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YanQin Lu, QianQian Bi, Wang Fu, LiLi Liu, Yin Zhang, XiaoYu Zhou, Jue Wang, "A Novel Clinical Nomogram to Predict Transient Symptomatic Associated with Infarction: The ABCD3-SLOPE Score", BioMed Research International, vol. 2021, Article ID 5597155, 8 pages, 2021. https://doi.org/10.1155/2021/5597155
A Novel Clinical Nomogram to Predict Transient Symptomatic Associated with Infarction: The ABCD3-SLOPE Score
Background. It is hard to differentiate transient symptoms associated with infarction (TSI) from transient ischemic stroke (TIA) without MRI in the early onset. However, they have distinct clinical outcomes and respond differently to therapeutics. Therefore, we aimed to develop a risk prediction model based on the clinical features to identify TSI. Methods. We enrolled 230 consecutive patients with transient neurologic deficit in the Department of Neurology, Tongji University Affiliated Tenth People’s Hospital from March 2014 to October 2019. All the patients were assigned into TIA group (DWI-negative) or TSI group (DWI-positive) based on MRI conducted within five days of onset. We summarized the clinical characteristics of TSI by univariate and multivariate analyses. And then, we developed and validated a nomogram to identify TSI by the logistic regression equation. Results. Of the 230 patients, 41.3% were diagnosed with TSI. According to the multivariate analysis, four independent risk factors, including smoking history, low-density lipoprotein cholesterol, brain natriuretic peptide precursor, and ABCD3 score, were incorporated into a nomogram. We developed a predictive model named ABCD3-SLOPE. The calibration curve showed good agreement between nomogram prediction and observation. The concordance index (C-index) of the nomogram for TSI prediction was 0.77 (95% confidence interval, 0.70-0.83), and it was well-calibrated. Conclusions. Smoking history, low-density lipoprotein cholesterol, brain natriuretic peptide precursor, and ABCD3 score were reliable risk factors for TSI. ABCD3-SLOPE was a potential tool to quantify the likelihood of TSI.
A transient episode of neurological dysfunction caused by focal brain, spinal cord, or retinal ischemia, without acute infarction, has been defined as a TIA [1, 2]. With the advent of diffusion-weighted imaging (DWI), it was reported that 29.3-41.5% percent of patients with transient clinical deficit had positive DWI [3–7]. To data, the normative definition for these DWI-positive cases was not yet well-established, while it was termed transient symptoms associated with infarction (TSI) . They are two distinct entities and contribute to different prognoses, even if they share the identical feature that neurological deficits fully recovered within 24 hours of onset. The risk of recurrent ischemic events is much higher in patients with TSI than those with TIA. One study found a rate of recurrent ischemic stroke of 10.8% among TSI patients in 90 days, while 4.3% in TIA . What is more, TSI patients were ten times more likely to suffer from ischemic stroke in 7 days and 4.3 times in 12 months than TIA patients [9, 10]. Therefore, early identification of TSI and aggressive treatment may reduce the risk of recurrent stroke.
The TSI diagnosis is exceptionally dependent on DWI sequence, but the MR image is unavailable at the first one or two days of onset in most medical facilities. Nowadays, there are ample studies of detection and early stroke risk prediction for TIA [8, 11–15], while few studies have investigated TSI’s clinical characteristics. For this reason, it is imperative to make investigations on the specific clinical characteristics of TSI and to develop a prediction model derived from clinical features for the identification of TSI.
The purpose of this research is to develop a practical risk prediction tool that can aid in rapidly and accurately triaging patients with high positive predictive value.
