Comparison of efficacy of lung ultrasound and chest X-ray in diagnosing pulmonary edema and pleural effusion in ICU patients: A single centre, prospective, observational study

Background and Aims While chest X-ray (CXR) has been a conventional tool in intensive care units (ICUs) to identify lung pathologies, computed tomography (CT) scan remains the gold standard. Use of lung ultrasound (LUS) in resource-rich ICUs is still under investigation. The present study compares the utility of LUS to that of CXR in identifying pulmonary edema and pleural effusion in ICU patients. In addition, consolidation and pneumothorax were analyzed as secondary outcome measures. Material and Methods This is a prospective, single centric, observational study. Patients admitted in ICU were examined for lung pathologies, using LUS by a trained intensivist;and CXR done within 4 hours of each other. The final diagnosis was ascertained by an independent senior radiologist, based on the complete medical chart including clinical findings and the results of thoracic CT, if available. The results were compared and analyzed. Results Sensitivity, specificity and diagnostic accuracy of LUS was 95%, 94.4%, 94.67% for pleural effusion;and 98.33%, 97.78%, 98.00% for pulmonary edema respectively. Corresponding values with CXR were 48.33%, 76.67%, 65.33% for pleural effusion;and 36.67%, 82.22% and 64.00% for pulmonary edema respectively. Sensitivity, specificity and diagnostic accuracy of LUS was 91.30%, 96.85%, 96.00% for consolidation;and 100.00%, 79.02%, 80.00% for pneumothorax respectively. Corresponding values with CXR were 60.87%, 81.10%, 78.00% for consolidation;and 71.3%, 97.20%, 96.00% for pneumothorax respectively. Conclusion LUS has better diagnostic accuracy in diagnosis of pleural effusion and pulmonary edema when compared with CXR and is thus recommended as an effective alternative for diagnosis of these conditions in acute care settings. Our study recommends that a thoracic CT scan can be avoided in most of such cases.

Rib Osteosynthesis for Sub-Acute Management of a Flail Chest in a Tertiary Centre in a Low-Middle Income Country of Sub-Saharan Africa: Case Report at Douala Laquintinie Hospital

Background: Costal fracture surgical is still a debate, therefore we shall select between early and delay surgical management. Case Report: We are reporting two cases of post road traffic clash delay ribs fractures osteosynthesis involving a 63-year-old man with multistage fractures on the left and pulmonary pinning of one of the costal arches, complicated by a homolateral haemothorax and a 41-year-old man with a bilateral flail chest. Conclusion: The simple postoperative course and the immediate postoperative improvement in the patient’s clinical respiratory condition enabled us to discuss the time frame for management, in this case the indication for early or later surgery.

Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

The diagnosis of COVID-19 requires chest computed tomography(CT).High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease,so it is of clinical importance to study super-resolution(SR)algorithms applied to CT images to improve the reso-lution of CT images.However,most of the existing SR algorithms are studied based on natural images,which are not suitable for medical images;and most of these algorithms improve the reconstruction quality by increasing the network depth,which is not suitable for machines with limited resources.To alleviate these issues,we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution(RFAFN).Specifically,we design a contextual feature extraction block(CFEB)that can extract CT image features more efficiently and accurately than ordinary residual blocks.In addition,we propose a feature-weighted cascading strategy(FWCS)based on attentional feature fusion blocks(AFFB)to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information.Finally,we suggest a global hierarchical feature fusion strategy(GHFFS),which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels.Numerous experiments show that our method performs better than most of the state-of-the-art(SOTA)methods on the COVID-19 chest CT dataset.In detail,the peak signal-to-noise ratio(PSNR)is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at×3 SR compared to the suboptimal method,but the number of parameters and multi-adds are reduced by 22K and 0.43G,respectively.Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.

