Risk factors for acute ischemic stroke following intravenous thrombolysis: a 2-center retrospective cohort study

Introduction

Concerns about stroke have focused on the associations between reperfusion therapy and neurological function after treatment. Acute ischemic stroke (AIS) is caused by a clot or embolus blocking an artery in the brain; this accounts for 69.6–70.8% of the total stroke population in China (1,2). Stroke is a leading cause of permanent disability worldwide (3). After stroke onset, survivors are at increased risk of poor outcomes and are often unable to independently perform their daily activities (4,5). These adverse consequences cause considerable expenses in health care and losses in the labor force and economy (6). Reperfusion therapy is the main treatment option for AIS, and can significantly improve patient outcomes (7,8). Intravenous thrombolysis in the early stage of acute cerebral infarction is a safe and reliable option (7,9-11). However, due to differences in age, stroke severity before thrombolysis, and past medical history, some patients can have complications, such as symptomatic intracranial hemorrhage, vasogenic edema, and even unfavorable neurological outcomes after thrombolysis (7,9-11). Therefore, factors associated with poor neurological function after intravenous thrombolysis for AIS patients need to be accurately determined to adjust the subsequent therapeutic regimens and clinical management. Some studies have found that National Institutes of Health Stroke Scale (NIHSS) score, computed tomography (CT), transcranial Doppler ultrasound, and blood lipids levels, are associated with stroke neurological function (12-22). The review of Shakti Shrestha has discussed main factors that can affect the outcomes of treatment in ischemic stroke (23).

The primary objectives of the study were to screen the risk factors for poor neurological function in AIS patients after intravenous thrombolysis and to guide the clinical strategies. In our study, univariate analysis and multivariate analysis were used to screen out the risk factors for poor prognosis in patients with acute ischemic stroke after intravenous thrombolysis, laying a foundation for later individualized prediction and intervention. We present the following article in accordance with the STROBE reporting checklist (available at https://apm.amegroups.com/article/view/10.21037/apm-21-3652/rc).

Methods

Study design

We used a 2-center cohort design in the present study. All of the patients had undergone intravenous thrombolysis between January 2018 and January 2020. Data of general information and intervention information of the patients were collected between February 2020 and April 2020. All the data using in the study came from official sites-Bigdata Observatory Platform for Stroke of China and were collected originally just for administrative purposes.

Setting

AIS patients from the advanced stroke centers of the Second Hospital of Hebei Medical University and Baoding No.1 Central Hospital were recruited between January 2018 and January 2020. All procedures performed in this study involving human participants were in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the ethics committee of Baoding No. 1 Central Hospital (No. 2021[012]) and had been put on record in the Second Hospital of Hebei Medical University. Individual consent for this retrospective analysis was waived. Intravenous thrombolysis strategies were classified into 2 types according to the current intravenous thrombolysis guidelines for stroke (7), as well as the actual thrombolytic regimens used at the stroke centers. Type 1 was based on recombinant tissue plasminogen activator (rt-PA), and type 2 used domestic urokinase (UK). Therapeutic regimens were developed by experienced and professionally trained attending physicians at the stroke centers. We assessed neurological function according to NIHSS score in medical records before, immediately after, and 24 h after intravenous thrombolysis; neurological function according to NIHSS score was also assessed on day 7 after stroke onset.

Participants

A total of 1,009 AIS patients who underwent intravenous thrombolysis at the 2 stroke centers from January 1, 2018 to January 1, 2020 were included in the present study. After excluding ineligible participants, 878 AIS patients were included in the present study.

The inclusion criteria were as follows: (I) met the current AIS diagnostic and treatment guidelines and intravenous thrombolytic therapy criteria (7); (II) underwent intravenous thrombolysis at the stroke center within 6 h after the onset of AIS; and (III) had complete clinical, demographic, and laboratory data. The exclusion criteria were as follows: (I) wake-up stroke; (II) received bridging therapy after intravenous thrombolysis; and (III) lost to follow up.

Based on the published literature (24,25), an NIHSS score ≥16 indicated that the neurological deficit after thrombolysis was extremely severe, and it was classified as 1. An NIHSS score <16 indicated that there was no severe neurological deficit after thrombolysis, and it was classified as 0.

Variables

General data included age, sex, body mass index (BMI), history of smoking, and history of alcohol consumption. Past medical history included hypertension, diabetes, coronary heart disease, arrhythmia, hyperlipemia, and stroke history. Systolic pressure, diastolic pressure, and emergency random peripheral blood glucose (GLU) were detected.

Data detected by routine blood test, coagulation function test, and biochemical test from the emergency laboratories in the 2 stroke centers were collected. The following reports were collected: brain CT examined before thrombolysis (CT1) and re-examined 24 h after thrombolysis (CT2); emergency electrocardiogram (ECG) examined before thrombolysis; head magnetic resonance imaging (MRI); carotid duplex ultrasound, ultrasound cardiogram (UCG), lower extremity artery ultrasound, and lower extremity venous color Doppler ultrasound examined after thrombolysis. Before thrombolysis, the NIHSS score, modified Rankin Scale before the stroke (mRS) score, and swallowing function were evaluated by experienced and professionally trained physicians. The Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification was adopted (26).