2.1. Patients and Study Design
We collected consecutive 348 patients manifested with transient neurological deficits hospitalized in the Department of Neurology, Shanghai Tenth People’s Hospital from March 2014 to October 2019. Patients with neurological dysfunction due to cerebral hemorrhage or other nonvascular causes (hypoglycemia, infection, space-occupying lesions, subdural hematoma, and epidural hematoma) were excluded. Also, patients without a brain MRI were excluded from this study. Two hundred and thirty patients were finally recruited. Brain MRI (Siemens 3.0T Avanto MRI) was required for all study participants. Standardized MRI sequences were necessary per protocol (at least T2-weighted, fluid-attenuated inversion recovery images and DWI sequences). All images were analyzed offline centrally by two experienced readers blinded to clinical and demographic data. The patients were divided into TIA group (DWI-negative) and TSI group (DWI-positive) based on DWI sequence results. We retrospectively analyzed the demographic characteristics, clinical manifestations, risk factors, laboratory examinations, imaging examinations, and other variables.
2.2. Grouping Standard
A transient episode of neurological dysfunction caused by focal brain, spinal cord, or retinal ischemia, without acute infarction, was defined as a TIA [1, 2]. Correspondingly, the patient with acute infarction was diagnosed with TSI. According to the definition, all the patients were divided into the TIA group (DWI-negative) and TSI group (DWI-positive) based on the DWI sequence results.
2.3. Statistical Analysis
Statistical analysis was performed using IBM SPSS Statistics, Version 22.0 (IBM; Armonk, NY). Categorical variables are expressed as a number or percentage, and continuous variables are expressed as means and SDs. Statistical analysis of categorical variables was done using the chi-square test, while continuous variables were compared using the -test or Mann-Whitney test, as appropriate.
First, a univariate analysis was performed. We used the forward stepwise regression method based on the maximum likelihood ratio to conduct multivariate logistic regression analysis of independent influencing factors in TSI patients. The odds ratio (OR) with 95% confidence intervals (CI) was calculated. indicates that the difference is statistically significant. We then established a risk prediction model and drawn the ROC curve of it and each clinical variable, calculated the sensitivity, specificity, and positive and negative predictive values correspondingly.
We formulated the nomogram based on the multivariate Cox regression model for TSI prediction through the package of rms in R, Version 3.6.3. The prediction performance of the nomogram was measured using the C-index and calibration curves.
3.1. Clinical Features
Of the 230 patients, 41.3% (95 of 230) were diagnosed with TSI. The demographic and clinical characteristics of the patients are listed in Table 1. Compared with the TIA group, there were more male (69.5 versus 51.9%; ), longer duration of symptoms (30 versus 10 min; ), higher SBP and DBP on admission ( versus mmHg and versus mmHg; and 0.026), and higher incidences of unilateral weakness and speech impairment (72.6% versus 50.4% and 38.9% versus 21.5%; and 0.006) in TSI group, whereas there were less memory loss (5.3% versus 14.8%; ) of atrial fibrillation and smoking, and drinking was higher (12.6% versus 3.0% and 48.4% versus 24.4% and 25.3% versus 14.1%; , <0.001, and 0.049).
PMH: past medical history; SBPa: the first systolic blood pressure after admission; DBPa: the first diastolic blood pressure after admission; dual TIAa prompting medical attention plus at least one other TIA in the preceding 7 days.
Biomarker and imaging characteristics are shown in Table 2. It indicated that creatinine (), uric acid (), total cholesterol (), low-density lipoprotein (), brain natriuretic peptide precursor (), red blood cell (), hemoglobin (), and hematocrit () in the group TSI are significantly higher than those in the TIA group, and the differences were statistically significant. Similarly, the percentages of patients with severe responsible vessel stenosis or occlusion (23.9% versus 14.0% and 9.1% versus 2.3%; ) and carotid plaque (76.3% versus 56.0%; ) were higher than that in the TIA group. Table 3 showed the multivariable analyses of clinical characteristics of TSI patients, and the smoking history, low-density lipoprotein cholesterol, brain natriuretic peptide precursor, and ABCD3 score are the independent risk factors of TSI.
