Development and validation of a clinical nomogram for differentiating hemorrhagic and ischemic stroke prehospital

doi:10.21203/rs.3.rs-2131276/v1

Download PDF

Research Article

Development and validation of a clinical nomogram for differentiating hemorrhagic and ischemic stroke prehospital

https://doi.org/10.21203/rs.3.rs-2131276/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 03 Mar, 2023

Read the published version in BMC Neurology →

You are reading this latest preprint version

Objectives: The early detection and identification of stroke are essential to the prognosis of patients with suspected stroke symptoms out-of-hospital. We aimed to develop a risk prediction model based on the FAST score to identify the different types of strokes early for emergency medical services (EMS).

Methods: This retrospective observational study enrolled 394 stroke patients at a single center from January 2020 to December 2021. Demographic data, clinical characteristics, and stroke risk factors with patients were collected from the EMS record database. Univariate and multivariate logistic regression analysis was used to identify the independent risk predictors. The nomogram was developed based on the independent predictors, in which the discriminative value and calibration of the nomogram were verified by the receiver operator characteristic (ROC) curve and calibration plots.

Results: A total of 31.90% (88/276) of patients were diagnosed with hemorrhagic stroke in the training set, while 36.40% (43/118) in the validation set. The nomogram was developed based on the multivariate analysis, including age, systolic blood pressure, hypertension, vomiting, arm weakness, and slurred speech. The area under the curve (AUC) of the ROC with nomogram was 0.796 (95% CI: 0.740-0.852,P<0.001) and 0.808 (95% CI:0.728-0.887, P<0.001) in the training set and validation set, respectively. In addition, the AUC with the nomogram was superior to the FAST score in both two sets. The calibration curve showed a good agreement with the nomogram and the decision curves analysis also demonstrated that the nomogram had a wider range of threshold probabilities than the FAST score in the prediction risk of hemorrhagic stroke.

Conclusions: This novel noninvasive clinical nomogram shows a good performance in differentiating hemorrhagic and ischemic stroke for EMS staff prehospital. Moreover, all of the variables of nomogram are acquired in clinical practice easily and inexpensively out-of-hospital.

hemorrhagic stroke

ischemic stroke

nomogram

emergency medical services

prehospital

Stroke is a severe manifestation of cardiovascular disease, which leads to the second cause of death in the world¹. Nearly 20 million people experience stroke annually, and the incidence is increasing because of the aging of the population [1, 2]. The ischemic stroke makes up about 88% of all strokes, and the remainder is hemorrhagic stroke [3, 4]. At present, endovascular therapy and intravenous tissue-type plasminogen activator are the most effective therapies for acute ischemic stroke in the therapeutic time window [5]. In addition, receiving operation and intervention treatment in time for hemorrhagic stroke is crucial [6]. Thus, it is critical to improve the prognosis of stroke patients for establish the stroke center network and stroke “Green Channel”, which shortens the treatment time and integrates the medical resources.

Almost 50% of stroke occurred out-of-hospital. Emergency medical services (EMS) are the first point to contract patients who appear suspected stroke symptoms [7]. Early identification of ischemic or hemorrhagic stroke from stroke patients could provide earlier diagnosis, referral to the appropriate emergency department, and given a better treatment decision [8]. Therefore, distinguishing hemorrhagic stroke from ischemia stroke has an important implication for EMS.

Some prehospital stroke scales were used for EMS to identify stroke in recent studies, such as the Face Arm Speech Test (FAST), Los Angeles Prehospital Stroke Screen (LAPSS), and Recognition of Stroke in the Emergency Room (ROSIER), parts of which had moderate-to-good sensitivity and the lower levels of specificity [9–12]. In addition, some studies have proved that serum markers had a certain advantage in distinguishing hemorrhagic stroke and ischemic stroke, including glial fibrillary acidic protein (GFAP), N-terminal proB-type natriuretic peptide (NT-proBNP), and retinol-binding protein 4 (RBP-4) [13, 14]. However, the difficulty to detect prehospital limited their application.

Therefore, this study aims to establish a simple and reliable clinical tool to identify ischemic and hemorrhagic stroke prehospital based on the easy-to-obtain prehospital clinical data and FAST scale. Moreover, the use of the tool would be convenient for EMS staff to identify stroke types early and improve emergency efficiency in future clinical applications.

2.1. Study design and participants

This retrospective observational study enrolled patients with suspected stroke symptoms onset from January 1, 2020 to December 1, 2021 in the second affiliated hospital of Wannan Medical College. All patients were admitted to the emergency department by the EMS. The study was approved by the Ethics Committee of the Second Affiliated Hospital of Wannan Medical College (Number: wyefyls202205) and followed the principles of the Declaration of Helsinki. The informed consent of patients was waived due to the retrospective observational design.

