Abnormal Sounds Research Articles

Nursing assessments of women with hypertensive disorders during labor in an academic medical center in Mississippi: a best practice implementation project

The objective of this project was to promote evidence-based practices regarding the nursing assessment of pregnant women with hypertensive disorders admitted to the intrapartum unit. Hypertension during pregnancy has been associated with adverse maternal and fetal outcomes. Ongoing evaluation and nursing care are key in the prevention of complications resulting from hypertensive disorders in pregnancy. The implementation of this best practice project was guided by the JBI Model of Evidence-based Healthcare and used the JBI Practical Application of Clinical Evidence System and Getting Research into Practice audit and feedback strategy to promote evidence-based practices regarding the nursing assessment of pregnant women with hypertensive disorders admitted to an intrapartum unit. Eight audit criteria that were representative of best-practice recommendations for nursing assessment of pregnant women with hypertensive disorders were used. A baseline audit was conducted, followed by the implementation of multiple strategies that were determined by the key stakeholders. The project was finalized with a follow-up audit to determine a change in compliance with best-practice recommendations. Baseline audits revealed an average 45% compliance rate with the eight best practice audit criteria. An on-site simulation event, which included a nursing assessment of normal and abnormal lung sounds along with the hands-on practice of deep tendon reflexes, was provided by project members. Evidence-based assessment guidelines were presented and reviewed with all participants. Input from the nursing staff was gathered regarding current documentation practices and electronic health record accessibility. As a result, an electronic health record change was requested, and improvements in nursing practice were observed for five of the eight audit criteria. Follow-up audits revealed an average compliance rate of 73% for all eight audit criteria, a 28% improvement. Continuing nursing education and ongoing competency refreshers can affect the quality of client care and outcomes by offering occasions to improve or strengthen clinical expertise and proficiency. For this project, the simulation training event improved nursing staff compliance with best practices.

Detection of Abnormal Sound in a Public Lavatory Using Maximum Likelihood Estimation

Detection of Abnormal Sound in a Public Lavatory Using Maximum Likelihood Estimation

Serological Detection, Isolation and Molecular Confirmation of Parainfluenza Virus-3 in Camels, Iraq

The objectives of this study were to detect and isolate the Parainfluenza-3 virus (PIV-3) in camels with naturally developed respiratory illness and to determine the titer of the isolates using the virus titration. Therefore, an overall 100 nasal swabs and jugular vein blood samples were collected from diseased camels in four districts in Wasit province (Iraq) from December (2019) to March (2020). The swabs were subjected to six subsequent passages on bovine kidney cell culture (BKCC) to isolate the virus and to confirm infection by molecular PCR assay. Fever (40°C), abundant runny nasal discharge, ocular discharge, coughing, depression, increased respiratory rate, abnormal breath sounds, and mainly wheezing are the most observed clinical signs. Positive findings were involved 24% by ELISA and 37% by RT-PCR. The age group from 1-2 years old showed a high infection rate, while the lower level was in the 4-6 years old group. Regarding season, the infection rate was high in winter compared to spring. Sheik Saad city appeared to have a higher infection rate than other districts. The positive samples inoculated into the Bovine kidney cell culture (BKCC) revealed the cytopathic effects (CPE) after three successive passages, which appeared as clumping and rounding with the progression of infection time at the 4th passage. Elongation and giant cell formation were shown in some isolates after the 5th and 6th passages until they reached complete detachments of the cells from the cell sheet. The titer of viral tissue culture infective dose (TCID50) of the 3rd passage was determined in BKCC cells at 10–3/0.05 ml, and the high titer was shown at the 5th and the 6th passages equal to 10-5/ 0.05 ml. In conclusion, PIV-3 is widespread among camels infected with respiratory illness; therefore, studies are necessary to detect the prevalence rate among camels in other Iraqi regions. Keywords: PIV-3, Fusion protein gene, Hemagglutination protein gene, ELISA, PCR

Development and validation of a clinical prediction rule for acute appendicitis in children in primary care

