Development of a novel score to predict newly diagnosed atrial fibrillation after ischemic stroke: The CHASE-LESS score

Journal Pre-proof Development of a novel score to predict newly diagnosed atrial fibrillation after ischemic stroke: The CHASE-LESS score Hsieh Cheng-Yang, Lee Cheng-Han, Sung Sheng-Feng PII:

S0021-9150(20)30018-6

DOI:

https://doi.org/10.1016/j.atherosclerosis.2020.01.003

Reference:

ATH 16162

To appear in:

Atherosclerosis

Received Date: 30 October 2019 Revised Date:

3 December 2019

Accepted Date: 9 January 2020

Please cite this article as: Cheng-Yang H, Cheng-Han L, Sheng-Feng S, Development of a novel score to predict newly diagnosed atrial fibrillation after ischemic stroke: The CHASE-LESS score, Atherosclerosis (2020), doi: https://doi.org/10.1016/j.atherosclerosis.2020.01.003. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V.

Author contributions Cheng-Yang Hsieh: Conceptualization, Formal analysis, Writing - Original Draft, Writing - Review & Editing. Cheng-Han Lee: Writing - Review & Editing. Sheng-Feng Sung: Conceptualization, Data Curation, Formal analysis, Writing Original Draft, Writing - Review & Editing, Supervision.

Development of a novel score to predict newly diagnosed atrial fibrillation after ischemic stroke: The CHASE-LESS score Cheng-Yang Hsieh1,2, Cheng-Han Lee3, Sheng-Feng Sung4,5,

1

Department of Neurology, Tainan Sin Lau Hospital, Tainan, Taiwan

2

School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of

Medicine, National Cheng Kung University, Tainan, Taiwan 3

Division of Cardiology, Department of Internal Medicine, National Cheng Kung University

Hospital and College of Medicine, Tainan, Taiwan 4

Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation

Chiayi Christian Hospital, Chiayi City, Taiwan 5

Department of Information Management and Institute of Healthcare Information

Management, National Chung Cheng University, Chiayi County, Taiwan

Corresponding Author Sheng-Feng Sung Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, 539 Zhongxiao Road, East District, Chiayi City 60002, Taiwan Email: [email protected]; [email protected];

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Abstract Background and aims: Prompt detection of atrial fibrillation (AF) is essential for optimal secondary stroke prevention, but routine long-term cardiac monitoring of all ischemic stroke patients is neither practical nor affordable. We aimed to develop and validate a risk score to identify patients at risk for newly diagnosed AF (NDAF) after ischemic stroke. Methods: Information on adult patients hospitalized for ischemic stroke without known AF was retrieved from a nationwide database. Primary outcome was NDAF within one year following index stroke. A stepwise Cox model was used to screen for predictors. Beta coefficients for the independent predictors were converted to integer points, which were summed to create a risk score. Results: We identified 4 positive predictors and 3 negative predictors. The CHASE-LESS score (Coronary, Heart failure, Age, stroke SEverity, – LipidEmia, Sugar, prior Stroke) comprises coronary artery disease (1 point), congestive heart failure (1 point), age (1 point for every 10 years), stroke severity (National Institutes of Health Stroke Scale; 1 point for 6–13 and 4 points for ≥14), hyperlipidemia (-1 point), diabetes (-1 point), and prior history of stroke or transient ischemic attack (-1 point). Overall, 6.0% (1,029/17,076) of patients developed NDAF. The incidence rate ranged from 8/1,000 person-years (CHASE-LESS ≤3) to 240/1,000 person-years (CHASE-LESS ≥10). The model achieved a c-index of 0.730 in the development cohort and 0.732 in the validation cohort. Conclusions: The CHASE-LESS score could aid clinicians to identify patients at risk of developing NDAF and help prioritize patients for advanced cardiac monitoring.

