1.Influencing Factors Analysis and Nomogram Model Construction of Mycoplasma Pneumonia in Children with Severe Pneumonia
Yanan KANG ; Xiuhui LI ; Peihui GONG
Journal of Medical Research 2023;52(11):69-73
Objective To analyze the influencing factors of severe pneumonia in children with mycoplasma pneumonia.Methods A total of 307 children with mycoplasma pneumonia hospitalized in Shanxi Children′s Hospital from March 2021 to February 2022 were se-lected as the study subjects and divided into severe group(200 cases)and non-severe group(107 cases).The differences of the clinical data between the two groups were compared,and a nomogram prediction model was established,and the model was internally validated.Results The severe group had more patients aged≤3 and<6 years,≥6 and≤10 years and autumn and winter(P<0.05).There were significant differences in the course of disease,peak body temperature,abnormal electrocardiogram findings,three concave signs positive,perioral cyanosis,extrapulmonary manifestations,and involvement of other systems between the two groups(P<0.05).There were significant differences between the two groups in 15 indicators including epstein-barr virus infection,antibody titer levels,and C-reactive protein among the laboratory test indicators(P<0.05).The Logistic regression analysis showed that long disease duration and el-evated platelet count,lactate dehydrogenase,and Th cell levels were positively correlated with the occurrence of severe pneumonia,and elevated NK cell levels were negatively correlated with the occurrence of severe pneumonia(P<0.05).The nomogram results showed that the probability of severe pneumonia was 92.8%,the calibration curve was basically consistent with the ideal curve,the area under the re-ceiver operating characteristic curve was0.819,and the decision curve showed a high net benefit value when the threshold probability was 4%-89%.Conclusion The nomogram model is helpful for early detection of severe pneumonia in children with mycoplasma pneumoni-a,and has important significance for preventing the development of severe pneumonia.
2.Factors Affecting Early-onset Sepsis in Preterm Infants and Construction of Nomogram Model
Peihui GONG ; Xiaoyun JIA ; Jiaxin SHEN
Journal of Medical Research 2024;53(2):122-126
Objective To analyze the factors influencing early-onset sepsis in preterm infants and construct nomogram model.Methods A total of 124 neonates with premature sepsis admitted to Shanxi Children's Hospital(Shanxi Maternal and Child Health Hos-pital)from January 2020 to December 2021 were collected.According to gestational age,the neonates were divided into premature group(n=33)and full-term group(n=91),and the clinical characteristics of the two groups were compared,and nomogram model was es-tablished to internally validate the predictiveness and accuracy of the model.Results Compared with the full-term group,the proportion of females in premature group was higher(x2=7.147,P<0.05),the 1min Apgarscore in premature group was lower(x2=-3.398,P<0.05),the proportion of perinatal mothers with pregnancy complications in premature group was higher(x2=7.846,P<0.05),the incidence of pneumonia and poor response in preterm infants of premature group were higher(x2=18.210,P<0.05;x2=14.814,P<0.05),but the incidence of jaundice in premature group was lower(x2=10.400,P<0.05).Multivariate Logistic regression analysis showed that female and pneumonia were risk factors for early-onset sepsis in preterm infants(P<0.05).The results of the nomogram model showed that the C-index of the model was 0.886.The predicted incidence was generally consistent with the actual incidence,the area under the receiver operator characteristic curve was 0.886,and the decision curve showed a high net benefit value at threshold proba-bilities of 4%-100%.Conclusion Female,preterm infants with pneumonia have a higher risk of early-onset sepsis.The nomogram model of premature sepsis constructed in this study has high clinical value and can provide a reference basis for clinical prevention of early-onset sepsis in preterm infants.