Guideline-Guided Prognostic Models of Mortality Following First-Ever Ischemic Stroke

doi:10.21203/rs.3.rs-4995784/v1

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Guideline-Guided Prognostic Models of Mortality Following First-Ever Ischemic Stroke

https://doi.org/10.21203/rs.3.rs-4995784/v1

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This study aimed to develop and validate guideline-guided prognostic models for predicting mortality in patients with first-ever ischemic stroke. The study included 899 adult patients (≥ 18 years) with confirmed diagnosis of first-ever ischemic stroke from the National Stroke Registry (NSR) of Malaysia (2009–2020). The primary outcome assessed was post stroke mortality within 90 days. Multivariable regression was used to develop (75%, n = 674) and internally validated (25%, n = 225). Model performance was evaluated using discrimination [area under the receiver operating characteristic curve (AUROC) and calibration [Hosmer-Lemeshow test (HLT)]. The final prognostic model included age ≥ 60 years [adjusted odds ratio, aOR 2.39, 95% confidence interval, CI (1.37–4.18)], Glasgow Coma Scale (GCS) score of ≤ 8 [2.66, (1.31–5.40)], diabetes [2.42 (1.41–4.16)], and non-adherence to antiplatelet therapy within 48 hours [2.30 (1.26–4.20)], dysphagia screening [2.90 (1.63–5.17)], antiplatelet therapy at discharge [3.63 (2.07–6.36)], lipid-lowering therapy [2.09 (1.10-4.00)], stroke education [39.61 (21.92–71.57)] and rehabilitation [10.75 (6.00-19.25)]. The prognostic model demonstrated good validation performance, with an AUROC of 0.94 (HL p = 0.630). The study demonstrates that the guideline-guided prognostic models developed are effective in predicting mortality after a first-ever ischemic stroke. The model holds considerable promise for enhancing clinical decision-making and patient care. Additionally, risk scores generated from these models could be valuable for creating risk calculators, aiding healthcare providers, patients, and other stakeholders in making well-informed clinical decisions in managing patients with stroke.

Health sciences/Health care

Health sciences/Medical research

Health sciences/Neurology

Ischemic stroke

Prognostic model

Mortality

Glasgow Coma Scale

National Stroke Registry

Malaysia

Stroke, the second leading cause of mortality and disability globally, has seen a significant increase in its burden, with 16 million new cases and over six million deaths annually [1]. Developing countries bear the majority of this burden, accounting for stroke-related deaths (~ 75%) and disability-adjusted life years (DALYs) (~ 81.0%) [2]. Ischemic stroke represents about 75–85% of all stroke cases, and approximately 75% are first-time [3]. Asia, home to over half of the world's population, faces a significant stroke burden [4]. In Malaysia, stroke ranks as the third leading cause of death and the second leading cause of disability [5]. Due to an aging population, the incidence of stroke in the country is expected to increase sharply over the next two decades [6].

Studies shows that the highest mortality risk following a stroke occurs within the first 30 days, with an average annual mortality rate of about 10% thereafter [7]. Approximately one-third of stroke patients do not survive beyond three years [8]. In Malaysia, studies have reported an overall mortality rate of 37%, with most deaths occurring within the first month [9, 10]. Factors such as deterioration of the Glasgow Coma Scale (GCS), middle cerebral artery infarct, atrial fibrillation, diabetes, and severe disability are linked with risen mortality after acute stroke [11]. However, the type of stroke care and process could play a significant role in both short and long term prognosis.

Clinical guidelines offer evidence-based recommendations for diagnosing, preventing, and treating diseases [12]. In Malaysia, the latest clinical practice guideline (CPG) for ischemic stroke was released in 2020 [13–15]. The CPG recommended key performance indicators, incorporated into the Malaysian National Stroke Registry (NSR) to evaluate stroke care quality. These indicators include thrombolysis, antithrombotic therapies, dysphagia screening, lipd lowering therapy, deep vein thrombosis (DVT) prophylaxis, anticoagulation for atrial fibrillation (AF), stroke education, and rehabilitation.

Clinical prediction models analyze patient data and treatment processes to estimate the likelihood of future events. These models are particularly useful in stroke care, helping clinicians set goals and make informed decisions based on predicted outcomes [16]. Although many prognostic models exist, most are not stroke-specific, lack generalizability, and have limited clinical utility, especially for long-term care [17]. Examples include the Framingham Score [18], QRISK [19], and EURO-SCORE [20]. For a risk score to be useful, it must be accurate, generalizable, and validated [17, 21]. However, no single stroke prognostic model is universally applicable across all patient subgroups and settings.

