Prognostic nomogram for the outcomes in acute stroke patients with intravenous thrombolysis

Zheng, Ping; Li, Min; Qiuyun, Lu; Xu, Chen; Qingke, Bai

doi:10.3389/fnins.2022.1017883

Cited by 12 publications

(9 citation statements)

References 31 publications

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“…Radiomics can be used to identify quantitative imaging indexes, which have the advantage of non-invasiveness; a novel nomogram, including clinical features and radiomics features, was constructed to predict neurological outcomes in ischemic stroke and exhibited good performance (18). Zheng et al (19) (20). Notably, a nomogram has also been utilized to predict mortality in patients with ischemic stroke (5,6).…”

Section: Discussionmentioning

confidence: 99%

Prediction of long-term mortality in patients with ischemic stroke based on clinical characteristics on the first day of ICU admission: An easy-to-use nomogram

Jin

Zeng

et al. 2023

Front. Neurol.

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BackgroundThis study aimed to establish and validate an easy-to-use nomogram for predicting long-term mortality among ischemic stroke patients.MethodsAll raw data were obtained from the Medical Information Mart for Intensive Care IV database. Clinical features associated with long-term mortality (1-year mortality) among ischemic stroke patients were identified using least absolute shrinkage and selection operator regression. Then, binary logistic regression was used to construct a nomogram, the discrimination of which was evaluated by the concordance index (C-index), integrated discrimination improvement (IDI), and net reclassification index (NRI). Finally, a calibration curve and decision curve analysis (DCA) were employed to study calibration and net clinical benefit, compared to the Glasgow Coma Scale (GCS) and the commonly used disease severity scoring system.ResultsPatients who were identified with ischemic stroke were randomly assigned into developing (n = 1,443) and verification (n = 646) cohorts. The following factors were associated with 1-year mortality among ischemic stroke patients, including age on ICU admission, marital status, underlying dementia, underlying malignant cancer, underlying metastatic solid tumor, heart rate, respiratory rate, oxygen saturation, white blood cells, anion gap, mannitol injection, invasive mechanical ventilation, and GCS. The construction of the nomogram was based on the abovementioned features. The C-index of the nomogram in the developing and verification cohorts was 0.820 and 0.816, respectively. Compared with GCS and the commonly used disease severity scoring system, the IDI and NRI of the constructed nomogram had a statistically positive improvement in predicting long-term mortality in both developing and verification cohorts (all with p < 0.001). The actual mortality was consistent with the predicted mortality in the developing (p = 0.862) and verification (p = 0.568) cohorts. Our nomogram exhibited greater net clinical benefit than GCS and the commonly used disease severity scoring system.ConclusionThis proposed nomogram has good performance in predicting long-term mortality among ischemic stroke patients.

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Section: Discussionmentioning

confidence: 99%

Prediction of long-term mortality in patients with ischemic stroke based on clinical characteristics on the first day of ICU admission: An easy-to-use nomogram

Jin

Zeng

et al. 2023

Front. Neurol.

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“…All prediction models were internally validated, and 26 of them were externally validated. Additionally, among the 28 eligible studies, 15 studies were multi-centered ( 27 – 29 , 39 , 45 , 47 , 52 , 55 , 58 – 64 ); five studies extracted their original data from databases ( 24 , 25 , 32 , 37 , 57 ); and the remaining 24 studies were single-centered. Most eligible studies focused on IS and 7 studies ( 23 , 32 , 39 , 40 , 48 , 60 , 63 ) on HS.…”

Section: Resultsmentioning

confidence: 99%

“…Due to distinct purposes, these included studies defined poor motor function in a different manner. Specifically, it was defined as Modified Rankin Scale >1 in 4 studies ( 26 , 31 , 45 , 58 ), Modified Rankin Scale >3 in 4 ( 39 , 53 , 60 , 63 ) studies, Modified Rankin Scale >4 in two ( 25 , 42 ) studies, and Modified Rankin Scale >2 in other 34 studies. In this study, a total of 13 primary studies using radiomics as predictive factors were identified ( 27 , 30 , 36 , 37 , 49 , 51 , 52 , 54 – 57 , 61 , 62 ), from which 33 prediction models were extracted.…”

Section: Resultsmentioning

confidence: 99%

Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis

Chi

Zhao

et al. 2023

Front. Neurol.

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BackgroundRecent studies have reported that machine learning (ML), with a relatively strong capacity for processing non-linear data and adaptive ability, could improve the accuracy and efficiency of prediction. The article summarizes the published studies on ML models that predict motor function 3–6 months post-stroke.MethodsA systematic literature search was conducted in PubMed, Embase, Cochorane and Web of Science as of April 3, 2023 for studies on ML prediction of motor function in stroke patients. The quality of the literature was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). A random-effects model was preferred for meta-analysis using R4.2.0 because of the different variables and parameters.ResultsA total of 44 studies were included in this meta-analysis, involving 72,368 patients and 136 models. Models were categorized into subgroups according to the predicted outcome Modified Rankin Scale cut-off value and whether they were constructed based on radiomics. C-statistics, sensitivity, and specificity were calculated. The random-effects model showed that the C-statistics of all models were 0.81 (95% CI: 0.79; 0.83) in the training set and 0.82 (95% CI: 0.80; 0.85) in the validation set. According to different Modified Rankin Scale cut-off values, C-statistics of ML models predicting Modified Rankin Scale>2(used most widely) in stroke patients were 0.81 (95% CI: 0.78; 0.84) in the training set, and 0.84 (95% CI: 0.81; 0.87) in the validation set. C-statistics of radiomics-based ML models in the training set and validation set were 0.81 (95% CI: 0.78; 0.84) and 0.87 (95% CI: 0.83; 0.90), respectively.ConclusionML can be used as an assessment tool for predicting the motor function in patients with 3–6 months of post-stroke. Additionally, the study found that ML models with radiomics as a predictive variable were also demonstrated to have good predictive capabilities. This systematic review provides valuable guidance for the future optimization of ML prediction systems that predict poor motor outcomes in stroke patients.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022335260, identifier: CRD42022335260.

