In-hospital fall prediction using machine learning algorithms and the Morse fall scale in patients with acute stroke: a nested case-control study.

BACKGROUND: Falls are one of the most common accidents in medical institutions, which can threaten the safety of inpatients and negatively affect their prognosis. Herein, we developed a machine learning (ML) model for fall prediction in patients with acute stroke and compared its accuracy with that of the existing fall risk prediction tool, the Morse Fall Scale (MFS). METHODS: This is a retrospective nested case-control study. The initial sample size was 8462 admitted to a single cerebrovascular specialty hospital with acute stroke. A total of 156 fall events occurred, and each fall case was randomly matched with six control cases. Six ML algorithms were used, namely, regularized logistic regression, support vector machine, naïve Bayes (NB), k-nearest neighbors, random forest, and extreme-gradient boosting (XGB). RESULTS: We included 156 in the fall group and 934 in the non-fall group. The mean ages of the fall and non-fall groups were 68.3 (± 12.2) and 65.3 (± 12.9) years old, respectively. The MFS total score was significantly higher in the fall group (54.3 ± 18.3) than in the non-fall group (37.7 ± 14.7). The area under the receiver operating curve (AUROC) of the MFS in predicting falls was 0.76 (0.73-0.79). XGB had the highest AUROC of 0.85 (0.78-0.92), and XGB and NB had the highest F1 score of 0.44. CONCLUSIONS: The AUROC values of all of ML algorithms were similar to those of the MFS in predicting fall risk in patients with acute stroke, allowing for accurate and efficient fall screening.

Machine learning predictive model for aspiration screening in hospitalized patients with acute stroke.

Dysphagia is a fatal condition after acute stroke. We established machine learning (ML) models for screening aspiration in patients with acute stroke. This retrospective study enrolled patients with acute stroke admitted to a cerebrovascular specialty hospital between January 2016 and June 2022. A videofluoroscopic swallowing study (VFSS) confirmed aspiration. We evaluated the Gugging Swallowing Screen (GUSS), an early assessment tool for dysphagia, in all patients and compared its predictive value with ML models. Following ML algorithms were applied: regularized logistic regressions (ridge, lasso, and elastic net), random forest, extreme gradient boosting, support vector machines, k-nearest neighbors, and naïve Bayes. We finally analyzed data from 3408 patients, and 448 of them had aspiration on VFSS. The GUSS showed an area under the receiver operating characteristics curve (AUROC) of 0.79 (0.77-0.81). The ridge regression model was the best model among all ML models, with an AUROC of 0.81 (0.76-0.86), an F1 measure of 0.45. Regularized logistic regression models exhibited higher sensitivity (0.66-0.72) than the GUSS (0.64). Feature importance analyses revealed that the modified Rankin scale was the most important feature of ML performance. The proposed ML prediction models are valid and practical for screening aspiration in patients with acute stroke.

The effects of socioeconomic and geographic factors on chronic phase long-term survival after stroke in South Korea.

The stroke incidence has increased rapidly in South Korea, calling for a national-wide system for long-term stroke management. We investigated the effects of socioeconomic status (SES) and geographic factors on chronic phase survival after stroke. We retrospectively enrolled 6994 patients who experienced a stroke event in 2009 from the Korean National Health Insurance database. We followed them up from 24 to 120 months after stroke onset. The endpoint was all-cause mortality. We defined SES using a medical-aid group and four groups divided by health insurance premium quartiles. Geographic factors were defined using Model 1 (capital, metropolitan, city, and county) and Model 2 (with or without university hospitals). The higher the insurance premium, the higher the survival rate tended to be (P < 0.001). The patient survival rate was highest in the capital city and lowest at the county level (P < 0.001). Regions with a university hospital(s) showed a higher survival rate (P = 0.006). Cox regression revealed that the medical-aid group was identified as an independent risk factor for chronic phase mortality. Further, NHIP level had a more significant effect than geographic factors on chronic stroke mortality. From these results, long-term nationwide efforts to reduce inter-regional as well as SES discrepancies affecting stroke management are needed.

Trends in Nursing Research on Life-Sustaining Treatment in South Korea after the Enforcement of the Act on Decisions on Life-Sustaining Treatment.

Purpose: This study investigated trends of nursing research on life-sustaining treatment in South Korea. Methods: The period for data search was set from January 2018 to December 2020. The major search terms used were advance directives and life-sustaining treatment. Of the 492 records identified in the initial search, 461 articles were excluded for various reasons. A total of 31 records were included in the final qualitative analysis. Results: Sixteen studies had nursing students as study subjects, while nine studies had nurses as study subjects. The majority of the studies employed cross-sectional descriptive surveys as their research design. The major themes that emerged from the studies were as follows attitudes toward withdrawal of life-sustaining treatment, knowledge of and attitudes toward advance directives, perceptions of a good death, and nurses' attitude toward life support care. Most of the studies reviewed concluded that attitudes toward withdrawal of life-sustaining treatment significantly impacted both knowledge of and attitudes toward advance directives and perceptions of a good death. Conclusion: To date, Korea still lacks extensive nursing research concerning life support care. Further research is needed to provide systematic education for nursing ethics and life support care, as well as the introduction of a specialist course. Furthermore, a multidisciplinary approach is necessary to provide diverse support systems and policy measures. In particular, since nurses are directly responsible for providing life support care, nurses' roles should be expanded in accordance with the Act on Decisions on Life-Sustaining Treatment.

A leaf-specific 27 kDa protein of potato Kunitz-type proteinase inhibitor is induced in response to abscisic acid, ethylene, methyl jasmonate, and water deficit.

The 22 kDa Kunitz-type potato proteinase inhibitor (22 kDa KPPI) was induced in tubers. However, the 27 kDa protein, which is immunologically related to the 22 kDa KPPI, was induced in leaves by wounding, hormones, and environmental stresses. The leaf-specific 27 kDa protein was induced in leaves that were treated with exogenous abscisic acid (ABA), ethephon, methyl jasmonate (MeJA), and water deficit. These results indicate that the 27 kDa protein in leaves could function as a defense protein against mechanical damages by herbivorous animals and abiotic environmental stresses that could induce plant hormones.