorable outcome. This study aimed to develop a nomogram for predicting 90-day poor outcomes in patients with LVO treated with MT.

Methods

A total of 187 patients who received MT were retrospectively analyzed. Factors associated with 90-day poor outcomes (defined as mRS of 4-6) were determined by univariate and multivariate logistic regression analyzes. One best-fit nomogram was established to predict the risk of a 90-day poor outcome, and a concordance index was utilized to evaluate the performance of the model. Additionally, 145 patients from a single stroke center were retrospectively recruited as the validation cohort to test the newly established nomogram.

Results

The overall incidence of 90-day poor outcomes was 45.16%, affecting 84 of 186 patients in the training set. Moreover, five variables, namely, age (odds ratio [OR]: 1.049, 95% CI [1.016-1.083]; p = 0.003), glucose level (OR: 1.163, 95% CI [1.038-1.303]; p = 0.009), baseline National Institute of Health Stroke Scale (NIHSS) score (OR: 1.066, 95% CI [0.995-1.142]; p = 0.069), unsuccessful recanalization (defined as a TICI grade of 0 to 2a) (OR: 3.730, 95% CI [1.688-8.245]; p = 0.001), and early neurological deterioration (END, defined as an increase of ≥4 points between the baseline NIHSS score and the NIHSS score at 24 h after MT) (OR: 3.383, 95% CI [1.411-8.106]; p = 0.006), were included in the nomogram to predict the potential risk of poor outcomes at 90 days following MT in LVO patients, with a C-index of 0.763 (0.693-0.832) in the training set and 0.804 (0.719-0.889) in the validation set.

Conclusion

The proposed nomogram provided clinical evidence for the effective control of these risk factors before or during the process of MT surgery in LVO patients.

Nomogram to predict 6-month mortality in acute ischemic stroke patients treated with endovascular treatment

Background

Acute Ischemic Stroke (AIS) presents significant challenges in evaluating the effectiveness of Endovascular Treatment (EVT). This study develops a novel prognostic model to predict 6-month mortality post-EVT, aiding in identifying patients likely to benefit less from this intervention, thus enhancing therapeutic decision-making.

Methods

We employed a cohort of AIS patients from Shenyang First People's Hospital, serving as the Validation set, to develop our model. LASSO regression was used for feature selection, followed by logistic regression to create a prognostic nomogram for predicting 6-month mortality post-EVT. The model's performance was validated using a dataset from PLA Northern Theater Command General Hospital, assessing discriminative ability (C-index), calibration (calibration plot), and clinical utility (decision curve analysis). Statistical significance was set at p < 0.05.

Results

The development cohort consisted of 219 patients. Six key predictors of 6-month mortality were identified: "Lack of Exercise" (OR, 4.792; 95% CI, 1.731-13.269), "Initial TICI Score 1" (OR, 1.334; 95% CI, 0.628-2.836), "MRS Score 5" (OR, 1.688; 95% CI, 0.754-3.78), "Neutrophil Percentage" (OR, 1.08; 95% CI, 1.042-1.121), "Onset Blood Sugar" (OR, 1.119; 95% CI, 1.007-1.245), and "Onset NIHSS Score" (OR, 1.074; 95% CI, 1.029-1.121). The nomogram demonstrated a high predictive capability with a C-index of 0.872 (95% CI, 0.830-0.911) in the development set and 0.830 (95% CI, 0.726-0.920) in the validation set.

Conclusion

Our nomogram, incorporating factors such as Lack of Exercise, Initial TICI Score 1, MRS Score 5, Neutrophil Percentage, Onset Blood Sugar, and Onset NIHSS Score, provides a valuable tool for predicting 6-month mortality in AIS patients post-EVT. It offers potential to refine early clinical decision-making and optimize patient outcomes, reflecting a shift toward more individualized patient care.