Background Recognising acute appendicitis in children presenting with acute abdominal pain in primary care is challenging. General practitioners (GPs) may benefit from a clinical prediction rule. Objectives To develop and validate a clinical prediction rule for acute appendicitis in children presenting with acute abdominal pain in primary care. Methods In a historical cohort study data was retrieved from GP electronic health records included in the Integrated Primary Care Information database. We assigned children aged 4–18 years presenting with acute abdominal pain (≤ 7 days) to development (2010–2012) and validation (2013–2016) cohorts, using acute appendicitis within six weeks as the outcome. Multiple logistic regression was used to develop a prediction model based on predictors with > 50% data availability derived from existing rules for secondary care. We performed internal and external temporal validation and derived a point score to stratify risk of appendicitis into three groups, i.e. low-risk, medium-risk and high-risk. Results The development and validation cohorts included 2,041 and 3,650 children, of whom 95 (4.6%) and 195 (5.3%) had acute appendicitis. The model included male sex, pain duration (<24, 24–48, > 48 h), nausea/vomiting, elevated temperature (≥ 37.3 °C), abnormal bowel sounds, right lower quadrant tenderness, and peritoneal irritation. Internal and temporal validation showed good discrimination (C-statistics: 0.93 and 0.90, respectively) and excellent calibration. In the three groups, the risks of acute appendicitis were 0.5%, 7.5%, and 41%, Conclusion Combined with further testing in the medium-risk group, the prediction rule could improve clinical decision making and outcomes.

Abnormal Respiratory Sound Identification Using Audio-Spectrogram Vision Transformer.

Respiratory disease, the third leading cause of deaths globally, is considered a high-priority ailment requiring significant research on identification and treatment. Stethoscope-recorded lung sounds and artificial intelligence-powered devices have been used to identify lung disorders and aid specialists in making accurate diagnoses. In this study, audio-spectrogram vision transformer (AS-ViT), a new approach for identifying abnormal respiration sounds, was developed. The sounds of the lungs are converted into visual representations called spectrograms using a technique called short-time Fourier transform (STFT). These images are then analyzed using a model called vision transformer to identify different types of respiratory sounds. The classification was carried out using the ICBHI 2017 database, which includes various types of lung sounds with different frequencies, noise levels, and backgrounds. The proposed AS-ViT method was evaluated using three metrics and achieved 79.1% and 59.8% for 60:40 split ratio and 86.4% and 69.3% for 80:20 split ratio in terms of unweighted average recall and overall scores respectively for respiratory sound detection, surpassing previous state-of-the-art results.

Electret-based flexible pressure sensor for respiratory diseases auxiliary diagnosis system using machine learning technique

Respiratory diseases have been increasingly affecting people worldwide, posing a major public health challenge due to rising morbidity and mortality rates. Subtle abnormal respiratory sounds s may present early in the pulmonary or respiratory tract diseases, therefore urging an instant intervention in critical clinical conditions. However, the current monitoring and signal analysis technologies pose significant challenges in achieving real-time, convenient, and accurate respiratory disease monitoring. Here, we propose a novel automatic auxiliary diagnosis system that utilizes a flexible electret-based self-powered sensor (FESS), signal processing technology, and machine learning algorithms. The FESS is based on a strain-enhanced laminated electret with an edge-to-edge hollow hemisphere array structure, which enables the system to detect and prevent various respiratory diseases with high accuracy rates of 99.43%, sensitivity of 98.30%, and specificity of 99.02%. Our system holds immense potential in reducing medical burden and improving the overall health of individuals.

Risk factors for disease severity among children with Covid-19: a clinical prediction model

BackgroundChildren account for a significant proportion of COVID-19 hospitalizations, but data on the predictors of disease severity in children are limited. We aimed to identify risk factors associated with moderate/severe COVID-19 and develop a nomogram for predicting children with moderate/severe COVID-19.MethodsWe identified children ≤ 12 years old hospitalized for COVID-19 across five hospitals in Negeri Sembilan, Malaysia, from 1 January 2021 to 31 December 2021 from the state’s pediatric COVID-19 case registration system. The primary outcome was the development of moderate/severe COVID-19 during hospitalization. Multivariate logistic regression was performed to identify independent risk factors for moderate/severe COVID-19. A nomogram was constructed to predict moderate/severe disease. The model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, and accuracy.ResultsA total of 1,717 patients were included. After excluding the asymptomatic cases, 1,234 patients (1,023 mild cases and 211 moderate/severe cases) were used to develop the prediction model. Nine independent risk factors were identified, including the presence of at least one comorbidity, shortness of breath, vomiting, diarrhea, rash, seizures, temperature on arrival, chest recessions, and abnormal breath sounds. The nomogram’s sensitivity, specificity, accuracy, and AUC for predicting moderate/severe COVID-19 were 58·1%, 80·5%, 76·8%, and 0·86 (95% CI, 0·79 – 0·92) respectively.ConclusionOur nomogram, which incorporated readily available clinical parameters, would be useful to facilitate individualized clinical decisions.