Exploring the Feasibility of Machine Learning to Predict Risk Stratification Within 3 Months in Chest Pain Patients with Suspected NSTE-ACS

Objective We aimed to assess the feasibility and superiority of machine learning(ML)methods to predict the risk of Major Adverse Cardiovascular Events(MACEs)in chest pain patients with NSTE-ACS.Methods Enrolled chest pain patients were from two centers,Beijing Anzhen Emergency Chest Pain Center Beijing Bo’ai Hospital,China Rehabilitation Research Center.Five classifiers were used to develop ML models.Accuracy,Precision,Recall,F-Measure and AUC were used to assess the model performance and prediction effect compared with HEART risk scoring system.Ultimately,ML model constructed by Naïve Bayes was employed to predict the occurrence of MACEs.Results According to learning metrics,ML models constructed by different classifiers were superior over HEART(History,ECG,Age,Risk factors,&Troponin)scoring system when predicting acute myocardial infarction(AMI)and all-cause death.However,according to ROC curves and AUC,ML model constructed by different classifiers performed better than HEART scoring system only in prediction for AMI.Among the five ML algorithms,Linear support vector machine(SVC),Naïve Bayes and Logistic regression classifiers stood out with all Accuracy,Precision,Recall and F-Measure from 0.8 to 1.0 for predicting any event,AMI,revascularization and all-cause death(vs.HEART≤0.78),with AUC from 0.88 to 0.98 for predicting any event,AMI and revascularization(vs.HEART≤0.85).ML model developed by Naïve Bayes predicted that suspected acute coronary syndrome(ACS),abnormal electrocardiogram(ECG),elevated hs-cTn I,sex and smoking were risk factors of MACEs.Conclusion Compared with HEART risk scoring system,the superiority of ML method was demonstrated when employing Linear SVC classifier,Naïve Bayes and Logistic.ML method could be a promising method to predict MACEs in chest pain patients with NSTE-ACS.

Chest wall osteochondroma resection with biologic acellular bovine dermal mesh reconstruction in pediatric hereditary multiple exostoses:A case report and review of literature

BACKGROUND Hereditary multiple exostoses is a rare genetic disorder characterized by the growth of multiple osteochondromas affecting primarily long bones.Chest wall lesions may represent a challenge,particularly in pediatric patients.Pain is a common manifestation.However,life-threatening complications can result from direct involvement of adjacent structures.Surgical resection with appropriate reconstruction is often required.CASE SUMMARY A 5-year-old male who was diagnosed with hereditary multiple exostoses presented with significant pain from a large growing chest wall exostosis lesion.After appropriate preoperative investigations,he underwent surgical resection with reconstruction of his chest wall using a biologic bovine dermal matrix mesh.CONCLUSION Resection of chest wall lesions in children represents a challenge.Preoperative planning to determine the appropriate reconstruction strategy is essential.

Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs.Chest X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and portability.In radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer variability.Using lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is advantageous.The current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on optimization.The data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s edges.Then,the OSDL model is applied to classify the CXRs under different severity levels based on CXR data.The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work.OSDL model,applied in this study,was validated using the COVID-19 dataset.The experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

COVID-19 Detection from Chest X-Ray Images Using Convolutional Neural Network Approach

COVID-19 is a respiratory illness caused by the SARS-CoV-2 virus, first identified in 2019. The primary mode of transmission is through respiratory droplets when an infected person coughs or sneezes. Symptoms can range from mild to severe, and timely diagnosis is crucial for effective treatment. Chest X-Ray imaging is one diagnostic tool used for COVID-19, and a Convolutional Neural Network (CNN) is a popular technique for image classification. In this study, we proposed a CNN-based approach for detecting COVID-19 in chest X-Ray images. The model was trained on a dataset containing both COVID-19 positive and negative cases and evaluated on a separate test dataset to measure its accuracy. Our results indicated that the CNN approach could accurately detect COVID-19 in chest X-Ray images, with an overall accuracy of 97%. This approach could potentially serve as an early diagnostic tool to reduce the spread of the virus.

Radiation Dose Survey of Pediatric Chest Computed Tomography Examinations: A Local Diagnostic Reference Levels Approach to Patient Safety

Objectives: The aim of this work was to initially establish both age and weight driven pediatric diagnostic reference levels (DRLs) for chest computed tomography (CT) examinations performed at tertiary care medical institution. Another aim was to compare the presented data with internationally published ones. This initial data shall serve as basis for establishing a national DRLs values for pediatric diagnostic CT examinations. Methods: Dosimetric indexes were collected for the chest examination for 93 patients during the past 2 years in a tertiary care medical city. Results: The results are within and below the international reported levels for chest CT in several countries. Conclusion: Continuous monitoring of the radiation doses received by the patients in computed tomography is continuous and ongoing process in order to ensure compliance and to optimize clinical imaging protocols. More extensive data acquisition and analysis are required to allow better understanding of the contributing factors leading to less patient radiation dose while preserving the clinical image quality. .