Participants of the following criteria were included in this study: 1) age > 18 years; 2) admission to the emergency department with a suspected diagnosis of stroke; 3) computed tomography (CT) or magnetic resonance imaging (MRI) scan during hospitalization. The exclusion criteria were:1) patients using EMS as a secondary transport; 2) data missed with EMS records; 3) patients died before CT or MRI was performed; 4) stroke caused by trauma.

A total of 670 patients were included in this study. Finally, 394 patients were enrolled in the final analysis according to the inclusion and exclusion criteria. All patients were divided into a training set (n = 276) and a validation set (n = 118) in a ratio of 7:3 (Fig. 1).

2.2. Data collection

Baseline demographic data, prehospital clinical characteristics, and stroke risk factors were collected from the EMS record database in the Wuhu Emergency Medical Center, including age, gender, blood pressure, clinical features, diabetes, hypertension, duration of symptom, etc. The prehospital vital signs (blood pressure and heart rate) were measured using an electrocardiogram blood pressure monitor (Mindray, China) by EMS personnel out-of-hospital.

2.3. Definition

Hemorrhagic stroke was defined as a new symptomatic neurologic deterioration accompanied by neuroimaging evidence of extravasation of blood into the brain parenchyma spontaneous and non-traumatic, including intraparenchymal hemorrhage and subarachnoid hemorrhage [15, 16]. Ischemic stroke was defined as a sudden neurologic dysfunction with imaging evidence of acute infarction by MRI or CT scan [17]. Transient cerebral ischemic attack (TIA) was defined as new neurologic symptoms lasting less than 24 hours with no new infarction or hemorrhage on neuroimaging [18]. The Face Arm Speech Test (FAST) contained items that unilateral facial droop (F) or arm weakness (A) and Slurred speech (S) [19]. Duration of symptom was defined as the duration since suspected stroke symptoms onset until the EMS arrived.

2.4. Statistical analysis

All statistical analyses were performed using R software (version 3.6.2, http://www.r-project.org) and SPSS software (version 22.0, IBM, New York, USA). Kolmogorov-Smirnov(K-S) test was used to discriminate normal distribution for all continuous variables. Continuous variables were expressed as mean ± standard deviation (SD) or interquartile range (IQR), which depended on the normal distribution. Categorical variables were expressed as numbers (percentages). Differences between the two groups for continuous variables were analyzed by the student’s test or Mann-Whitney U test, and the chi-squared test was used for categorical variables. Multivariate logistic regression models with the entering method were accomplished to determine the independent risk predictors of hemorrhagic stroke in the nomogram. The variables included factors with significance at P < 0.1 in the univariate analysis. Moreover, the final total score of the nomogram was constructed by a preliminary score of each predictor with a point ranging from 0-100, which was converted to the probability of hemorrhagic stroke (0-100%). Calibration was implemented to assess the fitting degree between the actual and nomogram-predicted hemorrhagic stroke by using a calibration plot with bootstraps of 1000 resamples.

The area under the curve (AUC) of receiver operating characteristic (ROC) was used to compare the discrimination ability of nomogram and traditional FAST score. Decision curve analysis (DCA) was further performed to estimate the clinical value of the nomogram and traditional FAST score with the net benefit for multiple threshold probabilities. All accuracy estimates and regression coefficients reported 95% confidence intervals (CI) and a two-tailed P < 0.05 was considered statistically significant.

3.1. Demographic and prehospital clinical characteristics of the study

A total of 394 patients who met the eligibility criteria were retrospectively enrolled during the period between Jan 2020 and Dec 2021. The incidence of hemorrhagic stroke in the training set was 31.90% (88/276), while 36.40% (43/118) in the validation set. There were no statistical differences between the two sets in all clinical characteristics (all P > 0.05) (Table 1).

