To the best of our knowledge, this study is the first to use forecasting models to analyze 30-day readmission in patients with stroke. Accuracy in predicting 30-day readmission in patients with stroke was compared among the five forecasting models. When all models were constructed using a given set of clinical inputs, the ANN model was clearly superior to other forecasting models. Additionally, unlike previous works in which the analyses were performed using a dataset for a single medical center, our study used prospective and longitudinal data from multiple medical institutions, which provides a more accurate depiction of current treatment for patients with stroke [10–13]. Additionally, unlike single-center series studies, our use of registry data provides more accurately depicts stroke treatment in large populations. Using registry data also minimizes referral bias or bias caused by the practices of a single physician or a single institution [25, 26].
Recent works have repeatedly demonstrated the superior performance of the ANN model compared to other models [9, 13]. The advantages offered by the unique characteristics of the ANN model have been confirmed by statistical analyses. For example, using an ANN model can enable more appropriate and more accurate processing of inputs that are incomplete or inputs that introduce noise [9, 27]. Another advantage is that linear and non-linear ANN models with good potential for use in large-scale medical databases can be constructed using data that are highly correlated but not normally distributed. Prognosis prediction is only one of the many applications of ANN models in clinical research in the medical field [27].
The comparisons of various models in the present study suggest that, by expanding the number of potential predictors, the ANN model facilitates systematic analysis of various diseases and facilitates comparisons of the effectiveness of research methods. Additionally, the proposed model can be extended to outcome prediction for treatments other than PAC and in patients other than patients with stroke.
The global sensitivity analysis of the weights of significant predictors of 30-day readmission in the patients with stroke in this study revealed that the best predictor was PAC. This finding is consistent with earlier reports that, compared to all other stroke treatment variables, PAC has the largest effect on outcome in terms of overall treatment cost, functional status after stroke, and duration of hospital stay before transfer to rehabilitative ward [25, 28]. Wang et al. coupled a natural experimental design with propensity score matching to assess the impact of PAC in stroke patients and to examine the longitudinal effects of PAC on functional status [25]. The study concluded that intensive rehabilitative PAC delivered on a per-diem basis substantially improves functional status in patients with stroke. Another recent study compared a wide range of functional domains between a stroke PAC group and a well-matched nationwide cohort of patients with stroke who did not receive PAC [29]. The authors similarly reported that the stroke PAC group had significantly better outcomes in terms of restoration of functional impairments, 90-day clinical outcomes, and healthcare utilization.
The present study found that, before rehabilitation, NG tube insertion was significantly associated with 30-day readmission (P < 0.001). During the study period, no patient with stroke required NG tube insertion after rehabilitation. Previous works indicate that an NG tube insertion may be beneficial in acute stroke. However, prolonged use is associated with poor prognosis [30, 31]. A large clinical trial recently reported that, at 6 months after stroke, survival and other medical outcomes are better in patients who have had NG tubes removed compared to those who still require NG tubes at 6 months [30]. As reported previously in Ho et al., our study found that removal of NG tube early after stroke is associated with reduced rate of readmission, reduced incidence of pneumonia, and reduced mortality [31].
Hemorrhagic stroke is associated with a higher readmission rate and higher mortality compared to ischemic stroke [32, 33]. It is generally more severe in hemorrhagic stroke than in ischemic stroke. In the first 3 months after stroke, readmission and mortality are higher in hemorrhagic stroke, and both readmission and mortality are independently associated with the hemorrhagic of the stroke lesion. In the present study, 30-day readmission was higher in the hemorrhagic patients with stroke compared to ischemic patients with stroke.
This prospective observational study of a cohort of patients with stroke in Taiwan analyzed data for patients treated at multiple healthcare institutions. The ANN model developed in this study improves accuracy in identifying correlations between predictors and 30-day readmission in patients with stroke. However, the proposed predictive model has many other potential clinical applications. For example, healthcare institutions can improve care quality by using the methods developed in this study to evaluate the effectiveness of medical treatment. Since the proposed ANN model accurately predicts 30-day readmission, healthcare administrators at other institutions can use the model to demonstrate the need for prompt and appropriate PAC for patients with stroke. A broader application of the model is in facilitating the formulation and promotion of healthcare policies and the development of decision-support systems in Taiwan, which would ultimately enhance the health of all citizens. However, further studies are needed to determine the true clinical relevance of the ANN model and to clarify whether clinicians can effectively use the model to predict prognosis and to optimize medical management for patients with stoke.
To confirm our data for the PAC program significantly associated with 30-day readmission in patients with stroke, Table 6 presents an international data comparison. The comparison includes this and three other selected studies of similar population in the United States and Taiwan [34–36]. These studies all shared the following features: 1) sample size was relatively large, 2) mean age of study sample was similar to that in the present study, 3) data sources from the State or National datasets, and, most importantly, 4) to explore 30-day readmission measures in patients with stroke. All of these previous studies are consistent with reported findings in the present study that multidiscipline PAC program can significantly reduce 30-day readmission in patients with stroke compared with the non-PAC program (P < 0.001).
Table 6
Post-acute care (PAC) program predictor significantly associated with 30-day readmission in patients with stroke
Authors (area) | No. of subjects | Age (range) | Data source | Findings |
Present Study (Taiwan) | 1,476 | 65.5 | PAC ward at one of four community hospitals (three regional hospitals and one district hospital) or a traditional non-PAC ward at one of two medical centers. | PAC program was the most significant variable affecting 30-day readmission. |
Kosar CM, et al. (2020) (U.S.) | 2,044,231 | 80.2 | The Medicare Provider Analysis and Review database. | For patients from the most rural counties, adjusted 30-day readmission rates were 0.3 (95% CI, -0.6 to -0.1) percentage points lower than those of patients receiving postacute care. |
Raman N, et al. (2020) (U.S.) | 1,613 | 74.4 | The State Inpatient Database in California. | Clinical predictors of 30-day readmissions included several comorbidities (i.e. liver disease, hypertension), and discharge to a postacute care facility. |
Hsieh CY, et al. (2018) (Taiwan) | 6,839 | 69.4 | Taiwan National Health Insurance claims datasets. | The 30-day readmission rates were 15.5% for the PAC group, compared to 30.4% for the non-PAC group. |