The time from onset to thrombolysis (OTT) and the time from door to needle (DNT) were recorded. Thrombolytic complications were observed after thrombolysis. Thrombolysis complications included bleeding in the skin and mucous membranes or gums, nasal cavity, digestive tract, urinary system, or other sites; reperfusion injury; allergies; and edema of the tongue or throat during thrombolysis.

Laboratory tests after thrombolysis included fasting venous blood glucose, glycosylated hemoglobin, homocysteine, thyroid-stimulating hormone, serum free thyroxine, serum free triiodothyronine, antinuclear antibody (ANA), antineutrophil cytoplasmic antibody (ANCA), and blood lipids.

The training regimen of rehabilitation within 72 h after thrombolysis was collected. Antiplatelet and anticoagulation therapies performed within 48 h and lipid-lowering therapy was also collected. Refer to data measurement.

These variables were independent variables (Table 1). In the statistical analyses, the factor considered potential confounders was OTT time. Factors considered potential effect modifier were OTT time and thrombolytics type.

When NIHSS score on day 7 after onset was ≥16, the dependent variable Y was denoted as 1.

Data measurement

In dichotomous variables, we define higher or more severe level as 1.

Two types of Thrombolytic drugs were used in the study, therefore, we defined the type of thrombolytic preparations as 2 classification variable. For example: the variable of thrombolytic type was defined as: 1: rt-PA; 2: UK.

The variable of TOAST was defined as a multivariate variable according to its own classification: 1: large artery atherosclerosis; 2: cardioembolism; 3: small artery atherosclerosis; 4: stroke of other determined etiology; 5: stroke of undetermined etiology.

The variable of “infarction position” was defined as a multivariate variable according to the brain magnetic resonance angiography (MRA) detected within 48 h of thrombolysis: 1: anterior circulation cerebral infarction (ACCI); 2: posterior circulation cerebral infarction (PCCI); 3: ACCI and PCCI: both anterior and posterior circulations had infarcts.

The variable of mRS score referred to the mRS score before the stroke onset, and was defined as a rank variable according to its own classification: 0: completely asymptomatic; 1: despite the presence of symptoms, there is no apparent disability and patients are able to perform all their usual duties and activities; 2: mildly handicapped; unable to perform all activities previously performed, but able to carry out personal affairs without help; 3: moderately handicapped; needs some assistance, but does not need assistance to walk; 4: severely disabled; unable to walk without assistance, and unable to care for their own physical needs; 5: severely disabled; bedridden, incontinent, requiring constant nursing and care; 6: death.

Water swallow test was used to evaluate swallow function in the study. The variable of swallow function was defined as a rank variable according to its result: 1: can successfully swallow water 1 time; 2: divide more than 2 times, can swallow without choking cough; 3: can swallow it all at once, but coughs; 4: swallow more than 2 times, but cough; 5: cough frequently and cannot swallow it all.

The variable of “ECG” was defined as a 2 classification variable according to its report detected in the emergency room: 0: the report suggested “normal ECG”; 1: manifestations, including atrial fibrillation, atrial flutter, various of ventricular or supraventricular arrhythmia, and ST-T changes.

The variable of “CT 1” was defined as a 2 classification variable according to its report detected 24 h after thrombolysis: 0: normal or no early infarction, 1: has early infarction.

The variable of “CT 2” was defined as a 2 classification variable according to its report detected within 48 h of thrombolysis: 0: had no intracranial hemorrhage or massive cerebral infarction; 1: had intracranial hemorrhage or massive cerebral infarction.

The variable of “Brain MRI” was defined as a 2 classification variable according to its report detected within 48 h of thrombolysis: 0: had no intracranial hemorrhage or massive cerebral infarction; 1: had intracranial hemorrhage or massive cerebral infarction.

The variable of “carotid duplex ultrasound” was defined as a 2 classification variable according to its report detected within 48 h of thrombolysis: 0: no carotid atherosclerosis; 1: had carotid atherosclerosis.

The variable of “UCG” was defined as a 2 classification variable according to its report detected within 48 h of thrombolysis: 0: heart was structurally and functionally normal; 1: heart was structurally or functionally abnormal.

The variable of “Lower extremity artery ultrasound” was defined as a 2 classification variable according to its report detected within 48 h of thrombolysis: 0: no arteriosclerosis in the lower limbs; 1: had arteriosclerosis in the lower limbs.

The variable of “Limp deep venous thrombosis, LDVT” was defined as a 2 classification variable according to its report detected within 48 h of thrombolysis: 0: no; 1: yes.

The variable of “ANA” was defined as a 2 classification variable according to its result detected within 48 h of thrombolysis: 0: negative; 1: positive. The variable of “ANCA” was defined as a 2 classification variable according to its result detected within 48 h of thrombolysis: 0: negative; 1: positive.

