Delay in the diagnosis of acute myocardial infarction: effect on quality of care and its assessment

Cardiac biomarkers and detection methods for myocardial infarction

Background

A significant heart attack known as a myocardial infarction (MI) occurs when the blood supply to the heart is suddenly interrupted, harming the heart muscles due to a lack of oxygen. The incidence of myocardial infarction is increasing worldwide. A relationship between COVID-19 and myocardial infarction due to the recent COVID-19 pandemic has also been revealed.

Objective

We propose a biomarker and a method that can be used for the diagnosis of myocardial infarction, and an aptamer-based approach.

Results

For the diagnosis of myocardial infarction, an algorithm-based diagnosis method was developed using electrocardiogram data. A diagnosis method through biomarker detection was then developed.

Conclusion

Myocardial infarction is a disease that is difficult to diagnose based on the aspect of a single factor. For this reason, it is necessary to use a combination of various methods to diagnose myocardial infarction quickly and accurately. In addition, new materials such as aptamers must be grafted and integrated into new ways.

Purpose of Review

The incidence of myocardial infarction is increasing worldwide, and some studies are being conducted on the association between COVID-19 and myocardial infarction. The key to properly treating myocardial infarction is early detection, thus we aim to do this by offering both tools and techniques as well as the most recent diagnostic techniques.

Recent Findings

Myocardial infarction is diagnosed using an electrocardiogram and echocardiogram, which utilize cardiac signals. It is required to identify biomarkers of myocardial infarction and use biomarker-based ELISA, SPR, gold nanoparticle, and aptamer technologies in order to correctly diagnose myocardial infarction.

S100A9 and SOCS3 as diagnostic biomarkers of acute myocardial infarction and their association with immune infiltration

Acute myocardial infarction (AMI) is one of the leading causes of death globally, with a mortality rate of over 20%. However, the diagnostic biomarkers frequently used in current clinical practice have limitations in both sensitivity and specificity, likely resulting in delayed diagnosis. This study aimed to identify potential diagnostic biomarkers for AMI and explored the possible mechanisms involved. Datasets were retrieved from the Gene Expression Omnibus. First, we identified differentially expressed genes (DEGs) and preserved modules, from which we identified candidate genes by LASSO (least absolute shrinkage and selection operator) regression and the SVM-RFE (support vector machine-recursive feature elimination) algorithm. Subsequently, we used ROC (receiver operating characteristic) analysis to evaluate the diagnostic accuracy of the candidate genes. Thereafter, functional enrichment analysis and an analysis of immune infiltration were implemented. Finally, we assessed the association between biomarkers and biological processes, infiltrated cells, clinical traits, tissues and time points. We identified nine preserved modules containing 1,016 DEGs and managed to construct a diagnostic model with high accuracy (GSE48060: AUC = 0.923; GSE66360: AUC = 0.973) incorporating two genes named S100A9 and SOCS3. Functional analysis revealed the pivotal role of inflammation; immune infiltration analysis indicated that eight cell types (monocytes, epithelial cells, neutrophils, CD8+ T cells, Th2 cells, NK cells, NKT cells and platelets) were likely involved in AMI. Furthermore, we observed that S100A9 and SOCS3 were correlated with inflammation, variably infiltrated cells, clinical traits of patients, sampling tissues and sampling time points. In conclusion, we suggested S100A9 and SOCS3 as diagnostic biomarkers of AMI and discovered their association with inflammation, infiltrated immune cells and other factors.

The discrepancy between admission and discharge diagnoses: Underlying factors and potential clinical outcomes in a low socioeconomic country

Objective

The discrepancy between admission and discharge diagnosis can lead to possible adverse patient outcomes. There are gaps in integrated studies, and less is understood about its characteristics and effects. Therefore, this study was conducted to determine the frequency, characteristics, and outcomes of diagnostic discrepancies at admission and discharge.

Design and data sources

This retrospective study reviewed the admitting and discharge diagnoses of adult patients admitted at Aga Khan University Hospital (AKUH), Internal Medicine Department between October 2018 and February 2019. The frequency and outcomes of discrepancies in patient diagnoses were noted among Emergency Department (ED) physician versus admitting physician, admitting physician versus discharge physician, and ED physician versus discharge physician for the full match, partial match, and mismatch diagnoses. The studied outcomes included interdepartmental transfer, Intensive Care Unit (ICU) transfer, in-hospital mortality, readmission within 30 days, and the length of stay. For simplicity, we only analyzed the factors for the discrepancy among ED physicians and discharge physicians.

