Web-based tool for dynamic functional outcome after acute ischemic stroke and comparison with existing models

Stroke is one of the leading causes of death and adult disability worldwide and acute ischemic stroke (AIS) accounts for about 85% of all stroke cases [1],[2]. At time of admission, clinicians are frequently requested to estimate the likelihood of good functional outcome in post-stroke period. During the past decades, a number of prognostic models for AIS have been developed [3]-[18]. Except the IScore and ASTRAL score [15],[17], majority of them have not been externally validated. Meanwhile, none of them is recommended by international stroke guidelines and widely used in routine clinical practice or clinical trials [3],[19]. In addition, the majority of existing models are designed to provide prognostic information for a single time point after AIS, such as at discharge [13], 30-day [10], 3-month [4]-[7],[9],[17], 6-month [14], 9-month [16] or 1-year [8],[11] after onset. Although few recently proposed risk models could provide dynamic prognostic information at multiple time points after AIS [15],[18], they mainly focused on mortality instead of functional status. In real-world practice, patients, family members, clinicians and researchers usually concern about not only the likelihood of survival, but also the likelihood of survival with function recovery after AIS.

In the study, we aimed to develop and validate a risk model to provide dynamic prognostic information on functional status at multiple time points (discharge, 3-month, 6-month, and 1-year) after AIS (Dynamic Functional Status after Acute Ischemic Stroke, DFS-AIS). Furthermore, we compared discrimination of the DFS-AIS and existing AIS models with regard to both functional outcome and mortality at discharge, 3-month, 6-month, and 1-year after AIS.

In the study, we developed and validated a risk model to predict functional outcome at multiple time points (discharge, 3-month, 6-month, and 1-year) after AIS by using information routinely available at presentation. A web-based risk model (the DFS-AIS) was developed based on the set of independent predictors of each outcome measures and showed good discrimination and calibration in large derivation and validation cohorts. When compared to 8 existing models, the DFS-AIS showed significantly better discrimination with regard to both functional outcome and mortality at discharge, 3-month, 6-month, and 1-year after AIS.

In order to preserve clinical utility of the model for decision-making during acute hospitalization and postdischarge, we used only patient characteristics available at presentation. We chose not to include variables related to in-hospital and postdischarge management, such as intravenous recombinant tissue plasminogen activator (rt-PA) [22], rehabilitation, and persistence of evidence-based secondary prevention medications [27]-[29], despite the fact that these therapies could influence functional status following AIS. This model therefor predicts the expected functional status following AIS at presentation.

For a clinical prognostic rule to become effective and widely used, it must be reliable, accurate and practicable [3]. For reliability, the DFS-AIS was developed based on large derivation and validation cohorts, which included consecutive patients of AIS from a nationwide stroke registry, was outside of clinical trials, and was more reflective of real-world clinical practice; In addition, by sensitivity analysis, the DFS-AIS demonstrated to be effective for patients with various clinical characteristics, such as different age, gender, time intervals from stroke onset to hospital arrival, and OCSP subtype. For accuracy, the DFS-AIS was proven to be accurate in risk-stratification and outcome prediction for both functional outcome and mortality at multiple time points after AIS. For practicability, the DFS-AIS consists of factors that are readily available at hospital presentation. In addition, by a web-based and user-friendly calculator, users could obtain prognostic information at multiple time points following AIS without doing complex calculation.

