Role of Static and Dynamic Intra-abdominal Pressure Monitoring in Acute Pancreatitis

Goenka, Mahesh Kumar; Goenka, Usha; Afzalpurkar, Shivaraj; Tiwari, S. C.; Agarwal, Rachit; Tiwary, Indrajeet Kumar

doi:10.1097/mpa.0000000000001544

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…Increased IAP has a multitude of effects on the kidney through a series of mechanisms that result in a decrease in the glomerular filtration rate (GFR) with oliguria, which usually is the first clinical evidence of kidney impairment [ 35 , 36 ]. Screening and intervention to decrease IAP and improve vital perfusion of the kidney are essential to minimize the negative effects [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients

Cheng

Mei

et al. 2020

Gastroenterology Research and Practice

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Background. Acute kidney injury (AKI) has long been recognized as a common and important complication of acute pancreatitis (AP). In the study, machine learning (ML) techniques were used to establish predictive models for AKI in AP patients during hospitalization. This is a retrospective review of prospectively collected data of AP patients admitted within one week after the onset of abdominal pain to our department from January 2014 to January 2019. Eighty patients developed AKI after admission (AKI group) and 254 patients did not (non-AKI group) in the hospital. With the provision of additional information such as demographic characteristics or laboratory data, support vector machine (SVM), random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost) were used to build models of AKI prediction and compared to the predictive performance of the classic model using logistic regression (LR). XGBoost performed best in predicting AKI with an AUC of 91.93% among the machine learning models. The AUC of logistic regression analysis was 87.28%. Present findings suggest that compared to the classical logistic regression model, machine learning models using features that can be easily obtained at admission had a better performance in predicting AKI in the AP patients.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients

Cheng

Mei

et al. 2020

Gastroenterology Research and Practice

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“…66.3% (65/98) of patients in group B achieved the expected energy goal within 72 hours, which was significantly higher than 37.1% (39/105) in group A (P < 0.05). e incidence of mechanical multiorgan dysfunction syndrome was lower in the B group than in the A group (P < 0.05) [7]. As shown in Figure 1, there was no statistical difference in mortality between the two groups (P > 0.05).…”

Section: Resultsmentioning

confidence: 80%

“…Goenka and other studies have shown that IAP is significantly positively correlated with bladder pressure and intragastric pressure, and bladder pressure has a good correlation with IAP; it can objectively reflect the dynamic changes of IAP, diagnose ACS in time, and quickly evaluate its influence on organ function. e A-PACHEII scoring system is an objective, simple, and practical scoring method for evaluating the condition of critically ill patients and predicting the prognosis [7]. Chang and other studies have confirmed that the dynamic changes of IAP are related to the body's cardiopulmonary function and abdominal organ diseases; when IAP is continuously or repeatedly elevated, it can cause abdominal hypertension (IAH); subsequent progress may lead to abdominal compartment syndrome (ACS) characterized by increased airway pressure, hypoxemia, dyspnea, oliguria, and anuria; it causes a series of pathophysiological changes in the body, leading to multiple organ failure or obstacles and endangering the lives of patients [8].…”

Section: Introductionmentioning

confidence: 99%

Application Effect of Intra-Abdominal Pressure Monitoring System in Early Enteral Nutrition Nursing of ICU Patients

Song

Zhao

2022

Contrast Media & Molecular Imaging

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In order to study and explore the application effect of intra-abdominal pressure monitoring in guiding ICU patients to implement early enteral nutrition therapy, we selected patients admitted to the emergency department of a hospital and classified them into groups A (n = 105) and B (n = 98). Among them, the A group gave early enteral nutrition treatment by monitoring the residual gastric mass, and the B group gave early enteral nutrition treatment by monitoring the stomach. It has been established that, compared to gastric residue monitoring, intra-abdominal pressure monitoring in ICU patients with early enteral nutrition therapy has an obvious advantage, it helps to improve the prognosis of patients, and intra-abdominal pressure combined with gastric residual monitoring scheme can effectively reduce the early enteral nutrition in the ICU patients, the incidence of abdominal distension, vomiting, and make it reach the goal as soon as possible. Early enteral nutrition in patients with increased tolerance is of great significance.

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“…The maximum horizontal stress of the metal mine is greater than the vertical stress, and the ratio of horizontal in situ stress to vertical in situ stress is 1.7 : 1.1. The in situ stress is mainly horizontal tectonic stress [26]. Under this stress distribution, the phenomenon of stress concentration in roadway or stope surrounding rock is more obvious, and the possibility of rock burst in deep stope is greater.…”

Section: Critical Depth Criterionmentioning

confidence: 99%

Mine Ground Pressure Monitoring and Early Warning Based on Deep Learning Data Analysis

Xiao

Deng

Xie

et al. 2022

Mobile Information Systems

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In order to ensure the safe mining of kilometer mining working surface threatened by impact ground pressure, a metal mine ground pressure monitoring and early warning based on deep learning data analysis are proposed. This paper expounds the theoretical basis of rock burst, analyzes the inducing factors of deep well rock burst, analyzes and introduces the classification of rock burst, focuses on the progressive failure process of rock burst and standard of rock fracture depth of deep ore and rock in a metal mine, carries out triaxial stress-strain test on the core in the laboratory, and evaluates the tendency of rock burst for deep ore and rock through elastic strain generation, strength brittleness coefficient method, and deformation brittleness coefficient method. The real-time monitoring and early warning system of rock burst can monitor the dynamic change of advance stress in the working face in real time and give real-time early warning to the dangerous area and degree of rock burst. The experimental results show that the working face enters the fault affected area when it advances 170 m in front of the fault. When the working face advances to 100 m in front of the fault, it enters the high stress area formed by the superposition of fault tectonic stress and mining stress. When the working face advances to 40 m in front of the fault, the stress reaches the maximum. Therefore, the system can accurately predict the impact risk area and its risk degree and realize the safe mining of high impact risk face.

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Role of Static and Dynamic Intra-abdominal Pressure Monitoring in Acute Pancreatitis

Cited by 6 publications

References 12 publications

Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients

Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients

Application Effect of Intra-Abdominal Pressure Monitoring System in Early Enteral Nutrition Nursing of ICU Patients

Mine Ground Pressure Monitoring and Early Warning Based on Deep Learning Data Analysis

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