The variable of “antithrombotic therapy” given within 48h after thrombolysis was defined as a 2 classification variable: 1: only antiplatelet or anticoagulant, 2: both antiplatelet and anticoagulant.

The variable of “rehabilitation” given within 48h after thrombolysis was defined as a multivariate variable: 1: none; 2: acupuncture; 3: kinesitherapy; 4: both acupuncture and kinesitherapy; 5: other therapy, including swallow function rehabilitation.

According to the relevant international standards, the time of admission was determined as the time the patient presented to the emergency stroke center (7).

Bias

Because both stroke centers are advanced stroke centers and have uniform thrombolytic criteria, their examination results are mutually recognized by Grade III, Grade A hospitals. Information bias was avoided.

Study size

The number of cases, according to inclusion and exclusion criteria, at the 2 stroke centers during the study period determined the sample size.

Quantitative variables

The endpoint of the study was defined as the status of the neurological function on 7^th day of stroke onset. We used NIHSS score to assess neurological function in the endpoint of the study. When NIHSS score on day 7 after stroke onset was ≥16, the dependent variable Y was denoted as 1. When NIHSS score on day 7 after onset was <16, the dependent variable Y was denoted as 0. We examined the association of the above independent variables with poor neurological function on day 7 after onset.

Statistical methods

Statistical analyses were performed using SPSS version 24.0 (IBM, Armonk, NY, USA). Univariable regression analyses were performed to observe the correlation between each variable and the endpoint. Multivariable regression analysis was performed using binary logistics regression forward stepwise iteration to screen the factors associated with the endpoint. Multivariate regression analysis was also used to deal with confounding factors. The process will be discussed in detail in a later study.

Continuous variables were presented as mean ± standard deviation; categorical variables and ranked variables were presented as n (%). The relationship between variables and the outcome was analyzed by binary logistics regression. Variables with P<0.20, which met clinical practice and sample size requirements, were selected for the multivariable regression analysis. The maximum likelihood method was used to select all the variables in the equation with “–2 times” the minimum log likelihood as the factors related to the endpoint. The odds ratio (OR) and 95% confidence interval (95% CI) were calculated.

Our missing data analysis procedures used missing at random assumptions. We independently analyzed 10 copies of the data, each with missing values suitably imputed, in the multivariate logistic regression analyses. We averaged estimates of the variables to give a single mean estimate, and adjusted standard errors according to Rubin’s rules.

As the study was retrospective and most patients were hospitalized for more than 7 days in the neurological departments of the 2 hospitals, few patients were lost to follow up. The sensitivity analyses were performed in a future study on adjusting and controling confounding factors.

Results

Participants

Fifteen patients were excluded because their hospital stays were less than 7 days. Seven patients were excluded due to missing data. Sixty-three patients were excluded due to wake-up stroke. Forty-six patients were excluded because of receiving bridging therapy after intravenous thrombolysis.

A flow diagram was used to show the selection process (Figure 1).

Figure 1 Flow diagram of study selection process.

Descriptive data

Descriptive statistics and univariable analysis of all variables in the complete set are shown in Table 1. The number of participants with missing data for each variable of interest is shown in Table 2.

The mean follow-up time in the study was 7.8 days, and the longest follow-up time was 8.5 days.

Data outcome

After univariable analyses, variables with P<0.20, which met clinical practice and sample size requirements, were selected for the multivariable analysis (Table 1). We have showed the symptom end points used in survival analysis (see Table 2). After multivariable analysis, the following variables were screened as factors associated with the endpoint: age, NIHSS 2, urea nitrogen (UREA), total cholesterol (CHOL), high-density lipoprotein cholesterol (HDL-C), CT2, LDVT, and lipid regulation (Table 3).

By comparing ORs and 95% CIs, variables of age (OR: 1.099, 95% CI: 1.052–1.194, P<0.001), NIHSS 2 (OR: 1.286, 95% CI: 1.201–1.377, P<0.001), CHOL (OR: 1.614, 95% CI: 1.036–2.514, P<0.05), UREA (OR: 1.205, 95% CI: 1.045–1.390, P<0.05), CT2 (OR: 6.153, 95% CI: 2.696–14.045, P<0.001), and LDVT (OR: 4.398, 95% CI: 1.560–12.398, P<0.01) were found to be associated with poor neurological function on day 7 after stroke onset. The variables lipid regulation (OR: 0.065, 95% CI: 0.02–0.215, P<0.001) and HDL-C (OR: 0.038, 95% CI: 0.007–0.202, P<0.001) were found to be protective factors for poor neurological function.

Continuous variable data participated in the statistics with prototype. The dependent variable was a dichotomous variable, which was defined by the NIHSS score on day 7 after onset.

