U-shaped relationship between hemoglobin level and severity of ischemic stroke

JAK2V617F mutation is highly prevalent in patients with ischemic stroke: a case-control study

Ischemic stroke has a high recurrence rate despite treatment. This underlines the significance of investigating new possible cerebrovascular risk factors, such as the acquired gene mutation JAK2V617F found in 3.1% of the general population. We aimed to investigate the prevalence of the JAK2V617F mutation in a population with ischemic stroke compared with that in matched controls. We enrolled 538 consecutive Danish patients with ischemic stroke (mean age, 69.5 ± 10.9 years; 39.2% female) within 7 days of symptom onset. Using multiple-adjusted conditional logistic regression analysis, we compared the prevalence of JAK2V617F with that in age- and sex-matched controls free of ischemic cerebrovascular disease (ICVD) from the Danish General Suburban Population Study. DNA was analyzed for JAK2V617F mutation using sensitive droplet digital polymerase chain reaction in patients and controls. Of the 538 patients with ischemic stroke, 61 (11.3%) had JAK2V617F mutation. There were no differences in patient demographics or cerebrovascular comorbidities between the patients with and without mutations. Patients with ischemic stroke were more likely to have the JAK2V617F mutation than matched controls, in whom the JAK2V617F prevalence was 4.4% (odds ratio, 2.37; 95% confidence interval, 1.57-3.58; P < .001). A subanalysis stratified by smoking history revealed that the association was strongest in current smokers (odds ratio, 4.78; 95% confidence interval, 2.22-10.28; P < .001). Patients with ischemic stroke were 2.4 times more likely to have the JAK2V617F mutation than matched controls without ICVD when adjusting for other cerebrovascular risk factors. This finding supports JAK2V617F mutation as a novel cerebrovascular risk factor.

A cardiologist's guide to machine learning in cardiovascular disease prognosis prediction

A modern-day physician is faced with a vast abundance of clinical and scientific data, by far surpassing the capabilities of the human mind. Until the last decade, advances in data availability have not been accompanied by analytical approaches. The advent of machine learning (ML) algorithms might improve the interpretation of complex data and should help to translate the near endless amount of data into clinical decision-making. ML has become part of our everyday practice and might even further change modern-day medicine. It is important to acknowledge the role of ML in prognosis prediction of cardiovascular disease. The present review aims on preparing the modern physician and researcher for the challenges that ML might bring, explaining basic concepts but also caveats that might arise when using these methods. Further, a brief overview of current established classical and emerging concepts of ML disease prediction in the fields of omics, imaging and basic science is presented.

Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation

BACKGROUND

Appropriate anticoagulant therapy for patients with atrial fibrillation (AF) requires assessment of stroke and bleeding risks. However, risk stratification schemas such as CHA2DS2-VASc and HAS-BLED have modest predictive capacity for patients with AF. Multilabel machine learning (ML) techniques may improve predictive performance and support decision-making for anticoagulant therapy. We compared the performance of multilabel ML models with the currently used risk scores for predicting outcomes in AF patients.

METHODS

This was a retrospective cohort study of 9670 patients, mean age 76.9 years, 46% women, who were hospitalized with non-valvular AF, and had 1-year follow-up. The outcomes were ischemic stroke (167), major bleeding (430) admissions, all-cause death (1912) and event-free survival (7387). Discrimination and calibration of ML models were compared with clinical risk scores by area under the curve (AUC). Risk stratification was assessed using net reclassification index (NRI).

RESULTS

Multilabel gradient boosting classifier chain provided the best AUCs for stroke (0.685 95% CI 0.676, 0.694), major bleeding (0.709 95% CI 0.703, 0.716) and death (0.765 95% CI 0.763, 0.768) compared to multi-layer neural networks and classifier chain using support vector machine. It provided modest performance improvement for stroke compared to AUC of CHA2DS2-VASc (0.652, NRI = 3.2%, p-value = 0.1), but significantly improved major bleeding prediction compared to AUC of HAS-BLED (0.522, NRI = 22.8%, p-value < 0.05). It also achieved greater discriminant power for death compared with AUC of CHA2DS2-VASc (0.606, p-value < 0.05). ML models identified additional risk features such as hemoglobin level, renal function.