Evaluación de la enseñanza en técnica auscultatoria de estudiantes de posgrado de medicina y su correlación con los hallazgos de mecánica respiratoria

IntroductionThe invention of the stethoscope allowed the incorporation of lung auscultation into the physical examination and into medical education. Its performance in critically ill patients and its correlation with mechanical ventilatory parameters is uncertain. ObjectiveTo explore whether the retraining of clinical postgraduate students in pulmonary auscultation improves performance and correlation indices with parameters of ventilatory mechanics. Materials and methodsDiagnostic test evaluation study was performed in an Intensive Care Unit in Medellín, from March to August 2022. Intubated patients, on mechanical ventilation and with a RASS score ≥−3 were included.Clinical postgraduate students underwent a pedagogical intervention on the semiology of lung auscultation. Patients were auscultated with a Littman® Classic II stethoscope before and after the intervention. Findings of abnormal respiratory sounds were noted according to the nomenclature of the international association. Simultaneously, an evaluation of ventilatory mechanics was performed. The kappa concordance index was used. A value of P<.05 (95% CI) was considered significant. ResultsFifty-three patients were analyzed. Abnormal lung auscultation was reported in 38% of the patients before the intervention and in 43% after it. A concordance of 94% and a kappa coefficient of 0.88 (P<.001) were found. When comparing the clinical findings with the parameters of ventilatory mechanics, a sensitivity of 55% and a specificity of 65% were found with an LR+1.54 and LR−0.7. ConclusionsLung auscultation continues to be part of the physical examination despite its low sensitivity and diagnostic specificity, especially in critically ill patients. Lung auscultation retraining has little impact on physical examination in the intubated patient. The correlation of these findings with ventilatory mechanics is poor and requires its concomitant use with other diagnostic methods.

Acoustic Based Fire Event Detection System in Underground Utility Tunnels

Underground utility tunnels (UUTs) are convenient for the integrated management of various infrastructure facilities. They ensure effective control of underground facilities and reduce occupied space. However, aging UUTs require effective management and preventive measures for fire safety. The fundamental problems in operating UUTs are the frequent occurrence of mold, corrosion, and damage caused to finishing materials owing to inadequate waterproofing, dehumidification, and ventilation facilities, which result in corrosion-related electrical leakage in wiring and cables. To prevent this, an abnormal sound detection technology is developed in this study based on acoustic sensing. An acoustic sensor is used to detect electric sparks in the moldy environments of UUTs using a system to collect and analyze the sound generated in the UUTs. We targeted the sound that had the highest impact on detecting electric sparks and performed U-Net-based noise reduction and two-dimensional convolutional neural network-based abnormal sound detection. A mock experiment was conducted to verify the performance of the proposed model. The results indicated that local and spatial features could capture the internal characteristics of both abnormal and normal sounds. The superior performance of the proposed model verified that the local and spatial features of electric sparks are crucial for detecting abnormal sounds.

Deep-Learning-Based Sound Classification Model for Concrete Pouring Work Monitoring at a Construction Site

In the present study, the utilization of sound data in research and technology is examined, data classification techniques are analyzed, and the applicability and necessity of these techniques are explored in order to propose an acoustic classification model that differentiates between normal and abnormal sounds during concrete pouring. The paper presents an experiment in which normal sound data occurring during concrete pouring, main noise data from construction, and symptom data that could affect structural quality or even cause a collapse incident were collected. By analyzing sound data from actual construction sites and experiments, a deep-learning-based classification model was developed with the aim of preventing events that could compromise the quality and safety of structures in advance. In the classification model, both CNN (convolutional neural network) and RNN (recurrent neural network) exhibited high accuracies of 94.38% and 93.26%, respectively, demonstrating remarkable performance in identifying the status of concrete placement. Unlike previous research that only collected and sorted normal construction-related sound data, the current study developed a sorting model that addresses quality- and safety-related matters by including sound data that may influence material separation, concrete leakage, and formwork collapse during concrete placement, and differentiating these sounds from normal concrete pouring sounds. The research findings are expected to contribute to the improvement of safety management and work efficiency at construction sites.

