Prevalence of diabetes and its effects on stroke outcomes: A meta‐analysis and literature review

Lau, Lik‐Hui; Lew, Jeremy F; Borschmann, Karen; Thijs, Vincent; Ekinci, Elif I

doi:10.1111/jdi.12932

Cited by 299 publications

(257 citation statements)

References 98 publications

(286 reference statements)

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“…A similar ranking for diabetes can also be observed in the logistic regression models. Although diabetes is known to be an important predictor for bad stroke outcome [44], a feature importance score that is at a similar level as age is unexpected. Another striking difference is the high relative importance given to sex by the logistic regression models, which is absent in the rankings provided by the modern methods.…”

Section: Plos Onementioning

confidence: 98%

Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome

et al. 2020

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State-of-the-art machine learning (ML) artificial intelligence methods are increasingly leveraged in clinical predictive modeling to provide clinical decision support systems to physicians. Modern ML approaches such as artificial neural networks (ANNs) and tree boosting often perform better than more traditional methods like logistic regression. On the other hand, these modern methods yield a limited understanding of the resulting predictions. However, in the medical domain, understanding of applied models is essential, in particular, when informing clinical decision support. Thus, in recent years, interpretability methods for modern ML methods have emerged to potentially allow explainable predictions paired with high performance. To our knowledge, we present in this work the first explainability comparison of two modern ML methods, tree boosting and multilayer perceptrons (MLPs), to traditional logistic regression methods using a stroke outcome prediction paradigm. Here, we used clinical features to predict a dichotomized 90 days post-stroke modified Rankin Scale (mRS) score. For interpretability, we evaluated clinical features' importance with regard to predictions using deep Taylor decomposition for MLP, Shapley values for tree boosting and model coefficients for logistic regression. With regard to performance as measured by Area under the Curve (AUC) values on the test dataset, all models performed comparably: Logistic regression AUCs were 0.83, 0.83, 0.81 for three different regularization schemes; tree boosting AUC was 0.81; MLP AUC was 0.83. Importantly, the interpretability analysis demonstrated consistent results across models by rating age and stroke severity consecutively amongst the most important predictive features. For less important features, some differences were observed between the methods. Our analysis suggests that modern machine learning methods can provide explainability which is compatible with domain knowledge interpretation and traditional method rankings. Future work should focus on replication of these findings in other datasets and further testing of different explainability methods.

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Section: Plos Onementioning

confidence: 98%

Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome

et al. 2020

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“…Patients with DM have been found to have an increased risk of ischaemic stroke compared to those without DM [10]. Furthermore, the proportion of patients with diabetes is higher in ischaemic stroke patients compared to haemorrhagic stroke patients [11]. This may be due to long-term consequences of DM, such as thrombotic complications and microvascular dysfunction.…”

Section: Introductionmentioning

confidence: 99%

Impact of Diabetes on Complications, Long Term Mortality and Recurrence in 608,890 Hospitalised Patients with Stroke

Szlachetka

Pana

Tiamkao

et al. 2020

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Background: Patients with diabetes mellitus (DM) have been found to be at an increased risk of suffering a stroke. However, research on the impact of DM on stroke outcomes is limited. Objectives: We aimed to examine the influence of DM on outcomes in ischaemic (IS) and haemorrhagic stroke (HS) patients. Methods: We included 608,890 consecutive stroke patients from the Thailand national insurance registry. In-hospital mortality, sepsis, pneumonia, acute kidney injury (AKI), urinary tract infection (UTI) and cardiovascular events were evaluated using logistic regressions. Long-term analysis was performed on first-stroke patients with a determined pathology (n = 398,663) using Royston-Parmar models. Median follow-ups were 4.21 and 4.78 years for IS and HS, respectively. All analyses were stratified by stroke sub-type. Results: Mean age (SD) was 64.3 (13.7) years, 44.9% were female with 61% IS, 28% HS and 11% undetermined strokes. DM was associated with in-hospital death, pneumonia, sepsis, AKI and cardiovascular events (odds ratios ranging from 1.13-1.78, p < 0.01) in both stroke types. In IS, DM was associated with long-term mortality and recurrence throughout the follow-up: HR max (99% CI) at t = 4108 days: 1.54 (1.27, 1.86) and HR (99% CI) = 1.27(1.23,1.32), respectively. In HS, HR max (t = 4108 days) for long-term mortality was 2.10 (1.87, 2.37), significant after day 14 post-discharge. HR max (t = 455) for long-term recurrence of HS was 1.29 (1.09, 1.53), significant after day 116 post-discharge. Conclusions: Regardless of stroke type, DM was associated with in-hospital death and complications, long-term mortality and stroke recurrence.

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“…1 The polyherbal formulations have been reported for the treatment of type 2 diabetes, a chronic metabolic disorder resulting from either insulin insufficiency or insulin dysfunction. 2,3 As recently reported, approximately onethird of all stroke patients have diabetes 4 , and diabetes mellitus is one of the risk factors associated with increased breast cancer mortality. 6,7 The genus Hunteria belongs to the family Apocynaceae.…”

Section: Introductionmentioning

confidence: 94%

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2019

TJNPR

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3(3):95-98 ISSN 2616-0684 (Print) ISSN 2616-0692 (Electronic) 95Hunteria umbellata K. Schum has been reported for the treatment of diabetes in Nigeria. In the present study, the ethanolic extract of dried leaves of Hunteria umbellata K. Schum was investigated for the chemical principles. The isolated pure compounds were characterized by NMR, IR and Mass spectral studies. Ursolic acid (1), oleanolic acid (2) and squalene (3) were the main constituents isolated from the extract.

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Prevalence of diabetes and its effects on stroke outcomes: A meta‐analysis and literature review

Abstract: Approximately one-third of all stroke patients have diabetes. Uniform methods to screen for diabetes after stroke are required to identify individuals with diabetes to design interventions aimed at reducing poor outcomes in this high-risk population.

Cited by 299 publications

References 98 publications

Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome

Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome

Impact of Diabetes on Complications, Long Term Mortality and Recurrence in 608,890 Hospitalised Patients with Stroke

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