Diagnostic and economic value of carcinoembryonic antigen,carbohydrate antigen 19-9,and carbohydrate antigen 72-4 in gastrointestinal cancers

BACKGROUND The diagnostic and economic value of carcinoembryonic antigen(CEA),carbohydrate antigen 19-9(CA19-9)and CA72-4 for gastrointestinal malignant tumors lacked evaluation in a larger scale.AIM To reassess the diagnostic and economic value of the three tumor biomarkers.METHODS A retrospective analysis of all 32857 subjects who underwent CEA,CA19-9,CA72-4,gastroscopy and colonoscopy from October 2006 to May 2018 was conducted.Then,we assessed the discrimination and clinical usefulness.Total cost,cost per capita and cost-effectiveness ratios were used to evaluate the economic value of two schemes(gastrointestinal endoscopy for all people without blood tests vs both gastroscopy and colonoscopy when blood tests were positive).RESULTS The analysis of 32857 subjects showed that CEA was a qualified biomarker for colorectal cancer(CRC),while the diagnostic efficiencies of CA72-4 were catastrophic for all gastrointestinal cancers(GICs).Regarding early diagnosis,only CEA could be used for early CRC.The combination of biomarkers didn’t greatly increase the area under the curve.The economic indicators of CEA were superior to those of CA19-9,CA72-4 and any combination.At the threshold of 1.8μg/L to 10.4μg/L,all four indicators of CEA were lower than those in the scheme that conducted gastrointestinal endoscopy only.Subgroup analysis implied that the health checkup of CEA for people above 65 years old was economically valuable.CONCLUSION CEA had qualified diagnostic value for CRC and superior economic value for GICs,especially for elderly health checkup subjects.CA72-4 was not suitable as a diagnostic biomarker.

Liver fibrosis index-based nomograms for identifying esophageal varices in patients with chronic hepatitis B related cirrhosis

BACKGROUND Esophageal varices(EV)are the most fatal complication of chronic hepatitis B(CHB)related cirrhosis.The prognosis is poor,especially after the first upper gastrointestinal hemorrhage.AIM To construct nomograms to predict the risk and severity of EV in patients with CHB related cirrhosis.METHODS Between 2016 and 2018,the patients with CHB related cirrhosis were recruited and divided into a training or validation cohort at The First Affiliated Hospital of Wenzhou Medical University.Clinical and ultrasonic parameters that were closely related to EV risk and severity were screened out by univariate and multivariate logistic regression analyses,and integrated into two nomograms,respectively.Both nomograms were internally and externally validated by calibration,concordance index(C-index),receiver operating characteristic curve,and decision curve analyses(DCA).RESULTS A total of 307 patients with CHB related cirrhosis were recruited.The independent risk factors for EV included Child-Pugh class[odds ratio(OR)=7.705,95%confidence interval(CI)=2.169-27.370,P=0.002],platelet count(OR=0.992,95%CI=0.984-1.000,P=0.044),splenic portal index(SPI)(OR=3.895,95%CI=1.630-9.308,P=0.002),and liver fibrosis index(LFI)(OR=3.603,95%CI=1.336-9.719,P=0.011);those of EV severity included Child-Pugh class(OR=5.436,95%CI=2.112-13.990,P<0.001),mean portal vein velocity(OR=1.479,95%CI=1.043-2.098,P=0.028),portal vein diameter(OR=1.397,95%CI=1.021-1.912,P=0.037),SPI(OR=1.463,95%CI=1.030-2.079,P=0.034),and LFI(OR=3.089,95%CI=1.442-6.617,P=0.004).Two nomograms(predicting EV risk and severity,respectively)were well-calibrated and had a favorable discriminative ability,with C-indexes of 0.916 and 0.846 in the training cohort,respectively,higher than those of other predictive indexes,like LFI(C-indexes=0.781 and 0.738),SPI(C-indexes=0.805 and 0.714),ratio of platelet count to spleen diameter(PSR)(C-indexes=0.822 and 0.726),King’s score(C-indexes=0.694 and 0.609),and Lok index(C-indexes=0.788 and 0.700).The areas under the curves(AUCs)of the two nomograms were 0.916 and 0.846 in the training cohort,respectively,higher than those of LFI(AUCs=0.781 and 0.738),SPI(AUCs=0.805 and 0.714),PSR(AUCs=0.822 and 0.726),King’s score(AUCs=0.694 and 0.609),and Lok index(AUCs=0.788 and 0.700).Better net benefits were shown in the DCA.The results were validated in the validation cohort.CONCLUSION Nomograms incorporating clinical and ultrasonic variables are efficient in noninvasively predicting the risk and severity of EV.