Chest Radiographs Based Pneumothorax Detection Using Federated Learning

Pneumothorax is a thoracic condition that occurs when a person’s lungs collapse,causing air to enter the pleural cavity,the area close to the lungs and chest wall.The most persistent disease,as well as one that necessitates particular patient care and the privacy of their health records.The radiologists find it challenging to diagnose pneumothorax due to the variations in images.Deep learning-based techniques are commonly employed to solve image categorization and segmentation problems.However,it is challenging to employ it in the medical field due to privacy issues and a lack of data.To address this issue,a federated learning framework based on an Xception neural network model is proposed in this research.The pneumothorax medical image dataset is obtained from the Kaggle repository.Data preprocessing is performed on the used dataset to convert unstructured data into structured information to improve the model’s performance.Min-max normalization technique is used to normalize the data,and the features are extracted from chest Xray images.Then dataset converts into two windows to make two clients for local model training.Xception neural network model is trained on the dataset individually and aggregates model updates from two clients on the server side.To decrease the over-fitting effect,every client analyses the results three times.Client 1 performed better in round 2 with a 79.0%accuracy,and client 2 performed better in round 2 with a 77.0%accuracy.The experimental result shows the effectiveness of the federated learning-based technique on a deep neural network,reaching a 79.28%accuracy while also providing privacy to the patient’s data.

New scoring system for acute chest pain risk stratification: Is it worth SVEAT-ing it?

The emergency room is a very potent environment in the hospital.With the growing demands of the population,improved accessibility to health resources,and the onslaught of the triple pandemic,it is extremely crucial to triage patients at presentation.In the spectrum of complaints,chest pain is the commonest.Despite it being a daily ailment,chest pain brings concern to every physician at first.Chest pain could span from acute coronary syndrome,pulmonary embolism,and aortic dissection(all potentially fatal)to reflux,zoster,or musculoskeletal causes that do not need rapid interventions.We often employ scoring systems such as GRACE/PURSUIT/TIMI to assist in clinical decision-making.Over the years,the HEART score became a popular and effective tool for predicting the risk of 30-d major adverse cardiovascular events.Recently,a new scoring system called SVEAT was developed and compared to the HEART score.We have attempted to summarize how these scoring systems differ and their generalizability.With an increasing number of scoring systems being introduced,one must also prevent anchorage bias;i.e.,tools such as these are only diagnosis-specific and not organ-specific,and other emergent differential diagnoses must also be kept in mind before discharging the patient home without additional workup.

Surgical Outcomes Following Partial Breast Reconstruction with Chest Wall Perforator Flaps

Introduction: In the last two decades, chest wall perforator flaps (CWPF) have become a versatile tissue replacement technique for partial breast reconstruction following breast-conserving surgery (BCS) in well-selected cases. We present the surgical outcome of 81 patients with chest wall perforator flaps used for breast-conserving surgery. Methods: We recorded the outcomes of three oncoplastic breast surgeons who performed partial breast reconstruction with chest wall perforator flaps from 1st January 2018 to 30th June 2022 at Sherwood Forest Hospitals NHS Foundation Trust. Data were collected on patient demographics, including age, BMI, smoking status, bra size, previous treatments, type of CWPF procedure, tumor size (measured clinically, via imaging and histologically), biopsy results, specimen weight, margins involvement, re-operation rate, surgical site infection (SSI), flap loss, flap shrinkage, hematoma, and seroma rates. Results: A total of 81 patients were included in this study, with an average age of 55.7 years and a body mass index (BMI) of 26.7 kg/m2. The bra size varied between A to FF with A (7.4%), B (28.3%), C (38.2%), D (13.6%), DD (11.1%), and FF (1.2%). 14.8% of the patients had neoadjuvant chemotherapy (NACT). For 45 patients, LICAP (lateral intercostal artery perforator), 16 AICAP (anterior intercostal artery perforator), 13 MICAP (medial intercostal artery perforator), and for seven patients, LTAP (lateral thoracic artery perforator) flaps were used. The average tumor was measured at 15.75 mm clinically, 19.1 mm via imaging, and 19.6 mm histologically. Biopsy showed that 16% of the tumors were ductal carcinoma in situ (DCIS), and 84% were invasive. 16% of patients had involved margins, and re-excision was required in 10 patients, and completion mastectomy was performed in 2 patients. A thirty-day SSI rate was 6.2%, with flap-related complications, including flap loss and shrinkage, at 3.7% and 4.9%, respectively. In addition, 3.7% had a hematoma, and 17.3% had other complications. Conclusion: Partial breast reconstruction with perforator flaps is an excellent volume replacement technique in breast-conserving surgery with acceptable complications in well-selected cases.