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1. Introduction About 7 to 12 percent of patients with a first ischemic stroke will experience a recurrent stroke within one year [1,2], and stroke recurrence is associated with a 10-fold increased risk of death or disability at five years after the first stroke [3]. Consequently, a thorough diagnostic evaluation of the underlying stroke mechanism followed by a mechanism-based strategy of secondary stroke prevention is crucial to improve post-stroke outcomes [4]. For example, the detection of atrial fibrillation (AF) in patients with ischemic stroke generally changes the strategy of stroke prevention because oral anticoagulation is superior to antiplatelet therapy under such circumstances [5]. Moreover, despite differences in baseline characteristics, stroke patients with newly diagnosed AF (NDAF) tend to have similar clinical outcomes as patients with known AF before stroke [6,7]. Finally, delayed identification of NDAF with resultant late initiation of oral anticoagulation is highly associated with recurrent stroke or transient ischemic attack (TIA) [8]. Given all the therapeutic and prognostic factors, timely detection of NDAF is essential for reduction in stroke recurrence. Fortunately, through adequate serial cardiac monitoring, it is possible to detect NDAF in up to 24% of patients with ischemic stroke or TIA [9]. However, considering the limited resources in healthcare, universal screening for AF in every stroke patient may be infeasible. Real-world data indicates that less than 10 percent of patients received ambulatory electrocardiogram (ECG) monitoring in the 12 months following stroke [8]. An appropriate prioritization method to select patients for prolonged ambulatory ECG monitoring is imperative. In this regard, a simple risk score is preferred to help physicians in their clinical practice. While several risk scores may serve this purpose [10–12], some of them do not consider stroke severity [10,12], which may play an important role in predicting NDAF after ischemic stroke [13]. Besides, none of them have been tested in stroke patients from non-Western

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countries, for whom the risk factors for AF might differ [14]. Therefore, we aimed to develop and validate a simple score to assess the future risk of NDAF after ischemic stroke based on a large nationwide dataset.

2. Materials and methods 2.1 Data source Taiwan's National Health Insurance (NHI) is a single-payer, universal, and compulsory health insurance which covers almost all of its residents. The NHI Research Database is composed of all inpatient/outpatient claims, prescription claims, and demographic data from the NHI program [15]. The data of this study was obtained from a subset of the NHI Research Database, which contains the claims data of one million enrollees randomly sampled in the year 2000. Because the data contains only de-identified information, this study was exempt from a full review by the Institutional Review Board of Ditmanson Medical Foundation Chiayi Christian Hospital (CYCH-IRB No. 2018020) and informed consent was deemed unnecessary.

2.2. Study population Adult patients hospitalized for ischemic stroke between 2000 and 2013 were identified based on the principal discharge diagnosis (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] diagnosis codes 433.xx and 434.xx) [16]. Patients who were diagnosed with AF (ICD-9-CM code 427.31) or had received any oral anticoagulant before the index hospitalization were excluded. Patients with missing claims items were also eliminated. Demographic information, diagnosis codes, and prescription data were retrieved from the database. ICD-9-CM diagnosis codes (Supplemental Table 1) from

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the inpatient and outpatient claims in the one-year lookback period were used to ascertain the presence of comorbidities based on validated algorithms [17]. Since the claims database did not record stroke severity, a validated method was used to estimate stroke severity as follows. Seven items from the inpatient claims for the index hospitalization were used to calculate the stroke severity index (SSI) [18], which was converted to the National Institutes of Health Stroke Scale (NIHSS) score with the equation: estimated NIHSS = 1.1722 × SSI − 0.7533 [18]. The event of interest was NDAF after stroke. The diagnosis of AF was ascertained if AF (ICD-9-CM code 427.31) was mentioned as a secondary discharge diagnosis of the index hospitalization, or on at least one subsequent inpatient claim, or on at least two subsequent outpatient claims [17]. The earliest date of documented AF was regarded as the onset of AF. All patients were traced until the diagnosis of AF, one year after stroke, death, disenrollment from the NHI program, or the end of the data period, whichever came first.

2.3. Statistical analysis Continuous variables were reported with means and standard deviations, and categorical variables with counts and percentages. Differences between groups were compared by Chisquare tests for categorical variables and t tests for continuous variables. Incidence rates are expressed as events per 1,000 person-years. The study population was randomly split into two cohorts as follows. A set of random numbers from a uniform distribution between 0 and 1 was generated, with a random number for each patient. Next, patients in the study population were sorted by the random numbers. The first two thirds of patients on the list comprised the score development cohort and the remaining one third comprised the validation cohort. A multivariable Cox proportional hazards analysis with backward variable selection (probability for removal >0.05) was

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performed to identify predictors of NDAF. Potential candidate predictors included age, sex, hypertension, diabetes mellitus, hyperlipidemia, coronary artery disease (CAD), congestive heart failure (CHF), chronic kidney disease, peripheral artery disease, prior stroke or TIA, and stroke severity [13,19,20]. Age was divided by ten and analyzed as a continuous variable [11]. Stroke severity was categorized into mild (NIHSS ≤5), moderate (NIHSS 6–13), and severe stroke (NIHSS >13) with mild severity as the reference group [13]. The proportional hazards assumption was checked by visual assessment of the log-log plot for each covariate in the final model. All statistical analyses were performed using Stata 15.1 (StataCorp, College Station, Texas) and R version 3.6.1 (R Foundation for Statistical Computing, Vienna, Austria). Twotailed p values <0.05 were considered statistically significant.

