BACKGROUND Although antibiotic therapy has become the primary treatment for acute unco-mplicated appendicitis,the management of acute complicated appendicitis nece-ssitates careful consideration of various treatment o...BACKGROUND Although antibiotic therapy has become the primary treatment for acute unco-mplicated appendicitis,the management of acute complicated appendicitis nece-ssitates careful consideration of various treatment options.AIM To analyze the clinical data of patients who underwent emergency appendectomy for acute complicated appendicitis with peri-appendiceal abscess or phlegmon,identify factors influencing the postoperative length of hospital stay(LOS),and improve treatment strategies.METHODS The clinical data of acute complicated appendicitis patients with peri-appendiceal abscess or phlegmon who underwent emergency appendectomy at The Depart-ment of Emergency Surgery,Zhongshan Hospital,Fudan University from January 2016 to March 2023 were retrospectively analyzed.RESULTS A total of 234 patients were included in our study.The duration of symptoms and the presence of an appendicolith were significantly correlated with the occurrence of peri-appendiceal abscess in patients with acute complicated appendicitis(P<0.001 and P=0.015,respectively).Patients with symptoms lasting longer than 72 h had a significantly longer postoperative LOS compared to those with symptoms lasting 72 h or less[hazard ratio(HR),1.208;95%CI:1.107-1.319;P<0.001].Additionally,patients with peri-appendiceal abscesses had a significantly longer postoperative LOS compared to those with phlegmon(HR,1.217;95%CI:1.095-1.352;P<0.001).The patients with peri-appendiceal abscesses were divided into two groups based on the median size of the abscess:Those with abscesses smaller than 5.0 cm(n=69)and those with abscesses 5.0 cm or larger(n=82).Patients with peri-appendiceal abscesses measuring 5.0 cm or larger had a significantly longer postoperative LOS than those with abscesses smaller than 5.0 cm(P=0.038).CONCLUSION The duration of symptoms and the presence of an appendicolith are significant risk factors for the formation of peri-appendiceal abscesses in patients with acute complicated appendicitis.Patients with peri-appendiceal abscesses experience a significantly longer postoperative LOS compared to those with peri-appendiceal phlegmon.展开更多
<b>Introduction:</b> Acute appendicitis (AA) is a common surgical disease which occurs in almost all age groups, and especially in childhood. Acute appendicitis is one of the most common causes of acute ab...<b>Introduction:</b> Acute appendicitis (AA) is a common surgical disease which occurs in almost all age groups, and especially in childhood. Acute appendicitis is one of the most common causes of acute abdomen. The lifetime occurrence of this disease is approximately 7%, with perforation rate of up to 20%. In spite of the well-known classical symptoms and clinical findings of acute appendicitis, early diagnosis can be sometimes challenging. For the treatment of simple appendicitis (SA) in children, the effectiveness of antibiotic treatment has been reported. We aimed to determine predictive value of combination NLR and PAS in pediatric patients with clinical suspicion of acute appendicitis and complicated appendicitis <b>Methods:</b> Our study was performed on 480 children admitted for suspected acute appendicitis and underwent appendectomy at the MNCMCH, Ulaanbaatar Mongolia, between May 2019 and December 2019. White blood count (WBC), Neutrophil, NLR and PAS were compared between groups. <b>Results:</b> The sensitivity, specificity, PPV, NPV of PAS + NLR for differentiating complicated and noncomplicated appendicitis were 86.8%, 89.4%, 92.1% and 76% respectively. The sensitivity, specificity, PPV, NPV of PAS + NLR for diagnosis of acute appendicitis were 90.5%, 68.1%, 97.68% and 32.6% respectively. <b>Conclusion:</b> In the era of conservative antibiotic-based management of uncomplicated acute appendicitis, we advocate that combination of NLR and PAS is a useful aid in predicting complicated appendicitis.展开更多
Acute appendicitis(AAp)remains one of the most common abdominal emergencies,requiring rapid and accurate diagnosis to prevent complications and unnecessary surgeries.Conventional diagnostic methods,including medical h...Acute appendicitis(AAp)remains one of the most common abdominal emergencies,requiring rapid and accurate diagnosis to prevent complications and unnecessary surgeries.Conventional diagnostic methods,including medical history,clinical assessment,biochemical markers,and imaging techniques,often present limitations in sensitivity and specificity,especially in atypical cases.In recent years,artificial intelligence(AI)has demonstrated remarkable potential in enhancing diagnostic accuracy through machine learning(ML)and deep learning(DL)models.This review evaluates the current applications of AI in both