User-friendly prognostic model for rectal neuroendocrine tumours: In the era of precision management

In this letter,we explore into the potential role of the recent study by Zeng et al.Rectal neuroendocrine tumours(rNETs)are rare,originate from peptidergic neurons and neuroendocrine cells,and express corresponding markers.Although most rNETs patients have a favourable prognosis,the median survival period significantly decreases when high-risk factors,such as larger tumours,poorer differentiation,and lymph node metastasis exist,are present.Clinical prediction models play a vital role in guiding diagnosis and prognosis in health care,but their complex calculation formulae limit clinical use.Moreover,the prognostic models that have been developed for rNETs to date still have several limitations,such as insufficient sample sizes and the lack of external validation.A high-quality prognostic model for rNETs would guide treatment and follow-up,enabling the precise formulation of individual patient treatment and follow-up plans.The future development of models for rNETs should involve closer collab-oration with statistical experts,which would allow the construction of clinical prediction models to be standardized and robust,accurate,and highly general-izable prediction models to be created,ultimately achieving the goal of precision medicine.

Establishment and evaluation of prediction model of recurrence after laparoscopic choledocholithotomy

BACKGROUND Choledocholithiasis is a common clinical bile duct disease,laparoscopic choledocholithotomy is the main clinical treatment method for choledocho-lithiasis.However,the recurrence of postoperative stones is a big challenge for patients and doctors.AIM To explore the related risk factors of gallstone recurrence after laparoscopic choledocholithotomy,establish and evaluate a clinical prediction model.METHODS A total of 254 patients who underwent laparoscopic choledocholithotomy in the First Affiliated Hospital of Ningbo University from December 2017 to December 2020 were selected as the research subjects.Clinical data of the patients were collected,and the recurrence of gallstones was recorded based on the postope-rative follow-up.The results were analyzed and a clinical prediction model was established.RESULTS Postoperative stone recurrence rate was 10.23%(26 patients).Multivariate Logistic regression analysis showed that cholangitis,the diameter of the common bile duct,the diameter of the stone,number of stones,lithotripsy,preoperative total bilirubin,and T tube were risk factors associated with postoperative recurrence(P<0.05).The clinical prediction model was ln(p/1-p)=-6.853+1.347×cholangitis+1.535×choledochal diameter+2.176×stone diameter+1.784×stone number+2.242×lithotripsy+0.021×preoperative total bilirubin+2.185×T tube.CONCLUSION Cholangitis,the diameter of the common bile duct,the diameter of the stone,number of stones,lithotripsy,preoperative total bilirubin,and T tube are the associated risk factors for postoperative recurrence of gallstone.The prediction model in this study has a good prediction effect,which has a certain reference value for recurrence of gallstone after laparoscopic choledocholi-thotomy.

Severe/critical COVID-19 early warning system based on machine learning algorithms using novel imaging scores

BACKGROUND Early identification of severe/critical coronavirus disease 2019(COVID-19)is crucial for timely treatment and intervention.Chest computed tomography(CT)score has been shown to be a significant factor in the diagnosis and treatment of pneumonia,however,there is currently a lack of effective early warning systems for severe/critical COVID-19 based on dynamic CT evolution.AIM To develop a severe/critical COVID-19 prediction model using a combination of imaging scores,clinical features,and biomarker levels.METHODS This study used an improved scoring system to extract and describe the chest CT characteristics of COVID-19 patients.The study also took into consideration the general clinical indicators such as dyspnea,oxygen saturation,alternative lengthening of telomeres(ALT),and androgen suppression treatment(AST),which are commonly associated with severe/critical COVID-19 cases.The study employed lasso regression to evaluate and rank the significance of different disease characteristics.RESULTS The results showed that blood oxygen saturation,ALT,IL-6/IL-10,combined score,ground glass opacity score,age,crazy paving mode score,qsofa,AST,and overall lung involvement score were key factors in predicting severe/critical COVID-19 cases.The study established a COVID-19 severe/critical early warning system using various machine learning algorithms,including XGBClassifier,Logistic Regression,MLPClassifier,RandomForestClassifier,and AdaBoost Classifier.The study concluded that the prediction model based on the improved CT score and machine learning algorithms is a feasible method for early detection of severe/critical COVID-19 evolution.CONCLUSION The findings of this study suggest that a prediction model based on improved CT scores and machine learning algorithms is effective in detecting the early warning signals of severe/critical COVID-19.