Recent data on stroke outcome prediction indicate that models based solely on clinical criteria have limited ability to accurately distinguish between individuals [22]. Therefore, this study aimed to develop and validate guideline-guided prognostic models for predicting mortality in patients with first-ever acute ischemic stroke.

Study design

This was a multi-centered observational cohort study aimed at developing and validating a guideline-guided prognostic model for mortality following a first-ever ischemic stroke in a Malaysian stroke population from 2009 to 2019. The study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [23] and the PROGnosis RESearch Strategy (PROGRESS) framework [24].

Study setting

The study included patients with first-ever ischemic stroke registered in the National Stroke Registry (NSR), a multi-ethnic, multi-centered, and population-based registry established in 2009 under the National Neurology Registry (NNeuR), Ministry of Health (MOH), Malaysia [13, 25]. Malaysia, located in Southeast Asia, consists of Peninsular Malaysia and East Malaysia. The country has thirteen states with a population of approximately 33 million as of 2021, and hosting the Malays, Chinese, Indians, and various other ethnic groups [6].

Study population

The study included adult patients (aged 18 years and above) with a confirmed diagnosis of first-ever ischemic stroke who were enrolled in the NSR between January 1, 2009, and December 31, 2019, and had three months of follow-up data after hospital discharge. A first-ever ischemic stroke is defined as the initial occurrence of an index stroke with a measurable neurological deficit lasting more than 24 hours due to presumed ischemic etiology [26, 27]. Patients with other stroke types, such as transient ischemic attack (TIA) and hemorrhagic stroke, those with recurrent stroke, those with terminal illnesses like cancer, those with significant missing data (> 50%), and those without follow-up data were excluded.

Study outcomes

The primary prognostic outcome assessed was 90-day mortality post stroke event, recorded as death, alive or censored. A 90-day follow-up was chosen because significant stroke outcomes are typically reported within three months after discharge [28].

Data collection

Data collection occurred between January 1, 2020, and January 4, 2022. The study collected data on various variables, including socio-demographic characteristics (e.g., age, gender, ethnicity), clinical characteristics [e.g., Glasgow Coma Scale (GCS) for counciouness assessment [29], Oxfordshire Community Stroke Project (OCSP) classification for stroke subtypes [30], National Institutes of Health Stroke Scale (NIHSS) for stroke severity [31]], comorbidities (e.g., diabetes, hypertension, hyperlipidemia), and the nine key performance indicators.

Model development

Since the model's effectiveness depends on collecting precise and trustworthy data on key predictors, choosing the right study design is essential. [32]. There are various methods for creating a predictive model. The approach of logistic regression, which enables multivariate modeling of a binary dependent variable (e.g., death or alive), is the most widely used and adopted in this study. The multivariate analysis calculates coefficients for each predictor in the final model [33].

Univariable analysis

To determine the associations between sociodemographic, clinical, and key performance indicators with 90 days mortality, a simple binary logistic regression (SLR) was used initially to screen all the independent variables for possible inclusion in the multiple binary logistic regression analysis (MLR) if significant at p < 0.25 or clinically relevant.

Multivariable analysis

The MLR was performed using the Backward LR method. Multicollinearity and interactions between variables were assessed. Model fit assumptions were evaluated using the Hosmer-Lemeshow test, and the Omnibus goodness-of-fit test was conducted. The model's explanatory power was further assessed using Cox and Snell's R² and Nagelkerke's R² [34].

Model validation

The three main areas of model performance that should be evaluated are clinical validity, calibration, and discrimination [35].

Calibration

Calibration, or the reliability of a model, refers to how closely the observed outcomes align with those predicted by the statistical model. Calibration was assessed using the Hosmer-Lemeshow (H-L) goodness-of-fit test. The model calculated individual risk scores, and patients were grouped into risk quintiles rather than deciles due to the relatively small number of observed outcome events. Calibration plots, which visually represent either the clinical risk group analysis or the H-L test, displayed the mean predicted probability of the outcome versus the observed outcome proportion for each group [35]. A well-calibrated model would have an intercept of 0 and a slope of 1 in the calibration plot [35].

Discrimination

Discrimination refers to a model's ability to differentiate between distinct outcomes. The receiver operating characteristic (ROC) curve is a commonly used statistical tool for evaluating a model's discrimination [35, 36]. It plots sensitivity against specificity across various thresholds of predicted risk. The area under the ROC curve (AUC) indicates the model's effectiveness in distinguishing between outcomes, such as mortality and functional disability in stroke patients. The AUC ranges from 0.0 to 1.0, where a value of 1.0 indicates perfect discrimination and 0.5 suggests that the model performs no better than chance. Higher AUC values signify better performance in distinguishing outcomes; thus, a value of 1.0 demonstrates accurate differentiation between outcomes, while a value closer to 0.5 indicates poor discrimination.