3.2. Model Evaluation
Table 4 shows the performance of LDL, NT-pro-BNP, ABCD2 score, ABCD3 score, and our combined risk prediction model. We used the area under the ROC curve to evaluate the diagnosis efficacy (Figure 1). Compared with the other models, the combined model consisting of smoking history, low-density lipoprotein cholesterol, brain natriuretic peptide precursor, and ABCD3 score had the largest area under the ROC curve, whose AUC is 0.762 (95% CI 0.701-0.823), which means it shows better performance.
3.3. Construction and Validation of the Nomogram
As the combined model had the best predictive performance, we built a nomogram based on this final model: the ABCD3-SLOPE score (Figure 2). The performance of the nomogram was evaluated by the concordance index (C-index) with 1000 bootstrap resamples, and we measure the discrimination of the model by the area under the ROC curve. A value of 1 indicates perfect discrimination, and 0.5 represents discriminative power equal to randomness. Furthermore, the calibration curve of the combined model (Figure 3) is close to the ideal curve, which demonstrated that it has a well-predictive performance.
Our study suggested 41.3% of the patients with transient neurologic deficits had positive DWI lesions on brain MRI. The rate of TSI in our research was concordant with those in previous studies which reported 29.3-41.5% of the patients with transient symptoms were found acute ischemic lesions by DWI [3–7]. The high incidence of TIS prompted us to develop a practical tool to identify TSI in patients with transient symptoms at the onset of stroke.
Our study found TSI was independently associated with the smoking history, low-density lipoprotein cholesterol, brain natriuretic peptide precursor, and ABCD3 score. We developed the model ABCD3-SLOPE, and it is a practical and convenient tool with the high prediction of TSI in patients with neurological deficits recovered within 24 hours of onset. The score had easy applicability due to the easy and rapid availability of these variables in emergency department at the onset of stroke, and the nomogram indicated the virtue of simplicity and intuition. As demonstrated in Table 4, compared with other models, the ABCD3-SLOPE model had high sensitivity (82.1%) and specificity (59.3%) to identify TSI when the patients presented transient symptoms. The concordance index (C-index) was 0.77 (95% confidence interval, 0.70-0.83) in the internal validation with 1000 bootstrap resamples, and the calibration curve demonstrated that the ABCD3-SLOPE had a well-predictive performance.
TSI is not only an independent entity different from TIA and acute cerebral infarction, but it is associated with a higher risk of recurrent ischemic events as well [4, 16–18]. The patient with a DWI lesion on baseline MRI was 4.3 times (9.1% versus 2.1%; ) more likely to suffer a subsequent stroke in 1 year than TIA . The 7-day risk of stroke was higher on DWI-positive than DWI-negative patients (1.2% versus 12.3%) . The subsequent risk of ischemic stroke after TIA was 2.4% in 2 days, 3.8% in 7 days, 4.1% in 30 days, and 4.7% in 90 days according to a systematic review and meta-analysis of 206455 unique patients . It is reasonable to assume higher rates of subsequent ischemic stroke in 30 or 90 days in the TSI population. There was a trend that minor stroke patients who received intravenous t-PA had favorable outcomes and the larger proportion of alive and independent defined as Oxfordshire Handicap Score of 0-2 . In the Austrian Stroke Unit Registry, rt-PA-treated patients with mild stroke had a good outcome in comparison to subjects without rt-PA . However, there is no evidence of evidence-based medicine on intravenous t-PA in TIA patients. Thrombolysis is warranted in the TSI population instead of TIA patients probably. Early initiation of existing treatments was associated with an 80% reduction in early recurrent stroke and reduced subsequent hospital bed-days, acute costs, and 6-month disability [22, 23]. Despite the fact that recognition of TSI is of much importance, it remains a challenge for the physician in emergency department, since the TSI diagnosis is exceptionally dependent on the DWI sequence. Chaturvedi et al. reported that no more than 51% of TIA or minor stroke had MR image within two days of initial presentation . In another multicenter study, MRI was performed in 58.5% of patients with TIA or minor stroke . Other validating and simple tools are needed for physicians to distinguish TSI from TIA as traditionally defined.