Development of a prediction model for enteral feeding intolerance in intensive care unit patients:A prospective cohort study

BACKGROUND Enteral nutrition(EN)is essential for critically ill patients.However,some patients will have enteral feeding intolerance(EFI)in the process of EN.AIM To develop a clinical prediction model to predict the risk of EFI in patients receiving EN in the intensive care unit.METHODS A prospective cohort study was performed.The enrolled patients’basic information,medical status,nutritional support,and gastrointestinal(GI)symptoms were recorded.The baseline data and influencing factors were compared.Logistic regression analysis was used to establish the model,and the bootstrap resampling method was used to conduct internal validation.RESULTS The sample cohort included 203 patients,and 37.93%of the patients were diagnosed with EFI.After the final regression analysis,age,GI disease,early feeding,mechanical ventilation before EN started,and abnormal serum sodium were identified.In the internal validation,500 bootstrap resample samples were performed,and the area under the curve was 0.70(95%CI:0.63-0.77).CONCLUSION This clinical prediction model can be applied to predict the risk of EFI.

Clinical Prediction Models for Hepatitis B Virus-related Acute-on-chronic Liver Failure:A Technical Report

Background and Aims:It is critical but challenging to predict the prognosis of hepatitis B virus-related acute-on-chronic liver failure(HBV-ACLF).This study systematically summarized and evaluated the quality and performance of available clinical prediction models(CPMs).Methods:A keyword search of articles on HBV-ACLF CPMs published in PubMed from January 1995 to April 2020 was performed.Both the quality and performance of the CPMs were assessed.Results:Fifty-two CPMs were identified,of which 31 were HBV-ACLF specific.The modeling data were mostly derived from retrospective(83.87%)and single-center(96.77%)cohorts,with sample sizes ranging from 46 to 1,202.Three-month mortality was the most common endpoint.The Asian Pacific Association for the Study of the Liver consensus(51.92%)and Chinese Medical Association liver failure guidelines(40.38%)were commonly used for HBV-ACLF diagnosis.Serum bilirubin(67.74%),the international normalized ratio(54.84%),and hepatic encephalopathy(51.61%)were the most frequent variables used in models.Model discrimination was commonly evaluated(88.46%),but model calibration was seldom performed.The model for end-stage liver disease score was the most widely used(84.62%);however,varying performance was reported among the studies.Conclusions:Substantial limitations lie in the quality of HBV-ACLF-specific CPMs.Disease severity of study populations may impact model performance.The clinical utility of CPMs in predicting short-term prognosis of HBV-ACLF remains to be undefined.

A study on the clinical prediction model of the yin deficiency type of perimenopausal syndrome

Background:The yin deficiency type of perimenopausal syndrome(PMS)as a common category of PMS based on the theory of traditional Chinese medicine(TCM)has a high prevalence with severe symptoms and long course of disease.Therefore,it is necessary to construct a prediction model to assist in diagnosis.Objective:This study aimed to investigate the independent predictors of the yin deficiency type of PMS and to develop a clinical prediction model of this disease.Methods:PMS patients who attended the Third Affiliated Hospital of Zhejiang Chinese Medical University between February 2020 and August 2023 were selected and divided chronologically into training and validation groups.Logistic regression analysis was applied in the training group to clarify the independent predictors of the yin deficiency type of PMS,and a nomogram was plotted.Internal and external validations were performed in the training and validation groups to evaluate the model’s accuracy,goodness of fit,and clinical adaptability.Results:Hot flashes and sweating(≥10 episodes/day),palpitations,emotional fluctuations,and abnormal sexual activity were independent predictors of the yin deficiency type of PMS(P>0.05).Based on the clinical prediction model constructed,the area under the receiver operating characteristic curve(AUR OC)in the training group was 0.989(95%CI 0.980–0.998),and the AUR OC in the validation group was 0.971(95%CI 0.940–0.999).This demonstrates that the model has superior prediction performance.The Hosmer-Lemeshow test was used to evaluate the model’s goodness of fit with P=0.596 for the training group and P=0.883 for the validation group,indicating a good fit.The decision curve analysis(DCA)curve and clinical impact curve(CIC)indicated good clinical adaptability.Conclusion:The model can accurately predict the occurrence of the yin deficiency type of PMS,which may help clinicians identify such patients at an early stage.