adult and pediatric AAp,focusing on clinical data-based models,radiological imaging analysis,and AI-assisted clinical decision support systems.ML models such as random forest,support vector machines,logistic regression,and extreme gradient boosting have exhibited superior diagnostic performance compared to traditional scoring systems,achieving sensitivity and specificity rates exceeding 90%in multiple studies.Additionally,DL techniques,particularly convolutional neural networks,have been shown to outperform radiologists in interpreting ultrasound and computed tomography images,enhancing diagnostic confidence.This review synthesized findings from 65 studies,demonstrating that AI models integrating multimodal data including clinical,laboratory,and imaging parameters further improved diagnostic precision.Moreover,explainable AI approaches,such as SHapley Additive exPlanations and local interpretable model-agnostic explanations,have facilitated model transparency,fostering clinician trust in AI-driven decision-making.This review highlights the advancements in AI for AAp diagnosis,emphasizing that AI is used not only to establish the diagnosis of AAp but also to differentiate complicated from uncomplicated cases.While preliminary results are promising,further prospective,multicenter studies are required for large-scale clinical implementation,given that a great proportion of current evidence derives from retrospective designs,and existing prospective cohorts exhibit limited sample sizes or protocol variability.Future research should also focus on integrating AI-driven decision support tools into routine emergency care workflows.展开更多
BACKGROUND Acute appendicitis(AAp)is a prevalent medical condition characterized by inflammation of the appendix that frequently necessitates urgent surgical procedures.Approximately two-thirds of patients with AAp ex...BACKGROUND Acute appendicitis(AAp)is a prevalent medical condition characterized by inflammation of the appendix that frequently necessitates urgent surgical procedures.Approximately two-thirds of patients with AAp exhibit characteristic signs and symptoms;hence,negative AAp and complicated AAp are the primary concerns in research on AAp.In other terms,further investigations and algorithms are required for at least one third of patients to predict the clinical condition and distinguish them from uncomplicated patients with AAp.AIM To use a Stochastic Gradient Boosting(SGB)-based machine learning(ML)algorithm to tell the difference between AAp patients who are complicated and those who are not,and to find some important biomarkers for both types of AAp by using modeling to get variable importance values.METHODS This study analyzed an open access data set containing 140 people,including 41 healthy controls,65 individuals with uncomplicated AAp,and 34 individuals with complicated AAp.We analyzed some demographic data(age,sex)of the patients and the following biochemical blood parameters:White blood cell(WBC)count,neutrophils,lymphocytes,monocytes,platelet count,neutrophil-tolymphocyte ratio,lymphocyte-to-monocyte ratio,mean platelet volume,neutrophil-to-immature granulocyte ratio,ferritin,total bilirubin,immature granulocyte count,immature granulocyte percent,and neutrophil-to-immature granulocyte ratio.We tested the SGB model using n-fold cross-validation.It was implemented with an 80-20 training-test split.We used variable importance values to identify the variables that were most effective on the target.RESULTS The SGB model demonstrated excellent performance in distinguishing AAp from control patients with an accuracy of 96.3%,a micro aera under the curve(AUC)of 94.7%,a sensitivity of 94.7%,and a specificity of 100%.In distinguishing complicated AAp patients from uncomplicated ones,the model achieved an accuracy of 78.9%,a micro AUC of 79%,a sensitivity of 83.3%,and a specificity of 76.9%.The most useful biomarkers for confirming the AA diagnosis were WBC(100%),neutrophils(95.14%),and the lymphocyte-monocyte ratio(76.05%).On the other hand,the most useful biomarkers for accurate diagnosis of complicated AAp were total bilirubin(100%),WBC(96.90%),and the neutrophil-immature granulocytes ratio(64.05%).CONCLUSION The SGB model achieved high accuracy rates in identifying AAp patients while it showed moderate performance in distinguishing complicated AAp patients from uncomplicated AAp patients.Although the model's accuracy in the classification of complicated AAp is moderate,the high variable importance obtained is clinically significant.We need further prospective validation studies,but the integration of such ML algorithms into clinical practice may improve diagnostic processes.展开更多
基金Supported by The National Natural Science Foundation of China,No.82373417The Natural Science Foundation of Shanghai,China,No.23ZR1409900The Clinical Research Fund of Zhongshan Hospital,Fudan University,China,No.ZSLCYJ202343.
