Development and validation of a model to estimate the risk of acute ischemic stroke in geriatric patients with primary hypertension

According to estimates by the World Health Organization, stroke is the second leading cause of death that will account for 7.8 million deaths and 23 million first-time ischemic stroke events by 2030 [1]. Many risk factors for stroke, such as hypertension, dyslipidemia, diabetes, smoking, and alcohol consumption, have been identified [2]. With rising levels of prosperity and an aging population, the prevalence of hypertension in China has increased from 23.4% in 1991 to 28.6% in 2011 (concerning approximately 300 million adults), which places a huge burden on public health resources [3]. Hypertensive patients commonly suffer acute ischemic strokes, especially among the elderly with multiple risk factors.

Considering the high fatality and disability rates resulting from stroke, we intended to develop a practical prediction model by integrating the common risk factors observed in the clinic. It is beneficial to estimate the risk of acute ischemic stroke in geriatric patients with primary hypertension so that appropriate preventive measures can be taken. Nomograms have been widely used for medical diagnosis and prognosis evaluation in recent years [4, 5] for their user-friendliness. Our aim was to provide an individualized clinical decision tool for physicians.

This study developed a practical nomogram that includes six variables that can be easily identified in the clinic to assist physicians in discriminating patients with high risk of stroke, enabling them to implement preventive measures as early as possible.

Blood pressure management is the most important variable that has a positive effect on stroke. With aging, the vascular elasticity decreases as a consequence of atherosclerosis. Thus, it is recommended that the systolic blood pressure in the elderly is less than 150 mmHg [15]. A meta-analysis reported that there was a 41% reduction in stroke for every blood pressure reduction of 10 mmHg systolic or 5 mmHg diastolic [16]. Although various hypertension guidelines indicate a certain goal of blood pressure control, few large-scale clinical evidence-based data focus on hypertension or stroke in very elderly patients. Professional doctors should be aware of this practical clinical problem and pay attention to the notion of individualized blood pressure management in elderly patients [17], without ignoring the symptoms and feelings of very elderly patients. In addition to the absolute value of blood pressure, blood pressure variability deserves attention. Excessive blood pressure fluctuation in the morning is a classic phenomenon. Kario used ambulatory blood pressure monitoring and magnetic resonance imaging and demonstrated that an exaggerated early morning blood pressure surge was independently associated with stroke in elderly hypertensive patients. The risk of stroke in patients with a morning blood pressure surge > 55 mmHg was 2.7 times higher than that in patients with a morning blood pressure surge < 55 mmHg. Pierdominico reached a similar conclusion that stroke had a relationship with an exaggerated early morning blood pressure surge independent of the 24-h average blood pressure [18, 19].

Smoking and alcoholism are controllable risk factors for stroke. Both played an important role in our prediction model, and these were valid for more than 90% of the males in our cohort. A large number of clinical studies in different races and populations have confirmed the strong association between smoking and stroke, while exposure to secondhand smoke should also be noted. Current smokers are at least two-to-four times more likely to have a stroke than those who never smoked or those who quit smoking 10 years ago [20]. Some epidemiological studies have demonstrated that the impact of drinking on stroke risk depends on the quantity. A small amount of red wine may reduce the risk of cardiovascular disease and stroke. However, alcohol abuse (> 60 g/day) is associated with an increased risk of stroke in the long term [21, 22].

CAS is a marker of systemic atherosclerosis that can be easily detected by ultrasound. According to studies from the 1980s, the annual risk of ipsilateral stroke was 3% in patients with a CAS ≥ 50%, which increased to 5.5% in patients with a CAS > 75%. With the widespread use of preventive drugs, the annual risk of stroke has been reduced to 0.34% for patients with a CAS ≥ 50% in contemporary studies [23, 24].

Other risk factors that are not included in our nomogram, such as age, total cholesterol and LDL-cholesterol [25‐27], were proven to be related to stroke by an abundance of clinical trials and should be considered by clinicians. It is worth noting that elderly patients usually present with multiple chronic diseases, such as hypertension, diabetes and coronary heart disease. The risk of ischemic stroke caused by pathological changes of organs caused by these diseases may be more serious than that caused by physiological aging [28]. Additionally, elderly patients often do not adhere to prescribed treatments. The direct visual display of the nomogram model can play a role in educating elderly patients and increase their compliance to treatment.

In the era of artificial intelligence, machine learning has become a popular method in data analysis. It utilizes mathematical models and training data to make predictions [29, 30]. The random forest, support vector machines, and backpropagation neural networks are three representative algorithms of machine learning that are increasingly used in the prediction of adverse events in clinical practice or biological research in tumor [31, 32]. Although these machine learning algorithms have attracted much attention with the availability of increasingly voluminous datasets (such as electronic medical records), the internal process of which is similar to a “black box” with poor interpretability and visualization, limit their practical application.

In a number of reports, the results of multivariable logistic regression analysis as the classic reference standard were compared with those of machine learning algorithms. In our study, the machine learning algorithms offered no obvious advantage over multivariable logistic regression in evaluating a binary categorical problem (whether or not patients will suffer an acute ischemic stroke). This conclusion is the same as that of several recent studies [14, 33].

Our prediction model based on multivariable logistic regression analysis not only has considerable accuracy but also can be visualized by a nomogram, which is convenient for its clinical application.