Clinical eHealth最新文献

Pulmonary alveolar proteinosis (PAP) is a rare lung disease characterized by the accumulation of large amounts of phospholipid protein-like surfactant material in the alveolar spaces, leading to severe hypoxemia and respiratory failure. The disease has an insidious onset and is difficult to diagnose in the early stages. Its clinical manifestations primarily include progressive dyspnea and coughing. Currently, whole lung lavage (WLL) is the preferred and effective treatment for PAP, as it improves alveolar ventilation function. Granulocyte colony-stimulating factor is used clinically in the treatment of a variety of disease. Nebulized granulocyte–macrophage colony-stimulating factor (GM-CSF) has a certain therapeutic effect on PAP. Here, we report a case of PAP treated with WLL combined with nebulized GM-CSF, which achieved good therapeutic effects during a 4-year follow-up period, to enhance understanding of the disease.

Considering the poor efficacy of drug treatment, the non-drug therapy is vital for Alzheimer’s disease (AD). As an ultimate form of Internet, Metaverse will become mainstream industry in the future. This thesis reviews advances in non-drug therapy (Reminiscence Therapy, Music Therapy, Horticultural Therapy, Animal-Assisted Therapy) of Alzheimer’s disease and introduce the Metaverse and its application in field of medicine. Finally, the author thinks Metaverse will be used to store human’s memories and proposed Metaverse Therapy for AD patients as one of the non-drug treatment in the future.

Pulmonary immune system, constituted with innate immune and adaptive immune, defenses against pathogens, eliminates senescent cells, and maintains pulmonary physiological homeostasis. The physiological changes in immune system were known as “immune senescence” with increasing age, characterized by increased susceptibility to infection and cancer, reduced response to vaccines, and accompanied by chronic low-grade inflammation. T lymphocytes, B lymphocytes and NK cell immune senescence are the main phenotype of immune senescence. T lymphocytes senescence characterized by immune deficiency and inflammation, caused by thymus degeneration, mitochondrial dysfunction, genetic and epigenetic changes, protein homeostasis imbalance with increasing age. B lymphocytes senescence phenotype is mainly manifested with the decrease of B lymphocytes quantity and quality, and the deficiency in transformation and recombination with increasing age. Meanwhile, with increasing age, NK cells showed changes in cell function, number of cells, and proportion of NK cell subsets. In recent years, a large number of studies have found that the above immune aging changes with increasing age promoted occurrence and development of pulmonary diseases, especially chronic obstructive pulmonary disease (COPD), lung cancer. Even more, there were new therapeutic approach that target to “immunosenescence” have been developed clinically, such as immunotherapy for patients with COPD and lung cancer. However, there are some confusion about the regulatory mechanism of immune senescence in lung diseases and clinical real world. Therefore, this article reviews immune aging, mechanisms of immune aging in the development and progression of lung diseases, mainly included COPD, lung cancer, as well as current immunotherapy targeting to immune senescence, problems, and future directions.

Objective

To develop a machine learning model to predict hospital mortality and identify risk factors in cancer-related sepsis patients.

Method

We obtained data from the Medical Information Mart for Intensive Care (MIMIC)-IV critical care data set, which included patients who diagnosed with cancer and fulfilled the definition of sepsis between 2008 and 2019. The data set was randomly split into a training set and a validation set. The dataset was imputed using the K-Nearest Neighbor (KNN) imputation model. An advanced machine learning model called CatBoost was established and then assessed by SHAP value.

Results

A total of 5081 patients were included in the final analysis. The cancer-related sepsis patients had a lower hospital survival (13.8% vs. 25.3%, P < 0.001) than non-cancer-related patients.

For cancer-related sepsis patients, ensemble learning algorithms were superior to others with better accuracy and larger AUC, such as CatBoost (AUC: 0.828), LightGBM (AUC: 0.818), and Random Forest Classifier (AUC: 0.803). An evaluation of the performance suggested that the CatBoost model with the most powerful discrimination to predict hospital mortality, outperformed other models with a sensitivity of 76% and a specificity of 74%. The best cutoff was 0.223 for the CatBoost model. In addition, CatBoost also outperformed other severity scores such as SAPS-II (AUC: 0.725) and SOFA (AUC: 0.682). Urine output and the minimum BUN level on admission were the most important features for the hospital mortality prediction of cancer-related sepsis, while the patients’ age and the urine output on admission for non-cancer-related patients.

Conclusion

For cancer-related sepsis patients, CatBoost model was a better prediction model. It was easy for clinicians to access by use of common clinical vital signs or laboratory examination parameters, which provides convenience for them to evaluate patient’s condition and make follow-up treatments.

Background

Timely diagnosis and treatment of silent cerebrovascular disease (SCD) are critical for future cerebrovascular disease prevention, whereas asymptomatic specificity of SCD can lead to a lack of reasonable healthcare utilization. In China, access barriers to care for SCD patients is rarely studied. This study aimed to estimate the access barriers to care for SCD patients in rural China and explored associated factors.

Methods

We constructed a demand-side questionnaire using the six-dimensional model of access barriers to care, and collected survey data in Guizhou province, China. Data from SCD patients were collected including demographics, health status, and self-perceived access barriers to care. Linear regression was used to estimate the association between access barriers to care and self-reported health status.