Acoustic HMMs to Detect Abnormal Respiration with Limited Training Data

In many situations, abnormal sounds, called adventitious sounds, are included with the lung sounds of a subject suffering from pulmonary diseases. Thus, a method to automatically detect abnormal sounds in auscultation was proposed. The acoustic features of normal lung sounds for control subjects and abnormal lung sounds for patients are expressed using hidden markov models (HMMs) to distinguish between normal and abnormal lung sounds. Furthermore, abnormal sounds were detected in a noisy environment, including heart sounds, using a heart-sound model. However, the F1-score obtained in detecting abnormal respiration was low (0.8493). Moreover, the duration and acoustic properties of segments of respiratory, heart, and adventitious sounds varied. In our previous method, the appropriate HMMs for the heart and adventitious sound segments were constructed. Although the properties of the types of adventitious sounds varied, an appropriate topology for each type was not considered. In this study, appropriate HMMs for the segments of each type of adventitious sound and other segments were constructed. The F1-score was increased (0.8726) by selecting a suitable topology for each segment. The results demonstrate the effectiveness of the proposed method.

Skeletal Fluorosis-Induced Morphological Changes of the Temporomandi bular Joint and Associated Functional Limitations among Adults in Ethiopia

Skeletal fluorosis is a chronic metabolic bone and joint disease caused by ingesting large amounts of fluoride. The clinical manifestations of the disease range from mild joint pain to severe bone deformities. Joint pain, stiffness of joints, and abnormal growth of the upper and lower extremities are some of the symptoms of the disease. In its severe form (crippling fluorosis), the disease can cause a rigid, stiffened spine that renders the patient virtually immobile. However, the adverse effects of skeletal fluorosis are not limited to the spine and the upper and lower extremities of the body. The joints and bones in the anatomical region of the oro-skull are also similarly affected by the condition. Despite that, there are insufficient data on the biological effects of skeletal fluorosis in this essential anatomical region. A one point, investigative, cross-sectional study was conducted to determine possible skeletal fluorosis-related morphologic changes in the T.M.J. and associated functional limitations among individuals diagnosed with skeletal fluorosis. The study included 9 participants with skeletal fluorosis and was conducted between February 4, 2023, and March 20, 2023. A two-dimensional orthopantomogram was used to rule out possible skeletal fluorosis-related morphological changes in the temporomandibular joint. The TheraBite TMJ ROM scale was used to evaluate the R.O.M. of the T.M.J. by measuring the distance between the maxillary and mandibular cutting edges (interincisal open distance). The temporomandibular joint was auditioned to with a stethoscope for possible noises. Radiographs of the 8 study participants showed morphologic changes such as ossification of various joint structures, osteophytosis, osteophytes, and hyperostosis. In addition, 6 study participants had varying degrees of temporomandibular joint ankylosis. Of these, 4 participants showed low-intensity crepitus sounds on stethoscope auscultation. The results of this study suggest that skeletal fluorosis should be considered as the first differential diagnosis in patients complaing of pain, abnormal sounds, and stiffness related to the temporomandibular joint.

Clinical, hematological, serum biochemical, and acute phase protein indices in pulmonary affected calves

This study was performed on 24 calves aged about 30-100 days old, fourteen calves were suffering from signs of pulmonary affections including fever, bilateral muco-purulent nasal discharge, cough and abnormal lung sounds upon auscultation and these calves were enrolled as pulmonary affected group. The other ten calves were apparently clinically healthy as proved by clinical examination and were used as a control healthy group. Blood and serum samples were collected from healthy and diseased calves (before and after treatment). Hematological analysis of diseased calves before treatment showed a significant decrease in the value of RBCs and Hb, with no significant changes in the value of PCV between healthy and diseased calves before and after treatment. The total leucocytic count showed a significant increase in its value in diseased calves with significant increase in neutrophils and lymphocyte (%) as compared with healthy ones. Serum biochemical analysis of pulmonary affected calves showed hypoproteinemia, hypoalbuminemia, hypoglycemia, hyponatremia, hypochloremia and hyperkalemia as compared with healthy control calves. Activities of serum AST, ALT showed significant elevations in pulmonary affected calves with no significant changes in levels of total and direct bilirubin as compared with healthy calves. Analysis of acute phase protein of diseased calves before treatment showed a significant increase in values of Haptoglobin (Hp), serum amyloid A (SAA), Ceruloplasmin (Cp) and C - reactive protein (CRP) as compared with their levels after treatment and in healthy calves.