Internal validation

A predictive model's performance in the data sample in which it was developed is probably too optimistic because the model was designed to fit this data best [35, 37]. Fewer outcomes and more candidate predictors increase the likelihood of over-fitting [38]. Internal validation of the model is recommended to estimate the potential for over-fitting. Inward approval (reproducibility) is a vital piece of model advancement. It decides the reproducibility of a created expectation demonstrated for the subordinate example and forestalls over-replicating current information. Resampling methods, for example, cross-approval and bootstrapping, can be performed; bootstrap approval seems most alluring for getting steady confidence-adjusted evaluations. The idealism is the decline between model execution (e.g., c-file) in the bootstrap test and the first example, which can modify the created model for over-fitting. To acquire these evaluations, we initially built up the expectation display in the improvement companion (n = 960 in our precedent). After that, we produce a bootstrap test by examining n with substitution from the first example. After producing no less than 100 bootstrap tests, the confidence revised model execution can be acquired by subtracting the evaluated mean of the good faith gauge an incentive from the c-record in the first example.

Sampling and sample size

All adult patients with a confirmed diagnosis of first-ever ischemic stroke enrolled in the Malaysian National Stroke Registry were included in the study.

Sample size calculation

Prognostic studies recommend a minimum sample size of approximately ten events per predictor variable (EPV) to develop binary prediction models and prevent overfitting (Riley et al., 2020).

Sample size for prediction model’s study:

Sample size, n = 10 events per predictor variable (EPV)

number of predictor variables: 10

mortality cases, n_m = 10 x 10 = 100

disability cases, n_d = 10 x 10 = 100

Sample size, n = 10 events per predictor variable (EPV) = 100

Statistical analysis

Statistical analyses were performed following the Statistical Analyses and Methods in the Published Literature (SAMPL) guidelines [39]. The patiens data collected in Microsoft Excel format were imported into IBM SPSS Statistics for Windows, version 26.0 (IBM Corp., Armonk, NY, USA) for staitical data analysis. Missing data were handled using multiple imputation methods. Descriptive statistics summarized categorical variables as frequencies and percentages, while continuous variables were summarized as means (standard deviations, SD).

Binary logistic regression analyzed outcomes related to mortality (dead or alive). A univariable analysis was conducted to identify significant variables for inclusion in the multivariable logistic regression (MLR). The backward elimination method was used for MLR, and multicollinearity was assessed via a correlation matrix. Model fit assumptions were evaluated using the Hosmer-Lemeshow and Omnibus tests. The models were presented as crude/adjusted odds ratios (c/aOR) with 95% confidence intervals (CIs) and p-values.

Sociodemographic and clinical characteristics of the study population

A total of 899 eligible patients with first-ever ischemic stroke were included in the study. The average (± SD) age of the patients was 60.1 ± 10.8, most of whom were 60 years and above. Most of the patients were males (60.8%), belonging to the Malay ethnic group (73.1%), followed by Chinese (14.2%) and Indians (2.4%). The Oxfordshire Community Stroke Project (OCSP) classified the patients into mostly LACI (39.0%) and PACI (29.0%). Others were TACI (15.1%), POCI (11.6%), and unclassified (5.2%). The patients had an average (± SD) GCS score of 14.1 ± 2.1, a scale used to assess the level of consciousness, and were grouped into mostly mild (13–15 points) (83.2%), followed by moderate (9–12 points) (14.1%) and severe (1–8 points) (2.7%). Furthermore, the NIHSS, the patients had an average (± SD) score of 7.9 (7.3), classified the patients as having majorly mild (1–4) (34.4%) and (moderate 5–15) (34.4%) strokes. Other NIHSS scores were none (0) (7.9%), moderate-severe (16–20) (7.7%), and severe (21–42) (7.5%). The study observed fewer patients arriving at the hospital within 3 hours of stroke onset (26.1%) with an average (± SD) hospital stay of 6.3 ± 6.1 days. The patients' common risk factors and comorbid diseases were hypertension 618 (68.7%) and diabetes 421 (46.8%). Another important risk factor was hyperlipidemia 192 (23.7%). The primary outcomes measured at the end of the study was all-cause mortality 266 (29.6%).; The summary of the Sociodemographic and clinical characteristics of the study population were shown in Table 1.

