Overview on Inflammatory Cardiomyopathy and Systolic Pressure Variation

Ananya Dubey^*

Department of Stem Cell and Research, Thailand

Received:01-Apr-2023, Manuscript No. srrm-23-96396; Editor assigned: 04-Apr-2023, Pre-QC No. srrm-23- 96396 (PQ); Reviewed: 18-Apr-2023, QC No. srrm-23-96396; Revised: 22- Apr-2023, Manuscript No. srrm-23- 96396 (R); Published: 29-Apr-2023, DOI: 10.37532/srrm.2023.6(2).43-45

Abstract

Inflammatory cardiomyopathy (ICM) is a condition characterized by inflammation of the heart muscle, or myocardium, which can lead to heart failure and other complications. The condition can be caused by a variety of factors, including viral infections, autoimmune disorders, and exposure to certain toxins or drugs. The symptoms of ICM can vary depending on the severity of the inflammation and damage to the heart muscle, but may include fatigue, shortness of breath, chest pain, irregular heartbeat, and swelling in the legs and ankles.

Keywords

Vascular • Inflammatory • Genetic

Introduction

Diagnosis of ICM typically involves a physical exam, blood tests, imaging tests such as echocardiography or MRI, and sometimes a biopsy of the heart muscle [1]. Treatment may involve medications to reduce inflammation, manage symptoms, and prevent complications, as well as lifestyle changes such as reducing stress and avoiding triggers that may exacerbate the condition [2]. ICM is a serious condition that can lead to significant morbidity and mortality, particularly if left untreated or not managed properly. However, with appropriate treatment and ongoing management, many people with ICM are able to manage their symptoms and lead full, active lives [3]. In inflammatory cardiomyopathy is a complex condition that can have significant implications for heart health and overall well-being. Anyone experiencing symptoms of ICM should seek prompt medical attention to receive an accurate diagnosis and appropriate treatment [4]. Systolic pressure variation (SPV) is a measurement of the change in systolic blood pressure during mechanical ventilation. SPV is commonly used to assess fluid responsiveness in critically ill patients. SPV is based on the concept that changes in intrathoracic pressure during the respiratory cycle cause changes in venous return, which in turn affect cardiac output. Several studies have investigated the use of SPV in predicting fluid responsiveness, and the results have been mixed. Some studies have suggested that SPV is a useful predictor of fluid responsiveness, while others have found no significant correlation between SPV and fluid responsiveness. The variability in results may be due to differences in patient populations, study designs, and measurement techniques [5].

SPV is also affected by several factors, including tidal volume, positive end-expiratory pressure (PEEP), and lung compliance. In addition, SPV may be less accurate in patients with arrhythmias, Valvular heart disease, or pulmonary hypertension. Overall, while SPV has shown promise as a predictor of fluid responsiveness in some studies, its clinical usefulness remains uncertain. SPV should be interpreted in the context of other hemodynamic parameters and clinical factors, and further research is needed to determine its optimal use in clinical practice [6].

Procedure

Systolic pressure variation (SPV) is a measure of the variability of blood pressure during mechanical ventilation. It is calculated by measuring the difference between the highest and lowest systolic blood pressure values during one respiratory cycle. SPV has been studied extensively in the critical care setting, and it has been found to be a useful indicator of fluid responsiveness in mechanically ventilated patients [7].

The rationale behind using SPV as a predictor of fluid responsiveness is that changes in intrathoracic pressure during mechanical ventilation can affect preload and afterload of the heart, leading to changes in stroke volume and cardiac output. In patients who are preload-dependent (i.e., those who will increase their cardiac output in response to fluid administration), changes in intrathoracic pressure will result in changes in stroke volume, which will be reflected in changes in systolic blood pressure. Thus, SPV can be used as an indicator of fluid responsiveness, with higher values indicating a greater likelihood of response to fluid administration [8].

Treatment

Cardiomyopathy is a term used to describe a group of diseases that affect the structure and function of the heart muscle. These conditions can lead to impaired cardiac function, reduced cardiac output, and the development of heart failure. There are several different types of cardiomyopathy, including dilated cardiomyopathy, hypertrophic cardiomyopathy, and restrictive cardiomyopathy [9].

Research has shown that SPV can be a useful tool for predicting fluid responsiveness in patients with cardiomyopathy. In patients with dilated cardiomyopathy, for example, SPV has been shown to be a reliable predictor of fluid responsiveness and can help guide fluid management strategies. In patients with hypertrophic cardiomyopathy, however, SPV may be less reliable as a predictor of fluid responsiveness.

