RESUMEN
【Objective】 To analyze the clinical characteristics of patients with coronavirus disease 2019 (COVID-19) in Xi’an so as to investigate the relationship between the dynamic changes of lymphocytes and the disease progression. 【Methods】 We retrospectively analyzed the clinical data of 15 patients with COVID-19 in The First Affiliated Hospital of Xi’an Jiaotong University from January 22 to February 16, 2020. 【Results】 Among the 15 patients with COVID-19, 8 were males and 7 were females, aged from 22 to 89 years. There were 12 ordinary cases (80%), 1 severe case (6.67%), and 2 critical cases(13.33%). There were 6 groups of family clusters.Most of the patients (14/15, 93.3%) had fever of different degrees. The average time from illness onset to admission was 2.80±1.66 days, and the average time from illness onset to diagnosis was 2.83±2.29 days. The main accompanying symptoms were dry cough (8/15, 53.33%) and shortness of breath (4/15, 26.67%). Nine patients (60%) who had low lymphocyte counts at admission, including of all of the critically ill patients (1 severe case and 2 critical cases) and 6 (6/12, 50%) ordinary patients. Lymphocyte counts in the ordinary cases increased gradually, but fluctuated in the severely ill patients. They were always at low level, or even decreased overall in critical cases. 【Conclusion】 In Xi’an City, COVID-19 mostly occurred in family clusters. Lymphocyte counts were reduced in most patients, especially in critically ill (severe and critical) ones. The lymphocyte count at admission and its kinetics during therapy may be an important predictor for the severity and prognosis of the disease.
RESUMEN
【Objective】 To establish a predictive model for patients with hemorrhage after CT-guided coaxial core needle lung biopsy （CCNB） based on logistic regression. 【Methods】 A total of 489 patients who had undergone CCNB were retrospectively recruited. The potential risk factors of hemorrhage after lung biopsy were analyzed by univariate and multivariate logistic regression， through which we screened the independent risk factors and established a prediction model for hemorrhage. We evaluated the discrimination， calibration and clinical usefulness of the model. 【Results】 There were 141 cases （42.6%） of hemorrhage in the development group and 66 cases （41.8%） of hemorrhage in the validation group； there was no case of severe hemorrhage or hemothorax. Multivariate logistic regression analysis showed that fibrinogen degradation products， pulmonary interstitial fibrosis， largest diameter and puncture depth were independent predictive factors of hemorrhage. Hemorrhage prediction model was established and presented in the form of a nomogram. Discrimination of the model： the AUC was 0.837 in the development group and 0.777 in the validation group. The calibration curve showed good agreement between predicted probability and actual probability of hemorrhage. The unreliability test yielded a P value of 0.849 in the development group and 0.147 in the validation group. The DCA curve showed that the hemorrhage predictive model could increase the benefit of patients. 【Conclusion】 The predictive model of hemorrhage in patients after CCNB based on logistic regression can be used in clinical practice.