2.4. Risk score and internal validation To facilitate the use of the prediction model, a simplified risk scoring system was created based on the regression coefficients of the multivariable Cox model [21]. Because age was significantly positively associated with the risk of NDAF [11], the coefficient of age (per 10 years) was used as the number of regression units that reflect 1 point in the final scoring system [22]. Next, the points assigned to other significant predictors were obtained by dividing each coefficient by that of age and rounding to the nearest integer. A total risk score was calculated by summing up all the points corresponding to the predictors present in any given patient. Harrell’s c-index, a concordance index analogous to the area under the receiver operating characteristic curve, was used to assess the discriminatory ability of the risk score. After establishing the score model, it was validated in the validation cohort. The discriminatory ability of the risk score was evaluated using Harrell’s c-index. Calibration was assessed by plotting the predicted versus observed risk for quintiles of the predicted

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probability. For a model that calibrates well, the data points in the calibration curve are located close to a 45-degree diagonal line. The Kaplan-Meier method was used to plot survival curves, and differences in time to the diagnosis of AF were assessed using the logrank test.

2.5. Existing simple risk scores For comparison, the CHADS2, CHA2DS2-VASc, AS5F, and C2HEST scores were tested in the validation cohort. The former two well-known scores were originally developed to predict stroke in patients with AF [23,24]. They were later found to predict NDAF after ischemic stroke [25]. The AS5F score, based solely on age and NIHSS, was devised to detect NDAF after ischemic stroke [11]. The C2HEST score was initially designed to predict incident AF in the general population [26]. It was found to perform well in predicting NDAF in patients with ischemic stroke [12]. Harrell’s c-indices were calculated for comparison of model performance. In addition, clinical usefulness and the net benefit, i.e., the ability to make better decisions with a model than without, were estimated with decision curve analysis [27].

3. Results 3.1. Patient characteristics A total of 19,022 adult patients hospitalized for ischemic stroke were identified. After excluding patients with known AF (n=1,780) or having received oral anticoagulants (n=108), and those without claims items (n=58), the study population consisted of 17,076 patients. Within one year after stroke, 1,029 (6.0%) patients were diagnosed with AF. In the study population (Table 1), patients with NDAF were older and more likely to be female, and had higher proportions of CAD, CHF, and hyperthyroidism but were less likely to have

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hypertension, diabetes mellitus, hyperlipidemia, and a history of prior stroke or TIA. In addition, stroke severity was significantly higher in patients with NDAF. Baseline characteristics of the validation and development cohorts were similar except for hypertension (Supplemental Table 2).

3.2. Score development and validation The multivariate Cox proportional hazards analysis resulted in 7 risk factors independently associated NDAF (Table 2). Among them, age, CAD, CHF, and stroke severity were positive predictors whereas diabetes mellitus, hyperlipidemia, and a history of prior stroke or TIA were negative predictors. The score of a patient is derived by summation of the points of the predictors, which are 1 point for CAD, 1 point for CHF, 1 point for every 10 years of age, 1 point for moderate stroke severity (NIHSS 6–13), 4 points for severe stroke severity (NIHSS ≥14), minus 1 point for hyperlipidemia, minus 1 point for diabetes, and minus 1 point for a prior history of stroke or TIA. The final risk score was named the CHASE-LESS score (Coronary, Heart failure, Age, stroke SEverity - LipidEmia, Sugar, prior Stroke). The total individual scores range between 1 and 15. Score categories were collapsed when the prevalence of a given score was <2%, that is, for ≤2 and ≥13 points. Fig. 1 demonstrates the incidence rate of NDAF in the development cohort, validation cohort, and the entire study population. The overall incidence rate of NDAF was 60.7/1,000 person-years. The median total score was 6 points. Around 14% of patients scored ≥10 points, showing an incident rate of NDAF of 240/1,000 person-years. In contrast, among the patients with a score of ≤3 points, the incidence rate of NDAF was only 8/1,000 person-years. The calibration plot (Supplemental Fig. 2) displays a close agreement between the predicted and observed probabilities, indicating the risk score was well calibrated.