Table 1

Clinical characteristics of patients in the training set and validation set
Variables	Total (n = 394)	Training set (n = 276)	Validation set (n = 118)	t/z/χ²	P value
Demographic characteristics
Age, years (mean ± SD)	71.01 ± 14.54	70.69 ± 14.74	71.77 ± 14.09	-0.677	0.499
Male, n (%)	233(59.10)	163(59.10)	70(59.30)	0.002	0.961
BMI, kg/m²(mean ± SD)	21.06 ± 3.55	20.90 ± 3.49	21.44 ± 3.69	-1.375	0.170
Prehospital clinical data
SBP, mmHg, IQR	132.00 (119.75, 150.00)	134.00 (118.00, 151.50)	130.00 (120.00, 150.00)	-0.486	0.627
DBP, mmHg, IQR	80.00 (68.00, 88.00)	80.00 (70.00, 86.00)	79.50 (64.75, 88.00)	-0.984	0.325
Heart rate, bpm, IQR	84.00 (71.75, 100.00)	84.50 (71.25, 100.00)	83.50 (71.75, 100.00)	-0.231	0.817
Past medical history
Hypertension, n (%)	108(27.40)	77(27.90)	31(26.30)	0.110	0.740
Diabetes mellitus, n (%)	80(20.30)	56(20.30)	24(20.30)	0.000	0.991
Atrial fibrillation, n (%)	86(21.80)	61(22.10)	25(21.20)	0.041	0.840
Coronary heart disease, n (%)	96(24.40)	69(25.00)	27(22.90)	0.201	0.654
Chronic renal failure, n (%)	73(18.50)	45(16.30)	28(23.70)	3.018	0.082
Prehospital clinical symptoms
Headache, n (%)	140(35.50)	100(36.20)	40(33.90)	0.197	0.658
Dizzy, n (%)	186(47.20)	122(44.20)	64(54.20)	3.340	0.068
Vomiting, n (%)	147(37.30)	100(36.20)	47(39.80)	0.458	0.499
FAST score
Facial droop, n (%)	170(43.10)	118(42.80)	52(44.10)	0.058	0.809
Arm weakness, n (%)	174(44.20)	122(44.20)	52(44.10)	0.001	0.980
Slurred speech, n (%)	155(39.30)	114(41.30)	41(34.70)	1.490	0.222
Duration of symptom, min, IQR	35.00 (25.00, 50.00)	35.00 (25.00, 50.00)	30.00 (25.00, 51.25)	-0.322	0.747
Stroke type				0.773	0.379
Hemorrhagic stroke	131(33.20)	88(31.90)	43(36.40)
Ischemic stroke	263(66.80)	188(68.10)	75(63.60)
Notes: BMI, body mass index; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; Bpm, beats per minute; FAST, Face-Arm-Speech-Time; IQR, interquartile range;

In the training set, the frequency of hypertension (P = 0.003) was higher in hemorrhagic stroke patients, compared to ischemic stroke patients. The incidence of headache (P = 0.029) and vomiting (P = 0.007) was significantly increased in hemorrhagic stroke. Meanwhile, a higher incidence of the FAST Scale (arm weakness, and slurred speech) was observed in hemorrhagic stroke patients (P = 0.004, and P = 0.002). In addition, patients with hemorrhagic stroke tended to have the lower age (P < 0.001) and higher systolic blood pressure (P = 0.006) (Table 2).

Table 2

Clinical characteristics of hemorrhagic and ischemic stroke patients in the training set
Variables	Total (n = 276)	Hemorrhagic stroke (n = 88)	Ischemic stroke (n = 188)	t/z/χ²	P value
Demographic characteristics
Age, years (mean ± SD)	70.69 ± 14.74	63.70 ± 16.98	73.96 ± 12.31	-5.685	< 0.001
Male, n (%)	163(59.10)	52(59.10)	111(59.00)	0.000	0.994
BMI, kg/m² (mean ± SD)	20.90 ± 3.49	21.25 ± 3.30	20.74 ± 3.57	1.133	0.258
Prehospital clinical data
SBP, mmHg, IQR	134.00 (118.00, 151.50)	143.50 (120.00, 160.00)	130.00 (116.25, 146.00)	-2.751	0.006
DBP, mmHg, IQR	80.00 (70.00, 86.00)	80.00 (70.00, 90.00)	80.00 (68.00, 85.00)	-1.447	0.148
Heart rate, bpm, IQR	84.50 (71.25, 100.00)	82.50 (70.25, 102.75)	85.00 (72.00, 98.75)	-0.083	0.934
Past medical history
Hypertension, n (%)	77(27.90)	35(39.80)	42(22.30)	9.056	0.003
Diabetes mellitus, n (%)	56(20.30)	14(15.90)	42(22.30)	1.533	0.216
Atrial fibrillation, n (%)	61(22.10)	18(20.50)	43(22.90)	0.204	0.652
Coronary heart disease, n (%)	69(25.00)	19(21.60)	50(26.60)	0.801	0.371
Chronic renal failure, n (%)	45(16.30)	14(15.90)	31(16.50)	0.015	0.903
Prehospital clinical symptoms
Headache, n (%)	100(36.20)	40(45.50)	60(31.90)	4.756	0.029
Dizzy, n (%)	122(44.20)	42(47.70)	80(42.60)	0.651	0.420
Vomiting, n (%)	100(36.20)	42(47.70)	58(30.90)	7.389	0.007
FAST score
Facial droop, n (%)	118(42.80)	44(50.00)	74(39.40)	2.772	0.096
Arm weakness, n (%)	122(44.20)	50(56.80)	72(38.30)	8.336	0.004
Slurred speech, n (%)	114(41.30)	48(54.50)	66(35.10)	9.343	0.002
Duration of symptom, min, IQR	35.00 (25.00, 50.00)	30.00 (25.00, 50.00)	35.00 (25.00, 50.00)	-0.772	0.440
Notes: BMI, body mass index; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; Bpm, beats per minute; FAST, Face-Arm-Speech-Time; IQR, interquartile range;