Table 1

Sociodemographic and clinical characteristics of the study population
Variable	Total, n (%) (N = 899)	Mortality, n (%) (N = 266)
Age (years)	61.1 (10.8)^a
Gender
Female	352 (39.2)	108 (40.6)
Male	547 (60.8)	158 (59.4)
Ethnicity
Malay	657 (73.1)	219 (82.3)
Chinese	128 (14.2)	27 (10.2)
India	22 (2.4)	11 (4.1)
Others	92 (10.2)	9 (3.4)
OCSP classification
TACI	136 (15.1)	38 (14.3)
PACI	261 (29.0)	104 (39.1)
LACI	351 (39.0)	80 (30.1)
POCI	104 (11.6)	26 (9.8)
Unclassified	47 (5.2)	18 (6.8)
GCS score	14.1 (2.1) ^a
Mild	748 (83.2)	214 (80.5)
Moderate	127 (14.1)	45 (16.9)
Severe	24 (2.7)	7 (2.6)
NIHSS score	7.9 (7.3) ^a
None	71 (7.9)	14 (5.3)
Mild	309 (34.4)	80 (30.1)
Moderate	383 (42.6)	131 (49.2)
Moderate-Severe	69 (7.7)	14 (5.3)
Severe	67 (7.5)	27 (10.2)
Risk factors
Hypertension	618 (68.7)	192 (72.2)
Diabetes	421 (46.8)	156 (58.6)
Hyperlipidemia	192 (21.4)	68 (25.6)
Ischemic heart disease	87 (9.7)	30 (11.3)
Atrial fibrillation	28 (3.1)	8 (3.0)
Arrival time (≤ 3 hours)	235 (26.1)	58 (21.8)
Keys: GSC: Glasgow Coma Scale; LACI: Lacunar infarct; mRS: modified Rankin Scale; NIHSS: National Institute of Health Stroke Scale; OCSP: Oxfordshire Community Stroke Project; PACI: Partial anterior circulation infarct; POCI: Posterior circulation infarct; SD: standard deviation; TACI: Total anterior circulation infarct.

Factors associated with mortality following first-ever ischemic stroke

The univariable analysis showed that the risk of mortality following the first-ever acute ischemic stroke significantly increased with increasing age (OR, 1.03 [95% CI, 1.01–1.02]; p < 0.001). Compared to the Malay ethnic group, Chinese (OR, 0.54 [95% CI, 0.34–0.84]; p = 0.007) and other ethnics (OR, 0.22 [95% CI, 0.11–0.44]; p < 0.001) had significantly lower risk of death. In addition, mortality significantly increased in patients with the PACI stroke subtype (OR, 1.03 [95% CI, 1.01–1.02]; p < 0.001) compared to TACI. Moreover, increasing the NIHSS score (OR, 1.03 [95% CI, 1.01–1.05]; p = 0.005) significantly increased the risk of death. The risk factors identified to increase the risk of death significantly were diabetes (OR, 1.03 [95% CI, 1.01–1.02]; p < 0.001) and hyperlipidemia (OR, 1.41 [95% CI, 1.01–1.98]; p = 0.047). Adherence to KPIs significantly decreased the risk of mortality in patients that received antiplatelet within 48 hrs. (OR, 0.16 [95% CI, 0.12–0.22]; p < 0.001), dysphagia screening (OR, 0.11 [95% CI, 0.08–0.15]; p < 0.001), antiplatelet upon discharge (OR, 0.10 [95% CI, 0.01–0.14]; p < 0.001), lipid-lowering agent (OR, 0.51 [95% CI, 0.37–0.72]; p < 0.001), stroke education (OR, 0.02 [95% CI, 0.01–0.03]; p < 0.001) and rehabilitation (OR, 0.08 [95% CI, 0.56 − 0.11]; p < 0.001). The summary of the factors associated with mortality following first-ever ischemic stroke were shown in Table 2.