The findings of the present study indicated that, for every 1-year increase in age, the risk of poor neurological function in hospital increased by 9.9% for AIS patients who underwent intravenous thrombolysis. For every point increase in the NIHSS score evaluated immediately after thrombolytic therapy, the risk of in-hospital poor neurological function after intravenous thrombolysis increased by 28.6%. For every 1 mmol/L increase in CHOL, the risk of poor neurological function after intravenous thrombolysis increased by 61.4%. For every 1 mmol/L increase in HDL-C, the risk of poor neurological function after intravenous thrombolysis reduced by 96.2%. The risk of poor neurological function for patients with lipid-regulating therapy 48 h within intravenous thrombolysis was 0.065 times as much as those without lipid-regulating therapy. The risk of poor neurological function for patients with abnormal report of brain CT scanned 24 h after intravenous thrombolysis was 6.153 times as much as those with normal report. The risk of poor neurological functional in patients with LDVT revealed in the lower extremity venous color Doppler ultrasound after intravenous thrombolysis was 4.398 times as much as that of patients without LDVT. For every 1 mmol/L increase in UREA, the risk of poor neurological function after intravenous thrombolysis increased by 20.5%. We also showed the blood pressure level and the deterioration of the nervous system among different subtypes of ischemic stroke (see Table 4) and showed the distribution characteristics of risk factors among high-risk stroke populations of different ages and genders (see Table 5).

The interaction, adjusting, and control confounding factors will be explained in a later study.

Discussion

Key results

AIS is as result of a thrombus or embolus block in the cerebral arteries and accounts for a large proportion of all stroke patients (4,5). At the early stage of acute cerebral infarction, intravenous thrombolysis is a safe and reliable option, but it is not entirely without risk (7,9-11). Identifying the risk factors for poor neurological function in AIS patients after intravenous thrombolysis in order to adjust clinical strategies is crucial for clinical patient management. The present study was based on the overall data of AIS patients from 2 advanced stroke centers following intravenous thrombolysis. The thrombolytics drugs included rt-PA and domestic UK. Univariable and multivariable analyses were performed to screen the variables related to the endpoint. By comparing the ORs and their 95% CIs, risk factors and protectors were identified. The results suggested that old age, high levels of CHOL and UREA, high NIHSS scored immediately after thrombolysis (NIHSS2), bad result of brain CT 24 h after thrombolysis, and LDVT could predict poor neurological function after thrombolysis for AIS. Lipid-regulating therapy and high level of HDL-C could reduce the risk of poor neurological function.

Limitations

The present study has some limitations. First, the follow-up time was short. Future studies should extend the follow-up time to 3 or 6 months after the onset of AIS. Second, it may be more meaningful to perform a multi-center study and comparative analysis with other regions.

Interpretation

Increasing age has an important impact on the incidence, mortality, and long-term outcome of AIS (27). In their study, Rejnö et al. suggested that age could predict the deterioration of neurological function after stroke (28). Advanced age could lead to the dysfunction of neurovascular units and neurodegenerative changes in stroke patients (29). In the present study, age was a risk factor for poor neurological function after intravenous thrombolysis (OR: 1.099, 95% CI: 1.052–1.194, P<0.001), and its clinical significance was that for every 1-year increase in age, the risk of poor neurological function in hospital increased by 9.9% for AIS patients who underwent intravenous thrombolysis. Therefore, the relationship between age and the endpoint in the present study was consistent with the previous published studies we have cited above.

Stroke severity can be determined according to the degree of neurological impairment (e.g., impairment of consciousness, language, behavior, visual field impairment, and motor impairment). NIHSS score is also increasingly used in clinical practice to assess changes in neurological function after intravenous thrombolysis and determine its therapeutic effect (30). In the present study, NIHSS2 was found to be a risk factor for poor neurological function after intravenous thrombolysis (OR: 1.286, 95% CI: 1.201–1.377, P<0.001), and the clinical significance was that, for every point increase in the NIHSS score evaluated immediately after thrombolytic therapy, the risk of in-hospital poor neurological function after intravenous thrombolysis increased by 28.6%. Therefore, the relationship between the variables of NIHSS2 and the endpoint in the present study was consistent with the previous published studies we have cited above.

The correlation between blood lipids and stroke outcome varies depending on the components of blood lipids. CHOL is an adjustable risk factor, and some scholars believe that CHOL level is closely associated with the onset of first ischemic stroke (31). Globally, high cholesterol levels (>185 mg/dL) were associated with a 24% increase in stroke-related disability-adjusted life years from 1990 to 2013 (32). Most studies have found that CHOL levels are positively correlated with the risk of ischemic stroke. In the present study, CHOL was a risk factor for the endpoint (OR: 1.614, 95% CI: 1.036–2.514, P<0.05). Its clinical significance was that, for every 1 mmol/L increase in CHOL, the risk of poor neurological function after intravenous thrombolysis increased by 61.4%. The correlation between CHOL and the endpoint in the present study was consistent with the previous published studies we have cited above.