In our study, we obtained a high global accuracy with the combination of four items that built the model of ABCD3-SLOPE: the smoking history, low-density lipoprotein cholesterol, brain natriuretic peptide precursor, and ABCD3 score. These items were featured with easy and quick availability. It provided a practical risk prediction tool for clinicians in rapid and accurate identification of TSI from TIA population at the earliest when MRI is not available. In consequence, more appropriate and precise treatments could be taken in time.
Some risk factors were found their associations with TSI in recent years. Jia et al. found that dysphasia was associated with acute DWI lesions in patients with transient neurological symptoms . In Crisostomo et al.’s study, patients with positive DWI were 9.6 times more likely to present with symptom duration greater than 1 hour , and longer duration of symptoms would yield a higher probability of persistent parenchymal damage that could be detected by MRI. Cigarette smoking has appreciable adverse effects on hemorheological parameters, plasma osmolality, and high fibrinogen level. RBC aggregation in smokers promotes endothelial dysfunction through arterial wall inflammation and the impact of hematocrit on the blood vessel, and the increased blood viscosity may play an adverse role in the dissolution of thrombus . Brain natriuretic peptide precursor is secreted by the cardiac ventricles, increasing serum levels correlate with the severity of left ventricle dysfunction. Left atrial appendage emptying flow velocity (LAAEV) can reflect the left atrial function, and it was independently associated with ischemic stroke in a prospective observational study . As we have known that DOT score , Dawson score , and ABCD scoring system  are clinical diagnostic tools designed for and from TIA population, they fail to be fully applicable to TSI population. The ABCD3-SLOPE model and nomogram is derived from the TSI population. Therefore, it is not only a simple risk assessment tool but of suitability to quantify the likelihood of TSI in the way of visualization.
Although the nomogram model demonstrated reasonable accuracy for the prediction of TSI, there are some limitations. First, the time delay from symptom onset to MRI examination is an essential factor that affects DWI positivity , and some patients might be underestimated if the MRI was performed within 24 hours of symptom onset. The time should be classified in the further study. Second, the current study was performed on the basis of a single-center, retrospective database, and it might not have the generalizability of a multicenter trial typically retains. However, we found the baseline characteristics of our patients such as age, gender, risk factors, and clinical symptoms were similar to a large sample size of 1910 patients , and we had reason to believe our cohort had a representative of a larger population. Third, although we had well internal validation, we did not have external validation. Hence, a further multicenter, larger validation study is clearly warranted.
Limb weakness, speech impairment, long duration of symptoms, high ABCD3 scores, and smoking history were the independent risk factors for TSI. The ABCD3-SLOPE and nomogram had a reasonable prediction performance on the recognition of TSI, and we need to design more robust research to explore the best critical ABCD3 scores and symptom duration to predict the risk of TSI and to provide more reliable evidence for other potential predictors.
The data are available from the corresponding author on reasonable request.
Conflicts of Interest
No conflict of interests by any of the authors.
LYQ and BQQ contributed equally. LYQ and WJ devised the study. LYQ and BQQ drafted the manuscript. LYQ and LLL analyzed and interpreted the data. FW and ZY collected the data. ZXY contributed to the critical revision of the manuscript. All authors read and approved the final manuscript. XiaoYu Zhou and Jue Wang are co-corresponding authors.
The work was supported by Shanghai Municipal Key Clinical Specialty (shslczdzk06102), Shanghai Science and Technology Commission (19401972804), and Start-up Fund of Shanghai Tenth People’s Hospital (04.03.16.002).
- W. N. Kernan, B. Ovbiagele, H. R. Black 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, vol. 45, no. 7, pp. 2160–2236, 2014.