Loudspeaker Abnormal Sound Classification Using Auditory Perception Weighted by Energy Entropy

In order to improve the classification accuracy of loudspeaker abnormal sounds, this paper proposes a method based on time-varying specific loudness weighted by energy entropy and principal component analysis. This method simulates human auditory perception mechanism to process loudspeaker sound response signal to obtain more effective features. The human hearing system can be divided into many parallel and functionally independent conduction pathways according to frequency. The energy of the acoustic signal in each pathway can be discerned. Therefore, the time-varying specific loudness is calculated firstly to build the quantitative correlation of loudspeaker’s acoustic response signals and human hearing sensations. Each sub band loudness is weighted by energy entropy to highlight the acoustic strength variation with time and frequency. Then, important features are extracted by two dimensional-principal component analysis (2D-PCA). Finally, the whale optimization algorithm-least squares support vector machine (WOA-LSSVM) is adopted for classification. Visual analysis of the extracted features shows that this method can extract loudspeaker response signal features with better discriminability. Classification experimental results show that the average accuracy of this method reached 98.4%, which is higher than the classification method based on traditional time-frequency domain statistical features. The loudspeaker abnormal sound classification method in this paper simulates human auditory perception to extract features and is able to improve classification accuracy and automation effectively.

Epidemiological, clinico-diagnostic and therapeutic aspects of bovine respiratory disease in Indian buffaloes

Bovine Respiratory Disease (BRD) is major health problem occurring worldwide in bovines, responsible for high morbidity and mortality in calves and heavy economic losses in terms of production losses in milch animals and cost of veterinary treatment. Hence the present study was planned to investigate epidemiological, clinico-diagnostic and therapeutic aspects of BRD in Indian buffaloes. A total of 805 buffaloes reported to TVCC, COVAS, Udgir during the study period were screened for Bovine respiratory disease. On the basis of history of varying degree of nasal discharge, coughing, dyspnoea and abnormal lung sounds, 37 buffaloes were found positive for BRD, indicating 4.60% overall prevalence. The haemoanalysis showed significant (P&lt;0.01) leukocytosis associated with neutrophilia. The radiographic examination of thorax revealed variable degree of congestion and diffuse consolidation of lungs. The bacteria isolated from nasal swabs were Staphylococcus sp., Streptococcus sp., E. coli, Corynebacterium sp., Klebsiella sp., Manheimia haemolytica, Brevundimonas sp. and Pseudomonas sp. Antibiogram of pure culture organisms isolated from BRD affected buffaloes revealed highest sensitivity for gentamicin (83.33%), followed by enrofloxacin (58.33%), ceftiofur sodium (50.00%), moxifloxacin (33.33%). Thirty-one BRD affected buffaloes were randomly distributed in four groups and treated with gentamicin 4 mg/ kg, enrofloxacin 5 mg/kg, moxifloxacin 5 mg/kg and ceftiofur 1.6 mg/kg respectively. All treated buffaloes from different groups clinically cured within 5 to 15 days. Based on results of current therapeutic trial, gentamicin proved most effective and economic in treatment of BRD in buffaloes.

Evaluation of Internet-Connected Real-Time Remote Auscultation: An Open-Label Randomized Controlled Pilot Trial.

The utility of remote auscultation was unknown. This study aimed to evaluate internet-connected real-time remote auscultation using cardiopulmonary simulators. In this open-label randomized controlled trial, the physicians were randomly assigned to the real-time remote auscultation group (intervention group) or the classical auscultation group (control group). After the training session, the participants had to classify the ten cardiopulmonary sounds in random order as the test session. In both sessions, the intervention group auscultated with an internet-connected electronic stethoscope. The control group performed direct auscultation using a classical stethoscope. The total scores for correctly identified normal or abnormal cardiopulmonary sounds were 97/100 (97%) in the intervention group and 98/100 (98%) in the control group with no significant difference between the groups (p &gt; 0.99). In cardiac auscultation, the test score in the control group (94%) was superior to that in the intervention group (72%, p &lt; 0.05). Valvular diseases were not misclassified as normal sounds in real-time remote cardiac auscultation. The utility of real-time remote cardiopulmonary auscultation using an internet-connected electronic stethoscope was comparable to that of classical auscultation. Classical cardiac auscultation was superior to real-time remote auscultation. However, real-time remote cardiac auscultation is useful for classifying valvular diseases and normal sounds.

Abnormal Alpha-1 Antitrypsin Levels and Other Risk Factors Associated with Lung Function Impairment at 6 and 12 Months after Hospitalization Due to COVID-19: A Cohort Study.