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“…A nomogram is a visualization method for clinical predictive models that are widely used in the eld of ischemic stroke. For example, Zheng et al [ 40] developed a simple nomogram to assess the neurological outcome of acute ischemic stroke treated with intravenous thrombolysis, and external validation demonstrated the robustness of the predictive model. Liu C et al [ 41] found that hyperglycemia was associated with poor functional prognosis of stroke and used it to construct a nomogram, which has good clinical application value in predicting the functional prognosis of stroke.…”

Section: Discussionmentioning

confidence: 99%

Blood-based biomarkers for prediction of 30-days mortality in patients with acute moderate and severe stroke in neurological intensive care unit: A prospective observational study

Chen

et al. 2023

Preprint

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Background Stroke is currently the second most common cause of death and disability-adjusted life years worldwide. However, there have been few comprehensive models reported for evaluating the short-term mortality of acute moderate and severe stroke patients. In this study, we aimed to investigate blood-based biomarkers of inflammation, immunity, nutritional metabolism and blood brain barrier damage at the early stages, explore the association of these biomarkers with 30-day mortality in acute moderate and severe stroke patients, and develop a nomogram specifically for 30-day mortality prediction in these patients. Methods A prospective observational study was conducted, enrolling 152 acute stroke patients (including acute ischemic stroke (AIS) or intracerebral hemorrhage (ICH)) who had NIHSS ＞14 or GCS＜8. The study was conducted between January 2020 and November 2022 in the neurological intensive care unit (NICU), and patients were consecutively enrolled. The laboratory parameters and clinical characteristics of the patients were collected by the research team. Blood biomarkers, including IL-10, MIP-1β, TNF-α, nNOS, iNOS, MMP-9, S-100β, and ET-1, were measured using ELISA within the first 24 hours following symptom onset. The least absolute shrinkage and selection operator (LASSO) regression optimized predictive clinical, biomarker, and combined models.Univariate and multivariate logistic regression analyses were performed to construct a nomogram model for predicting the 30-day mortality risk of acute moderate and severe stroke patients.The bootstrapping validation method, a resampling technique, was used to internally validate the nomogram model. The performance of the nomogram was evaluated on the basis of its calibration, discrimination, and clinical usefulness. Results The 30-day mortality rate of acute moderate and severe stroke patients in the neurological intensive care unit (NICU) was 23.68%. Surviving stroke patients showed lower neutrophils, neutrophil-to-lymphocyte ratio (NLR), platelet count/platelet volume ratio (PPR), procalcitonin (PCT), IL-6, IL-10, TNF-α, and higher lymphocytes/monocytes ratio(LMR), lymphocytes than non-surviving patients, while there were no significant differences in monocyte, albumin, prealbumin, transferrin, MIP-1β, nNOS, iNOS, MMP-9, S-100β and ET-1 between non-surviving and surviving patients. The LASSO regression identified 3 variables (IL-10 (P<0.001), NIHSS (P=0.015) and cerebral herniation (P<0.001)), and a predictive model of 30-day mortality in acute moderate and severe stroke patients was subsequently established. The area under the curve (AUC) of the predictive model was 0.885 (95% CI: 0.808-0.963). The model achieved a concordance index of 0.877 [95% CI (0.775, 0.979)] and had a well-fitted calibration curve and good clinical application value. Conclusions This study found that blood biomarkers, including neutrophils, NLR, PPR, PCT, IL-6, IL-10, TNF-α, LMR, and lymphocytes measured within the first 24 hours following symptom onset, were associated with 30-day mortality in acute moderate and severe stroke patients. Specifically, higher levels of IL-10 exhibited a greater predictive power.Furthermore, we constructed a predictive nomogram model, which may facilitate early prognosis identification, enhance communication between patients and clinicians, and improve patient management.

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Prognostic nomogram for the outcomes in acute stroke patients with intravenous thrombolysis

Cited by 12 publications

References 31 publications

Prediction of long-term mortality in patients with ischemic stroke based on clinical characteristics on the first day of ICU admission: An easy-to-use nomogram

Prediction of long-term mortality in patients with ischemic stroke based on clinical characteristics on the first day of ICU admission: An easy-to-use nomogram

Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis

Blood-based biomarkers for prediction of 30-days mortality in patients with acute moderate and severe stroke in neurological intensive care unit: A prospective observational study

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