Individual with concurrent chest wall tuberculosis and triplenegative essential thrombocythemia:A case report

BACKGROUND Chest wall tuberculosis(TB)and triple-negative essential thrombocythemia(TNET)are rare medical conditions,and their combination is extremely rare globally.Only one case of TB peritonitis with thrombocytosis has been reported,which was identified in 1974.CASE SUMMARY Herein,we report the case of a 23-year-old man with concurrent chest wall mass and TN-ET.The patient presented to a local hospital due to having a headache and low-grade fever for 2 d,with their bodily temperature fluctuating at around 36.8°C.Hematological analysis showed a high platelet count of 1503×109/L.Subsequently,the patient visited our hospital for further investigation.Computed tomography of the chest suggested a submural soft tissue density shadow in the left lower chest wall.After surgical resection,the pathological findings of the swelling were reported as TB with massive caseous necrosis.According to the World Health Organization diagnostic criteria,the patient was diagnosed with TN-ET,as they met the requirement of four main criteria or the first three main criteria and one secondary criterion.The patient was eventually diagnosed with chest wall TB with TN-ET,which is extremely rare.CONCLUSION Chest wall TB is rare.TN-ET diagnosis requires secondary factor exclusion and satisfaction of primary diagnostic criteria.miRNA,combined with the methylation process,could explain suppressor of cytokine signaling(SOCS)1 and SOCS3 downregulation in ET-JAK2V617F-negative patients.The miRNA could participate in JAK2 pathway activation.SOCS3 may be a novel MPN biomarker.

Histogram Matched Chest X-Rays Based Tuberculosis Detection Using CNN

Tuberculosis(TB)is a severe infection that mostly affects the lungs and kills millions of people’s lives every year.Tuberculosis can be diagnosed using chest X-rays(CXR)and data-driven deep learning(DL)approaches.Because of its better automated feature extraction capability,convolutional neural net-works(CNNs)trained on natural images are particularly effective in image cate-gorization.A combination of 3001 normal and 3001 TB CXR images was gathered for this study from different accessible public datasets.Ten different deep CNNs(Resnet50,Resnet101,Resnet152,InceptionV3,VGG16,VGG19,DenseNet121,DenseNet169,DenseNet201,MobileNet)are trained and tested for identifying TB and normal cases.This study presents a deep CNN approach based on histogram matched CXR images that does not require object segmenta-tion of interest,and this coupled methodology of histogram matching with the CXRs improves the accuracy and detection performance of CNN models for TB detection.Furthermore,this research contains two separate experiments that used CXR images with and without histogram matching to classify TB and non-TB CXRs using deep CNNs.It was able to accurately detect TB from CXR images using pre-processing,data augmentation,and deep CNN models.Without histogram matching the best accuracy,sensitivity,specificity,precision and F1-score in the detection of TB using CXR images among ten models are 99.25%,99.48%,99.52%,99.48%and 99.22%respectively.With histogram matching the best accuracy,sensitivity,specificity,precision and F1-score are 99.58%,99.82%,99.67%,99.65%and 99.56%respectively.The proposed meth-odology,which has cutting-edge performance,will be useful in computer-assisted TB diagnosis and aids in minimizing irregularities in TB detection in developing countries.