3.2. Multivariate logistic regression analyses of independent risk factors for hemorrhagic stroke

Multivariate logistic regression analysis was used to calculate the adjusted odds ratio (aOR) value of each independent risk factor in the training set (Table 3). After multivariable adjustment, systolic blood pressure (aOR:1.014, 95% CI: 1.002–1.025, P = 0.023), hypertension (aOR:2.440, 95% CI: 1.291–4.613, P = 0.006), age (aOR:0.942, 95% CI: 0.921–0.964, P < 0.001), and vomiting (aOR:2.741, 95% CI: 1.465–5.129, P = 0.002) remained significant after adjusting for confounders. Besides, arm weakness (aOR:2.559, 95% CI: 1.397–4.687, P = 0.002) and slurred speech (aOR:2.072, 95% CI: 1.142–3.760, P = 0.017) were independently associated with hemorrhagic stroke.

Table 3

Univariate analysis and multivariate logistic regression analysis for the risk factors associated with hemorrhagic stroke in the training set.
Variables	Unadjusted OR (95% CI)	P-value	Adjusted OR (95% CI)	P-value
Age, years	0.951(0.932–0.970)	< 0.001	0.942(0.921–0.964)	< 0.001
SBP, mmHg	1.015(1.004–1.025)	0.005	1.014(1.002–1.025)	0.023
Hypertension	2.296(1.327–3.970)	0.003	2.440(1.291–4.613)	0.006
Headache	1.778(1.057–2.989)	0.030	1.408(0.761–2.607)	0.276
Vomiting	2.046(1.216–3.443)	0.007	2.741(1.465–5.129)	0.002
Facial droop	1.541(0.925–2.566)	0.097	1.756(0.959–3.218)	0.068
Arm weakness	2.120(1.268–3.545)	0.004	2.559(1.397–4.687)	0.002
Slurred speech	2.218(1.325–3.714)	0.002	2.072 (1.142–3.760)	0.017
Notes: SBP, Systolic blood pressure; OR, Odds ratio; CI, confidence intervals.

3.3. Establishment of nomogram in predicting hemorrhagic stroke

A clinical nomogram to predict hemorrhagic stroke was developed based on multivariate logistic regression analysis using the 6 independent risk factors. Each of the major significant predictors was assigned with points ranging from 0 to 100, and the preliminary scores were summarized as the total predictive score. Based on the total score, a visualized percentage to predict the risk of hemorrhagic stroke was shown in the nomogram (Fig. 2). The calibration plots with 1000 Bootstrap resamples were described, which demonstrated a good-predictive performance between the predicted probability of hemorrhagic stroke and the actual observations, with data points on the plots close to the ideal curve (Fig. 3).

3.4. Validation of nomogram with receiver operating characteristic (ROC) curve

The AUC of ROC was analyzed to investigate the discrimination of the nomogram, which was 0.796 (95% CI:0.740–0.852, P < 0.001) in the training set and 0.808 (95% CI:0.728–0.887, P < 0.001) in the validation set.

Furthermore, we compared the discrimination between the nomogram and the FAST score, and the results indicated the AUC of the nomogram was superior to that of the FAST score in both the training set (AUC = 0.796 vs 0.660, P < 0.001) and validation set (AUC = 0.808 vs 0.664, P = 0.004), indicating the novel nomogram had higher predictive efficiency (Fig. 4).

3.5. Clinical use compared nomogram with the FAST score

Decision curve analysis (DCA) curves were applied to compare the clinical validity of the nomogram and the FAST score, suggesting that the nomogram could augment net benefits and demonstrate a wider range of threshold probabilities than the FAST score in the prediction of hemorrhagic stroke (Fig. 5).

Stroke is a major cause of death and long-term cognitive impairment in China [20]. Timely treatment is critical to the prognosis of stroke patients, which reduces mortality and improves neurological prognosis [21]. Efficient pre-hospital assessment is essential for EMS to differentiate between hemorrhagic and ischemic stroke [22]. Traditionally, ischemic and hemorrhagic stroke had the common risk factors. However, the risk factors for identifying the different subtypes of stroke are unclear.

This study established a practical and convenient tool based on the FAST score and combined with age, systolic blood pressure, hypertension, and vomiting to predict the risk of hemorrhagic stroke in patients with suspected stroke symptoms for EMS staff. All independent predictors were acquired in clinical practice easily and inexpensively out-of-hospital. This nomogram has proven clinical utility and is useful for risk decision-making in patients with hemorrhagic stroke during pre-hospital first aid.