Table 2

Factors associated with mortality following first-ever ischemic stroke: univariable model
Variables	OR (95% CI)	p-value
Age (Years)	1.03 (1.02–1.05)	< 0.001
Gender (Female)	1.09 (0.81–1.46)	0.565
Ethnicity
Malay	1	1
Chinese	0.54 (0.34–0.84)	0.007
India	2.00 (0.85–4.69)	0.111
Others	0.22 (0.11–0.44)	< 0.001
Stroke subtype
TACI	1	1
PACI	1.71 (1.09–2.68)	0.019
LACI	0.76 (0.49–1.19)	0.235
POCI	0.86 (0.48–1.54)	0.610
Unclassified	1.60 (0.80–3.22)	0.186
GCS score (1–15)	0.95 (0.89–1.02)	0.134
NIHSS score (0–42)	1.03 (1.01–1.05)	0.005
Arrival time (≤ 3 hours)	0.73 (0.52–1.02)	0.069
Risk factors
Hypertension	1.26 (0.92–1.73)	0.150
Diabetes	1.97 (1.47–2.63)	< 0.001
Hyperlipidemia	1.41 (1.01–1.98)	0.047
Ischemic heart disease	1.29 (0.81–2.05)	0.294
Atrial fibrillation	0.95 (0.41–2.19)	0.905
KPIs adherence
Thrombolysis	0.43 (0.09–1.95)	0.272
Antiplatelet within 48 hrs.	0.16 (0.12–0.22)	< 0.001
Dysphagia screening	0.11 (0.08–0.15)	< 0.001
DVT prophylaxis	1.01 (0.73–1.40)	0.968
Antiplatelet upon discharge	0.10 (0.07–0.14)	< 0.001
Anticoagulant for AF	0.92 (0.57–1.50)	0.744
Lipid-lowering agent	0.51 (0.37–0.72)	< 0.001
Stroke education	0.02 (0.01–0.03)	< 0.001
Rehabilitation	0.08 (0.56 − 0.11)	< 0.001
Keys: KPIs: Key performance indicators; GSC: Glasgow Coma Scale; LACI: Lacunar infarct; mRS: modified Rankin Scale; NIHSS: National Institute of Health Stroke Scale; OCSP: Oxfordshire Community Stroke Project; PACI: Partial anterior circulation infarct; POCI: Posterior circulation infarct; SD: standard deviation; TACI: Total anterior circulation infarct; OR: Crude odds ratio; Statistical significance at p < 0.05 using Simple Logistic Regression.

Predictors of mortality following first-ever ischemic stroke

After adjusting for confounders, the independent factors associated with increased risk of mortality after first-ever ischemic stroke include increasing age (aOR, 1.06 [95% CI, 1.03–1.10]; p < 0.001), increasing NIHSS score (aOR, 1.08 [95% CI, 1.02–1.13]; p = 0.004) and having diabetes (aOR, 2.29 [95% CI, 1.20–4.37]; p = 0.012). Adherence to KPIs significantly decreased the risk of death in patients that received antiplatelet within 48 hrs. (aOR, 0.40 [95% CI, 0.19–0.81]; p = 0.011), dysphagia screening (aOR, 0.30 [95% CI, 0.15–0.61]; p = 0.001), antiplatelet upon discharge (aOR, 0.17 [95% CI, 0.08–0.35]; p < 0.001), lipid-lowering agent (aOR, 0.37 [95% CI, 0.17–0.82]; p = 0.013), stroke education (aOR, 0.02 [95% CI, 0.01–0.05]; p < 0.001) and rehabilitation (aOR, 0.08 [95% CI, 0.04–0.16]; p < 0.001). The summary of the predictors of mortality following first-ever acute ischemic stroke for the multivariable model were shown in Table 3.

Table 3

Predictors of mortality following first-ever ischemic stroke: multivariable model
Variables	β (SE)	aOR (95% CI)	p-values
Age	0.060 (0.017)	1.06 (1.03–1.10)	< 0.001
NIHSS score	0.072 (0.025)	1.08 (1.02–1.13)	0.004
Risk factors
Diabetes	0.830 (0.329)	2.29 (1.20–4.37)	0.012
KPIs adherence
Antiplatelet in 48 hrs.	-0.926 (0.366)	0.40 (0.19–0.81)	0.011
Dysphagia screening	-1.191 (0.356)	0.30 (0.15–0.61)	0.001
Antiplatelet upon discharge	-1.760 (0.366)	0.17 (0.08–0.35)	< 0.001
Lipid-lowering therapy	-0.986 (0.399)	0.37 (0.17–0.82)	0.013
Stroke education	-3.770 (0.368)	0.02 (0.01–0.05)	< 0.001
Rehabilitation	-2.543 (0.364)	0.08 (0.04–0.16)	< 0.001
Intercept	1.005 (1.089)
Keys: KPIs: key performance indicators; β: regression coefficient; SE: standard error; GCS: Glasgow coma score; LACI: Lacunar infarct; mRS: modified Rankin Scale; NIHSS: National Institute of Health Stroke Scale; OCSP: Oxfordshire Community Stroke Project; PACI: Partial anterior circulation infarct; POCI: Posterior circulation infarct; RM: Malaysian Ringgit; SD: standard deviation; TACI: Total anterior circulation infarct; IHD: Ischemic heart disease; *: Statistical significance at p < 0.05 using Multiple Logistic Regression

Model validation for predicting mortality in first-ever ischemic stroke

Both development and validation models demonstrated excellent discrimination (AUC) and calibration (Hosmer-Lemeshow X² test). The development and validation models showed the AUC were 0.965 (95% CI, 0.95–0.98) vs 0.941 (95% CI, 0.92–0.96), the H-L X² test were 15.52 (p = 0.05) vs 4.35 (p = 0.630), the Omnibus goodness-of-fit X² test were 582.49 (p < 0.001) vs 142.53 (p < 0.001) and Nagelkerke R2 values were 0.800 and 0.741, respectively. The summary of the model validation and performance results are shown in Table 4 and Fig. 1.