- T. Fitzpatrick, S. Gocan, C. Q. Wang et al., “How do neurologists diagnose transient ischemic attack: a systematic review,” International Journal of Stroke, vol. 14, no. 2, pp. 115–124, 2019.
- J. Yuan, Z. Jia, Y. Song et al., “Incidence and predictors of acute ischemic lesions on brain magnetic resonance imaging in patients with a clinical diagnosis of transient ischemic attack in China,” Frontiers in Neurology, vol. 10, p. 764, 2019.
- M. Al-Khaled and J. Eggers, “MRI findings and stroke risk in TIA patients with different symptom durations,” Neurology, vol. 80, no. 21, pp. 1920–1926, 2013.
- M. Brazzelli, F. M. Chappell, H. Miranda et al., “Diffusion-weighted imaging and diagnosis of transient ischemic attack,” Annals of Neurology, vol. 75, no. 1, pp. 67–76, 2014.
- H. Ay, W. J. Koroshetz, T. Benner et al., “Transient ischemic attack with infarction: a unique syndrome?” Annals of Neurology, vol. 57, no. 5, pp. 679–686, 2005.
- S. Anticoli, F. R. Pezzella, C. Pozzessere et al., “Transient ischemic attack fast-track and long-term stroke risk: role of diffusion-weighted magnetic resonance imaging,” Journal of Stroke and Cerebrovascular Diseases, vol. 24, no. 9, pp. 2110–2116, 2015.
- B. Song, L. Pei, H. Fang et al., “Validation of the RRE-90 scale to predict stroke risk after transient symptoms with infarction: a prospective cohort study,” PLoS One, vol. 10, no. 9, article e0137425, 2015.
- J. M. Boulanger, S. B. Coutts, M. Eliasziw, S. Subramaniam, J. Scott, and A. M. Demchuk, “Diffusion-weighted imaging-negative patients with transient ischemic attack are at risk of recurrent transient events,” Stroke, vol. 38, no. 8, pp. 2367–2369, 2007.
- T. Tong, Z. Yao, and X. Feng, “Combined diffusion- and perfusion-weighted imaging: a new way for the assessment of hemispheric transient ischemic attack patients,” International Journal of Developmental Neuroscience, vol. 29, no. 1, pp. 63–69, 2011.
- D. Dutta, “Diagnosis of TIA (DOT) score--design and validation of a new clinical diagnostic tool for transient ischaemic attack,” BMC Neurology, vol. 16, no. 1, p. 20, 2016.
- J. Dawson, K. E. Lamb, T. J. Quinn et al., “A recognition tool for transient ischaemic attack,” QJM, vol. 102, no. 1, pp. 43–49, 2009.
- H. Ay, E. M. Arsava, S. C. Johnston et al., “Clinical- and imaging-based prediction of stroke risk after transient ischemic attack: the CIP model,” Stroke, vol. 40, no. 1, pp. 181–186, 2009.
- T. Kiyohara, M. Kamouchi, Y. Kumai et al., “ABCD3 and ABCD3-I scores are superior to ABCD2 score in the prediction of short- and long-term risks of stroke after transient ischemic attack,” Stroke, vol. 45, no. 2, pp. 418–425, 2014.
- L. S. Dolmans, E. R. Lebedeva, D. Veluponnar et al., “Diagnostic accuracy of the explicit diagnostic criteria for transient ischemic attack: a validation study,” Stroke, vol. 50, no. 8, pp. 2080–2085, 2019.
- S. B. Coutts and B. Cucchiara, “Stroke risk after TIA: DWI is only part of the answer,” Neurology, vol. 80, no. 21, pp. 1914-1915, 2013.
- A. Arhami Dolatabadi, A. Meisami, H. Hatamabadi et al., “Improving the prediction of stroke or death after transient ischemic attack (TIA) by adding diffusion-weighted imaging lesions and TIA etiology to the ABCD2 score,” Journal of Stroke and Cerebrovascular Diseases, vol. 22, no. 7, pp. e25–e30, 2013.