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For clinical practicality, we performed a stratification of the total risk scores into 4 risk categories: low (1–3), low intermediate (4–6), high intermediate (7–9), and high risk (≥10). Fig. 2 illustrates the Kaplan-Meier survival curves for the development and validation cohorts, in which patients were stratified by the risk categories. Table 3 gives the patient numbers at risk and the incident rates of NDAF across the four tiers of risk in the entire study population. The c-index of the CHASE-LESS score was 0.730 (95% confidence interval [CI], 0.711– 0.748) for the score development cohort. In the validation cohort, the CHASE-LESS score showed good discriminative ability with a c-index of 0.732 (95% CI, 0.703–0.761), which was significantly higher than the CHADS2 score (0.536; 95% CI, 0.505–0.567; p<0.0001), the CHA2DS2-VASc score (0.578; 95% CI, 0.547–0.609; p<0.0001), the AS5F score (0.709; 95% CI, 0.680–0.738; p= 0.002), and the C2HEST scores (0.615; 95% CI, 0.584–0.647; p<0.0001). Supplemental Fig. 1 shows the net benefit curves across risk thresholds for the risk scores. The CHASE-LESS score demonstrated higher clinical usefulness than all the other scores.

4. Discussion We developed and validated a simple risk score for predicting NDAF after ischemic stroke. The CHASE-LESS score showed reasonable discrimination and calibration.

4.1. Positive predictors Several clinical variables were positively and significantly associated with NDAF. As expected, age was a strong predictor of NDAF [28]. Besides, in line with the results from a non-stroke cohort in the Framingham Heart Study [29], heart diseases including CAD and CHF were identified as predictors. Finally, a greater stroke severity was found to increase the risk of NDAF. Actually, a higher NIHSS score has been shown to predict NDAF [11,30,31].

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Some reasons have been proposed to explain this finding [32]. On the one hand, a more severe stroke may suggest an arterial occlusion caused by a larger cardiac thrombus due to a preexisting higher burden of paroxysmal AF, which is naturally easier to detect after a stroke. On the other hand, a stroke with higher severity, such as a larger anterior circulation infarct, has a higher chance of involving the insular cortex, and may thus predispose to AF [33]. Adding stroke severity to existing risk scores, such as the CHADS2 and CHA2DS2-VASc scores, was shown to improve the performance of predicting NDAF [13].

4.2. Negative predictors One characteristic of the CHASE-LESS score was the inclusion of variables negatively associated with NDAF, which were, hyperlipidemia, diabetes, and prior stroke or TIA. All these variables are traditional risk factors for stroke. The reasons for their negative association with NDAF are described as follows. The CHASE-LESS score was developed in patients who had experienced a stroke. Hyperlipidemia, diabetes, and prior stroke or TIA all contribute to stroke occurrence. Because patients with these factors bear a higher risk of ischemic stroke than those without any of these factors, they are less likely to possess other risk factors for ischemic stroke, such as occult paroxysmal AF. In other words, patients with hyperlipidemia, diabetes, or prior stroke do not require paroxysmal AF to develop ischemic stroke and vice versa. Consequently, NDAF was less common in patients with any of these factors who developed ischemic stroke. This leads to a phenomenon in which these factors seem to “protect” stroke patients from having AF. Such a phenomenon resembles the index event bias, which is caused by dependence between risk factors induced by conditioning on the outcome [34]. This partly explains why the CHADS2 and CHA2DS2-VASc scores performed suboptimally in predicting NDAF after ischemic stroke since both scores include diabetes and prior stroke as positive predictors [13].

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4.3. Comparison with other simple risk scores Up to now, several simple risk scores with a similar purpose to the CHASE-LESS score have been studied, including CHADS2 [25], CHA2DS2-VASc [25], HAVOC [10], AS5F [11], and C2HEST scores [12]. All of them were tested in stroke cohorts from Western countries, with C-indices ranging from 0.700 to 0.770. Except for the AS5F score, none of them included stroke severity as a predictor, which was shown to be an independent predictor of NDAF [13]. Hence, CHADS2, CHA2DS2-VASc, and C2HEST scores only achieved an unsatisfactory model performance in our validation cohort (c-indices 0.536 to 0.615). In contrast, the AS5F score, despite its simplicity, attained an acceptable c-index of 0.709. However, this score has its own limitations. The AS5F score was developed from prospective study cohorts undergoing 72-hour Holter-ECG monitoring after TIA or stroke [11], and therefore, its ability to predict the long-term risk of NDAF is unknown. Furthermore, probably because of its low number of outcomes, the AS5F score had inadequate power to retain other variables in the prediction model [35].

