Establishment of a Chinese critical care database from electronic healthcare records in a tertiary care medical center

Case Report: Management of cerebral arterial gas embolism via transfer to an outpatient hyperbaric chamber

Gas embolisms can be caused by iatrogenic interventions, resulting in various manifestations. We present a patient who experienced loss of consciousness and simultaneous paralysis during a percutaneous needle biopsy of the lung. A CT scan of the head revealed a cerebral arterial gas embolism. Because the treating hospital did not have access to hyperbaric oxygen for immediate treatment, the patient was transferred to an outpatient wound care facility. There, the patient initially improved when treated with hyperbaric oxygen therapy but deteriorated with resumption of ambient pressure. Continued treatment occurred at another hospital where the patient's condition normalized. The initial transfer of the patient to another facility was notable because it was a transfer from a rural hospital, a higher-level facility, to an offsite wound care center with a hyperbaric chamber, a lower-level facility that could provide a higher level of care. This case report demonstrates the importance of immediate treatment of iatrogenic gas embolism with hyperbaric oxygen, which often is not available at many hospitals, and highlights the necessity to adapt to the transport of the patient from a higher-level facility to a lower-level facility when such transportation is necessary to provide effective and immediate care. This report is not recommending routinely transferring such patients to a lower level of care facility. However, when deemed clinically necessary and safe by bedside emergency physicians/critical care pulmonary physicians, it is a viable option. Explicit guidelines for transfers to lower-level facilities should be established to avoid delays in these situations.

Chest computed tomography for patients with sepsis in the emergency intensive care unit

Sepsis is a systemic inflammatory response syndrome (SIRS) caused by infection, which may lead to multiple organ dysfunction in susceptible patient. The most frequently involved organs/systems include the lung, kidney and circulation system. It is well established that sepsis is a risk factor for acute lung injury. While overt pulmonary infiltrates can be well captured by human operators, subtle structural changes of the lung might be ignored. Since the advantage of chest computed tomography (CT) is its capability of providing fine structural changes in high spatial resolution, the study of chest CT by means of computer science may provide further insights into the underlying pathophysiology. The integration of chest CT into the study of sepsis is limited partly due to the lack of well-curated database. The study aims to establish a database comprising detailed clinical tabular data, as well as the raw chest CT images. The database is intended to support a wide array of research studies involving radiomics in sepsis patients, helping to reduce barriers to the reproducibility of clinical research.

Real-Time Cardiac Abnormality Monitoring and Nursing for Patient Using Electrocardiographic Signals

INTRODUCTION

Cardiovascular disease nursing is a critical clinical application that necessitates real-time monitoring models. Previous models required the use of multi-lead signals and could not be customized as needed. Traditional methods relied on manually designed supervised algorithms, based on empirical experience, to identify waveform abnormalities and classify diseases, and were incapable of monitoring and alerting abnormalities in individual waveforms.

METHODS

This research reconstructed the vector model for arbitrary leads using the phase space-time-delay method, enabling the model to arbitrarily combine signals as needed while possessing adaptive denoising capabilities. After employing automatically constructed machine learning algorithms and designing for rapid convergence, the model can identify abnormalities in individual waveforms and classify diseases, as well as detect and alert on abnormal waveforms.

RESULT

Effective noise elimination was achieved, obtaining a higher degree of loss function fitting. After utilizing the algorithm in Section 3.1 to remove noise, the signal-to-noise ratio increased by 8.6%. A clipping algorithm was employed to identify waveforms significantly affected by external factors. Subsequently, a network model established by a generative algorithm was utilized. The accuracy for healthy patients reached 99.2%, while the accuracy for APB was 100%, for LBBB 99.32%, for RBBB 99.1%, and for P-wave peak 98.1%.

CONCLUSION

By utilizing a three-dimensional model, detailed variations in electrocardiogram signals associated with different diseases can be observed. The clipping algorithm is effective in identifying perturbed and damaged waveforms. Automated neural networks can classify diseases and patient identities to facilitate precision nursing.

Development and Implementation of a Dynamically Updated Big Data Intelligence Platform Using Electronic Medical Records for Secondary Hypertension

Background

The accurate identification and diagnosis of secondary hypertension is critical,especially while cardiovascular heart disease continues to be the leading cause of death. To develop a big data intelligence platform for secondary hypertension using electronic medical records to contribute to future basic and clinical research.

Methods

Using hospital data, the platform, named Hypertension DATAbase at Urumchi (UHDATA), included patients diagnosed with hypertension at the People's Hospital of Xinjiang Uygur Autonomous Region since December 2004. The electronic data acquisition system, the database synchronization technology, and data warehouse technology (extract-transform-load, ETL) for the scientific research big data platform were used to synchronize and extract the data from each business system in the hospital. Standard data elements were established for the platform, including demographic and medical information. To facilitate the research, the database was also linked to the sample database system, which includes blood samples, urine specimens, and tissue specimens.

Results

From December 17, 2004, to August 31, 2022, a total of 295,297 hypertensive patients were added to the platform, with 53.76% being males, with a mean age of 59 years, and 14% with secondary hypertension. However, 75,802 patients visited the Hypertension Center at our hospital, with 43% (32,595 patients) being successfully diagnosed with secondary hypertension. The database contains 1458 elements, with an average fill rate of 90%. The database can continuously include the data for new hypertensive patients and add new data for existing hypertensive patients, including post-discharge follow-up information, and the database updates every 2 weeks. Presently, some studies that are based on the platform have been published.

Conclusions

Using computer information technology, we developed and implemented a big database of dynamically updating electronic medical records for patients with hypertension, which is helpful in promoting future research on secondary hypertension.

The application of artificial intelligence in the management of sepsis

Sepsis is a complex and heterogeneous syndrome that remains a serious challenge to healthcare worldwide. Patients afflicted by severe sepsis or septic shock are customarily placed under intensive care unit (ICU) supervision, where a multitude of apparatus is poised to produce high-granularity data. This reservoir of high-quality data forms the cornerstone for the integration of AI into clinical practice. However, existing reviews currently lack the inclusion of the latest advancements. This review examines the evolving integration of artificial intelligence (AI) in sepsis management. Applications of artificial intelligence include early detection, subtyping analysis, precise treatment and prognosis assessment. AI-driven early warning systems provide enhanced recognition and intervention capabilities, while profiling analyzes elucidate distinct sepsis manifestations for targeted therapy. Precision medicine harnesses the potential of artificial intelligence for pathogen identification, antibiotic selection, and fluid optimization. In conclusion, the seamless amalgamation of artificial intelligence into the domain of sepsis management heralds a transformative shift, ushering in novel prospects to elevate diagnostic precision, therapeutic efficacy, and prognostic acumen. As AI technologies develop, their impact on shaping the future of sepsis care warrants ongoing research and thoughtful implementation.