Table 4

Model validation for predicting mortality following first-ever ischemic stroke
Performance measures	Models
	Development	Validation
Discrimination (AUC)	0.965 (0.95–0.98)	0.941 (0.92–0.96)
Calibration (H-L test)	X² = 15.52 (p = 0.050)	X² = 4.35 (p = 0.630)
Omnibus test (Goodness-of-fit)	X² = 582.49 (p < 0.001)	X² = 142.53 (p < 0.001)
Nagelkerke R square (R²)	0.800	0.741
Keys: AUC: area under the curve; H-L: Hosmer-Lemeshow; CI: confidence interval, X²: chi-square

The study developed and validated guideline-guided prognostic models of 90-day in patients with a first-ever ischemic stroke in a Malaysian stroke population with favorable validation performance. Significant prognostic indicators of stroke typically become evident within 30–90 dyas after a patient's discharge, with minimal changes observed beyond six months [28].

The average age of the patients was 60.1 (± 10.8) years, the majority of whom were 60 years and above, mostly the male gender belonging to the Malay ethnic group. Studies conducted in Malaysia reported a comparable average age of patients with ischemic stroke of 59.0 to 62.8 years [40–43]. The prevalence of older patients with stroke varies among countries. Comparable demographics, especially the higher age and male gender, were shown in neighboring countries like Singapore [44, 45], Thailand [46–48], Indonesia [49] and slightly higher in China [50–52] and Taiwan [53, 54]. However, studies from Western countries, particularly the US, UK and the EU, reported older age of 70 and above years among their stroke population [55–58]. Geographic disparities worldwide often impact sociodemographic characteristics. Stroke can occur at any age, but most cases involve people over 65, making it an age-related disease [59]. Age is the primary non-modifiable risk factor for stroke, with older stroke patients facing poorer functional recovery, higher mortality, and increased morbidity compared to younger patients [60]. The role of gender in ischemic stroke is significantly modified by patient age [61]. At an early age, the burden of acute ischemic stroke is higher in men with poorer functional outcomes [62]. However, it becomes more frequent and unbearable for elderly women, likely due to longer lifespans, hormonal differences, menopause and lifestyle factors [60]. Furthermore, the ethnic composition consisted of 73.1% Malay, followed by Chinese, Indians and others. The ethnic distribution represents the racial distribution of Malaysia's predominantly Malay population [63].

In this study, the distribution of stroke subtypes according to the OCSP classification was majorly LACI (39%), followed by PACI (29%), POCI (15%) and TACI (11%). The higher number of patients with LACI and lower with TACI stroke subtypes are consistent with previous studies from China [64, 65]. However, it is important to acknowledge that the distribution of stroke subtypes may vary based on factors such as the study environment, sample size, and inclusion criteria (e.g., first-time or recurrent stroke) [66]. These variations can impact the generalizability of the findings and should be considered when interpreting the results. Larger sample sizes and studies conducted in diverse populations can help provide a more comprehensive understanding of the distribution of stroke subtypes and improve the generalizability of the findings.

The GCS score was used to assess the level of consciousness in the studied stroke population. Due to the high capability of the GCS to predict outcomes (morbidity and mortality) after stroke [67], the scale is routinely used to evaluate stroke patients upon admission. In the present study, the average GCS score was recorded as 14.1 (2.1), with the majority grouped as mild 13–15 points (83.2%), followed by moderate 9–12 points (14.1%) and severe 1–8 points (2.7%). Previous studies on acute ischemic stroke have reported comparable average GCS scores and more patients with mild stroke [40]. Moreover, a higher percentage of mild GCS scores observed in this study may be attributed to a higher prevalence of the LACI subtype, which typically presents milder symptoms [66]. In the current study, many patients with minor GCS scores were diagnosed with LACI ischemic stroke subtype. This finding highlights the importance of considering the specific stroke subtypes when interpreting GCS scores and assessing the severity of the condition [68].

The National Institutes of Health Stroke Scale (NIHSS) is a tool healthcare providers use to assess and quantify the severity of stroke symptoms in patients. It is a standardized assessment tool that evaluates the patient's ability to perform various physical and cognitive tasks [69]. The NIHSS assigns numerical scores to 15 stroke-related categories from 0 (neurologically intact) to 42 (general score) (severe and comatose). Most of the stroke patients in this study were classified using the NIHSS as moderate (5–15 points) 42.6% and mild (1–4 points) 34.4% stroke with an average score of 7.9 (± 7.3). Similar studies on acute ischemic stroke reported comparable average NIHSS scores, with the majority having mild to moderate stroke upon admission [70, 71].

