Evaluation of the Elderly Patient with Acute Chest Pain

Effects of G.H.3. On mental symptoms and health-related quality of life among older adults: results of a three-month follow-Up study in Shanghai, China

OBJECTIVE

To explore the effects of daily use of Gerovital H3 (G.H.3.) tablets on relieving mental symptoms and improving health-related quality of life among Chinese older adults population.

METHODS

In a randomized, placebo-controlled, double-blinded study, totally 100 eligible participants were randomly allocated into the G.H.3. group or the placebo group, administered either G.H.3. or placebo tablets and were followed up for three months. All of the participants were required to report their subjective feelings about quality of life, low mood, and anxiety by filling out Self-Rating Depression Scale (SDS), Self-Rating Anxiety Scale (SAS) and a 36-item Short-Form Health Survey (SF-36 scale). Physicians were responsible for evaluating the related mental health indications through physical examinations at the baseline and at the end of the intervention period.

RESULTS

Participants were men and women between 50 and 89 years of age, with a median of 62.53 years. Before the intervention, the demographic characteristics and the baseline SF-36 scores, low mood, and anxiety statuses were comparable (p > 0.05). After the 12-week intervention, the scores of role-physical (RP), bodily pain (BP), general health (GH), vitality (VT), mental health (MH) and health transition (HT), mental composite score (MCS) of the G.H.3. group were higher than the placebo group (p < 0.05), There were no significant differences in other domains in SF-36 and PCS between the two groups(p > 0.05), the scores of SDS and SAS in the G.H.3. group were both lower than the placebo group(p < 0.01), the prevalence rates of low moods in the G.H.3. group and the placebo group were 20.8 % and 34.0 % respectively, no significant difference was found (χ (2) =2.127,p = 0.145), while the prevalence rate of clinical anxiety concerns in the G.H.3. group was 2.1 %, which was significantly lower than the placebo group, 22.0 % (χ (2) =9.040,p < 0.001).

CONCLUSIONS

Preliminarily use of G.H.3. shows positive effects in supporting mental health and improving general health and well-being while promoting the recovery of cognitive function among older adults. Most of SF-36 domains including PF, RP, BP, GH, VT, RE, MH, and HT, as well as the overall quality of life in MCS might benefit from taking G.H.3. tablets. Average levels of low moods and anxiety concerns were both reduced and the prevalence rate of clinical anxiety concerns were reduced.

Nursing home nurses conceptualize how to care for residents with cardiac vulnerability

BACKGROUND

With ageing, older people face cardiovascular problems as the major cause of disability and death. Although immediate medical attention is a major factor in determining outcomes of cardiac problems, lack of personnel (i.e. registered nurse, certified nursing assistant and home care aide) in nursing homes without residing doctor limits the awareness of such problems, thus making it difficult to initiate timely and appropriate intervention.

AIM

The aim of this study was to conceptualize critical care for nursing home residents with cardiac vulnerability and develop practical knowledge in nursing practice.

METHODS

Conventional content analysis was performed on date from interviews with 30 nurses from 10 nursing homes in South Korea between July and November 2010.

RESULTS

The analysis revealed three major cardiac problems resulting from residents' cardiac vulnerability: angina, myocardial infarction (MI) and cardiogenic shock. Through content analysis, we extracted 6 themes and 21 subthemes for nurses' conceptualization of critical care for nursing home residents with cardiac vulnerability. In nursing homes without a residing doctor, nurses assessed the physical, functional and cognitive conditions along with the responses and symptoms of residents when emergency situations related to the cardiac problems occurred. Moreover, with a lack of infrastructures of a hospital, nurses provided critical care to the elderly by using personal practice strategies based on their personal experience in facilities along with practical knowledge of nurses while following the management principles of emergencies.

CONCLUSIONS AND RELEVANCE TO CLINICAL PRACTICE

We found that nurses conceptualized critical nursing care for cardiac problems at nursing homes, which are different from those of general hospitals. The results of this study will provide basis for the development of care guidelines and educational materials that can be used by novice nurses or nursing students.

Application of pattern recognition tools for classifying acute coronary syndrome: an integrated medical modeling

OBJECTIVE

The classification of Acute Coronary Syndrome (ACS), using artificial intelligence (AI), has recently drawn the attention of the medical researchers. Using this approach, patients with myocardial infarction can be differentiated from those with unstable angina. The present study aims to develop an integrated model, based on the feature selection and classification, for the automatic classification of ACS.

METHODS

A dataset containing medical records of 809 patients suspected to suffer from ACS was used. For each subject, 266 clinical factors were collected. At first, a feature selection was performed based on interviews with 20 cardiologists; thereby 40 seminal features for classifying ACS were selected. Next, a feature selection algorithm was also applied to detect a subset of the features with the best classification accuracy. As a result, the feature numbers considerably reduced to only seven. Lastly, based on the seven selected features, eight various common pattern recognition tools for classification of ACS were used.

RESULTS

The performance of the aforementioned classifiers was compared based on their accuracy computed from their confusion matrices. Among these methods, the multi-layer perceptron showed the best performance with the 83.2% accuracy.

CONCLUSION

The results reveal that an integrated AI-based feature selection and classification approach is an effective method for the early and accurate classification of ACS and ultimately a timely diagnosis and treatment of this disease.