文摘BACKGROUND Although antibiotic therapy has become the primary treatment for acute unco-mplicated appendicitis,the management of acute complicated appendicitis nece-ssitates careful consideration of various treatment options.AIM To analyze the clinical data of patients who underwent emergency appendectomy for acute complicated appendicitis with peri-appendiceal abscess or phlegmon,identify factors influencing the postoperative length of hospital stay(LOS),and improve treatment strategies.METHODS The clinical data of acute complicated appendicitis patients with peri-appendiceal abscess or phlegmon who underwent emergency appendectomy at The Depart-ment of Emergency Surgery,Zhongshan Hospital,Fudan University from January 2016 to March 2023 were retrospectively analyzed.RESULTS A total of 234 patients were included in our study.The duration of symptoms and the presence of an appendicolith were significantly correlated with the occurrence of peri-appendiceal abscess in patients with acute complicated appendicitis(P<0.001 and P=0.015,respectively).Patients with symptoms lasting longer than 72 h had a significantly longer postoperative LOS compared to those with symptoms lasting 72 h or less[hazard ratio(HR),1.208;95%CI:1.107-1.319;P<0.001].Additionally,patients with peri-appendiceal abscesses had a significantly longer postoperative LOS compared to those with phlegmon(HR,1.217;95%CI:1.095-1.352;P<0.001).The patients with peri-appendiceal abscesses were divided into two groups based on the median size of the abscess:Those with abscesses smaller than 5.0 cm(n=69)and those with abscesses 5.0 cm or larger(n=82).Patients with peri-appendiceal abscesses measuring 5.0 cm or larger had a significantly longer postoperative LOS than those with abscesses smaller than 5.0 cm(P=0.038).CONCLUSION The duration of symptoms and the presence of an appendicolith are significant risk factors for the formation of peri-appendiceal abscesses in patients with acute complicated appendicitis.Patients with peri-appendiceal abscesses experience a significantly longer postoperative LOS compared to those with peri-appendiceal phlegmon.
文摘<b>Introduction:</b> Acute appendicitis (AA) is a common surgical disease which occurs in almost all age groups, and especially in childhood. Acute appendicitis is one of the most common causes of acute abdomen. The lifetime occurrence of this disease is approximately 7%, with perforation rate of up to 20%. In spite of the well-known classical symptoms and clinical findings of acute appendicitis, early diagnosis can be sometimes challenging. For the treatment of simple appendicitis (SA) in children, the effectiveness of antibiotic treatment has been reported. We aimed to determine predictive value of combination NLR and PAS in pediatric patients with clinical suspicion of acute appendicitis and complicated appendicitis <b>Methods:</b> Our study was performed on 480 children admitted for suspected acute appendicitis and underwent appendectomy at the MNCMCH, Ulaanbaatar Mongolia, between May 2019 and December 2019. White blood count (WBC), Neutrophil, NLR and PAS were compared between groups. <b>Results:</b> The sensitivity, specificity, PPV, NPV of PAS + NLR for differentiating complicated and noncomplicated appendicitis were 86.8%, 89.4%, 92.1% and 76% respectively. The sensitivity, specificity, PPV, NPV of PAS + NLR for diagnosis of acute appendicitis were 90.5%, 68.1%, 97.68% and 32.6% respectively. <b>Conclusion:</b> In the era of conservative antibiotic-based management of uncomplicated acute appendicitis, we advocate that combination of NLR and PAS is a useful aid in predicting complicated appendicitis.
