ABSTRACT
Objective To explore the effect of neutrophil to lymphocyte ratio(NLR)and other related in-dicators on the prognosis of advanced non-small cell lung cancer patients treated with programmed death 1(PD-1)inhibitor and construct a nomogram prediction model.Methods A total of 198 patients with advanced non-small cell lung cancer who received PD-1 inhibitor treatment in the hospital from February 2020 to April 2022 were selected and followed up to August 2022.According to the clinical outcome,they were divided into the death group(46 cases)and the survival group(152 cases).The clinical data of the death group and the survival group were recorded,and the prognostic factors of advanced non-small cell lung cancer patients trea-ted with PD-1 inhibitor were analyzed.Receiver operating characteristic(ROC)curve was used to analyze the predictive value of NLR,platelet to lymphocyte ratio(PLR)and lymphocyte to monocyte ratio(LMR)for the prognosis of patients.Multivariate Logistic regression model was used to analyze the independent risk factors affecting the prognosis of patients.A prediction nomogram model for the prognosis of patients was construc-ted using R software 4.0"rms"package,and the calibration curve was used to internally validate the nomo-gram prediction model.Results Compared with the survival group,the proportion of smoking,TNM stageⅣ,ECOG score 2,and NLR,PLR,LMR were higher(P<0.05).The area under the curve of NLR,PLR and LMR were 0.707,0.793 and 0.819,respectively,and the optimal cut-off value were 4.72%,179.21%and 3.44%,respectively.Smoking,TNM stage,ECOG score,NLR,PLR,and LMR were independent risk factors for the prognosis of advanced non-small cell lung cancer patients treated with PD-1 inhibitor(P<0.05).The internal validation results show that the nomogram inhibitor treatment of PD-1 model prediction the prognosis of patients with advanced non-small cell lung cancer C-index was 0.847(95%CI 0.769-0.902),the calibra-tion curve tends to be the ideal curve.The threshold value of the nomogram model for predicting the prognosis of patients with advanced non-small cell lung cancer treated with PD-1 inhibitor was>0.22.The nomogram prediction model provided a net clinical benefit,and the net clinical benefit was higher than that of smoking,TNM stage,ECOG score,NLR,PLR and LMR.Conclusion Based on smoking,TNM stage,ECOG score,NLR,PLR,and LMR,a nomogram prediction model for the prognosis of advanced non-small cell lung cancer patients treated with PD-1 inhibitor is constructed,which has important clinical application value.
ABSTRACT
Objective:To analyze the influence of serological indexes on the liver cirrhosis (LC) in patients with chronic hepatitis B, and to construct a nomogram model.Methods:The clinical data of 220 patients with chronic hepatitis B in Xianning Central Hospital from January 2019 to December 2021 were retrospectively analyzed. Among them, 42 patients developed LC (LC group), and 178 cases did not develop LC (non-LC group). The patient′s fasting peripheral venous blood was taken in the morning. The platelet, red blood cell, white blood cell, fasting blood glucose, alanine aminotransferase (ALT), aspartate aminotransferase (AST), triacylglycerol (TG), total cholesterol (TC), total bilirubin (TBiL), albumin, globulin, alkaline phosphatase (ALP), γ-glutamyltransferase (GGT), prothrombin time (PT), thrombin time (TT), D-dimer (D-D), alpha-fetoprotein (AFP) and C-reactive protein (CRP) were detected. Receiver operating characteristic (ROC) curve was used to analyze the efficacy of each index in predicting LC in patients with chronic hepatitis B. Multivariate Logistic regression was used to analyze the independent risk factors for