A nomogram model incorporating blood biomarkers predicts 3-week functional outcomes in stroke patients

 Predictions are absolutely fucking useless! Give us protocols that deliver recovery and then we can discuss if you can stay employed!

A nomogram model incorporating blood biomarkers predicts 3-week functional outcomes in stroke patients

Suzhen Ye¹Ting Ding²Xin Gao²Xuezhen Zhou¹Meihong Xiu^3*Yu Xia^1*

• 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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