文摘Acute appendicitis(AAp)remains one of the most common abdominal emergencies,requiring rapid and accurate diagnosis to prevent complications and unnecessary surgeries.Conventional diagnostic methods,including medical history,clinical assessment,biochemical markers,and imaging techniques,often present limitations in sensitivity and specificity,especially in atypical cases.In recent years,artificial intelligence(AI)has demonstrated remarkable potential in enhancing diagnostic accuracy through machine learning(ML)and deep learning(DL)models.This review evaluates the current applications of AI in both adult and pediatric AAp,focusing on clinical data-based models,radiological imaging analysis,and AI-assisted clinical decision support systems.ML models such as random forest,support vector machines,logistic regression,and extreme gradient boosting have exhibited superior diagnostic performance compared to traditional scoring systems,achieving sensitivity and specificity rates exceeding 90%in multiple studies.Additionally,DL techniques,particularly convolutional neural networks,have been shown to outperform radiologists in interpreting ultrasound and computed tomography images,enhancing diagnostic confidence.This review synthesized findings from 65 studies,demonstrating that AI models integrating multimodal data including clinical,laboratory,and imaging parameters further improved diagnostic precision.Moreover,explainable AI approaches,such as SHapley Additive exPlanations and local interpretable model-agnostic explanations,have facilitated model transparency,fostering clinician trust in AI-driven decision-making.This review highlights the advancements in AI for AAp diagnosis,emphasizing that AI is used not only to establish the diagnosis of AAp but also to differentiate complicated from uncomplicated cases.While preliminary results are promising,further prospective,multicenter studies are required for large-scale clinical implementation,given that a great proportion of current evidence derives from retrospective designs,and existing prospective cohorts exhibit limited sample sizes or protocol variability.Future research should also focus on integrating AI-driven decision support tools into routine emergency care workflows.
文摘BACKGROUND Acute appendicitis(AAp)is a prevalent medical condition characterized by inflammation of the appendix that frequently necessitates urgent surgical procedures.Approximately two-thirds of patients with AAp exhibit characteristic signs and symptoms;hence,negative AAp and complicated AAp are the primary concerns in research on AAp.In other terms,further investigations and algorithms are required for at least one third of patients to predict the clinical condition and distinguish them from uncomplicated patients with AAp.AIM To use a Stochastic Gradient Boosting(SGB)-based machine learning(ML)algorithm to tell the difference between AAp patients who are complicated and those who are not,and to find some important biomarkers for both types of AAp by using modeling to get variable importance values.METHODS This study analyzed an open access data set containing 140 people,including 41 healthy controls,65 individuals with uncomplicated AAp,and 34 individuals with complicated AAp.We analyzed some demographic data(age,sex)of the patients and the following biochemical blood parameters:White blood cell(WBC)count,neutrophils,lymphocytes,monocytes,platelet count,neutrophil-tolymphocyte ratio,lymphocyte-to-monocyte ratio,mean platelet volume,neutrophil-to-immature granulocyte ratio,ferritin,total bilirubin,immature granulocyte count,immature granulocyte percent,and neutrophil-to-immature granulocyte ratio.We tested the SGB model using n-fold cross-validation.It was implemented with an 80-20 training-test split.We used variable importance values to identify the variables that were most effective on the target.RESULTS The SGB model demonstrated excellent performance in distinguishing AAp from control patients with an accuracy of 96.3%,a micro aera under the curve(AUC)of 94.7%,a sensitivity of 94.7%,and a specificity of 100%.In distinguishing complicated AAp patients from uncomplicated ones,the model achieved an accuracy of 78.9%,a micro AUC of 79%,a sensitivity of 83.3%,and a specificity of 76.9%.The most useful biomarkers for confirming the AA diagnosis were WBC(100%),neutrophils(95.14%),and the lymphocyte-monocyte ratio(76.05%).On the other hand,the most useful biomarkers for accurate diagnosis of complicated AAp were total bilirubin(100%),WBC(96.90%),and the neutrophil-immature granulocytes ratio(64.05%).CONCLUSION The SGB model achieved high accuracy rates in identifying AAp patients while it showed moderate performance in distinguishing complicated AAp patients from uncomplicated AAp patients.Although the model's accuracy in the classification of complicated AAp is moderate,the high variable importance obtained is clinically significant.We need further prospective validation studies,but the integration of such ML algorithms into clinical practice may improve diagnostic processes.