Development and validation of a prognostic nomogram model for Chinese patients with primary small cell carcinoma of the esophagus

BACKGROUND Primary small cell carcinoma of the esophagus(PSCE)is a highly invasive malignant tumor with a poor prognosis compared with esophageal squamous cell carcinoma.Due to the limited samples size and the short follow-up time,there are few reports on elucidating the prognosis of PSCE,especially on the establishment and validation of a survival prediction nomogram model covering general information,pathological factors and specific biological proteins of PSCE patients.AIM To establish an effective nomogram to predict the overall survival(OS)probability for PSCE patients in China.METHODS The nomogram was based on a retrospective study of 256 PSCE patients.Univariate analysis and multivariate Cox proportional hazards regression analysis were used to examine the prognostic factors associated with PSCE,and establish the model for predicting 1-,3-,and 5-year OS based on the Akaike information criterion.Discrimination and validation were assessed by the concordance index(C-index)and calibration curve and decision curve analysis(DCA).Histology type,age,tumor invasion depth,lymph node invasion,detectable metastasis,chromogranin A,and neuronal cell adhesion molecule 56 were integrated into the model.RESULTS The C-index was prognostically superior to the 7th tumor node metastasis(TNM)staging in the primary cohort[0.659(95%CI:0.607-0.712)vs 0.591(95%CI:0.517-0.666),P=0.033]and in the validation cohort[0.700(95%CI:0.622-0.778)vs 0.605(95%CI:0.490-0.721),P=0.041].Good calibration curves were observed for the prediction probabilities of 1-,3-,and 5-year OS in both cohorts.DCA analysis showed that our nomogram model had a higher overall net benefit compared to the 7th TNM staging.CONCLUSION Our nomogram can be used to predict the survival probability of PSCE patients,which can help clinicians to make individualized survival predictions.

Maximum Net Benefit Indicator and Its Applications

Receiver operating characteristics(ROC)curve and the area under the curve(AUC)value are often used to illustrate the diagnostic ability of binary classifiers.However,both ROC and AUC focus on high accuracy in theory,which may not be effective for practical applications.In addition,it is difficult to judge which one is better when the ROC curves are intersect and the AUC values are equal.Decision curve analysis(DCA)methods improve ROC by incorporating accuracy and consequences.However,similar to ROC,DCA requires a quantitative indicator to objectively determine which one is better when DCA curves intersect.A DCA-based statistical indicator named maximum net benefit(MNB)is constructed for evaluating clinical treatment regimens rather than just accuracy as in ROC and AUC.As a simple and effective statistical indicator,the construction process of MNB is given theoretically.Moreover,the MNB can still provide effective identification when the AUC values are equal,which is proved by theory.Furthermore,the feasibility and effectiveness of the proposed MNB are verified by gene selection and classifier performance comparison on actual data.

Circulating biomarker-and magnetic resonance-based nomogram predicting long-term outcomes in dilated cardiomyopathy

Background:Dilated cardiomyopathy(DCM)has a high mortality rate and is the most common indication for heart transplantation.Our study sought to develop a multiparametric nomogram to assess individualized all-cause mortality or heart transplantation(ACM/HTx)risk in DCM patients.Methods:The present study is a retrospective cohort study.The demographic,clinical,blood test,and cardiac magnetic resonance imaging(CMRI)data of DCM patients in the tertiary center(Fuwai Hospital)were collected.The primary endpoint was ACM/HTx.The least absolute shrinkage and selection operator(LASSO)Cox regression model was applied for variable selection.Multivariable Cox regression was used to develop a nomogram.The concordance index(C-index),area under the receiver operating characteristic curve(AUC),calibration curve,and decision curve analysis(DCA)were used to evaluate the performance of the nomogram.Results:A total of 218 patients were included in the present study.They were randomly divided into a training cohort and a validation cohort.The nomogram was established based on eight variables,including mid-wall late gadolinium enhancement,systolic blood pressure,diastolic blood pressure,left ventricular ejection fraction,left ventricular end-diastolic diameter,left ventricular end-diastolic volume index,free triiodothyronine,and N-terminal pro-B type natriuretic peptide.The AUCs regarding 1-year,3-year,and 5-year ACM/HTx events were 0.859,0.831,and 0.840 in the training cohort and 0.770,0.789,and 0.819 in the validation cohort,respectively.The calibration curve and DCA showed good accuracy and clinical utility of the nomogram.Conclusions:We established and validated a circulating biomarker-and CMRI-based nomogram that could provide a personalized prediction of ACM/HTx for DCM patients,which might help risk stratification and decision-making in clinical practice.