We found that age was an important independent factor to distinguish hemorrhagic stroke from ischemic stroke. The incidence of stroke among young adults has increased in the past two decades [23]. The patients with hemorrhagic stroke were younger than ischemic stroke [24, 25]. One study of 1,880 non-fatal stroke patients in Japan found that the mean age was 74.1 years for ischemic stroke, and 68.2 years for hemorrhagic stroke [26]. Further, a recent study also confirmed that the median age of patients was 74 (66–82) years for ischemic stroke, 70 (59–79) years for intracerebral hemorrhage, and 64 (53–75) years for subarachnoid hemorrhage among the 183,080 stroke patients [27]. Thus, younger patients who suspected stroke may have an increased risk of hemorrhagic stroke prehospital, which was associated with the poorer blood pressure control and an increased proportion of subarachnoid hemorrhage [28].

At present, hypertension has been recognized as the most important risk factor affecting the occurrence of stroke [29, 30]. This may be related to cerebral vascular remodeling caused by the decrease in the diameter of the cerebrovascular lumen and the increase in the thickness of the vascular wall when hypertension occurs [31]. The elevated blood pressure that occurred in the hyperacute phase of stroke was often associated with sympathetic overactivity [32, 33]. Rawshani et al’s study found that systolic blood pressure was a risk factor that affected cerebrovascular accidents [34]. Importantly, Katsanos confirmed that the lower the systolic blood pressure, the lower risk of hemorrhagic stroke happened, which was consistent with our results [35]. Therefore, we should pay more attention to uncontrolled systolic blood pressure in hypertensive patients, which induced the increased risk of hemorrhagic stroke [36]. Furthermore, we should focus on individual blood pressure treatment goals to reduce the risk of hemorrhagic stroke in hypertensive patients.

The FAST score is a traditional tool for identifying strokes with large vessel occlusion [37]. For patients suspected of acute stroke, questions including facial drooping, arm weakness, and slurred speech should be evaluated according to the FAST score [38, 39]. It was worth noting that stroke was the leading cause of adult-acquired disability [40]. In our study, we found that a higher incidence of slurred speech and arm weakness was observed in hemorrhagic stroke patients. Slurred speech was a manifestation of progressive central nervous system damage [41], always manifested as dysarthria, and was caused by weak, slow, or uncoordinated muscle control [42–44]. The appearance of arm weakness might be related to the regulation of hand function by the corticoreticulospinal tract [45]. Therefore, slurred speech and arm weakness played the important roles in the diagnosis of stroke, especially in hemorrhagic stroke.

Intracranial pressure could be increased after ischemic or hemorrhagic stroke [46, 47]. The typical clinical manifestations of elevated intracranial pressure were headache, vomiting, and even loss of consciousness [48]. Especially in hemorrhagic stroke, blood could extravasate into surrounding brain tissue due to blood vessel ruptures [49]. Our study also demonstrated that vomiting was an important clinical manifestation to distinguish hemorrhagic stroke. More importantly, in contrast to other symptoms, vomiting was a typical symptom that can be assessed even in patients with unconsciousness [50]. In addition, vomiting was the most common manifestation in children with hemorrhagic stroke [51], and it was rarely presented in children with ischemic stroke [52].

Our study has several limitations. First, it was a retrospective single-center study, in which potential selection bias and recall bias were inevitable. Second, this study was not external validation because of the smaller sample size, and multicenter studies should be conducted future. Furthermore, other stroke-related risk factors, such as alcohol consumption, smoking, and exercise habits were not included in our research, which were important factors affecting stroke and may have influenced our results.

In summary, we identified several associated risk factors that could differentiate hemorrhagic and ischemic stroke prehospital. In addition, we developed a clinical nomogram based on the FAST score for differentiating hemorrhagic and ischemic stroke.

Acknowledgements

Not applicable.

Authors’ contributions

Hailong Zhang and Sheng Ye designed the research. Sheng Ye and Huiqing Pan drafted the manuscript. Weijia Li and Jingqiang Wang contributed to data collection. Sheng Ye, Huiqing Pan analyzed the data and discussed the results. Hailong Zhang revised the final manuscript.

Funding

This work was supported by the Key Research Fund Project of Wannan Medical College (grant number: WK2021ZF24, WK2021ZF25) and the “Summit Plan” of the second affiliated hospital of Wannan Medical College.

Availability of data and materials

The data and R codes are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

The research was approved by the Medical Ethics Committee of the Second Affiliated Hospital of Wannan Medical College (No. WYEFYLS202205). The informed consent of patients was waived by the Medical Ethics Committee of the Second Affiliated Hospital of Wannan Medical College due to the retrospective observational design.

Consent for publication

Not applicable.