CONCLUSIONS

Multilabel ML models can outperform clinical risk stratification scores for predicting the risk of major bleeding and death in non-valvular AF patients.

Both Ends of Values in the Hemoglobin Spectrum Are Associated with Adverse Stroke Outcomes

BACKGROUND AND PURPOSE

Existing studies on the association between hemoglobin values and stroke outcomes mostly focus on the lower side and mortality, often the only and primary endpoint. The current study was conducted to assess the association between hemoglobin concentration and a variety of poor stroke outcomes in patients with acute ischemic stroke.

METHODS

We studied 8,321 patients enrolled in the China National Stroke Registry (CNSR) between 2007 and 2008. Patients were divided into 7 groups, and a logistic regression model was used to evaluate the association. Endpoints of interest included 1-year all-cause mortality, stroke recurrence, combined endpoint, and stroke disability. Stroke disability was defined as a modified Rankin Scale of 2-6.

RESULTS

Patients with low and high hemoglobin values (≤11.6 g/dL and >16.1 g/dL) had higher proportion of poststroke adverse events than those in other groups. As compared with the fourth group of hemoglobin values of 13.5-14.2 g/dL, the adjusted odds ratios (ORs) with 95% confidence interval (CI) of low hemoglobin values (≤11.6 g/dL) were 2.25 (1.72-2.93) for all-cause mortality, 1.30 (1.04-1.61) for stroke recurrence, 1.63 (1.33-2.01) for combined endpoint, and 1.37 (1.12-1.67) for stroke disability, respectively. And, the ORs of high hemoglobin values (>16.1 g/dL) for adverse stroke outcomes were 1.72 (1.25-2.37), 1.43 (1.13-1.82), 1.43 (1.13-1.81), and 1.31 (1.06-1.63), respectively. Stratified analysis showed significant interactions between sex and categories of hemoglobin values for all-cause mortality (p = 0.05), stroke recurrence (p = 0.03), and combined endpoint (p = 0.01) but not for stroke disability (p = 0.24).

CONCLUSIONS

Our study found both low and high hemoglobin values were associated with adverse stroke outcomes including all-cause mortality, stroke recurrence, combined endpoint, and stroke disability, which showed a U-shaped association. And, significant interactions between sex and hemoglobin concentration on all-cause mortality and stroke recurrence were also identified.

Association Between Iron Status and Risk of Chronic Kidney Disease in Chinese Adults

Background: Even though it is well-known that iron deficiency is the result of chronic kidney disease (CKD), whether iron will affect kidney function and disease in the general population is not clear. We thus conducted a nationwide cross-sectional study using data from the China Health and Nutrition Survey (CHNS) to assess the relationship of iron status with estimated glomerular filtration rate (eGFR) and CKD among general adults. Methods: A total of 8,339 adults from the China Health and Nutrition Survey in the wave of 2009 were included to assess the association between iron status and eGFR/CKD. Serum ferritin (SF), transferrin, soluble transferrin receptor (sTfR), and hemoglobin (Hb) were measured. The relationship of iron status and eGFR was evaluated by using multi-variable linear regression model. The effect of iron status on the odds of CKD was calculated by logistic regression model. Results: For the association between iron status and eGFR, every 100 μg/L increase in SF was correlated with 0.26 ml/min per 1.73 m2 (95% CI: 0.08-0.44) decrease in eGFR, and every 5 mg/L increase in sTfR was associated with a decrease of 6.00 ml/min per 1.73 m2 (95% CI: 3.79-8.21) in eGFR. There were no significant associations between Hb or transferrin with eGFR. For the association between iron status and CKD, every 5 g/L increase in sTfR was associated with an odds ratio of 3.72 (95% CI: 2.16-6.13) for CKD. The concentrations of Hb were associated with the odds of CKD in a U-shaped manner, with the lowest risk in the Hb range of 136-141 g/L. There was a positive correlation between SF concentration and CKD prevalence but not in a dose-response manner. The odds of CKD for participants in the highest tertile increased by 28% (98% CI: 1-63%) compared with those in the lowest tertile. Conclusion: The concentration of SF and sTfR was positively correlated with the odds of CKD, and Hb was associated with the odds of CKD in a U-shaped manner. Further large prospective researches are warranted to confirm these findings.