Predicting failure to recover IS STUPIDER THAN HELL! Deliver recovery you blithering idiots!
Send me personal hate mail on this: oc1dean@gmail.com. I'll print your complete statement with your name and title(If you can't stand by your name don't bother replying anonymously) and my response in my blog. Or are you afraid to engage with my stroke-addled mind? No excuses are allowed! You're medically trained; it should be simple to precisely state EXACTLY WHERE I'M WRONG.
Exactly what in this research gets survivors recovered? 100% recovery is the only goal in stroke; NOT PREDICTIONS, BIOMARKERS, PROGNOSTICATION, OR ASSESSMENTS! I'd fire anyone doing these!
Platelet to high-density lipoprotein cholesterol ratio predicts clinical outcomes after acute ischemic stroke: a prospective cohort study
Abstract
Background:
The platelet/high-density lipoprotein cholesterol ratio (PHR), a marker of hypercoagulable states and disordered lipid metabolism, has been confirmed as a predictor of cardiovascular disease. However, the effects of PHR on the prognosis of acute ischemic stroke (AIS) remain unknown. We aimed to assess the associations of PHR with the risk of clinical outcomes in patients with AIS.
Methods:
This prospective observational study included 820 patients (median age, 68 years; female, 34.6%; median NIHSS at admission, 3) with AIS. The median time from symptom onset to admission was 2 days (interquartile range [IQR], 0–4), and from admission to blood sampling was 15 h (IQR, 12–19). PHR was calculated as platelet count (PC; 109 cells/L)/HDL-C (mmol/L) at admission. PHR was analyzed both as a continuous variable and in tertile form (tertile 1-tertile 3). To analyze the associations between PHR and clinical outcomes including all-cause death, stroke recurrence and poor functional outcome at 3 months, 6 months and 1 year, we used multivariable Cox and logistic regression, Kaplan–Meier survival curves, restricted cubic splines, subgroup analysis, concordance statistic (C-statistic), net reclassification index (NRI), and integrated discrimination improvement index (IDI).
Results:
The median PHR was 202.155 (IQR, 153.120–262.365). Kaplan–Meier survival curves identified tertile 3 as the group with the highest risk for all-cause death and stroke recurrence. After adjustment, multivariable Cox regression (tertile 1 as reference) showed that the highest PHR tertile 3 was associated with increased risk for both all-cause death and stroke recurrence across all three follow-up intervals (3 months, 6 months and 1 year). In parallel, multivariable logistic regression (tertile 1 as reference) showed that tertile 3 was associated with a greater likelihood of poor functional outcome across the same three time points. Continuous PHR showed a positive dose–response relationship with clinical outcomes. Subgroup analysis revealed significant interactions of age (p < 0.05) with PHR for all-cause death, and of BMI (p < 0.05) with PHR for mRS 3–6. A basic model’s predictive ability was strengthened by the addition of PHR (C-statistic, NRI, IDI).
Conclusion:
A higher PHR level in patients with AIS is strongly associated with an increased risk of all-cause death, stroke recurrence and poor functional outcome. As a valuable predictive biomarker, PHR may provide a simple and effective tool for predicting clinical outcomes in patients with AIS.
Graphical Abstract
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