Nomogram model of functional outcome for endovascular treatment in patients with acute basilar artery occlusion

Background and purpose

The efficacy and safety of endovascular treatment (EVT) in acute basilar artery occlusion (ABAO) has been confirmed by four randomized clinical trials. Nevertheless, the predictors of a 90-day favorable outcome after EVT have not been elucidated. We attempted to establish a nomogram for the prediction of a 90-day favorable outcome in ABAO patients with EVT.

Methods

Clinical data of ABAO patients with EVT were obtained from two nationwide clinical trial registries in China. Factors associated with a 90-day favorable outcome were screened by multivariable step-wise regression on the basis of univariable analysis. A nomogram was established to predict 90-day favorable outcome after EVT.

Results

The proportion of ABAO patients with a favorable outcome was 41.53% (157/378). Seven variables, including baseline National Institutes of Health Stroke Scale (NIHSS) <20 [odds ratio (OR): 8.330; P-value < 0.0001], posterior circulation Alberta Stroke Program Early CT (pc-ASPECT) score ≥7 (OR: 1.948; P-value = 0.0296), Pons-Midbrain Index (PMI) score < 2 (OR: 2.108; P-value = 0.0128), Posterior Circulation Collateral Score (PC-CS) ≥5 (OR: 3.288; P-value < 0.0001), local anesthesia (OR: 0.389; P-value = 0.0017), time from onset to recanalization (OTR) <330 min (OR: 2.594; P-value = 0.0013), and no occurrence of early neurological deterioration (END; OR: 0.039; P-value < 0.0001) were included into the nomogram, with C-index values of 0.8730 and 0.8857 in the training and the internal validation set, respectively.

Conclusions

The proposed nomogram provided a reliable prognostic scale, which can be employed in clinical settings for the selection and clinical management of ABAO patients.

Registration

https://www.clinicaltrials.gov, identifier: NCT03370939.

Dynamic Prediction of Mechanical Thrombectomy Outcome for Acute Ischemic Stroke Patients Using Machine Learning

The unfavorable outcome of acute ischemic stroke (AIS) with large vessel occlusion (LVO) is related to clinical factors at multiple time points. However, predictive models used for dynamically predicting unfavorable outcomes using clinically relevant preoperative and postoperative time point variables have not been developed. Our goal was to develop a machine learning (ML) model for the dynamic prediction of unfavorable outcomes. We retrospectively reviewed patients with AIS who underwent a consecutive mechanical thrombectomy (MT) from three centers in China between January 2014 and December 2018. Based on the eXtreme gradient boosting (XGBoost) algorithm, we used clinical characteristics on admission (“Admission” Model) and additional variables regarding intraoperative management and the postoperative National Institute of Health stroke scale (NIHSS) score (“24-Hour” Model, “3-Day” Model and “Discharge” Model). The outcome was an unfavorable outcome at the three-month mark (modified Rankin scale, mRS 3–6: unfavorable). The area under the receiver operating characteristic curve and Brier scores were the main evaluating indexes. The unfavorable outcome at the three-month mark was observed in 156 (62.0%) of 238 patients. These four models had a high accuracy in the range of 75.0% to 87.5% and had a good discrimination with AUC in the range of 0.824 to 0.945 on the testing set. The Brier scores of the four models ranged from 0.122 to 0.083 and showed a good predictive ability on the testing set. This is the first dynamic, preoperative and postoperative predictive model constructed for AIS patients who underwent MT, which is more accurate than the previous prediction model. The preoperative model could be used to predict the clinical outcome before MT and support the decision to perform MT, and the postoperative models would further improve the predictive accuracy of the clinical outcome after MT and timely adjust therapeutic strategies.

Nomogram to predict 3-month unfavorable outcome after thrombectomy for stroke

Background

Mechanical thrombectomy (MT) is an effective treatment for large-vessel occlusion in acute ischemic stroke, however, only some revascularized patients have a good prognosis. For stroke patients undergoing MT, predicting the risk of unfavorable outcomes and adjusting the treatment strategies accordingly can greatly improve prognosis. Therefore, we aimed to develop and validate a nomogram that can predict 3-month unfavorable outcomes for individual stroke patient treated with MT.