Value of ultrasound guided biopsy combined with Xpert Mycobacterium tuberculosis/resistance to rifampin assay in the diagnosis of chest wall tuberculosis

BACKGROUND The thoracic wall lesions,particularly chest wall tuberculosis,and chest wall tumors and other pyogenic wall and actinomycetes infections,almost always present as a diagnostic challenge.AIM To explore the value of ultrasound-guided biopsy combined with the Xpert Mycobacterium tuberculosis/resistance to rifampin(MTB/RIF)assay to diagnose chest wall tuberculosis.METHODS We performed a retrospective study of patients with chest wall lesions from March 2018 to March 2021.All patients received the ultrasound-guided biopsy for pathology examination,acid-fast Bacillus staining,mycobacterial culture,and Xpert MTB/RIF analysis.The sensitivity,specificity,and area under the curve(AUC)were calculated for these diagnostic tests,either individually or combined.Rifampicin resistance results were compared between the mycobacterial culture and the Xpert MTB/RIF assay.RESULTS In 31 patients with the chest wall lesion biopsy,22 patients were diagnosed with chest wall tuberculosis.Of them,3,6,and 21 patients tested positive for mycobacterial culture,acid-fast stain,and Xpert MTB/RIF assay,respectively.The rifampicin resistance results of the 3 culture-positive patients were consistent with their Xpert MTB/RIF assay results.When considering the sensitivity,specificity,and AUC value,the Xpert MTB/RIF assay(95.5%,88.9%,and 0.92,respectively)was a better choice than the acid-fast Bacillus stain(27.3%,100.0%,and 0.64,respectively)and mycobacterial culture(13.6%,100.0%,0.57,respectively).No complications were reported during the procedure.CONCLUSION Ultrasound guided biopsy combined with Xpert MTB/RIF has high value in the diagnosis of chest wall tuberculosis,and can also detect rifampicin resistance.

胸部CT图像质量评价与辐射剂量的临床应用研究

目的:归纳和分析影响胸部CT图像质量的因素,进一步对照分析常规剂量组和低剂量组两组图像质量有无差异,以论证胸部CT低剂量检查的可行性。方法:选取150例胸部CT查体的患者,A组为常规剂量组,管电压120kV,管电流自动调节;B组为低剂量组,管电压100kV,管电流自动调节。参照胸部CT的图像质量评价标准对常规剂量组和低剂量组图像质量进一步作出主观评价。对辐射剂量、信噪比等客观数据进行统计学处理,以P<0.05为差异有统计学意义。并与主观评价相比较。结果:主、客观评价两组图像质量均无统计学意义,A组辐射剂量与B组辐射剂量有统计学意义。结论:影响图像质量的因素包括患者的因素、设备的因素以及操作者的因素,胸部CT采用100kV低剂量检查可以作为常规扫描方案,获得的图像可以满足影像诊断的要求,具有可行性。

面向肺炎CT图像识别的DL-CTNet模型

肺炎常缺乏明显呼吸系症状,症状多不典型,易发生漏诊、错诊.利用深度学习技术辅助医务人员安全、高效地检测感染者是一种有效途径.针对COVID-19感染者CT图像的磨玻璃影、铺路石征、血管扩张等特点,提出一种可有效地提取CT图像中的局部与全局特征的轻量级模型——DL-CTNet.输入预处理的CT图像后,首先采用空洞卷积和动态双路径多尺度特征融合(D-DMFF)模块的2个支路提取浅层特征;然后使用局部与全局特征拼接模块(LGFC)中的D-DMFF模块提取局部特征、Swin Transformer提取全局特征,并通过拼接获得深层特征;最后经过全连接层输出分类标签.实验结果表明,在2个CT图像数据集上,验证了LGFC模块以及DL-CTNet的低复杂度与有效性;DL-CTNet的分类准确率高达98.613%,与其他方法相比,其能更准确地识别肺炎的CT图像.