Results

A total of 162 SCD patients were included in the analysis. The questionnaire’s measures are adoptable with reliability (Cronbach’s α = 0.86) and validity (KMO = 0.774, Bartlett’s test p-value < 0.05). The average score of access barriers to care for SCD patients in Guizhou was 13.41 (SD = 4.08). Average scores vary across the six dimensions, and affordability has the highest score of 3.07 (SD = 0.13), indicating the highest level of access barriers in terms of affordability. The lowest scored dimension is acceptability which score is 1.62 (SD = 0.33), indicating SCD patients had a relatively high willingness in receiving healthcare services. Regression outcome reported that self-reported worse health status was significantly associated with higher level of access barriers (p-value < 0.01).

Conclusion

This study estimated overall and by-dimension access barriers to care for patients with SCD in rural China and investigated the association between health status and access barriers to care. The varied level of different dimensions of access barriers to care suggested that interventions designed to facilitate healthcare utilization should be specific and target those SCD patients who are in poorer health status and have difficulty affording healthcare expenses.

3D-printing of medicines is an innovative manufacturing method that is characterised by a high degree of digitalisation and automation and enables patient-specific care. Its integration into routine healthcare processes currently fails mainly due to the requirements of a digital environment. Our hospital was the first hospital in Europe to introduce a fully comprehensive patient record in 2011 and to digitalise and automate the drug supply process.

The aim of our study is to evaluate the integration of a machine-learning assisted 3D printing of medicines into the already existing, fully digital medication process of the hospital (closed-loop medication management, CLMM). Here, the design of this feasibility study and first results of subprojects are presented.

First, a suitable and clinically relevant active ingredient (levodopa/carbidopa) was identified in a multi-step approach by an interdisciplinary panel of experts using defined evaluation criteria, taking into account galenic, clinical and machine learning aspects. In the next step, a galenic formulation using a suitable printing technology for manufacturing a drug according to pharmaceutical quality criteria in different dosages is to be developed and to be evaluated for compliance with quality criteria according to the European Pharmacopoeia. Furthermore, an IT concept was developed and adapted to the hospital's current IT infrastructure. Likewise, a machine learning algorithm is to be developed to determine the optimal dose for each individual patient using data from smart wearable devices. For this purpose, a clinical trial was set up as a proof-of-principle study for the use of wearables to detect and grade clinical symptoms from Parkinson’s Disease. Finally, the process is to be connected to the digital medication process of the hospital taking into account regulatory requirements.

Thus, this interdisciplinary feasibility study will provide important insights into the possibilities of integrating patient-specific 3D printing of medicines into everyday clinical practice in the hospital.

Omicron variants of SARS-CoV-2 have been become the dominant variant family among more than 100 countries and regions around the world. There are still limited data on how inactivated COVID-19 vaccines prevent Omicron-related symptomatic infection, transmission, hospital admission, and death3. Recently, Dawei Yang et al. published a paper in the to explore the effect of inactivated COVID-19 vaccines on Omicron from the perspective of real-world observation data.

Background

Recently, Professor Chunxue Bai and colleagues have proposed a definition of the Metaverse in Medicine as the medical Internet of Things (MIoT) facilitated using AR and/or VR glasses.

Methods

A multi-disciplinary panel of doctors and IT experts from Asia, the United States, and Europe analyzed published articles regarding expert consensus on the Medical Internet of Things, with reference to study results in the field of metaverse technology.

Findings

It is feasible to implement the three basic functions of the MIoT, namely, comprehensive perception, reliable transmission, and intelligent processing, by applying a metaverse platform, which is composed of AR and VR glasses and the MIoT system, and integrated with the technologies of holographic construction, holographic emulation, virtuality-reality integration, and virtuality-reality interconnection. In other words, through interactions between virtual and real cloud experts and terminal doctors, we will be able to carry out medical education, science popularization, consultation, graded diagnosis and treatment, clinical research, and even comprehensive healthcare in the metaverse. The interaction between virtual and real cloud experts and terminal users (including terminal doctors, patients, and even their family members) could also facilitate different medical services, such as disease prevention, healthcare, physical examination, diagnosis and treatment of diseases, rehabilitation, management of chronic diseases, in-home care, first aid, outpatient attendance, consultation, etc. In addition, it is noteworthy that security is a prerequisite for the Metaverse in Medicine, and a reliable security system is the foundation to ensure the normal operation of such a platform.

Conclusion

The application of a Cloud Plus Terminal platform could enable interaction between virtual and real cloud experts and terminal doctors, in order to realize medical education, science popularization, consultation, graded diagnosis and treatment, clinical research, and even comprehensive healthcare in the metaverse.

Background

The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans.

Goal

This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result.

Methods

With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan University (NCT04275947, B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. The effects of different diagnostic factors were ranked based on the results from a single factor analysis, with 0.05 as the significance level for factor inclusion and 0.1 as the significance level for factor exclusion. Independent variables were selected by the step-forward multivariate logistic regression analysis to obtain the probability model.

Findings

We applied the statistical method of a multivariate regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are accessible. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases).

Discussion

With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results.