Methods

We analyzed 258 patients with acute ischemic stroke who underwent MT from January 2018 to February 2021. The primary outcome was a 3-month unfavorable outcome, assessed using the modified Rankin Scale (mRS), 3–6. A nomogram was generated based on a multivariable logistic model. We used the area under the receiver-operating characteristic curve to evaluate the discriminative performance and used the calibration curve and Spiegelhalter’s Z-test to assess the calibration performance of the risk prediction model.

Results

In our visual nomogram, gender (odds ratio [OR], 3.40; 95%CI, 1.54–7.54), collateral circulation (OR, 0.46; 95%CI, 0.28–0.76), postoperative mTICI (OR, 0.06; 95%CI, 0.01–0.50), stroke-associated pneumonia (OR, 5.76; 95%CI, 2.79–11.87), preoperative Na (OR, 0.82; 95%CI, 0.72–0.92) and creatinine (OR, 1.02; 95%CI, 1.01–1.03) remained independent predictors of 3-month unfavorable outcomes in stroke patients treated with MT. The area under the nomogram curve was 0.8791 with good calibration performance (P = 0.873 for the Spiegelhalter’s Z-test).

Conclusions

A novel nomogram consisting of gender, collateral circulation, postoperative mTICI, stroke-associated pneumonia, preoperative Na and creatinine can predict the 3-month unfavorable outcomes in stroke patients treated with MT.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-022-02633-1.

Nijeholt G, van Es A, van der Lugt A, Majoie C, Burke J, Roozenbeek B, Lingsma H, Dippel D. Outcome Prediction Models for Endovascular Treatment of Ischemic Stroke: Systematic Review and External Validation

Supplemental Digital Content is available in the text.

Prediction models for outcome of patients with acute ischemic stroke who will undergo endovascular treatment have been developed to improve patient management. The aim of the current study is to provide an overview of preintervention models for functional outcome after endovascular treatment and to validate these models with data from daily clinical practice.

A Predictive Model for Functional Outcome in Patients with Acute Ischemic Stroke Undergoing Endovascular Thrombectomy

INTRODUCTION

Endovascular thrombectomy (EVT) is a well-established treatment of acute ischemic stroke. Variability in outcomes among thrombectomy patients results in a need for patient centered approaches to recovery. Identifying key factors that are associated with outcomes can help prognosticate and direct resources for continued improvement post-treatment. Thus, we developed a comprehensive predictive model of short-term outcomes post-thrombectomy.

METHODS

This is a retrospective chart review of adult patients who underwent EVT at our institution over the last four years. Primary outcome was dichotomized 90-day mRS (mRS 0-2 v mRS 3-6). Bivariate analyses were conducted, followed by logistic regression modelling via a backward-elimination approach to identify the best fit predictive model.

RESULTS

326 thrombectomies were performed; 230 cases were included in the model. In the final predictive model, adjusting for age, gender, race, diabetes, and presenting NIHSS, pre-admission mRS = 0-2 (OR 18.1; 95% 3.44-95.48; p < 0.001) was the strongest predictor of a good outcome at 90-days. Other independent predictors of good outcomes included being a non-smoker (OR 5.4; 95% CI 1.53-19.00; p = 0.01) and having a post-thrombectomy NIHSS<10 (OR 9.7; 95% CI 3.90-24.27; p < 0.001). A decompressive hemicraniectomy (DHC) was predictive of a poor outcome at 90-days (OR 0.07; 95% CI 0.01-0.72; p = 0.03). This model had a Sensitivity of 79%, a Specificity of 89% and an AUC=0.89.

CONCLUSION

Our model identified low pre-admission mRS score, low post-thrombectomy NIHSS, non-smoker status and not requiring a DHC as predictors of good functional outcomes at 90-days. Future works include developing a prognostic scoring system.