职业性尘肺病患者胸部CT影像表现与肺通气功能的相关性

目的探讨职业性尘肺病患者胸部CT影像表现与肺通气功能相关性。方法选取2019年1月至2021年12月焦作市疾病预防控制中心收集的86例职业性尘肺病患者纳入尘肺病组,早期煤尘肺病HRCT分期法分为Ⅰ期33例、Ⅱ期29例和Ⅲ期24例,另选取86例同期行健康体检者纳入健康对照(HC)组,均接受胸部CT检查和肺通气功能检查,采用Camiciotoli视觉评分法评估两组胸部CT影像肺间质纤维化评分,记录呼气流量峰值(PEF)、第1秒用力呼气容积(FEV_(1))、FEV_(1)/用力肺活量(FCV)、最大自主通气量(MMV)。分析职业性尘肺病患者胸部CT影像表现;比较尘肺病组和HC组胸部CT影像肺纤维化评分和肺通气功能指标水平;比较不同分期职业性尘肺病患者胸部CT影像肺纤维化评分和肺通气功能指标水平;Pearson相关性分析职业性尘肺病患者胸部CT影像肺纤维化评分与肺通气功能指标的关系。结果职业性尘肺病患者胸部CT影像主要表现为小结节影、小结节聚集、纤维斑块形和小叶间隔增厚、胸膜斑;尘肺病组胸部CT影像肺纤维化评分为(20.16±5.01)分,其FEV_(1)、PEF、FEV_(1)/FVC值、MVV低于HC组(P<0.05);Ⅲ期职业性尘肺病患者胸部CT影像肺纤维化评分高于Ⅰ期、Ⅱ期患者,FEV_(1)、PEF、FEV_(1)/FVC值、MVV低于Ⅰ期、Ⅱ期患者(P<0.05);Pearson相关性分析,职业性尘肺病患者胸部CT影像肺纤维化评分与FEV_(1)、PEF、FEV_(1)/FVC值、MVV呈负相关(r=-0.625,-0.865,-0.636,-0.673,P<0.05)。结论职业性尘肺病患者胸部CT影像肺纤维化评分与肺通气功能指标之间存在负相关性,可作为临床判断职业性尘肺病和评估病情进展的可靠的影像学指标。

张仲景宗和法治疗胸痹刍议

和法古往今来都备受争议,张仲景运用和法治疗胸痹更具有典型特色。本文简要分析了和法之和解、和中、调和的内涵和《伤寒论》中张仲景对和法的诠释与应用。旨在总结归纳张仲景于《伤寒论》中运用和法,抓住胸痹主要病机,从营卫、气血、阴阳三方面辨证治疗胸痹的遣方用药。

带蒂大网膜移植术治疗恶性肿瘤放射性胸部溃疡并胸骨骨髓炎的临床疗效分析

目的探讨带蒂大网膜移植术治疗恶性肿瘤放射治疗(放疗)后引发的胸部溃疡并胸骨骨髓炎的临床疗效。方法回顾性分析4例恶性肿瘤放射性胸部溃疡并胸骨骨髓炎患者的临床资料,均行带蒂大网膜移植手术治疗,观察治疗效果。结果4例患者均顺利实施手术治疗,手术时间85~150 min,出血量200~700 ml,住院天数22~99 d。1例患者术后溃疡面积明显缩小,仅残留针状窦道,出院后门诊换药6个月后治愈;其余患者均顺利治愈出院,无二次手术。1例患者术后1年因肺癌肝转移死亡,其余3例随访至今胸骨骨髓炎无复发。结论对于恶性肿瘤放射性胸部溃疡并胸骨骨髓炎患者,带蒂大网膜移植术是一种安全有效的手术方式。

肺泡灌洗术联合肺表面活性物质治疗1例儿童外源性脂质性肺炎并文献复习

目的:探讨经支气管镜肺泡灌洗术联合肺表面活性物质治疗1例儿童外源性脂质性肺炎的应用价值。方法:收集我院收治的1例外源性脂质性肺炎患儿的临床诊疗资料,并进行文献分析。结果:患儿意外吸入油性稀释液体后出现咳嗽、气促进行性加重,血氧饱和度降低,血炎症指标增高,胸部计算机断层扫描(CT)见双肺多发性病变,确诊外源性脂质性肺炎后及时予以经支气管镜肺泡灌洗术,并首次联合应用肺表面活性物质经支气管镜注入治疗,同时予以无创正压机械通气、抗感染、糖皮质激素雾化等治疗。治疗后,患儿咳嗽、气促、低氧血症等临床表现及胸部影像学短期内明显好转。结论:儿童外源性脂质性肺炎早期应用经支气管镜肺泡灌洗术联合肺表面活性物质治疗临床疗效好,有较大临床推广价值。