Additionally, SPV may be a useful tool for monitoring the response to treatment in patients with cardiomyopathy. Changes in SPV over time can indicate changes in cardiac function and may help guide the adjustment of treatment strategies [10].

Discussion

Several studies have evaluated the accuracy of SPV in predicting fluid responsiveness, and overall, it has been found to be a moderately accurate predictor. However, SPV is affected by several factors, including tidal volume, respiratory rate, and underlying lung disease, which can limit its accuracy in certain patient populations.

SPV is a useful measure of fluid responsiveness in mechanically ventilated patients. However, its accuracy may be limited by certain patient factors, and it should be used in conjunction with other clinical indicators to guide fluid management in critically ill patients.

Systolic pressure variation (SPV) is a measure of the change in blood pressure between the highest and lowest values during a respiratory cycle. It is commonly used as an indicator of fluid responsiveness in critically ill patients.

Several studies have shown that SPV can predict fluid responsiveness with a high degree of accuracy. One study found that an SPV greater than 13% was associated with a positive fluid response in 85% of patients. Another study found that an SPV greater than 10% had a sensitivity of 92% and specificity of 100% for predicting fluid responsiveness.

However, there are also some limitations to the use of SPV. SPV may not accurately predict fluid responsiveness in patients with arrhythmias, severe lung disease, or spontaneous breathing efforts. Additionally, SPV may not be reliable in patients who are not fully sedated and mechanically ventilated.

Overall, SPV is a useful tool for predicting fluid responsiveness in critically ill patients who are fully sedated and mechanically ventilated. However, its use should be considered in conjunction with other clinical factors, and its limitations should be taken into account when interpreting the results.

Systolic pressure variation (SPV) refers to the changes in systolic blood pressure that occur during the respiratory cycle. This variation is measured as the difference between the maximum and minimum systolic blood pressure values over a single respiratory cycle. SPV is influenced by changes in intrathoracic pressure that occur during the respiratory cycle, which in turn affect venous return to the heart and stroke volume.

Conclusion

Overall, while SPV may have some utility in predicting fluid responsiveness and monitoring treatment response in patients with cardiomyopathy, its use should be considered in the context of other clinical information and should be interpreted with caution. It is important to consult with a healthcare professional to determine the appropriate use of SPV in the management of cardiomyopathy.

References

1. Davari Dolatabadi A, Khadem SEZ, Asl BM et al. Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM. Comput Methods Programs Bio. 138, 117–126 (2017).

Indexed at, Google Scholar, Crossref

2. Patidar S, Pachori RB, Rajendra Acharya U et al. Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals. Knowl Based Syst. 82, 1–10 (2015).

Indexed at, Google Scholar, Crossref

3. Giri D, Acharya UR, Martis RJ et al. Automated diagnosis of coronary artery disease affected patients using LDA, PCA, ICA and discrete wavelet transform. Knowl Based Syst. 37, 274–282 (2013).

Google Scholar, Crossref

4. Maglaveras N, Stamkopoulos T, Diamantaras K et al. ECG pattern recognition and classification using non-linear transformations and neural networks: a review. Int J Med Inform. 52,191–208 (1998).

Indexed at, Google Scholar, Crossref

5. Rajkumar R, Anandakumar K, Bharathi A, et al. Coronary artery disease (CAD) prediction and classification-a survey. Breast Cancer. 90, 945-955 (2006).

Google Scholar

6. Lee G, Hwang J.A Novel Index to Detect Vegetation in Urban Areas Using UAV-Based Multispectral. Images Appl Sci. 11: 3472 (2021).

Indexed at, Google Scholar

7. Zou X, Mõttus M. Sensitivity of Common Vegetation Indices to the Canopy Structure of Field Crops. RSE. 9, 994 (2017).

Indexed at, Google Scholar

8. Vukasinovic.Real Life impact of anesthesia strategy for mechanical thrombectomy on the delay, recanalization and outcome in acute ischemic stroke patients. J Neuroradiol. 95, 391-392 (2019).

Indexed at, Google Scholar, Crossref

9. Salinet ASM. Do acute stroke patients develop hypocapnia? A systematic review and meta-analysis. J Neurol Sci. 15, 1005-1010 (2019).

   Indexed at, Google Scholar, Crossref

10. Jellish WS. General Anesthesia versus conscious sedation for the endovascular treatment of acute ischemic stroke. J Stroke Cerebrovasc Dis. 25, 338-341 (2015).

    Indexed at, Google Scholar, Crossref