Development and validation of a dynamic nomogram of prehospital delay in patients with first-episode acute ischemic stroke

 If you would just create EXACT 100% RECOVERY PROTOCOLS REGARDLESS OF TIME PRESENTED, it would be the simple logical thing to do. WHY HAS NO ONE DONE THAT YET? INCOMPETENCE, STUPIDITY, IGNORANCE? None are valid excuses. You'll want them when you are the 1 in 4 per WHO that has a stroke

 and by then it will be too late for them to change their recovery trajectory! 

Development and validation of a dynamic nomogram of prehospital delay in patients with first-episode acute ischemic stroke

Fangyan Li a b, 

Lei Zhang b, 

Ruilei Zhang c, 

Yaoyao Liu b, 

Tinglin Zhang b

Highlights

• •
  Binary logistic regression was used to screen out the influencing factors of prehospital delay in patients with first-episode acute ischemic stroke. The R 4.3.3 software (packages "rms" and "regplot") was used to construct a risk prediction model, and the "DynNom" package and Shiny Apps software were used to visualize the dynamic nomogram.
• •
  Six independent risk factors, including living alone, regular physical examination, NIHSS score, illness perception, perceived barriers to healthcare-seeking decisions, and health literacy for stroke, were screened to construct a dynamic nomogram.
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  The nomogram has good discrimination and calibration. In addition, the predictors selected by this prediction model are easy to operate and convenient for obtaining data, which has good universality and promotion value.
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  The online dynamic nomogram, which reduces the manual measurement of traditional nomogram, can directly generate the probability of a patient experiencing prehospital delay, greatly improving the screening efficiency and providing a reliable basis for long-term follow-up and validation.

Abstract

Background

To develop and validate a dynamic nomogram for prehospital delay in patients with first-episode acute ischemic strokes (AIS).

Methods

Select 468 first-episode AIS patients from Jinzhou between September 2023 and March 2024, and the patients were investigated with questionnaires. SPSS 27.0 software was used to identify the influencing factors of prehospital delay, and a dynamic nomogram was constructed based on R-shiny.

Results

including living alone, regular physical examination, NIHSS scores, illness perception, perceived barriers to healthcare-seeking decisions, and health literacy for stroke, were screened to construct a dynamic nomogram. For the training set, the AUC was 0.847, and the validation set's AUC was 0.834. The calibration curve was close to the ideal curve, and DCA results confirmed that the nomogram performed well in terms of clinical applicability.

Conclusion

The dynamic nomogram in this study has good discrimination and calibration, and the included indicators are simple and easy to obtain.

Introduction

The 2019 Global Burden of Disease Study estimates that there are 101 million prevalent cases of stroke, 12.2 million incident cases of stroke, and 6.55 million stroke-related deaths globally. Stroke is now the second-most common cause of death and disability globally.1 As the world's largest developing nation, home to around 25 % of the global population, China is the country with the highest number of stroke sufferers worldwide.2 Stroke has emerged as China's primary cause of death and disability since 2015.3 As a major chronic non-communicable disease, it poses a major threat to the health of Chinese citizens. The disease burden of stroke is increasing due to the ageing population, the ubiquity of unhealthy lifestyles, and the increased exposure to stroke risk factors.4,5 Stroke is a major global public health concern because of its high incidence, high death, high disability, and high recurrence rates.1 These features severely impair patients' quality of life and place a significant financial and caregiving burden on families and society.6, 7, 8

About 81 % of stroke cases are acute ischemic strokes (AIS), which are the most prevalent kind of stroke.2 Studies have shown that early intravenous injection of recombinant tissue plasminogen activator (rt-PA) for thrombolysis is the key to the treatment of acute ischemic stroke, which is very important for restoring the blood supply of brain tissue, saving the neurological function of patients, and improving the prognosis.9,10 However, intravenous thrombolysis is limited by a strict time window and is usually required to be performed within 3 to 4.5 h after onset.11, 12, 13 With the improvement of thrombolytic technology and the continuous improvement of clinical research, the time window for intravenous thrombolysis can be extended to 6–9 h for eligible patients.14 The earlier the time of intravenous thrombolysis, the greater the benefit to patients.15 The risk of bleeding following thrombolysis will rise if thrombolytic treatment is not administered within the ideal time frame.16 At the same time, intravenous thrombolysis over time will increase the possibility of ischemia-reperfusion injury and neurotoxicity, resulting in higher disability and mortality.17, 18 In a word, intravenous thrombolytic therapy has an obvious time limit and timeliness, and timely treatment within the thrombolytic time window is very important for the prognosis of patients.

Despite the significant benefits of thrombolytic therapy, the practical application of thrombolytic treatment, especially in developing countries, is not optimistic. From 2019 to 2020, the thrombolysis rate of AIS patients in China was only 5.64 %,19 which is still far from Europe, the United States, and other countries. The thrombolysis rate was 11.7∼18.2 % in the US in 2017 and 16.3 % in Germany in 2019.20,21 If contraindications are excluded, the primary cause of AIS patients' poor reperfusion treatment rate is the prehospital delay,22 and the prehospital delay time accounts for a larger proportion of the total delay time. Prehospital delay refers to the time between the onset or awakening time of stroke patients and their arrival at a hospital with treatment capacity,23 including patient delay and transport delay.24 Although the healthcare system has made much effort to improve its quality in recent years compared to those arriving, the prehospital delay phenomenon has not improved.25 According to statistics, up to 69.3 % of stroke patients arrive at the hospital three hours or more after the stroke begins, and 55.3 % arrive six hours or more after the stroke starts.26 The primary cause of this predicament is the failure to identify stroke symptoms and indicators in certain patients, particularly those experiencing their first episode of stroke, which results in a postponement of medical attention and the loss of the optimal window of opportunity for thrombolytic therapy.27, 28 Therefore, early identification and rapid screening of high-risk groups for prehospital delays are key to improving the survival rate of AIS patients, and the development of convenient and fast early warning tools is necessary.

Most current studies have focused on analyzing the status of prehospital delays in stroke patients and the factors affecting them.29, 30, 31 Lack of relevant early identification tools. Therefore, this study aims to explore the influencing factors of prehospital delays in first-ever AIS patients and construct a risk prediction model for prehospital delays in first-ever AIS patients. Providing clinical healthcare professionals with a quick and easy screening tool may enable them to correctly identify high-risk patients and take targeted actions to reduce the frequency of prehospital delays, thereby improving the treatment success rate and overall quality of life of AIS patients.

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