LC in patients with chronic hepatitis B. The R software "rms" package was used to construct a nomogram model to predict the LC in patients with chronic hepatitis B, the correction curve was used to internally verify the prediction model, and the decision curve evaluated the efficacy of the prediction model.Results:The TBiL, ALP, GGT, PT, TT, D-D, AFP and CRP in LC group were significantly higher than those in non-LC group: (50.57 ± 5.61) μmol/L vs. (46.69 ± 3.92) μmol/L, (105.23 ± 30.60) U/L vs. (75.14 ± 26.45) U/L, (68.73 ± 19.47) U/L vs. (50.39 ± 14.21) U/L, (13.88 ± 1.98) s vs. (13.01 ± 2.10) s, (18.88 ± 2.56) s vs. (15.98 ± 2.43) s, (2.62 ± 1.04) mg/L vs. (1.34 ± 0.63) mg/L, (4.19 ± 1.95) μg/L vs. (2.66 ± 1.21) μg/L and (8.54 ± 1.22) mg/L vs. (7.47 ± 0.79) mg/L, the platelet, ALT, AST and albumin were significantly lower than those in the non-LC group: (129.63 ± 32.66) × 10 9/L vs. (183.53 ± 56.31) ×10 9/L, (131.27 ± 22.19) U/L vs. (157.57 ± 38.67) U/L, (112.76 ± 19.57) U/L vs. (125.16 ± 21.84) U/L and (29.79 ± 6.17) g/L vs. (33.52 ± 5.89) g/L, and there were statistical differences ( P<0.01 or <0.05); there were no statistical differences in red blood cell, white blood cell, fasting blood glucose, TG, TC and globulin between the two groups ( P>0.05). ROC curve analysis result showed that the area under the curve (AUC) of AFP, platelet, ALT, AST, ALP, GGT, TBiL, albumin, D-D, CRP, PT and TT for predicting LC in patients with chronic hepatitis B were 0.731, 0.798, 0.723, 0.676, 0.766, 0.762, 0.710, 0.673, 0.856, 0.759, 0.603 and 0.786, and the optimal cut-off values were 4.64 μg/L, 162.56 × 10 9/L, 155.67 U/L, 122.37 U/L, 95.17 U/L, 68.96 U/L, 49.95 μmol/L, 28.8 g/L, 1.64 mg/L, 8.55 mg/L, 12 s and 18 s. Multivariate Logistic regression analysis result showed that AFP (>4.64 μg/L), platelet (≤162.56 × 10 9/L), ALP (>95.17 U/L), GGT (>68.96 U/L), D-D (>1.64 mg/L) and TT (>18 s) were independent risk factors for LC in patients with chronic hepatitis B ( OR = 1.278, 1.428, 1.488, 1.356, 1.513 and 1.369; 95% CI 1.109 to 1.369, 1.269 to 1.623, 1.217 to 1.894, 1.127 to 1.669, 1.342 to 1.878 and 1.169 to 1.583; P<0.05 or <0.01). The AFP, platelet, ALP, GGT, D-D and TT were used as predictors to construct a nomogram model for predicting the LC in patients with chronic hepatitis B. The correction curve of the nomogram model to predict the LC in patients with chronic hepatitis B was close to the ideal curve (C-index was 0.739, 95% CI 0.615 to 0.876); the decision curve analysis result showed that the prediction model had higher clinical net benefit when the risk threshold > 0.26 than a single index, and that it had significantly additional clinical net benefit. Conclusions:The AFP, platelets, ALP, GGT, D-D and TT are independent risk factors for LC in patients with chronic hepatitis B, and the nomogram model constructed based on these factors could provide important guidance for the prevention and treatment of LC in patients with chronic hepatitis B.
ABSTRACT
OBJECTIVE To know more clearly about the situation of Hepatitis B virus markers in clinical medical workers and take further interventional strategies to protect high risk medical workers.METHODS Hepatitis B virus markers in doctors,nurses and medical checkers who have contacted with patients′ blood,body fluid,or other occupational hazard situation,were detected by of ELISA.RESULTS Among the 587 medical workers detected,311 were with deficiency of active immunity(52.98%),196 were HBV infectors(33.39%).CONCLUSIONS Medical workers are in high risk groups of HBV infection.Medical institutions should attend to their self-protection and encourage them to take HBV vaccine to prevent iatrogenic transmission.