To the best of our knowledge, we are the first to systematically compare AIS risk models in a single population. During the past decades, a multitude of AIS predictive models have been developed. Although each of these prognostic rules has inherent strengths and limitations, none of them is widely accepted and used in routine clinical practice or clinical trials [3],[19]. With many AIS prognostic rules available, identification of the most accurate and reliable model(s) would be of great value to patients, family members, clinicians, and researchers. In this study, we found that the DFS-AIS consistently demonstrated significant better discrimination than 8 compared models for both functional outcome and mortality at discharge, 3-month, 6-month, and 1-year after AIS (Figure 2, Table S1, and S2). Though promising, one should exercise caution when interpreting the results: first, the study populations for derivation and validation of these AIS risk models are different. The clinical characteristics of our study population were different from those of western cohorts used to develop compared models [13],[15],[17],[18], such as with younger age of AIS onset, higher proportion of male gender, less severity of neurological deficit on admission, and lower rate of mortality at different time points following AIS. It is not our primary aim to compare difference of these cohorts and it is hard to explain the potential reasons due to sorts of difference in study design and population. Second, there might be complex genetic, social, culture, economic factors as well as regional management philosophies and preferences that are difficult to account for when prognostic models are developed or applied to a distinct population. Finally, the intended outcome (functional outcome vs. mortality), outcome assessment methods (Barthel Index vs. mRS), and timing of follow-up (in-hospital vs. 30-day vs. 3-month vs. 6-month vs. 1-year after AIS) are different for these AIS models. One might argue it is not fair to directly compare these models for functional outcome and mortality at various time points after AIS as that they were not designed in that way. However, our intention was not to show limitation of these models beyond the context under which they are developed, but to provide evidence on which model is more appropriate for what kind of outcome (functional outcome vs. mortality) and at what specific time points (discharge, 3-month, 6-month, and 1-year) after AIS.

By offering prediction of functional outcome at multiple time points following AIS, rather than only mortality, the DFS-AIS can be useful for patients, family members, clinicians and researchers, whose primary concern is not the likelihood of survival, but rather the likelihood of survival with recovery of function. This is also important since different clinical care contexts or clinical research may focus on short-term or long-term functional outcome after AIS differently. Meanwhile, the DFS-AIS could be used to monitor the performance of hospitals or stroke units in stroke care. Those hospitals or stroke units which deviate in outcome from the expected outcome (e.g. worse outcome) need to be monitored in detail to identify the potential targets for improvement. In addition, like other prognostic tools, the DFS-AIS may help in randomized clinical trials to stratify study population, in nonrandomized clinical trials to control for case-mix variation, and in controlled clinical trials to select suitable patients and to reduce required sample size [19],[30],[31].

Our study has limitations that deserve comment. First, like all observational studies, we cannot rule out the possibility that additional variable (unmeasured confounders) might have some impact on functional status after stroke, such as intravenous rt-PA, neuroimaging characteristics, rehabilitation, and persistence of evidence-based secondary prevention medications. However, given our emphasis on early prediction using information at presentation to predict dynamic functional outcome after AIS, the DFS-AIS might be helpful to guide subsequent in-hospital and post-discharge management. Second, our study included only hospitalized stroke patients and those patients died in emergency department or treated in outpatient clinics were not included. Meanwhile, like most registries, our registry required informed consent and selection bias was inevitable [32]. Third, due to that the Medical Research Council score, volume of ischemia on diffusion-weighted imaging, Charlson comorbidity index, Consortium for the Investigation of Vascular Impairment of Cognition scale score, body temperature, and oxygen administration were not routinely collected in the CNSR, several other AIS models cannot be externally validated in the study [4]-[6],[8],[11],[14],[16]. Finally, the DFS-AIS is not externally validated in the study. Meanwhile, both the derivation and validation cohorts originated from Asian population, and therefore, the score needed to be further validated in non-Asian populations.

The DFS-AIS is a valid clinical model for predicting functional outcome at discharge, 3-month, 6-month, and 1-year after AIS. For clinical practice, the DFS-AIS can be useful for patients, family members, and clinicians by offering prediction of functional outcome at multiple time points following AIS. For clinical research, the DFS-AIS may help in randomized clinical trials to stratify study population, in nonrandomized clinical trials to control for case-mix variation, and in controlled clinical trials to select suitable patients and to reduce required sample size. Further validation of the DFS-AIS in different populations is required.