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.

Nomograms and risk score models for predicting survival in rectal cancer patients with neoadjuvant therapy

BACKGROUND Colorectal cancer is a common digestive cancer worldwide.As a comprehensive treatment for locally advanced rectal cancer(LARC),neoadjuvant therapy(NT)has been increasingly used as the standard treatment for clinical stage II/III rectal cancer.However,few patients achieve a complete pathological response,and most patients require surgical resection and adjuvant therapy.Therefore,identifying risk factors and developing accurate models to predict the prognosis of LARC patients are of great clinical significance.AIM To establish effective prognostic nomograms and risk score prediction models to predict overall survival(OS)and disease-free survival(DFS)for LARC treated with NT.METHODS Nomograms and risk factor score prediction models were based on patients who received NT at the Cancer Hospital from 2015 to 2017.The least absolute shrinkage and selection operator regression model were utilized to screen for prognostic risk factors,which were validated by the Cox regression method.Assessment of the performance of the two prediction models was conducted using receiver operating characteristic curves,and that of the two nomograms was conducted by calculating the concordance index(C-index)and calibration curves.The results were validated in a cohort of 65 patients from 2015 to 2017.RESULTS Seven features were significantly associated with OS and were included in the OS prediction nomogram and prediction model:Vascular_tumors_bolt,cancer nodules,yN,body mass index,matchmouth distance from the edge,nerve aggression and postoperative carcinoembryonic antigen.The nomogram showed good predictive value for OS,with a C-index of 0.91(95%CI:0.85,0.97)and good calibration.In the validation cohort,the C-index was 0.69(95%CI:0.53,0.84).The risk factor prediction model showed good predictive value.The areas under the curve for 3-and 5-year survival were 0.811 and 0.782.The nomogram for predicting DFS included ypTNM and nerve aggression and showed good calibration and a C-index of 0.77(95%CI:0.69,0.85).In the validation cohort,the C-index was 0.71(95%CI:0.61,0.81).The prediction model for DFS also had good predictive value,with an AUC for 3-year survival of 0.784 and an AUC for 5-year survival of 0.754.CONCLUSION We established accurate nomograms and prediction models for predicting OS and DFS in patients with LARC after undergoing NT.

Evaluation of a risk factor scoring model in screening for undiagnosed diabetes in China population

Objective:To develop a risk scoring model for screening for undiagnosed type 2 diabetes in Chinese population.Methods:A total of 5348 subjects from two districts of Jinan City,Shandong Province,China were enrolled.Group A (2985) included individuals from east of the city and Group B (2363) from west of the city.Screening questionnaires and a standard oral glucose tolerance test (OGTT) were completed by all subjects.Based on the stepwise logistic regression analysis of Group A,variables were selected to establish the risk scoring model.The validity and effectiveness of this model were evaluated in Group B.Results:Based on stepwise logistic regression analysis performed with data of Group A,variables including age,body mass index (BMI),waist-to-hip ratio (WHR),systolic pressure,diastolic pressure,heart rate,family history of diabetes,and history of high glucose were accepted into the risk scoring model.The risk for having diabetes increased along with aggregate scores.When Youden index was closest to 1,the optimal cutoff value was set up at 51.At this point,the diabetes risk scoring model could identify diabetes patients with a sensitivity of 83.3% and a specificity of 66.5%,making the positive predictive value 12.83% and negative predictive value 98.53%.We compared our model with the Finnish and Danish model and concluded that our model has superior validity in Chinese population.Conclusions:Our diabetes risk scoring model has satisfactory sensitivity and specificity for identifying undiagnosed diabetes in our population,which might be a simple and practical tool suitable for massive diabetes screening.

SVEAT score outperforms HEART score in patients admitted to a chest pain observation unit

BACKGROUND Timely and accurate identification of subgroup at risk for major adverse cardiovascular events among patients presenting with acute chest pain remains a challenge.Currently available risk stratification scores are suboptimal.Recently,a new scoring system called the Symptoms,history of Vascular disease,Electrocardiography,Age,and Troponin(SVEAT)score has been shown to outperform the History,Electrocardiography,Age,Risk factors and Troponin(HEART)score,one of the most used risk scores in the United States.AIM To assess the potential usefulness of the SVEAT score as a risk stratification tool by comparing its performance to HEART score in chest pain patients with low suspicion for acute coronary syndrome and admitted for overnight observation.METHODS We retrospectively reviewed medical records of 330 consecutive patients admitted to our clinical decision unit for acute chest pain between January 1st to April 17th,2019.To avoid potential biases,investigators assigned to calculate the SVEAT,and HEART scores were blinded to the results of 30-d combined endpoint of death,acute myocardial infarction or confirmed coronary artery disease requiring revascularization or medical therapy[30-d major adverse cardiovascular event(MACE)].An area under receiving-operator characteristic curve(AUC)for each score was then calculated.C-statistic and logistic model were used to compare RESULTS A 30-d MACE was observed in 11 patients(3.33%of the subjects).The AUC of SVEAT score(0.8876,95%CI:0.82-0.96)was significantly higher than the AUC of HEART score(0.7962,95%CI:0.71-0.88),P=0.03.Using logistic model,SVEAT score with cut-off of 4 or less significantly predicts 30-d MACE(odd ratio 1.52,95%CI:1.19-1.95,P=0.001)but not the HEART score(odd ratio 1.29,95%CI:0.78-2.14,P=0.32).CONCLUSION The SVEAT score is superior to the HEART score as a risk stratification tool for acute chest pain in low to intermediate risk patients.