HDL-C is a strong and independent negative predictor of cardiovascular and cerebrovascular diseases. The beneficial effect of HDL-C is largely due to its key role in the reverse transport of cholesterol, namely the transport of excess cholesterol from peripheral tissues to the liver. There is increasing evidence that HDL-C also has anti-inflammatory, antioxidant, and vasodilator characteristics, reducing atherosclerosis (33). Gu et al. analyzed 6 cohort studies involving 267,500 Chinese patients and showed that low density lipoprotein cholesterol (LDL-C) and triglyceride (TG) levels were positively correlated with ischemic stroke, while HDL-C levels showed a negative correlation (34). In their study, Li et al. suggested that the ATP-binding cassette transporter A1/apolipoprotein E/HDL-C signaling pathway might be involved in the myelination and oligodendrocyte cytogenesis of ischemic brain tissues after stroke, which could repair the white matter damage of the central nervous system caused by stroke and promote the white matter remodeling of ischemic brain tissues, thereby facilitating the recovery of neurological function in the late stage of ischemic stroke (35). Therefore, HDL-C is associated with the functional outcome of ischemic stroke and might be a protective factor for the bad outcome. In the present study, HDL-C was a protective factor for poor neurological function after intravenous thrombolysis, which was consistent with the previous published studies we have cited above. After multivariable analysis, the OR of HDL-C was 0.038 and 95% CI was 0.007–0.202. The clinical significance was that, for every 1 mmol/L increase in HDL-C, the risk of poor neurological function after intravenous thrombolysis reduced by 96.2%.

As a result, lipid-regulating therapy is important. In the present study, we also found that the variable of lipid regulation was the protective factor to the endpoint (OR: 0.065, 95% CI: 0.02–0.215, P<0.001). Its clinical significance was that the risk of poor neurological function for patients with lipid-regulating therapy 48 h within intravenous thrombolysis was 0.065 times as much as those without lipid-regulating therapy. Intravenous thrombolysis and endovascular thrombectomy can quickly achieve reperfusion to reduce disability (7,9-11,36). However, intravenous thrombolysis has an increased risk of symptomatic intracerebral hemorrhage (37). Non-contrast CT can exclude intracranial hemorrhage, and it is crucial to recheck the head CT as soon as possible after thrombolysis (after 24 h, and before the administration of antiplatelet drugs) (7). In the present study, CT2 was a risk factor for the endpoint (OR: 6.153, 95% CI: 2.696–14.045, P<0.001). Its clinical significance was that the risk of poor neurological function for patients with an abnormal report of brain CT scanned 24 h after intravenous thrombolysis was 6.153 times as much as those with a normal report, which was consistent with the previous published studies we have cited above.

LDVT or lower extremity venous thrombosis is a severe comorbidity of ischemic stroke. Paralysis after stroke is a common cause of lower extremity venous thrombosis. Pan et al. analyzed clinical characteristics and accessible biochemical parameters to develop and validate a nomogram for predicting the risk of deep vein thrombosis in patients with acute stroke within 14 days (38). Liu et al. conducted a study on 679 stroke patients (including 507 with ischemic stroke and 172 with hemorrhagic stroke) and found that 21.1% of patients with ischemic stroke (n=107) were affected by deep vein thrombosis (39). Intermuscular veins, especially fibular veins, were the most susceptible. Ha et al. studied Asian AIS patients with lower extremity deep venous thrombosis, and found that females and higher NIHSS scores were independently associated with lower extremity deep venous thrombosis (40). Compared with D-dimer screening, lower extremity deep venous color Doppler ultrasound of patients with severe neurological deficits might be more conducive to diagnosing deep vein thrombosis in Asian AIS patients. Decreased activities of the lower extremities or joint contractures caused by stroke and other reasons could be the main contributors to deep vein thrombosis of the lower extremities, which can further lead to a prolonged rehabilitation process (41). After completing the acute phase of treatment, most stroke patients need rehabilitation. It usually takes months or even years for patients to have full restoration of their extremity function. Paralyzed limbs might be restricted in daily activities, such as turning over, getting up, and moving short distances, due to decreased activities. After lower extremity venous thrombosis, the rehabilitation exercise of the paralyzed limbs will further decrease, which will lead to a longer recovery time of the extremity function, and severe neurological deficits and poor outcome. Therefore, LDVT could be a risk factor for poor neurological function after stroke. In the present study, we found that LDVT was a risk factor for the endpoint (OR: 4.398, 95% CI: 1.560–12.398, P<0.05), and its clinical significance was that the risk of poor neurological function in patients with LDVT revealed in the lower extremity venous color Doppler ultrasound after intravenous thrombolysis was 4.398 times as much as that of patients without LDVT.

Peng et al. found that both high and low levels of blood urea nitrogen (BUN) were associated with high risk of ischemic stroke (42). Deng et al found that elevated BUN/creatinine (Cr) is associated with poor 3-month outcome in AIS patients with high HDL-C levels (43). In the present study, UREA (or BUN) was found to be a risk factor for poor neurological function after intravenous thrombolysis. The OR value was 1.205 and 95% CI was 1.045–1.390 (P<0.05). The clinical significance was that, for every 1 mmol/L increase in UREA, the risk of poor neurological function after intravenous thrombolysis increased by 20.5%. Our next research plan is to make use of these risk factors to establish a prediction model and provide individualized prediction and intervention for patients.

