Community-acquired pneumonia: Trends in and research on drug resistance and advances in new antibiotics

Pneumonia-Plus: a deep learning model for the classification of bacterial, fungal, and viral pneumonia based on CT tomography

OBJECTIVES

This study aims to develop a deep learning algorithm, Pneumonia-Plus, based on computed tomography (CT) images for accurate classification of bacterial, fungal, and viral pneumonia.

METHODS

A total of 2763 participants with chest CT images and definite pathogen diagnosis were included to train and validate an algorithm. Pneumonia-Plus was prospectively tested on a nonoverlapping dataset of 173 patients. The algorithm's performance in classifying three types of pneumonia was compared to that of three radiologists using the McNemar test to verify its clinical usefulness.

RESULTS

Among the 173 patients, area under the curve (AUC) values for viral, fungal, and bacterial pneumonia were 0.816, 0.715, and 0.934, respectively. Viral pneumonia was accurately classified with sensitivity, specificity, and accuracy of 0.847, 0.919, and 0.873. Three radiologists also showed good consistency with Pneumonia-Plus. The AUC values of bacterial, fungal, and viral pneumonia were 0.480, 0.541, and 0.580 (radiologist 1: 3-year experience); 0.637, 0.693, and 0.730 (radiologist 2: 7-year experience); and 0.734, 0.757, and 0.847 (radiologist 3: 12-year experience), respectively. The McNemar test results for sensitivity showed that the diagnostic performance of the algorithm was significantly better than that of radiologist 1 and radiologist 2 (p < 0.05) in differentiating bacterial and viral pneumonia. Radiologist 3 had a higher diagnostic accuracy than the algorithm.

CONCLUSIONS

The Pneumonia-Plus algorithm is used to differentiate between bacterial, fungal, and viral pneumonia, which has reached the level of an attending radiologist and reduce the risk of misdiagnosis. The Pneumonia-Plus is important for appropriate treatment and avoiding the use of unnecessary antibiotics, and provide timely information to guide clinical decision-making and improve patient outcomes.

CLINICAL RELEVANCE STATEMENT

Pneumonia-Plus algorithm could assist in the accurate classification of pneumonia based on CT images, which has great clinical value in avoiding the use of unnecessary antibiotics, and providing timely information to guide clinical decision-making and improve patient outcomes.

KEY POINTS

• The Pneumonia-Plus algorithm trained from data collected from multiple centers can accurately identify bacterial, fungal, and viral pneumonia. • The Pneumonia-Plus algorithm was found to have better sensitivity in classifying viral and bacterial pneumonia in comparison to radiologist 1 (5-year experience) and radiologist 2 (7-year experience). • The Pneumonia-Plus algorithm is used to differentiate between bacterial, fungal, and viral pneumonia, which has reached the level of an attending radiologist.

Health Status and Individual Care Needs of Disabled Elderly at Home in Different Types of Care

For the disabled, paying attention to their health status is the starting point to discovering their survival problems, while meeting their care needs is the end point to solving their survival problems. As the country with the largest number of disabled elderly in the world, how to ensure this group could obtain appropriate home care is a major public health issue facing China. Thus, we conducted a cross-sectional study from October to December 2020 to explore the basic characteristics and health status of disabled elderly in different types of care who are living at home in 37 streets in Shanghai, as well as the individual care needs and its relevance. We observed the significant differences in the number of diagnoses (p = 0.03), smoking (p = 0.009), drinking (p = 0.016), exercise (p = 0.001), activity of daily living (p < 0.0001), and the quality of life (p < 0.0001) across care types. The care needs of the disabled elderly are diversified, of which a vast majority of them have not been fully guaranteed. The urgent need for improving the identification accuracy of care needs of disabled elderly, as well as the development of elaborate and personalized care programs for them, is needed.

Rational medication management mode and its implementation effect for the elderly with multimorbidity: A prospective cohort study in China

Background

As one of the countries with the most serious degree of aging, the incidence of potentially inappropriate drug use among the elderly is as high as 30. 4% in Chinese communities, and the lack of effective medication management and poor medication compliance at home are the main factors. Given these situations, we constructed a Rational Medication Management Mode based on family physician service, carried out an empirical research and evaluated the implementation effect.

Methods

A prospective cohort study was conducted from September to December 2021 to analyze the implementation effect of the Rational Medication Management Mode by comparing the outcome indicators between the intervention group and control group. The primary outcome of this study was medication number and polypharmacy (taking 5 or more medications) at 90 days. The secondary outcomes included the situation for behavioral self-management and knowledge-belief-behavior of rational medication use.

Results

A total of 618 elderly patients (309 in the intervention group and 309 in the control group) with multimorbidity were included in this study, those were all available at follow-up at 90 days. At 90 days, the number of medications was achieved by 3.88 (1.48), and patients with polypharmacy were reduced by 59.55% in the intervention group, having a significant difference compared with the control group (P < 0.001). Patients with medication reminders, intermittent medication and adverse drug reactions were achieved in 294 (95.15%), 47 (15.21%), and 51 (16.51%) respectively in the intervention group (P < 0.001). The knowledge, belief, behavior security and behavior compliance of rational medication use of elderly patients were all greatly improved in the intervention group at 90 days (P < 0.0001).

Conclusion

The Rational Medication Management Mode based family physician service, which provides the support of manuals and pillboxes, can decrease the elderly patients' number of drugs with multimorbidity, reduce the incidence of polypharmacy, enhance behavioral self-management, increase the knowledge and belief of rational medication use, and improve the security and compliance of medication usage behavior. In order to provide a practical basis for rational medication management of elderly patients with multimorbidity under the background of long-term prescriptions in China.