Results

Out of 537 admissions, there were 25.3–27.2% admissions with full match diagnoses while 18.6–19.4% and 45.3–47.9% had mismatch and partial match diagnoses respectively. The discrepancy resulted in an increased number of interdepartmental transfers (5–5.8%), ICU transfers (5.6–8.7%), in-hospital mortality (8–11%), and readmissions within 30 days in ED (14.4%-16.7%). A statistically significant difference was observed for the ward’s length of stay with the most prolonged stay in partially matched diagnoses (6.3 ± 5.4 days). Among all the factors that were evaluated for the diagnostic discrepancy, older age, multi-morbidities, level of trainee clerking the patient, review by ED faculty, incomplete history, and delay in investigations at ED were associated with significant discrepant diagnoses.

Conclusions

Diagnostic discrepancies are a relevant and significant healthcare problem. Fixed patient or physician characteristics do not readily predict diagnostic discrepancies. To reduce the diagnostic discrepancy, emphasis should be given to good history taking and thorough physical examination. Patients with older age and multi-morbidity should receive significant consideration.

Improving emergency department door-to-electrocardiogram time in ST segment elevation myocardial infarction

For patients presenting to emergency departments (ED) with a suspected acute coronary syndrome, time of arrival until an electrocardiogram is performed is an important quality metric. In our ED routine quality monitoring found that mean door-to-electrocardiogram (D2ECG) time did not meet our goal and national benchmark of 10 minutes. We describe the use of quality improvement tools to assess and decrease our D2ECG time. The ED quality improvement committee identified 2 main causes of D2ECG >10 minutes: (1) priority delay (eg, completing triage and registration data entry tasks before ECG), and (2) failure to recognize patients with nonchest pain ST Elevation Myocardial Infarction (STEMI) symptoms. Interventions included are-designed patient prioritization process for triage, staff assignment to provide immediate ECG testing, continuous feedback and a triage staff educational initiative to identify high risk patients. Mean time to ECG before intervention was 21.28 +/- 5.49 minutes. After the intervention period, the mean D2ECG for STEMI decreased to 9.47 +/- 2.48 minutes representing a 55% improvement. A D2ECG time of less than 10 minutes time can be achieved by the implementation of patient prioritization triage process changes, assigning specific personnel to obtain the ECG, continuous feedback by reviewing cases that fall outside the 10-minute goal and by ED staff education regarding STEMI symptoms other than chest pain.

Discrepancy between admission and discharge diagnoses as a predictor of hospital length of stay

CONTEXT

The addition of clinical data or present on admission (POA) codes to administrative databases improves the accuracy of predicting clinical outcomes, such as inpatient mortality. Other POA information may also explain variation in hospital outcomes, such as length of stay (LOS), but this potential has not been previously explored.

OBJECTIVES

To assess whether a discrepancy between the diagnosis coded at the time of admission and the diagnoses coded at discharge independently explains variation in LOS for general internal medicine patients.

DESIGN, SETTING, AND PATIENTS

A retrospective data review of patients age 18 years and older admitted to general internal medicine units at a large, urban academic medical center between July 2005 and June 2006. A generalized linear regression model was constructed to adjust for patient factors known to be associated with LOS.

OUTCOME MEASURE

Average LOS among patients with a discrepancy between the admitting and discharge diagnosis codes versus those patients without a discrepancy.

MAIN RESULTS

The average LOS for patients without a discrepancy between the admitting and discharge diagnosis codes, adjusted for comorbid conditions, was 3.4 days compared to 4.2 days with a discrepancy (0.76-day increase; P < 0.01).

CONCLUSIONS

Diagnosis discrepancy is associated with longer LOS. Diagnosis discrepancy on admission may be a marker of diagnosis uncertainty or poor patient assessment/documentation. Further research is needed to understand the underlying reasons for this discrepancy and its association with LOS, and, potentially, clinical outcomes.