4.4. Clinical applications and significance Extended and serial cardiac monitoring is effective in increasing detection rates of AF after TIA or stroke [9]. As seen in the EMBRACE [36] and CRYSTAL AF [ 37] trials, in patients with unremarkable 24-hour ECG recordings, either noninvasive 30-day ambulatory ECG monitoring or long-term monitoring with an insertable cardiac monitor could significantly increase the detection of AF. However, such strategies are resource-intensive and costly. Even in patients with no obvious cause of stroke, prolonged (>24 hour) cardiac monitoring is routinely performed in only 19% of hospitals in high-income countries [38], let alone those hospitals in middle- or low-income areas. Without the use of risk-stratification

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tools, the number needed to screen is 16 (1000/60.7) to detect a case of AF yearly (Table 3). Although only 14% of patients were categorized as high risk for NDAF according to the CHASE-LESS score, the number needed to screen would be decreased to 4 (1000/240.4) in this category. In this way, long-term ECG monitoring can be reserved for patients with a high CHASE-LESS score and a negative 24-hour Holter. On the other hand, more than half of the patients were categorized as having a low risk for NDAF, including 44.3% in the low intermediate and 6.8% in the low risk category. The number needed to screen would be 42 (1000/24.0) and 120 (1000/8.3) for each risk category, respectively. Since unselected ECG monitoring and additional workup procedures may lead to unnecessary use of healthcare resources and even cause harm to patients [39], it is possible to minimize the potential harms and maximize the efficiency of limited medical resources with the use of the CHASE-LESS score. Moreover, the CHASE-LESS score has several advantages. First, all of its items are readily available from patient history and physical examination. In contrast, many risk models require additional effort to obtain necessary variables, such as blood biomarkers [40], ECG features [41], and echocardiographic parameters [41,42], leading to more consumption of healthcare resources. Second, it is easily calculated at the bedside because the score was designed using an integer point system. Furthermore, the score can be determined upon hospital admission so that high-risk patients can be prioritized for advanced cardiac monitoring as soon as possible. Finally, the CHASE-LESS score was developed from a nationwide representative stroke cohort that includes patients with various underlying stroke mechanisms rather than from the more homogenous populations typically found in clinical trials. Thus, selection bias might be reduced, leading to a greater generalizability.

4.5. Limitations

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Several limitations are worth noting. First, the CHASE-LESS score was developed using claims data, and therefore, this study has the limitations inherent to all claims database analyses. For example, the ascertainment of comorbidities was based solely on diagnosis codes even though we used validated algorithms [17]. Therefore, the coefficient estimates obtained from the regression model might be biased. Second, we were unable to determine how AF was diagnosed and how avidly AF was sought. Paroxysmal AF may go undetected because long-term cardiac monitoring is not routinely used in stroke patients, resulting in under-diagnosis of AF. Furthermore, even if AF is actually detected, it may not be accurately and completely coded in the claims data. In general, a claims diagnosis of AF has high specificity but lower sensitivity [17]. Consequently, we may have missed some cases of AF and the risk of NDAF was underestimated, undermining the accuracy of the prediction model. Third, the sex differences in the risk of developing AF were not addressed. Because the risk of incident AF differs between females and males and such sex differences vary with increasing age [43], the underrepresentation of females in the development cohort might lead to a bias in the Cox model. Whether risk prediction will benefit from sex-specific risk models awaits further research. Finally, we were unable to account for the type of ischemic stroke because this information was unavailable in the claims database used for this study. It is possible that the predictors selected in the final model and the weight assigned to each predictor may vary across different types of ischemic stroke.

4.6. Conclusions The CHASE-LESS score is a simple risk score based on readily obtainable clinical characteristics. This novel score could aid clinicians to identify patients at risk of developing NDAF and help prioritize patients for advanced cardiac monitoring in real-world practice.

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Conflicts of interest The authors declared they do not have anything to disclose regarding conflict of interest with respect to this manuscript.

Financial support This research was supported in part by the Ministry of Science and Technology [grant number MOST 107-2314-B-705-001].

Author contributions Cheng-Yang Hsieh: Conceptualization, Formal analysis, Writing - Original Draft, Writing - Review & Editing. Cheng-Han Lee: Writing - Review & Editing. Sheng-Feng Sung: Conceptualization, Data Curation, Formal analysis, Writing - Original Draft, Writing Review & Editing, Supervision.