Respiratory function deficits are common sequelae for COVID-19. In this study, we aimed to identify the medical conditions that may influence lung function impairment at 12 months after SARS-CoV2 infection and to analyze the role of alpha-1 antytripsin (AAT) deficiciency (AATD). A cohort study was conducted on hospitalized COVID-19 pneumonia patients in Granada (Spain) during the first infection wave who were referred to a post-COVID-19 hospital clinic. The patients were monitored with three follow-up visits from May 2020 to May 2021. Previous medical history, hospital admission data, baseline parameters and physical examination data were collected at the first visit. Pulmonary function tests were performed at 6 and 12 months together with the determination of AAT level and AATD genotype. After 12 months, 49 out of 157 patients (31.2%) continued to have lung function impairment. A multivariate analysis showed a statistically significant association of lung function impairment with: higher Charlson index; pneumonia with a central and/or mixed distribution; anemia on admission; time in intensive care; need for corticosteroid boluses; abnormal respiratory sounds at 6 months; elevated lactate dehydrogenase at 12 months; abnormal AAT; and MZ genotype. Our results suggest that these medical conditions predispose COVID-19 patients to develop long-term lung function sequelae.

How Early Can We Predict the Need for VPI Surgery?

This retrospective study reviewed documentation of children with moderate-to-severe VPI at age 3 years who did not undergo VPI surgery until age 5 years. Based on the national cleft register, 959 patients with syndromic and nonsyndromic CP were treated by the craniofacial team at Helsinki University Hospital, Finland between 2000 and 2014. Eighty-six patients fulfilled the study inclusion criteria. The speech pathologist evaluated velopharyngeal function at age 3, 5, and 8 years. Of the 86 children presenting with moderate-to-severe VPI at age 3 years, 94% still had moderate-to-severe VPI at age 5 years, even though speech therapy was offered to 77%. Of those whose velopharyngeal function improved by age 5 years, function regressed to incompetent over time. Overall, 93% underwent VPI surgery and 82% underwent VPI surgery between ages 5 and 8 years. Only 23% at age 8 years still had moderate-to-severe VPI. Speech therapy alone did not improve VPI speech characteristics. Moderate-to-severe VPI did not improve from 3 to 5 years or improved but subsequently relapsed. This suggests that the decision for VPI surgery can be made for children aged 3 years with moderate-to-severe VPI.

Proposal of a new method for learning of diesel generator sounds and detecting abnormal sounds using an unsupervised deep learning algorithm

This study is to find a method to learn engine sound after the start-up of a diesel generator installed in nuclear power plant with an unsupervised deep learning algorithm (CNN autoencoder) and a new method to predict the failure of a diesel generator using it. In order to learn the sound of a diesel generator with a deep learning algorithm, sound data recorded before and after the start-up of two diesel generators was used. The sound data of 20 min and 2 h were cut into 7 s, and the split sound was converted into a spectrogram image. 1200 and 7200 spectrogram images were created from sound data of 20 min and 2 h, respectively. Using two different deep learning algorithms (CNN autoencoder and binary classification), it was investigated whether the diesel generator post-start sounds were learned as normal. It was possible to accurately determine the post-start sounds as normal and the pre-start sounds as abnormal. It was also confirmed that the deep learning algorithm could detect the virtual abnormal sounds created by mixing the unusual sounds with the post-start sounds. This study showed that the unsupervised anomaly detection algorithm has a good accuracy increased about 3% with comparing to the binary classification algorithm.

Incarcerated Small Bowel inside A Slide Hernia Composed of Colon Wall Triggered by Left Bochdalek Hernia in Adult:An Extremely Rare Etiology of Acute Intestinal Volvulus

Acute abdomen triggered by acute bowel obstruction is common in clinical practice, which may be caused by various reasons such as inflammation, tumors, intussusception and incarcerated hernia,etc. Here by, we describe a young man who was a heavy phsical worker presented with an abdominal pain and a sudden onset for nearly a day duration, accompanied with frequent nausea and vomiting, which could be partially relieved on his standing position. Empty feeling with mild tenderness was palpable on his right lower quadrant abdomen and abnormal bowel sounds were heard in his left chest cavity. Acute abdominal intestinal incarcerated obstruction was demonstrated on computed tomography (CT) of the abdomen. Urgent laparascopic exploration unveiled a Bochdalek hernia with a 3 cm defect in diameter that was subsequently closed by continual suture after the incarcerated reduced. The key to success is early identification of characterized features on images such as chest X-ray and abdominal CT, combined with the abdominal pain attack relieved by standing or semi-recombent position. In addition, the defect of Bodalek hernia is recommended to take a continual suture with a slowly absorbed barbed suture so as to strenthen the firmeness of the diaphragm.