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Page 10 of 12 Hsu et al. Vessel Plus 2021;5:2 I http://dx.doi.org/10.20517/2574-1209.2020.45
[46]
to predict 30 days of survival with 0.84 to 0.88 AUC. Muscari et al. proposed a multiple regression
[8]
model to predict 9-month mRS with an AUC of 0.84. Teale et al. reviewed 17 models using two to eleven
[27]
variables to predict 30-180 days outcome, and their AUCs ranged from 0.75 to 0.88. Wouters et al.
built a multivariate model utilizing baseline NIHSS and age to predict 90-day mRS, and the AUC was
[25]
0.86. Jampathong et al. reviewed 23 prognostic models for complete recovery in ischemic stroke, and
the pooled AUC of these models was 0.78. Although different prognostic models were attempted with
[8]
reasonable performance, they were built to a unique model with fewer cases and specific populations .
This study proposed unique prognostic models for the nationwide Taiwanese population with significant
performance improvements than previously described. In our study, the AUC of our statistical LR models
at admission and discharge were 0.85-0.87 and 0.95-0.96 higher than any previously reported and with
fewer selected features between 2-18 and 2-8 in four different subgroups (male, remale, ischemia, and
hemorrhage). In addition, the sample sizes of previous studies ranged only from hundreds to thousands,
and this study employed 37,094 stroke patients with high-quality datasets that were clinically validated by
machine learning methods previously reported .
[11]

Our current study has some limitations that may have prevented us from achieving even greater
performance. First, the prediction model was based on a prospective cohort study dataset in a specific
population based on TSR; thus, our specific findings were limited to variables available from the registry.
Some important prognostic variables were not included, such as pre-stroke medical history, previous
acute events, lifestyle information, and socioeconomic status. Second, heparin, IA thrombolysis, IV t-PA,
Foley, and rehabilitation showed strong adverse effects on stroke outcomes [Table 1]; the results were likely
confounded by indications (e.g., stroke severity), and relatively unbalanced case numbers in each subgroup,
and these factors were not selected in the final models. In a future study, we aim to build separate models
for specific patient populations to improve performance further and work toward establishing the clinical
tools to help improve stroke care and outcomes.

In conclusion, modeling of clinical assessment variables for stroke outcome prediction was found to be
population-specific. The study proposed prognostic models for predicting stroke outcomes with exceptional
performance that employed a significantly large sample size of nationwide stroke patients of the Taiwanese
population. Our study identified important clinical variables collected at admission and discharge to
build prediction models in four different patient subgroups, and these variables can be further reduced to
only a few (2-18) variables with similar performance. The results might provide insight information for
interventions to improve stroke care and outcomes. Our proposed models achieved significantly better
prediction performance than previously reported models. It should be noted that prognostication or triage
in the acute stroke period is critical but complicated, and current prediction models will need to be further
investigated and validated in prospective studies before being developed into useful tools to assist clinicians
in emergency settings.

DECLARATION
Authors’ contribution
Conceived the idea of the study, implemented the logistic regression approaches and drafted the
manuscript: Hsu KC
Performed the statistic analyze and interpreted the results: Lin CH, Johnson KR
Provided practical suggestion to this study: Hsu CY
Processed and provided dataset: Tsai CH, Chen PL, Chang WL, Yeh PY, Wei CY
Provided key support, coordinated cooperative organizations and input practical concerns to the study:
Fann YC
Contributed to the review of the manuscript and approved the final version: Hsu KC, Lin CH, Johnson KR,
Fann YC, Hsu CY, Tsai CH, Chen PL, Chang WL, Yeh PY, Wei CY