- 1The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- 2Qingdao Mental Health Center, Qingdao, China
- 3Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
Objective: Accurate prediction of functional
outcomes of stroke remains clinically challenging. The present study was
designed to identify baseline biomarkers in demographic, clinical data,
and blood biomarkers to predict 3-week outcomes in stroke patients.
Methods: A prospective cohort of two hundred
patients with stroke was recruited at the hospital and followed for
3 weeks. We applied the Barthel Index (BI) to measure the activities of
daily living functions in stroke patients. The good outcome or poor
outcome groups were classified based on the BI scores. A logistic
regression analysis was performed to identify independent predictors,
which were subsequently integrated into a nomogram. Discrimination and
calibration values of the nomogram were analyzed, and its utility was
assessed using decision curve analysis.
Results: Four blood biomarkers, including PT
(OR = 1.45, 95% CI: 1.05–2.12), FIB (OR = 1.49, 95% CI: 1.14–2.00), RBG
(OR = 1.20, 95% CI: 1.03–1.40), and UA (OR = 1.00, 95% CI: 0.99–1.00)
were independent predictors of the 3-week functional outcomes after a
stroke. The nomogram incorporating these biomarkers demonstrated
moderate discriminative ability (AUC values = 0.714, 95%CI:
0.641–0.786), with satisfactory calibration and positive net benefit on
DCA across clinically relevant threshold probabilities.
Conclusion: We developed a pragmatic nomogram
integrating readily available blood biomarkers to predict 3-week
functional outcomes in stroke patients. While validation in larger
cohorts is warranted, our findings provide new evidence in early risk
stratification and personalized rehabilitation planning, potentially
improving post-stroke care efficiency.
1 Introduction
Stroke is a serious health issue with significant global
impacts. It affects 15 million patients worldwide each year. The
effects of a stroke vary from mild to severe and may lead to long-term
disabilities in 2/3 of patients or even death in approximately 1/3 of
patients (Adamson et al., 2004; Johnston et al., 2009). About 80% of strokes are ischemic and occur under the age of 50 years (Maaijwee et al., 2014).
Prolonged hospitalization for stroke treatment results in a significant
increase in healthcare costs. Rehabilitation for stroke sequelae is
also a long-term and potentially costly process, including physical
therapy, occupational therapy, speech therapy, etc. However, despite
significant advances in basic research, there is a lack of reliable
blood biomarkers to predict prognosis in this patient population (Whiteley et al., 2009).
Patients who have experienced a stroke typically face a
range of motor, sensory, language, cognitive, and psychological
disorders that can significantly impact their self-care abilities and
overall quality of life (Anderson et al., 1995; Kalra and Langhorne, 2007; Winstein et al., 2016). The aftermath of a stroke can lead to long-term disability and affect a person’s ability to function independently (Kalra and Langhorne, 2007).
Post-stroke evaluation is critical for optimal stroke care. Currently,
not all patients with stroke achieve the same level of recovery, even
after hospitalization and intensive rehabilitation (Jørgensen et al., 1997).
Stroke survivors face many challenges that may affect their ability to
care for themselves and maintain a good quality of life. A study in
stroke survivors found that 5 years after stroke, a large proportion of
participants reported impaired of health-related quality of life
(HRQoL), mainly related to pain and discomfort (Segerdahl et al., 2023).
The study found that higher age and longer hospitalization were
predictors of impaired HRQoL related to mobility, self-care, and daily
activities. These findings highlight the importance of identifying
patients at a high risk for poor HRQoL, who may benefit from specialized
attention and psychological support.
The early prediction of prognosis in patients with
stroke, including the long-term functional outcomes in patients, is of
great interest. In clinical practice, once a diagnosis of stroke has
been confirmed, appropriate treatment must be administrated promptly to
ensure maximum benefit to the patient and to prevent complications (Adams et al., 2003; Duncan et al., 2005; Winstein et al., 2016). Previous studies have observed a relationship between blood biomarkers and diagnosis and poor stroke outcomes (Whiteley et al., 2008; Welsh et al., 2009; Whiteley et al., 2009; Monbailliu et al., 2017; Lai et al., 2019; Amalia, 2021; Barba et al., 2023).
For example, a study with 270 patients with stroke reported after
controlling for stroke severity and age, IL-6 and N-terminal pro-brain
natriuretic peptides correlated with poor outcomes at month 3 (Whiteley et al., 2012).
Another study used demographic, clinical, and biochemical
characteristics obtained within 2 days after strokes to develop models
that accurately predict stroke mortality and morbidity at 90 days (Fernandez-Lozano et al., 2021).
Although there have been many previous studies of candidate biomarkers,
none have yet demonstrated sufficient sensitivity and specificity in
studies with small sample sizes for routine management of stroke.
We hypothesized that an integrated approach that included
biomarkers, demographic data, clinical presentations, and laboratory
parameters measured in blood samples would improve the prediction of
poor outcomes after stroke. A nomogram is a statistical tool that
evaluates and calculates the precise risk of long-term outcomes for
patients with stroke (Kim et al., 2016).
Considering the complex underlying mechanism and varying prognosis
biomarkers for functional outcomes after stroke, an effective nomogram
model has not yet been established for patients with stroke (van Alebeek et al., 2018).
Studies have shown that the Barthel index (BI) is a good functional
outcome measure in patients with stroke and reported that BI scores of
more than 40 at discharge can predict better outcomes (Mahoney and Barthel, 1965; Nakao et al., 2010).
Also, a strong correlation between the BI scale and the National
Institutes of Health Stroke Scale (NIHSS), another good scale to assess
the severity of stroke and functional outcomes, has been reported (Bathla et al., 2023).
Therefore, this study aimed to measure the demographic, clinical data,
and laboratory indicators and then establish a predictive model to
predict the 3-week outcome after strokes.
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