Hypertension (68.7%) and diabetes (46.8%) were the most common risk factors among the patients with acute ischemic stroke, followed by cigarette smoking 845 (39.2%) and hyperlipidemia (23.7%). Studies from other countries showed comparable results with hypertension, diabetes and hyperlipidemia consistently predominant [70–72]. These risk factors were prevalent in many Asian studies, including studies from northern China and Southeast Asia [46, 73, 74]. Specifically, a retrospective study of patients with acute ischemic stroke reported stroke risk factors of hypertension (77.5% vs 80.4%), diabetes (41.5% vs 38.7%) and dyslipidemia (47.9% vs 62.0%), in Taiwan vs Portugal, respectively [53, 71]. The ability to identify and target at-risk groups for stroke prevention or improved clinical management depends on understanding each risk factor's role and how they interact and affect the many subtypes of stroke. In this study, the primary outcomes measured among patients with first-ever acute ischemic stroke were mortality (29.6%). The findings highlight the significant impact of acute ischemic stroke on mortality rates resulting from the condition.

Our study showed that the clinical prediction models could reliably predict 90-day mortality following a first-ever ischemic stroke with good validation performance. Age emerged as a significant predictor, with research indicating that post-stroke mortality increases with advancing age. The 30-day fatality rate rises from 9% in those aged 65 to 74 to 13.1% for those aged 74 to 84, and reaches 23% for individuals over 85 [75]. Additionally, 28% of patients over 80 died within 90 days, compared to 13% of those under 80 [76]. Thus, advancing age is a key risk factor for increased mortality after stroke.

The study's results indicate that certain factors significantly increase mortality risk after acute ischemic stroke. Increasing age and NIHSS scores were found to increase the risk of mortality, while having diabetes was also found to be a significant risk factor. On the other hand, adherence to KPI guidelines was shown to decrease the risk of death in patients who received antiplatelet within 48 hrs., underwent dysphagia screening, received antiplatelet upon discharge, were prescribed lipid-lowering agents, received stroke education, or underwent rehabilitation. These findings are consistent with previous research highlighting the importance of adherence to evidence-based guidelines in improving outcomes after stroke [77, 78]. Additionally, the study highlights the importance of dysphagia screening in reducing mortality risk, consistent with previous studies showing a higher incidence of pneumonia and mortality in stroke patients with dysphagia [79]. The study's findings suggest that providing stroke education to patients and their caregivers and offering rehabilitation services can significantly decrease mortality risk after stroke.

The results of both development and validation models showed excellent discrimination and calibration. This study evaluated discrimination using the AUC, and calibration using the Hosmer-Lemeshow X² test and the Omibus goodness-of-fit X² test. The development and validation models had similar discrimination, with AUCs of 0.965 and 0.941, respectively. This suggests that the models could accurately distinguish between individuals with and without the stroke outcome of mortality. However, the calibration of the two models differed slightly. The HL test showed that the development model had a p = 0.050, indicating borderline significance, while the validation model had a p = 0.630, indicating good calibration. The OG test showed that the development model had a much higher X² value than the validation model (582.49 vs 142.53), indicating that the development model may be overfitting the data. It is important to note that both models had high Nagelkerke R² values (0.800 and 0.741), indicating that the models explained a large proportion of the variability in the outcome. These results suggest that both models had good discriminatory power and were well-calibrated, although the development model may be overfitting the data.

It is worth comparing the results of this study with those of previous studies that have developed and validated prediction models for mortality following stroke. For example, a study by Xian et al. (2016) developed and validated a prediction model for inpatient mortality among acute ischemic stroke patients [80]. They reported an AUC of 0.88 for the validation model, which is lower than the AUC reported in the present study. However, their model included fewer variables than the present study and was developed using data from a single hospital, whereas the present study used data from multiple centers. In another study by Saposnik et al. (2011), a prediction model was developed and validated for functional outcomes among acute ischemic stroke patients [81]. They reported an AUC of 0.79 for the validation model, which is lower than the AUC reported in the present study. However, their model focused on functional outcomes rather than mortality and used different predictor variables. The present study developed and validated a prediction model for mortality among acute ischemic stroke patients, and the results suggest that the model has good discriminatory power and is well-calibrated.