- F. Purroy, P. E. Jiménez-Caballero, G. Mauri-Capdevila et al., “Predictive value of brain and vascular imaging including intracranial vessels in transient ischaemic attack patients: external validation of the ABCD3-I score,” European Journal of Neurology, vol. 20, no. 7, pp. 1088–1093, 2013.
- S. Shahjouei, A. Sadighi, D. Chaudhary et al., “A 5-Decade analysis of incidence trends of ischemic stroke after transient ischemic attack: a systematic review and meta-analysis,” JAMA Neurology, vol. 78, no. 1, p. 77, 2021.
- P. Khatri, D. Tayama, G. Cohen et al., “Effect of intravenous recombinant tissue-type plasminogen activator in patients with mild stroke in the third international stroke trial-3: post hoc analysis,” Stroke, vol. 46, no. 8, pp. 2325–2327, 2015.
- S. Greisenegger, L. Seyfang, S. Kiechl, W. Lang, and J. Ferrari, “Thrombolysis in patients with mild Stroke,” Stroke, vol. 45, no. 3, pp. 765–769, 2014.
- R. Luengo-Fernandez, A. M. Gray, and P. M. Rothwell, “Effect of urgent treatment for transient ischaemic attack and minor stroke on disability and hospital costs (EXPRESS study): a prospective population-based sequential comparison,” Lancet Neurology, vol. 8, no. 3, pp. 235–243, 2009.
- P. M. Rothwell, M. F. Giles, A. Chandratheva et al., “Effect of urgent treatment of transient ischaemic attack and minor stroke on early recurrent stroke (EXPRESS study): a prospective population-based sequential comparison,” Lancet, vol. 370, no. 9596, pp. 1432–1442, 2007.
- S. Chaturvedi, S. Ofner, F. Baye et al., “Have clinicians adopted the use of brain MRI for patients with TIA and minor stroke?” Neurology, vol. 88, no. 3, pp. 237–244, 2017.
- P. Amarenco, P. C. Lavallée, J. Labreuche et al., “One-year risk of stroke after transient ischemic attack or minor stroke,” The New England Journal of Medicine, vol. 374, no. 16, pp. 1533–1542, 2016.
- Z. Jia, Y. Song, and W. Hu, “Dysphasia is associated with diffusion-weighted MRI abnormalities in patients with transient neurological symptoms,” Neurological Sciences, vol. 41, no. 7, pp. 1765–1771, 2020.
- R. A. Crisostomo, M. M. Garcia, and D. C. Tong, “Detection of diffusion-weighted MRI abnormalities in patients with transient ischemic attack: correlation with clinical characteristics,” Stroke, vol. 34, no. 4, pp. 932–937, 2003.
- Y. Cai, W. Xu, H. Liu et al., “Effects of cigarette smoking on older Chinese men treated with clopidogrel monotherapy or aspirin monotherapy: a prospective study,” Platelets, vol. 31, no. 5, pp. 667–673, 2020.
- R. M. Melduni, H.-C. Lee, K. R. Bailey et al., “Real-time physiologic biomarker for prediction of atrial fibrillation recurrence, stroke, and mortality after electrical cardioversion: a prospective observational study,” American Heart Journal, vol. 170, no. 5, pp. 914–922, 2015.
- T. J. Quinn, A. C. Cameron, J. Dawson, K. R. Lees, and M. R. Walters, “ABCD2 scores and prediction of noncerebrovascular diagnoses in an outpatient population: a case-control study,” Stroke, vol. 40, no. 3, pp. 749–753, 2009.
- M. Al-Khaled, C. Matthis, T. F. Münte, J. Eggers, and QugSS2-Study, “The incidence and clinical predictors of acute infarction in patients with transient ischemic attack using MRI including DWI,” Neuroradiology, vol. 55, no. 2, pp. 157–163, 2013.
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