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.

Comparing 11 early warning scores and three shock indices in early sepsis prediction in the emergency department

BACKGROUND:This study aimed to evaluate the discriminatory performance of 11 vital sign-based early warning scores(EWSs)and three shock indices in early sepsis prediction in the emergency department(ED).METHODS:We performed a retrospective study on consecutive adult patients with an infection over 3 months in a public ED in Hong Kong.The primary outcome was sepsis(Sepsis-3 definition)within 48 h of ED presentation.Using c-statistics and the DeLong test,we compared 11 EWSs,including the National Early Warning Score 2(NEWS2),Modified Early Warning Score,and Worthing Physiological Scoring System(WPS),etc.,and three shock indices(the shock index[SI],modified shock index[MSI],and diastolic shock index[DSI]),with Systemic Inflammatory Response Syndrome(SIRS)and quick Sequential Organ Failure Assessment(qSOFA)in predicting the primary outcome,intensive care unit admission,and mortality at different time points.RESULTS:We analyzed 601 patients,of whom 166(27.6%)developed sepsis.NEWS2 had the highest point estimate(area under the receiver operating characteristic curve[AUROC]0.75,95%CI 0.70-0.79)and was significantly better than SIRS,qSOFA,other EWSs and shock indices,except WPS,at predicting the primary outcome.However,the pooled sensitivity and specificity of NEWS2≥5 for the prediction of sepsis were 0.45(95%CI 0.37-0.52)and 0.88(95%CI 0.85-0.91),respectively.The discriminatory performance of all EWSs and shock indices declined when used to predict mortality at a more remote time point.CONCLUSION:NEWS2 compared favorably with other EWSs and shock indices in early sepsis prediction but its low sensitivity at the usual cut-off point requires further modification for sepsis screening.

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.

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.

PREDICTION OF THE THERAPEUTIC EFFECTIVENESS OF NEW DRUGS FROM CLINICAL PHARMACOLOGY STUDIES

The development of new drugs for therapeutic purposes has become very expensive and time-consuming in American and European countries.It is estimated that on the average 50 to 100 million dollars and 10 or more years from the time of patenting are required to make a new drug available for general prescription. Every new drug needs to be charac-

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.

Towards automated calculation of evidence-based clinical scores

AIM To determine clinical scores important for automated calculation in the inpatient setting.METHODS A modified Delphi methodology was used to create consensus of important clinical scores for inpatient practice. A list of 176 externally validated clinical scores were identified from freely available internet-based services frequently used by clinicians. Scores were categorized based on pertinent specialty and a customized survey was created for each clinician specialty group. Clinicians were asked to rank each score based on importance of automated calculation to their clinical practice in three categories-"not important", "nice to have", or "very important". Surveys were solicited via specialty-group listserv over a 3-mo interval. Respondents must have been practicing physicians with more than 20% clinical time spent in the inpatient setting. Within each specialty, consensus was established for any clinical score with greater than 70% of responses in a single category and a minimum of 10 responses. Logistic regression was performed to determine predictors of automation importance.RESULTS Seventy-nine divided by one hundred and forty-four(54.9%) surveys were completed and 72/144(50%) surveys were completed by eligible respondents. Only the critical care and internal medicine specialties surpassed the 10-respondent threshold(14 respondents each). For internists, 2/110(1.8%) of scores were "very important" and 73/110(66.4%) were "nice to have". For intensivists, no scores were "very important" and 26/76(34.2%) were "nice to have". Only the number of medical history(OR = 2.34; 95%CI: 1.26-4.67; P < 0.05) and vital sign(OR = 1.88; 95%CI: 1.03-3.68; P < 0.05) variables for clinical scores used by internists was predictive of desire for automation. CONCLUSION Few clinical scores were deemed "very important" for automated calculation. Future efforts towards score calculator automation should focus on technically feasible "nice to have" scores.

Applications of artificial intelligence in, early detection of cancer, clinical diagnosis and personalized medicine

Artificial intelligence(AI)refers to the simulation of human intelligence in machines programmed to convert raw input data into decision-making actions,like humans.AI programs are designed to make decisions,often using deep learning and computer-guided programs that analyze and process raw data into clinical decision making for effective treatment.New techniques for predicting cancer at an early stage are needed as conventional methods have poor accuracy and are not applicable to personalized medicine.AI has the potential to use smart,intelligent computer systems for image interpretation and early diagnosis of cancer.AI has been changing almost all the areas of the medical field by integrating with new emerging technologies.AI has revolutionized the entire health care system through innovative digital diagnostics with greater precision and accuracy.AI is capable of detecting cancer at an early stage with accurate diagnosis and improved survival outcomes.AI is an innovative technology of the future that can be used for early prediction,diagnosis and treatment of cancer.

A predictive score for retinopathy of prematurity by using clinical risk factors and serum insulin-like growth factor-1 levels

AIM：To detect the impact of insulin-like growth factor-1（IGF-1）and other risk factors for the early prediction of retinopathy of prematurity（ROP）and to establish a scoring system for ROP prediction by using clinical criteria and serum IGF-1 levels.METHODS：The study was conducted with 127 preterm infants.IGF-1 levels in the 1st day of life,1st,2nd,3rd and4th week of life was analyzed.The score was established after logistic regression analysis,considering the impact of each variable on the occurrences of any stage ROP.A validation cohort containing 107 preterm infants was included in the study and the predictive ability of ROP score was calculated.RESULTS：Birth weights（BW）,gestational weeks（GW）and the prevalence of breast milk consumption were lower,respiratory distress syndrome（RDS）,bronchopulmonarydysplasia（BPD）and necrotizing enterocolitis（NEC）were more frequent,the duration of mechanical ventilation and oxygen supplementation was longer in patients with ROP（P〈0.05）.Initial serum IGF-1 levels tended to be lower in newborns who developed ROP.Logistic regression analysis revealed that low BW（〈1250 g）,presence of intraventricular hemorrhage（IVH）and formula feeding increased the risk of ROP.Afterwards,the scoring system was validated on 107 infants.The negative predictive values of a score less than 4 were 84.3%,74.7%and 79.8%while positive predictive values were 76.3%,65.5%and71.6%respectively.CONCLUSION：In addition to BW〈1250 g and IVH,formula consumption was detected as a risk factor for the development of ROP.Breastfeeding is important for prevention of ROP in preterm infants.