Competing interests

All authors have read and approved the final manuscript and declared no competing interests in this work.

Katan M, Luft A. Global Burden of Stroke. Semin Neurol. 2018;38(2):208–11. doi:10.1055/s-0038-1649503.
Jones SP, Bray JE, Gibson JM, et al. Characteristics of patients who had a stroke not initially identified during emergency prehospital assessment: a systematic review. Emerg Med J. 2021;38(5):387–93. doi:10.1136/emermed-2020-209607.
Wu S, Wu B, Liu M, et al. Stroke in China: advances and challenges in epidemiology, prevention, and management. Lancet Neurol. 2019;18(4):394–405. doi:10.1016/s1474-4422(18)30500-3.
Barthels D, Das H. Current advances in ischemic stroke research and therapies. Biochim Biophys Acta Mol Basis Dis. 2020;1866(4):165260. doi:10.1016/j.bbadis.2018.09.012.
Uchida K, Yoshimura S, Hiyama N, et al. Clinical Prediction Rules to Classify Types of Stroke at Prehospital Stage. Stroke 2018;49(8):1820-27. doi: 10.1161/STROKEAHA.118.021794.
Kase CS, Hanley DF. Intracerebral Hemorrhage: Advances in Emergency Care. Neurol Clin. 2021;39(2):405–18. doi:10.1016/j.ncl.2021.02.002.
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(9):949 – 84. doi: 10.1177/1747493018786616.
Millin MG, Gullett T, Daya MR. EMS management of acute stroke–out-of-hospital treatment and stroke system development (resource document to NAEMSP position statement). Prehosp Emerg Care. 2007;11(3):318–25. doi:10.1080/10903120701347885.
Zhelev Z, Walker G, Henschke N, et al. Prehospital stroke scales as screening tools for early identification of stroke and transient ischemic attack. Cochrane Database Syst Rev. 2019;4:CD011427. doi:10.1002/14651858.CD011427.pub2.
Han F, Zuo C, Zheng G. A systematic review and meta-analysis to evaluate the diagnostic accuracy of recognition of stroke in the emergency department (ROSIER) scale. BMC Neurol. 2020;20(1):304. doi:10.1186/s12883-020-01841-x.
Bergs J, Sabbe M, Moons P. Prehospital stroke scales in a Belgian prehospital setting: a pilot study. Eur J Emerg Med. 2010;17(1):2–6. doi:10.1097/MEJ.0b013e32831040ec.
Harbison J, Hossain O, Jenkinson D, et al. Diagnostic accuracy of stroke referrals from primary care, emergency room physicians, and ambulance staff using the face arm speech test. Stroke. 2003;34(1):71–6. doi:10.1161/01.str.0000044170.46643.5e.
Bustamante A, Penalba A, Orset C, et al. Blood Biomarkers to Differentiate Ischemic and Hemorrhagic Strokes. Neurology. 2021;96(15):e1928-e39. doi:10.1212/WNL.0000000000011742.
Luger S, Jaeger HS, Dixon J, et al. Diagnostic Accuracy of Glial Fibrillary Acidic Protein and Ubiquitin Carboxy-Terminal Hydrolase-L1 Serum Concentrations for Differentiating Acute Intracerebral Hemorrhage from Ischemic Stroke. Neurocrit Care. 2020;33(1):39–48. doi:10.1007/s12028-020-00931-5.
Morotti A, Goldstein JN. Diagnosis and Management of Acute Intracerebral Hemorrhage. Emerg Med Clin North Am. 2016;34(4):883–99. doi:10.1016/j.emc.2016.06.010.
Dastur CK, Yu W. Current management of spontaneous intracerebral haemorrhage. Stroke Vasc Neurol. 2017;2(1):21–9. doi:10.1136/svn-2016-000047.
Mendelson SJ, Prabhakaran S. Diagnosis and Management of Transient Ischemic Attack and Acute Ischemic Stroke: A Review. JAMA. 2021;325(11):1088–98. doi:10.1001/jama.2020.26867.
Amarenco P, Lavallee PC, Monteiro Tavares L, et al. Five-Year Risk of Stroke after TIA or Minor Ischemic Stroke. N Engl J Med. 2018;378(23):2182–90. doi:10.1056/NEJMoa1802712.
Purrucker JC, Hametner C, Engelbrecht A, et al. Comparison of stroke recognition and stroke severity scores for stroke detection in a single cohort. J Neurol Neurosurg Psychiatry. 2015;86(9):1021–8. doi:10.1136/jnnp-2014-309260.
Chao BH, Yan F, Hua Y, et al. Stroke prevention and control system in China: CSPPC-Stroke Program. Int J Stroke. 2021;16(3):265–72. doi:10.1177/1747493020913557.
Karlinski M, Kozera-Strzelinska D, Sienkiewicz-Jarosz H, et al. Reliability of prehospital diagnosis of acute cerebrovascular accident. Neurol Neurochir Pol 2022;56(1):89–95. doi: 10.5603/PJNN S.a2022.0011.
Ebinger M, Audebert HJ. Shifting acute stroke management to the prehospital setting. Curr Opin Neurol. 2022;35(1):4–9. doi:10.1097/WCO.0000000000001012.
Hussain A, Lee M, Rana J, et al. Epidemiology and risk factors for stroke in young individuals: implications for prevention. Curr Opin Cardiol 2021;36(5):565–71. doi: 10.1097/HCO.000000000 0000894.
Henriksson KM, Farahmand B, Asberg S, et al. Comparison of cardiovascular risk factors and survival in patients with ischemic or hemorrhagic stroke. Int J Stroke. 2012;7(4):276–81. doi:10.1111/j.1747-4949.2011.00706.x.
Tsai CF, Jeng JS, Anderson N, et al. Comparisons of Risk Factors for Intracerebral Hemorrhage versus Ischemic Stroke in Chinese Patients. Neuroepidemiology. 2017;48(1–2):72–8. doi:10.1159/000475667.
Takashima N, Arima H, Kita Y, et al. Long-Term Survival after Stroke in 1.4 Million Japanese Population: Shiga Stroke and Heart Attack Registry. J Stroke. 2020;22(3):336–44. doi:10.5853/jos.2020.00325.
Toyoda K, Yoshimura S, Nakai M, et al. Twenty-Year Change in Severity and Outcome of Ischemic and Hemorrhagic Strokes. JAMA Neurol. 2022;79(1):61–9. doi:10.1001/jamaneurol.2021.4346.