Regarding the strategies of screening variables into the multivariable regression, the baseline variables considered clinically relevant, and had a univariable relationship with the outcome, were included in the multivariable risk regression model (44). The variables were carefully selected. It must ensure the conciseness of the final model at first. As candidate variables that might impact the outcome event, from the perspective of clinical specialty, its role must first be acceptable to people, and should be reasonably explained from a certain physiological mechanism or pathway. The baseline variables of the present study included demographic data [e.g., sex, age, and BMI, lifestyle (e.g., smoking and alcohol consumption)], medical history (e.g., hypertension, diabetes, coronary heart disease, arrhythmia, hyperlipidemia, and past stroke), examinations (blood test indicators and other examination items), treatments (thrombolytics type, DNT, and OTT time), and exposure/treatment factors (NIHSS score after thrombolysis, subsequent related antiplatelet, anticoagulation, lipid regulation, and rehabilitation). From the above variables, and by referring to the previously published literature, the variables screened by univariable regression analysis with P>0.2 and conformed to clinical practice, were summarized and used as key candidate variables for multifactor regression analysis.

Second, the variables were screened from the results of the univariable analysis. The relationship between traditional univariable analysis and univariable regression analysis was essentially equivalent. In the univariable analysis, differences in single factors among groups were analyzed by t-test, χ²-test, and analysis of variance. Through these univariable analysis methods, distribution differences of the means or percentages between 2 or among multiple groups can be simply and directly observed. Univariable regression analysis included only 1 factor in the regression model for fitting when constructing the regression model. Therefore, the univariable regression analysis was equivalent to the traditional univariable analysis methods used in the present study. The t-test was equivalent to simple linear regression, while analysis of variance was equivalent to multiple linear regression. Similarly, the results of the analysis of variance and the univariable linear regression were equal in value to a certain extent, but they might not have exactly the same P value. In the present study, the baseline variables were analyzed by univariable regression analysis individual, and the P value is shown in Table 1.

Statistical significance in the univariable analyses was set at P<0.05, and the inclusion criteria were appropriately extended to P<0.20 in the present study (45), effectively avoiding the omission of some important variables, as their real effects might be underestimated or neglected due to the limitation of the P value.

When performing the multivariable regression analysis, categorical variables with 3 or more variables need to set dummy variables because parametric regression is made in the framework of a generalized linear model, and the latter has a linear trend. If dummy variables were not set, the categorical variables with 3 or more categories were selected into the multivariable adjustment, and the relationship between different categories of the variable had equivalent effects on the outcome during the statistical analysis. However, many medical variables were multicategorical variables, and there was no such equidistant relationship. To better explain which variable had a greater impact on the outcome, a reference must be set, namely the dummy variable.

Generalizability

The data of the present study were from the 2 provincial advanced stroke centers. The facilities, management, and skill level of the medical staff at the 2 centers were configured to a uniform standard. The examination reports could be mutually recognized. Data collection and statistical analysis were done by the same person. In the study, the variables of Age, NIHSS2, CHOL, UREA, CT2, and LDVT were found to be risk factors of poor neurological function after thrombolysis for AIS. Lipid regulation and HDL-C were found to be protective factors of poor neurological function. As a result, the conclusion of the study has credibility and generalizability.

Acknowledgments

The authors would like to thank all of the study participants who were enrolled in this study.

Funding: None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://apm.amegroups.com/article/view/10.21037/apm-21-3652/rc

Data Sharing Statement: Available at https://apm.amegroups.com/article/view/10.21037/apm-21-3652/dss

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://apm.amegroups.com/article/view/10.21037/apm-21-3652/coif). The authors have no conflicts of interest to declare.