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 risk score to guide targeted hepatitis C testing among human immunodeficiency virus patients in Cambodia

BACKGROUND The World Health Organization recommends testing all human immunodeficiency virus(HIV)patients for hepatitis C virus(HCV).In resource-constrained contexts with low-to-intermediate HCV prevalence among HIV patients,as in Cambodia,targeted testing is,in the short-term,potentially more feasible and cost-effective.AIM To develop a clinical prediction score(CPS)to risk-stratify HIV patients for HCV coinfection(HCV RNA detected),and derive a decision rule to guide prioritization of HCV testing in settings where‘testing all’is not feasible or unaffordable in the short term.METHODS We used data of a cross-sectional HCV diagnostic study in the HIV cohort of Sihanouk Hospital Center of Hope in Phnom Penh.Key populations were very rare in this cohort.Score development relied on the Spiegelhalter and Knill-Jones method.Predictors with an adjusted likelihood ratio≥1.5 or≤0.67 were retained,transformed to natural logarithms,and rounded to integers as score items.CPS performance was evaluated by the area-under-the-ROC curve(AUROC)with 95% confidence intervals(CI),and diagnostic accuracy at the different cut-offs.For the decision rule,HCV coinfection probability≥1% was agreed as test-threshold.RESULTS Among the 3045 enrolled HIV patients,106 had an HCV coinfection.Of the 11 candidate predictors(from history-taking,laboratory testing),seven had an adjusted likelihood ratio≥1.5 or≤0.67:≥50 years(+1 point),diabetes mellitus(+1),partner/household member with liver disease(+1),generalized pruritus(+1),platelets<200×10^(9)/L(+1),aspartate transaminase(AST)<30 IU/L(-1),AST-to-platelet ratio index(APRI)≥0.45(+1),and APRI<0.45(-1).The AUROC was 0.84(95%CI:0.80-0.89),indicating good discrimination of HCV/HIV coinfection and HIV mono-infection.The CPS result≥0 best fits the test-threshold(negative predictive value:99.2%,95%CI:98.8-99.6).Applying this threshold,30%(n=926)would be tested.Sixteen coinfections(15%)would have been missed,none with advanced fibrosis.CONCLUSION The CPS performed well in the derivation cohort,and bears potential for other contexts of low-to-intermediate prevalence and little onward risk of transmission(i.e.cohorts without major risk factors as injecting drug use,men having sex with men),and where available resources do not allow to test all HIV patients as recommended by WHO.However,the score requires external validation in other patient cohorts before any wider use can be considered.

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.

Predicting the Risk of Preterm Birth Throughout Pregnancy Based on a Novel Transcriptomic Signature

Objective::This study focused on the prediction of preterm birth(PTB).It aimed to identify the transcriptomic signature essential for the occurrence of PTB and evaluate its predictive value in early,mid,and late pregnancy and in women with threatened preterm labor(TPTL).Methods::Blood transcriptome data of pregnant women were obtained from the Gene Expression Omnibus database.The activity of biological signatures was assessed using gene set enrichment analysis and single-sample gene set enrichment analysis.The correlation among molecules in the interleukin 6(IL6)signature and between IL6 signaling activity and the gestational week of delivery and latent period were evaluated by Pearson correlation analysis.The effects of molecules associated with the IL6 signature were fitted using logistic regression analysis;the predictive value of both the IL6 signature and IL6 alone were evaluated using receiver operating characteristic curves and pregnancy maintenance probability was assessed using Kaplan-Meier analysis.Differential analysis was performed using the DEseq2 and limma algorithms.Results::Circulatory IL6 signaling activity increased significantly in cases with preterm labor than in those with term pregnancies(normalized enrichment score(NES)=1.857,P=0.001).The IL6 signature(on which IL6 signaling is based)was subsequently considered as the candidate biomarker for PTB.The area under the curve(AUC)values for PTB prediction(using the IL6 signature)in early,mid,and late pregnancy were 0.810,0.695,and 0.779,respectively;these values were considerably higher than those for IL6 alone.In addition,the pregnancy curves of women with abnormal IL6 signature differed significantly from those with normal signature.In pregnant women who eventually had preterm deliveries,circulatory IL6 signaling activity was lower in early pregnancy(NES=-1.420,P=0.031)and higher than normal in mid(NES=1.671,P=0.002)and late pregnancy(NES=2.350,P<0.001).In women with TPTL,the AUC values for PTB prediction(or PTB within 7 days and 48 hours)using the IL6 signature were 0.761,0.829,and 0.836,respectively;the up-regulation of IL6 signaling activity and its correlation with the gestational week of delivery(r=-0.260,P=0.001)and latency period(r=-0.203,P=0.012)were more significant than in other women.Conclusion::Our findings suggest that the IL6 signature may predict PTB,even in early pregnancy(although the predictive power is relatively weak in mid pregnancy)and is particularly effective in symptomatic women.These findings may contribute to the development of an effective predictive and monitoring system for PTB,thereby reducing maternal and fetal risk.