A diagnostic prediction model for hypertension in Han and Yugur population from the China National Health Survey (CNHS)

Background:The prevalence of hypertension is high among Chinese adults,thus,identifying non-hypertensive individuals at high risk for intervention will help to improve the efficiency of primary prevention strategies.Methods:The cross-sectional data on 9699 participants aged 20 to 80 years were collected from the China National Health Survey in Gansu and Hebei provinces in 2016 to 2017,and they were nonrandomly split into the training set and validation set based on location.Multivariable logistic regression analysis was performed to develop the diagnostic prediction model,which was presented as a nomogram and a website with risk classification.Predictive performances of the model were evaluated using discrimination and calibration,and were further compared with a previously published model.Decision curve analysis was used to calculate the standardized net benefit for assessing the clinical usefulness of the model.Results:The Lasso regression analysis identified the significant predictors of hypertension in the training set,and a diagnostic model was developed using logistic regression.A nomogram with risk classification was constructed to visualize the model,and a website(https://chris-yu.shinyapps.io/hypertension_risk_prediction/)was developed to calculate the exact probabilities of hypertension.The model showed good discrimination and calibration,with the C-index of 0.789(95%confidence interval[CI]:0.768,0.810)through internal validation and 0.829(95%CI:0.816,0.842)through external validation.Decision curve analysis demonstrated that the model was clinically useful.The model had a higher area under receiver operating characteristic curves in training and validation sets compared with a previously published diagnostic model based on Northern China population.Conclusion:This study developed and validated a diagnostic model for hypertension prediction in Gansu Province.A nomogram and a website were developed to make the model conveniently used to facilitate the individualized prediction of hypertension in the general population of Han and Yugur.

A Nomogram for Predicting the Likelihood of Obstructive Sleep Apnea to Reduce the Unnecessary Polysomnography Examinations

Background：The currently available polysomnography （PSG） equipments and operating personnel are facing increasing pressure,such situation may result in the problem that a large number of obstructive sleep apnea （OSA） patients cannot receive timely diagnosis and treatment,we sought to develop a nomogram quantifying the risk of OSA for a better decision of using PSG,based on the clinical syndromes and the demographic and anthropometric characteristics.Methods：The nomogram was constructed through an ordinal logistic regression procedure.Predictive accuracy and performance characteristics were assessed with the area under the curve （AUC） of the receiver operating characteristics and calibration plots,respectively.Decision curve analyses were applied to assess the net benefit of the nomogram.Results：Among the 401 patients,73 （18.2%） were diagnosed and grouped as the none OSA （apnea-hypopnea index [AHI] 〈5）,67 （16.7%） the mild OSA （5 ≤ AHI 〈 15）,82 （20.4%） the moderate OSA （15 ≤ AHI 〈 30）,and 179 （44.6%） the severe OSA （AHI ≥ 30）.The multivariable analysis suggested the significant factors were duration of disease,smoking status,difficulty of falling asleep,lack of energy,and waist circumference.A nomogram was created for the prediction of OSA using these clinical parameters and was internally validated using bootstrapping method.The discrimination accuracies of the nomogram for any OSA,moderate-severe OSA,and severe OSA were 83.8%,79.9%,and 80.5%,respectively,which indicated good calibration.Decision curve analysis showed that using nomogram could reduce the unnecessary polysomnography （PSG） by 10% without increasing the false negatives.Conclusions：The established clinical nomogram provides high accuracy in predicting the individual risk of OSA.This tool may help physicians better make decisions on PSG arrangement for the patients referred to sleep centers.

A nomogram for individualized estimation of survival among adult patients with adrenocortical carcinoma after surgery:a retrospective analysis and multicenter validation study

Background:Clinical outcome of adrenocortical carcinoma(ACC)varies because of its heterogeneous nature and reliable prognostic prediction model for adult ACC patients is limited.The objective of this study was to develop and externally validate a nomogram for overall survival(OS)prediction in adult patients with ACC after surgery.Methods:Based on the data from the Surveillance Epidemiology,and End Results(SEER)database,adults patients diagnosed with ACC between January 1988 and December 2015 were identified and classified into a training set,comprised of 404 patients diagnosed between January 2007 and December 2015,and an internal validation set,com-prised of 318 patients diagnosed between January 1988 and December 2006.The endpoint of this study was OS.The nomogram was developed using a multivariate Cox proportional hazards regression algorithm in the training set and its performance was evaluated in terms of its discriminative ability,calibration,and clinical usefulness.The nomogram was then validated using the internal SEER validation,also externally validated using the Cancer Genome Atlas set(TCGA,82 patients diagnosed between 1998 and 2012)and a Chinese multicenter cohort dataset(82 patients diag-nosed between December 2002 and May 2018),respectively.Results:Age at diagnosis,T stage,N stage,and M stage were identified as independent predictors for OS.A nomo-gram incorporating these four predictors was constructed using the training set and demonstrated good calibration and discrimination(C-index 95%confidence interval[CI],0.715[0.679-0.751]),which was validated in the internal validation set(C-index[95%CI],0.672[0.637-0.707]),the TCGA set(C-index[95%CI],0.810[0.732-0.888])and the Chi-nese multicenter set(C-index[95%CI],0.726[0.633-0.819]),respectively.Encouragingly,the nomogram was able to successfully distinguished patients with a high-risk of mortality in all enrolled patients and in the subgroup analyses.Decision curve analysis indicated that the nomogram was clinically useful and applicable.Conclusions:The study presents a nomogram that incorporates clinicopathological predictors,which can accurately predict the OS of adult ACC patients after surgery.This model and the corresponding risk classification system have the potential to guide therapy decisions after surgery.