Timing of angiography and outcomes in patients with non-ST-segment elevation myocardial infarction: Insights from the evaluation and management of patients with acute chest pain in China registry

Reassess Hospital Costs and Mortality Between Myocardial Infarction With and Without ST-Segment Elevation in a Modern Context

BACKGROUND

Patients with ST-segment elevation myocardial infarction (STEMI) may have higher hospitalization costs and poorer prognosis than non-ST-segment elevation myocardial infarction (NSTEMI).

METHODS

A single-center retrospective study was conducted on 758 STEMI patients and 386 NSTEMI patients from January 1, 2020 to May 30, 2023 aimed to investigate the differences in cost and mortality.

RESULTS

STEMI patients had higher maximal troponin I (15,222.5 (27.18, 40,000.00) vs. 2731.5 (10.73, 27,857.25), p < 0.001) and lower left ventricular ejection fraction (LVEF) (56% (53%, 59%) vs. 57% (55%, 59%), p < 0.001) compared to NSTEMI patients. The clinical symptoms were mainly persistent or interrupted chest pain/distress in either STEMI or NSTEMI patients. STEMI patients had a significantly higher risk of combined hypotension than NSTEMI patients (8.97% vs. 3.89%, p = 0.002), and IABP was much more frequently used in the STEMI group with a statistical difference (2.90% vs. 0.52%, p = 0.015). STEMI patients have statistically higher hospitalization costs (RMB, ¥) (31,667 (25,337.79, 39,790) vs. 30,506.91 (21,405.96, 40,233.75), p = 0.006) and longer hospitalization days (10 (8, 11) vs. 9 (8, 11), p = 0.001) compared to NSTEMI patients. Although in-hospital mortality was higher in STEMI patients, the difference was not statistically significant (3.56% vs. 2.07%, p = 0.167). Multivariable logistic regression was performed and found that systolic blood pressure and NT-proBNP were risk factors for patient death (OR ≥ 1).

CONCLUSION

STEMI patients are more likely comorbid cardiogenic shock, heart failure complications with higher hospitalization costs and longer hospitalization days. And relatively more use of acute mechanical circulatory support devices such as IABP.

TRIAL REGISTRATION

ChiCTR2300077885.

Design and implementation of a smart Internet of Things chest pain center based on deep learning

The data input process for most chest pain centers is not intelligent, requiring a lot of staff to manually input patient information. This leads to problems such as long processing times, high potential for errors, an inability to access patient data in a timely manner and an increasing workload. To address the challenge, an Internet of Things (IoT)-driven chest pain center is designed, which crosses the sensing layer, network layer and application layer. The system enables the construction of intelligent chest pain management through a pre-hospital app, Ultra-Wideband (UWB) positioning, and in-hospital treatment. The pre-hospital app is provided to emergency medical services (EMS) centers, which allows them to record patient information in advance and keep it synchronized with the hospital's database, reducing the time needed for treatment. UWB positioning obtains the patient's hospital information through the zero-dimensional base station and the corresponding calculation engine, and in-hospital treatment involves automatic acquisition of patient information through web and mobile applications. The system also introduces the Bidirectional Long Short-Term Memory (BiLSTM)-Conditional Random Field (CRF)-based algorithm to train electronic medical record information for chest pain patients, extracting the patient's chest pain clinical symptoms. The resulting data are saved in the chest pain patient database and uploaded to the national chest pain center. The system has been used in Liaoning Provincial People's Hospital, and its subsequent assistance to doctors and nurses in collaborative treatment, data feedback and analysis is of great significance.