Acute pancreatitis:A review of diagnosis,severity prediction and prognosis assessment from imaging technology,scoring system and artificial intelligence

Acute pancreatitis(AP)is a potentially life-threatening inflammatory disease of the pancreas,with clinical management determined by the severity of the disease.Diagnosis,severity prediction,and prognosis assessment of AP typically involve the use of imaging technologies,such as computed tomography,magnetic resonance imaging,and ultrasound,and scoring systems,including Ranson,Acute Physiology and Chronic Health Evaluation II,and Bedside Index for Severity in AP scores.Computed tomography is considered the gold standard imaging modality for AP due to its high sensitivity and specificity,while magnetic resonance imaging and ultrasound can provide additional information on biliary obstruction and vascular complications.Scoring systems utilize clinical and laboratory parameters to classify AP patients into mild,moderate,or severe categories,guiding treatment decisions,such as intensive care unit admission,early enteral feeding,and antibiotic use.Despite the central role of imaging technologies and scoring systems in AP management,these methods have limitations in terms of accuracy,reproducibility,practicality and economics.Recent advancements of artificial intelligence(AI)provide new opportunities to enhance their performance by analyzing vast amounts of clinical and imaging data.AI algorithms can analyze large amounts of clinical and imaging data,identify scoring system patterns,and predict the clinical course of disease.AI-based models have shown promising results in predicting the severity and mortality of AP,but further validation and standardization are required before widespread clinical application.In addition,understanding the correlation between these three technologies will aid in developing new methods that can accurately,sensitively,and specifically be used in the diagnosis,severity prediction,and prognosis assessment of AP through complementary advantages.

Understanding serum inflammatory markers in pediatric Mycoplasma pneumoniae pneumonia

This editorial reflects on the research,which investigates the potential of serum markers to predict the severity of Mycoplasma pneumoniae infections.Mycoplasma pneumoniae pneumonia(MPP)is a prevalent cause of respiratory infections in children,often leading to significant morbidity.Predicting the severity of MPP can significantly enhance patient management and outcomes.This editorial reviews the role of specific laboratory markers:(1)Lactate dehydrogenase;(2)Interleukin(IL)-6;(3)IL-10;(4)Tumor necrosis factor-α;and(5)D-dimer in predicting the severity of MPP in pediatric patients.Elevated levels of these markers are strongly associated with severe cases of MPP,providing clinicians with valuable tools for early diagnosis and targeted intervention.

A Nomogram for Predicting the Severity of COVID-19 Using Laboratory Examination and CT Findings

Background: The outbreak of COVID-19 has a significant impact on the health of people around the world. In the clinical condition of COVID-19, the condition of critical cases changes rapidly with a high mortality rate. Therefore, early prediction of disease severity and active intervention play an important role in the prognosis of severe patients. Methods: All the patients with COVID-19 in Taizhou city were retrospectively included and segregated into the non-severe and severe group according to the severity of the disease. The clinical manifestations, laboratory examination results, and imaging findings of the 2 groups were analyzed for comparing the differences between the 2 groups. Univariate and multivariate logistic regression were used for screening the factors that could predict the disease, and the nomogram was constructed. Results: A total of 143 laboratory-confirmed cases were included in the study, including 110 non-severe patients and 33 severe patients. The median age of patients was 47 years (range, 4 - 86 years). Fever (73.4%) and cough (63.6%) were the most common initial clinical symptoms. By using the method of multivariate logistic regression, the variables to construct nomogram include age (OR: 1.052, 95% CI: 1.020 - 1.086, P = 0.001), body temperature (OR: 2.252, 95% CI: 1.139 - 4.450, P = 0.020), lymphocyte count (OR: 1.128, 95% CI: 1.000 - 1.272, P = 0.049), ADA (OR: 1.163, 95% CI: 1.023 - 1.323, P = 0.021), PaO2 (OR: 0.972, 95% CI: 0.953 - 0.992, P = 0.007), IL-10 (OR: 1.184, 95% CI: 1.037 - 1.351, P = 0.012), and bronchiectasis (OR: 3.818, 95% CI: 1.694 - 8.605, P = 0.001). The AUC of the established nomogram was 0.877. Conclusions: This study analyzed the cases of patients with COVID-19 in Taizhou city and constructed a model to predict the illness severity. When patients showed the features including older age, high body temperature, low lymphocyte count, low ADA value, low PaO2, high IL-10, and bronchiectasis sign in CT predicts a greater likelihood of severe COVID-19.

Advances in prognostic factors in acute pancreatitis:a mini-review

BACKGROUND:Early assessment of the severity of acute pancreatitis is essential to the proper management of the disease.It is dependent on the criteria of the Atlanta classification system.DATA SOURCES:PubMed search of recent relevant articles was performed to identify information about the severity and prognosis of acute pancreatitis.RESULTS:The scoring systems included the Ranson’s or Glasgow’s criteria ≥3,the APACHE II classification system ≥8,and the Balthazar’s criteria ≥4 according to the computed tomography enhanced scanning findings.The single factors on admission included age >65 years,obesity,hemoconcentration(>44%),abnormal chest X-ray,creatinine >2 mg/dl,C-reactive protein>150 mg/dl,procalcitonin >1.8 ng/ml,albumin <2.5 mg/dl,calcium <8.5 mg/dl,early hyperglycemia,increased intra-abdominal pressure,macrophage migration inhibitory factor,or a combination of IL-10 >50 pg/ml with calcium <6.6 mg/dl.CONCLUSION:The prediction of the severity of acute pancreatitis is largely based on well defined multiple factor scoring systems as well as several single risk factors.

Sarcopenia diagnosed using masseter muscle area predictive of early mortality following severe traumatic brain injury

Traumatic brain injury（TBI）represents a global pandemic and is currently a leading cause of injury related death worldwide.Unfortunately,those who survive initial injury often suffer devastating functional,social,and economic consequences.

Predicting the severity of traffic accidents on mountain freeways with dynamic traffic and weather data

Traffic accident severity prediction is essential for dynamic traffic safety management.To explore the factors influencing the severity of traffic accidents on mountain freeways and to predict the severity of traffic accidents,four models based on machine learning algorithms are constructed using support vector machine(SVM),decision tree classifier(DTC),Ada_SVM and Ada_DTC.In addition,random forest(RF)is used to calculate the importance degree of variables and the accident severity influences with high importance levels form the RF dataset.The results show that rainfall intensity,collision type,number of vehicles involved in the accident and toad section type are important variables influencing accident severity.The RF feature selection method improves the classification performance of four machine leaming algorithms,resulting in a 9.3%,5.5%,7.2% and 3.6% improvement in prediction accuracy for SVM,DTC,Ada_SVM and Ada_DTC,respectively.The combination of the Ada_SVM integrated algorithm and RF feature selection method has the best prediction performance,and it achieves 78.9% and 88.4% prediction precision and accuracy,respectively.