Minhas JS, Moullaali TJ, Rinkel GJE, et al. Blood Pressure Management After Intracerebral and Subarachnoid Hemorrhage: The Knowns and Known Unknowns. Stroke 2022;53(4):1065-73. doi: 10.1161/STROKEAHA.121.036139.
Feigin VL, Krishnamurthi RV, Parmar P, et al. Update on the Global Burden of Ischemic and Hemorrhagic Stroke in 1990–2013: The GBD 2013 Study. Neuroepidemiology. 2015;45(3):161–76. doi:10.1159/000441085.
Castilla-Guerra L, Fernandez-Moreno Mdel C. Chronic Management of Hypertension after Stroke: The Role of Ambulatory Blood Pressure Monitoring. J Stroke. 2016;18(1):31–7. doi:10.5853/jos.2015.01102.
Pires PW, Rogers CT, McClain JL, et al. Doxycycline, a matrix metalloprotease inhibitor, reduces vascular remodeling and damage after cerebral ischemia in stroke-prone spontaneously hypertensive rats. Am J Physiol Heart Circ Physiol. 2011;301(1):H87–97. doi:10.1152/ajpheart.01206.2010.
Ko SB, Yoon BW. Blood Pressure Management for Acute Ischemic and Hemorrhagic Stroke: The Evidence. Semin Respir Crit Care Med. 2017;38(6):718–25. doi:10.1055/s-0037-1608777.
Montano A, Hanley DF, Hemphill JC. 3rd. Hemorrhagic stroke. Handb Clin Neurol. 2021;176:229–48. doi:10.1016/B978-0-444-64034-5.00019-5.
Rawshani A, Rawshani A, Sattar N, et al. Relative Prognostic Importance and Optimal Levels of Risk Factors for Mortality and Cardiovascular Outcomes in Type 1 Diabetes Mellitus. Circulation 2019;139(16):1900-12. doi: 10.1161/CIRCULATIONAHA.118.037454.
Katsanos AH, Filippatou A, Manios E, et al. Blood Pressure Reduction and Secondary Stroke Prevention: A Systematic Review and Metaregression Analysis of Randomized Clinical Trials. Hypertension. 2017;69(1):171–79. doi:10.1161/HYPERTENSIONAHA.116.08485.
Kernan WN, Ovbiagele B, Black HR, et al. Guidelines for the prevention of stroke in patients with stroke and transient ischemic attack: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2014;45(7):2160 – 236. doi: 10.1161/STR.00000000 00000024.
Frank B, Fabian F, Brune B, et al. Validation of a shortened FAST-ED algorithm for smartphone app guided stroke triage. Ther Adv Neurol Disord. 2021;14:17562864211057639. doi:10.1177/17562864211057639.
Puolakka T, Virtanen P, Kinnunen J, et al. Prehospital identification of large vessel occlusion using the FAST-ED score. Acta Neurol Scand. 2021;144(4):400–07. doi:10.1111/ane.13474.
Lima FO, Silva GS, Furie KL, et al. Field Assessment Stroke Triage for Emergency Destination: A Simple and Accurate Prehospital Scale to Detect Large Vessel Occlusion Strokes. Stroke. 2016;47(8):1997–2002. doi:10.1161/STROKEAHA.116.013301.
Vafadar AK, Cote JN, Archambault PS. Effectiveness of functional electrical stimulation in improving clinical outcomes in the upper arm following stroke: a systematic review and meta-analysis. Biomed Res Int. 2015;2015:729768. doi:10.1155/2015/729768.
Purrucker JC, Hametner C, Engelbrecht A, Bruckner T, Popp E, Poli S. Comparison of stroke recognition and stroke severity scores for stroke detection in a single cohort. J Neurol Neurosurg Psychiatry. 2015;86(9):1021–8. doi:10.1136/jnnp-2014-309260.
Gordon C, Ellis-Hill C, Ashburn A. The use of conversational analysis: nurse-patient interaction in communication disability after stroke. J Adv Nurs. 2009;65(3):544–53. doi:10.1111/j.1365-2648.2008. 04917.x.
Kent RD, Vorperian HK, Kent JF, et al. Voice dysfunction in dysarthria: application of the Multi-Dimensional Voice Program™. J Commun Disord. 2003;36(4):281–306. doi:10.1016/ s0021-9924(03)00016 – 9.
Mitchell C, Bowen A, Tyson S, et al. Interventions for dysarthria due to stroke and other adult-acquired, non-progressive brain injury. Cochrane Database Syst Rev. 2017;1:CD002088. doi:10.1002/14651858.CD002088.pub3.
Taga M, Charalambous CC, Raju S, et al. Corticoreticulospinal tract neurophysiology in an arm and hand muscle in healthy and stroke subjects. J Physiol. 2021;599(16):3955–71. doi:10.1113/JP281681.
Freeman WD. Management of Intracranial Pressure. Continuum (Minneap Minn). 2015;21(5 Neurocritical Care):1299–323. doi:10.1212/CON.0000000000000235.
Holbrook J, Saindane AM. Imaging of Intracranial Pressure Disorders. Neurosurgery. 2017;80(3):341–54. doi:10.1227/NEU.0000000000001362.
Leinonen V, Vanninen R, Rauramaa T. Raised intracranial pressure and brain edema. Handb Clin Neurol. 2017;145:25–37. doi:10.1016/B978-0-12-802395-2.00004-3.
Syahrul S, Maliga HA, Ilmawan M, et al. Hemorrhagic and ischemic stroke in patients with coronavirus disease 2019: incidence, risk factors, and pathogenesis - a systematic review and meta-analysis. F1000Res. 2021;10:34. doi:10.12688/f1000research.42308.1.
Shigematsu K, Shimamura O, Nakano H, et al. Vomiting should be a prompt predictor of stroke outcome. Emerg Med J. 2013;30(9):728–31. doi:10.1136/emermed-2012-201586.
Gerstl L, Badura K, Heinen F, et al. Childhood haemorrhagic stroke: a 7-year single-centre experience. Arch Dis Child. 2019;104(12):1198–202. doi:10.1136/archdischild-2018-316749.
Yock-Corrales A, Mackay MT, Mosley I, et al. Acute childhood arterial ischemic and hemorrhagic stroke in the emergency department. Ann Emerg Med 2011;58(2):156–63. doi: 10.1016/j.ann emergmed.2010.10.013.