The study has several strengths and limitations to consider when interpreting the findings. One of the limitations is that the study utilized administrative data from a registry. However, it provides a large and representative sample of patients with acute ischemic stroke but could have data accuracy and completeness issues. Despite this, efforts were made to ensure data quality through strict quality control measures. However, caution is still needed when interpreting the findings and applying the models to other populations. As an observational registry-based study, the findings may be subject to inherent biases and confounding factors that could influence the results. The generalizability of the findings to other populations should be approached with caution, as the study focused on a specific patient cohort. External validation of the developed models is necessary to assess their applicability in different settings. Moreover, it is crucial to note that the study only calculated the direct cost of inpatient care, which may not fully represent the overall cost of stroke care, including indirect and post-discharge expenses. To strengthen future research in this area, it is recommended to conduct external validation studies to assess the generalizability of the findings. Incorporating diverse populations and utilizing multi-center studies can enhance the representativeness of the results. Additionally, expanding the cost analysis to include indirect costs and long-term follow-up can provide a more comprehensive understanding of the economic impact of stroke care. By addressing these limitations and building upon the study's strengths, future research can contribute to a more robust and nuanced understanding of the costs and outcomes associated with acute ischemic stroke care.

One strength of the study is the use of a large and representative sample of patients from a national data source, which enhances the generalizability of the findings to the wider stroke population in Malaysia. This increases the reliability of the prediction models developed in the study and makes them more applicable to clinical practice. Another strength of the study is the development of valid guideline-guided prediction models, which could help healthcare providers identify patients at high risk of poor outcomes and guide treatment decisions. This can ultimately lead to better outcomes for stroke patients and more efficient use of healthcare resources. Furthermore, the study provided a cost estimate of inpatient stroke care, which can help stakeholders better understand the economic burden of stroke and allocate resources more effectively. This information can inform policy decisions and resource allocation to improve the quality and accessibility of stroke care.

The significance of the current study lies in the fact that it included patients with first-ever acute ischemic stroke due to the need for a homogenous population with consistent baseline clinical features. This unique patient group aimed to minimize potential confounding factors and draw definite conclusions specific to patients with their first-ever acute ischemic stroke. Furthermore, considering that ischemic stroke is the most prevalent type of stroke, our research can impact a significant proportion of the stroke population, enhancing the relevance and applicability of our findings. Accurate outcome prediction in acute ischemic stroke is essential for enabling clinicians to make timely, informed decisions. Recognizing the potential long-term health and financial impacts of acute ischemic stroke is important for patients, families, and society. A precise prognosis ensures that adequate resources are allocated to support stroke survivors and allows for the assessment of the long-term effects of comprehensive strategies related to stroke awareness, prevention, and treatment, emphasizing the importance of structured stroke unit care and rehabilitation.

The study established a validated population-based guideline-guided prognostic model that predicts mortality and functional disability following a first-ever acute ischemic stroke. The model showed promising validation performance and potential clinical utility. These models can aid in predicting post-stroke outcomes, offering valuable support for patient management and clinical decision-making. Moreover, the model scores may serve as the foundation for developing user-friendly web-based risk calculators, facilitating informed clinical decision-making by healthcare providers, patients, and other stakeholders. The current study highlights the stroke-registry's continued relevance as a national stroke registry that supports active initiatives to enhance evidence-based stroke care and innovative research.

Acknowledgment

We also thank the Director-General of Health Malaysia for permission to publish this article. We also thank the research assistants, the Malaysian National Stroke Registry (NSR), and all staff for facilitating the data collection process.

Funding

None

Author Contributions

MM was involved in the Conceptualizatio; Methodology and software; Data curation, Formal analysis and investigation; Writing – original draft preparation; Writing – review and editing; Resources and validation; Author have read and approved the final manuscript.

Competing interests

The author(s) declare no competing interests.

Data availability statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Ethics declarations

This study was performed in line with the principles of the Declaration of Helsinki. The study was registered with the National Medical Research Register (NMRR) of Malaysia with registration number NMRR-19-3846-52122(IIR) and approved by the Medical Research and Ethics Committee, Ministry of Health Malaysia with approval number KKM/NIHSEC/P20-307(6).

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No competing interests reported.

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Guideline-Guided Prognostic Models of Mortality Following First-Ever Ischemic Stroke

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Abstract

Figures

Introduction

Methods

Study design

Study setting

Study population

Study outcomes

Data collection

Model development

Univariable analysis

Multivariable analysis

Model validation

Calibration

Discrimination

Internal validation

Sampling and sample size

Sample size calculation

Statistical analysis

Results

Sociodemographic and clinical characteristics of the study population

Factors associated with mortality following first-ever ischemic stroke

Predictors of mortality following first-ever ischemic stroke

Model validation for predicting mortality in first-ever ischemic stroke

Discussion

Conclusion

Declarations

References

Additional Declarations

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