Acknowledgements This study is based in part on data from the National Health Insurance Research Database provided by the National Health Insurance Administration, Ministry of Health and Welfare and managed by National Health Research Institutes. The interpretation and conclusions contained herein do not represent those of National Health Insurance Administration, Ministry of Health and Welfare or National Health Research Institutes. We would like to thank Ms. Li-Ying Sung for English language editing.

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Tables Table 1. Baseline characteristics of the study population

All (N=17,076)

NDAF (N=1,029)

No AF (N=16,047)

p

Age, mean (SD)

68.8 (12.5)

75.0 (10.5)

68.4 (12.5)

<0.001

Female

6,815 (39.9)

473 (46.0)

6,342 (39.5)

<0.001

Hypertension

12,891 (75.5)

743 (72.2)

12,148 (75.7)

0.011

Diabetes mellitus

6,787 (39.7)

304 (29.5)

6,483 (40.4)

<0.001

Hyperlipidemia

5,404 (31.6)

182 (17.7)

5,222 (32.5)

<0.001

Coronary artery disease

3,538 (20.7)

277 (26.9)

3,261 (20.3)

<0.001

Congestive heart failure

1,530 (9.0)

171 (16.6)

1,359 (8.5)

<0.001

Chronic kidney disease

715 (4.2)

43 (4.2)

672 (4.2)

0.989

Peripheral artery disease

769 (4.5)

47 (4.6)

722 (4.5)

0.918

Prior stroke or transient ischemic attack

2,835 (16.6)

127 (12.3)

2,708 (16.9)

<0.001

Chronic obstructive pulmonary disease

2,295 (13.4)

148 (14.4)

2147 (13.4)

0.360

104 (0.6)

15 (1.5)

89 (0.6)

<0.001

Hyperthyroidism Stroke severity

<0.001

Estimated NIHSS ≤5

10,686 (62.6)

389 (37.8)

10,297 (64.2)

Estimated NIHSS 6–13

3,455 (20.2)

200 (19.4)

3,255 (20.3)

Estimated NIHSS >13

2,935 (17.2)

440 (42.8)

2,495 (15.6)

Data are numbers (percentage) unless specified otherwise. AF, atrial fibrillation; NDAF, newly diagnosed atrial fibrillation; NIHSS, National Institutes of Health Stroke Scale; SD, standard deviation.

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Table 2. Cox model for newly diagnosed atrial fibrillation

HR (95% CI)

Coefficient

p

Points

Age (per 10 years)

1.36 (1.27–1.45)

0.305

<0.001

1

Diabetes mellitus

0.68 (0.57–0.80)

-0.390

<0.001

-1

Hyperlipidemia

0.64 (0.52–0.78)

-0.451

<0.001

-1

Coronary artery disease

1.26 (1.06–1.50)

0.229

0.009

1

Congestive heart failure

1.44 (1.17–1.78)

0.367

0.001

1

Prior stroke or transient ischemic attack

0.65 (0.52–0.81)

-0.435

<0.001

-1

Moderate (estimated NIHSS 6–13)

1.47 (1.20–1.82)

0.389

<0.001

1

Severe (estimated NIHSS >13)

3.54 (2.98–4.20)

1.263

<0.001

4

Stroke severity

CI, confidence interval; HR, hazard ratio; NIHSS, National Institutes of Health Stroke Scale.

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Table 3. Risk categories and corresponding incidence rate of newly diagnosed atrial fibrillation

Risk score

Risk category

N (%)

Incidence rate (/1,000 person-years)

1–3

Low

1,157 (6.8)

8.3

4–6

Low intermediate

7,571 (44.3)

24.0

7–9

High intermediate

5,933 (34.7)

68.3

≥10

High

2,415 (14.1)

240.4

17,076 (100)

60.7

Overall

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Figure legends Fig. 1 Incidence rates of newly diagnosed atrial fibrillation (NDAF) across risk scores. At risk means the proportion of all patients with the respective score.

Fig. 2 Cumulative risk of newly diagnosed atrial fibrillation (NDAF). Cumulative risk of NDAF in the development (A) and validation (B) cohorts.

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Highlights Prompt detection of AF is essential for optimal secondary stroke prevention The CHASE-LESS score consists of seven simple clinical variables The score can identify stroke patients at risk of newly diagnosed AF The score helps prioritize patients for longer-term advanced cardiac monitoring