No competing interests reported.

Download PDF

Journal Publication

published 03 Mar, 2023

Read the published version in BMC Neurology →

Editorial decision: Major revision
20 Feb, 2023
Reviews received at journal
12 Feb, 2023
Reviewers agreed at journal
18 Jan, 2023
Reviews received at journal
06 Nov, 2022
Reviewers agreed at journal
22 Oct, 2022
Reviewers agreed at journal
13 Oct, 2022
Reviewers invited by journal
13 Oct, 2022
Editor assigned by journal
13 Oct, 2022
Editor invited by journal
11 Oct, 2022
Submission checks completed at journal
11 Oct, 2022
First submitted to journal
04 Oct, 2022

You are reading this latest preprint version

Development and validation of a clinical nomogram for differentiating hemorrhagic and ischemic stroke prehospital

Status:

Journal Publication

Version 1

Abstract

Figures

1. Background

2. Methods

2.1. Study design and participants

2.2. Data collection

2.3. Definition

2.4. Statistical analysis

3. Results

3.1. Demographic and prehospital clinical characteristics of the study

3.2. Multivariate logistic regression analyses of independent risk factors for hemorrhagic stroke

3.3. Establishment of nomogram in predicting hemorrhagic stroke

3.4. Validation of nomogram with receiver operating characteristic (ROC) curve

3.5. Clinical use compared nomogram with the FAST score

4. Discussion

5. Conclusions

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1