Ethical statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All procedures performed in this study involving human participants were in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the ethics committee of Baoding No. 1 Central Hospital (No. 2021[012]) and had been put on record in the Second Hospital of Hebei Medical University. Individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Wang D, Liu J, Liu M, et al. Patterns of Stroke Between University Hospitals and Nonuniversity Hospitals in Mainland China: Prospective Multicenter Hospital-Based Registry Study. World Neurosurg 2017;98:258-65. [Crossref] [PubMed]
2. Wang W, Jiang B, Sun H, et al. Prevalence, Incidence, and Mortality of Stroke in China: Results from a Nationwide Population-Based Survey of 480 687 Adults. Circulation 2017;135:759-71. [Crossref] [PubMed]
3. Diener HC, Hankey GJ. Primary and Secondary Prevention of Ischemic Stroke and Cerebral Hemorrhage: JACC Focus Seminar. J Am Coll Cardiol 2020;75:1804-18. [Crossref] [PubMed]
4. Campbell BCV, De Silva DA, Macleod MR, et al. Ischaemic stroke. Nat Rev Dis Primers 2019;5:70. [Crossref] [PubMed]
5. Sacco RL, Kasner SE, Broderick JP, et al. An updated definition of stroke for the 21st century: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2013;44:2064-89. [Crossref] [PubMed]
6. Rajsic S, Gothe H, Borba HH, et al. Economic burden of stroke: a systematic review on post-stroke care. Eur J Health Econ 2019;20:107-34. [Crossref] [PubMed]
7. Powers WJ, Rabinstein AA, Ackerson T, et al. Guidelines for the Early Management of Patients With Acute Ischemic Stroke: 2019 Update to the 2018 Guidelines for the Early Management of Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke 2019;50:e344-418. [Crossref] [PubMed]
8. Turc G, Bhogal P, Fischer U, et al. European Stroke Organisation (ESO)- European Society for Minimally Invasive Neurological Therapy (ESMINT) guidelines on mechanical thrombectomy in acute ischemic stroke. J Neurointerv Surg 2019;11:535-8. [Crossref] [PubMed]
9. American College of Emergency Physicians Clinical Policies Subcommittee (Writing Committee) on Use of Intravenous tPA for Ischemic Stroke:; Brown MD, Burton JH, et al. Clinical Policy: Use of Intravenous Tissue Plasminogen Activator for the Management of Acute Ischemic Stroke in the Emergency Department. Ann Emerg Med 2015;66:322-333.e31.
10. Minematsu K, Toyoda K, Hirano T, et al. Guidelines for the intravenous application of recombinant tissue-type plasminogen activator (alteplase), the second edition, October 2012: a guideline from the Japan Stroke Society. J Stroke Cerebrovasc Dis 2013;22:571-600.
11. Boulanger JM, Lindsay MP, Gubitz G, et al. Canadian Stroke Best Practice Recommendations for Acute Stroke Management: Prehospital, Emergency Department, and Acute Inpatient Stroke Care, 6th Edition, Update 2018. Int J Stroke 2018;13:949-84.
12. Adeoye AM, Ogah OS, Ovbiagele B, et al. Prevalence and Prognostic Features of ECG Abnormalities in Acute Stroke: Findings From the SIREN Study Among Africans. Glob Heart 2017;12:99-105. [Crossref] [PubMed]
13. Manea MM, Comsa M, Minca A, et al. Brain-heart axis--Review Article. J Med Life 2015;8:266-71. [PubMed]
14. Kocan MJ. The brain-heart connection: cardiac effects of acute ischemic stroke. J Cardiovasc Nurs 1998;13:57-68; quiz 97. [Crossref] [PubMed]
15. Bozluolcay M, Ince B, Celik Y, et al. Electrocardiographic findings and prognosis in ischemic stroke. Neurol India 2003;51:500-2. [PubMed]
16. Purushothaman S, Salmani D, Prarthana KG, et al. Study of ECG changes and its relation to mortality in cases of cerebrovascular accidents. J Nat Sci Biol Med 2014;5:434-6. [Crossref] [PubMed]
17. Heiss WD. Malignant MCA Infarction: Pathophysiology and Imaging for Early Diagnosis and Management Decisions. Cerebrovasc Dis 2016;41:1-7. [Crossref] [PubMed]
18. Godoy D, Piñero G, Cruz-Flores S, et al. Malignant hemispheric infarction of the middle cerebral artery. Diagnostic considerations and treatment options. Neurologia 2016;31:332-43. [Crossref] [PubMed]
19. Sarkar S, Ghosh S, Ghosh SK, et al. Role of transcranial Doppler ultrasonography in stroke. Postgrad Med J 2007;83:683-9. [Crossref] [PubMed]
20. Mazya MV, Ahmed N, Azevedo E, et al. Impact of Transcranial Doppler Ultrasound on Logistics and Outcomes in Stroke Thrombolysis: Results From the SITS-ISTR. Stroke 2018;49:1695-700. [Crossref] [PubMed]
21. Bharosay A, Bharosay VV, Bandyopadhyay D, et al. Effect of lipid profile upon prognosis in ischemic and haemorrhagic cerebrovascular stroke. Indian J Clin Biochem 2014;29:372-6. [Crossref] [PubMed]
22. Lv G, Wang GQ, Xia ZX, et al. Influences of blood lipids on the occurrence and prognosis of hemorrhagic transformation after acute cerebral infarction: a case-control study of 732 patients. Mil Med Res 2019;6:2. [Crossref] [PubMed]
23. Shrestha S, Poudel RS, Thapa LJ, et al. Intravenous Thrombolysis and Risk Factors for Ischemic Stroke. JNMA J Nepal Med Assoc 2014;52:745-50. [Crossref] [PubMed]
24. Saver JL, Altman H. Relationship between neurologic deficit severity and final functional outcome shifts and strengthens during first hours after onset. Stroke 2012;43:1537-41. [Crossref] [PubMed]
25. Frankel MR, Morgenstern LB, Kwiatkowski T, et al. Predicting prognosis after stroke: a placebo group analysis from the National Institute of Neurological Disorders and Stroke rt-PA Stroke Trial. Neurology 2000;55:952-9. [Crossref] [PubMed]
26. Chung JW, Park SH, Kim N, et al. Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification and vascular territory of ischemic stroke lesions diagnosed by diffusion-weighted imaging. J Am Heart Assoc 2014;3:001119. [Crossref] [PubMed]
27. Knoflach M, Matosevic B, Rücker M, et al. Functional recovery after ischemic stroke--a matter of age: data from the Austrian Stroke Unit Registry. Neurology 2012;78:279-85. [Crossref] [PubMed]
28. Rejnö Å, Nasic S, Bjälkefur K, et al. Changes in functional outcome over five years after stroke. Brain Behav 2019;9:e01300. [Crossref] [PubMed]
29. Cai W, Zhang K, Li P, et al. Dysfunction of the neurovascular unit in ischemic stroke and neurodegenerative diseases: An aging effect. Ageing Res Rev 2017;34:77-87. [Crossref] [PubMed]
30. Wang J, Fang X, Wang D, et al. Effect of intravenous thrombolysis with alteplase on clinical efficacy, inflammatory factors, and neurological function in patients with acute cerebral infarction. Braz J Med Biol Res 2021;54:e10000. [Crossref] [PubMed]
31. Shishkova VN, Adasheva TV, Remenik AY, et al. Prognostic significance of clinical-anthropometric, biochemical, metabolic, vascular-inflammatory and molecular-genetic markers in the development of the first ischemic stroke. Zh Nevrol Psikhiatr Im S S Korsakova 2018;118:4-11. [Crossref] [PubMed]
32. Feigin VL, Roth GA, Naghavi M, et al. Global burden of stroke and risk factors in 188 countries, during 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet Neurol 2016;15:913-24. [Crossref] [PubMed]
33. Nagao M, Nakajima H, Toh R, et al. Cardioprotective Effects of High-Density Lipoprotein Beyond its Anti-Atherogenic Action. J Atheroscler Thromb 2018;25:985-93. [Crossref] [PubMed]
34. Gu X, Li Y, Chen S, et al. Association of Lipids With Ischemic and Hemorrhagic Stroke: A Prospective Cohort Study Among 267 500 Chinese. Stroke 2019;50:3376-84. [Crossref] [PubMed]
35. Li L, Li R, Zacharek A, et al. ABCA1/ApoE/HDL Signaling Pathway Facilitates Myelination and Oligodendrogenesis after Stroke. Int J Mol Sci 2020;21:4369. [Crossref] [PubMed]
36. Campbell BCV, Khatri P. Stroke. Lancet 2020;396:129-42. [Crossref] [PubMed]
37. Nisar T, Hanumanthu R, Khandelwal P. Symptomatic Intracerebral Hemorrhage after Intravenous Thrombolysis: Predictive Factors and Validation of Prediction Models. J Stroke Cerebrovasc Dis 2019;28:104360. [Crossref] [PubMed]
38. Pan X, Wang Z, Chen Q, et al. Development and Validation of a Nomogram for Lower Extremity Deep Venous Thrombosis in Patients after Acute Stroke. J Stroke Cerebrovasc Dis 2021;30:105683. [Crossref] [PubMed]
39. Liu XC, Chen XW, Li ZL, et al. Anatomical distribution of lower-extremity deep venous thrombosis in patients with acute stroke. J Stroke Cerebrovasc Dis 2020;29:104866. [Crossref] [PubMed]
40. Ha SH, Kim YJ, Heo SH, et al. Prediction of deep vein thrombosis by ultrasonography and D-dimer in Asian patients with ischemic stroke. BMC Neurol 2020;20:257. [Crossref] [PubMed]
41. Low FZ, Lim JH, Kapur J, et al. Effect of a Soft Robotic Sock Device on Lower Extremity Rehabilitation Following Stroke: A Preliminary Clinical Study With Focus on Deep Vein Thrombosis Prevention. IEEE J Transl Eng Health Med 2019;7:4100106. [Crossref] [PubMed]
42. Peng R, Liu K, Li W, et al. Blood urea nitrogen, blood urea nitrogen to creatinine ratio and incident stroke: The Dongfeng-Tongji cohort. Atherosclerosis 2021;333:1-8. [Crossref] [PubMed]
43. Deng L, Wang C, Qiu S, et al. Association between Blood Urea Nitrogen-to-creatinine Ratio and Three-Month Outcome in Patients with Acute Ischemic Stroke. Curr Neurovasc Res 2019;16:166-72. [Crossref] [PubMed]
44. Stone GW, Maehara A, Lansky AJ, et al. A prospective natural-history study of coronary atherosclerosis. N Engl J Med 2011;364:226-35. [Crossref] [PubMed]
45. Kang SJ, Cho YR, Park GM, et al. Predictors for functionally significant in-stent restenosis: an integrated analysis using coronary angiography, IVUS, and myocardial perfusion imaging. JACC Cardiovasc Imaging 2013;6:1183-90. [Crossref] [PubMed]

(English Language Editor: R. Scott)