文献计量学是利用数学与统计学的相关方法分析某一主题的研究现状[1].CiteSpace可视化分析软件能够实现对文献的定量分析和可视化分析,使研究者快捷、准确了解该领域文献的基础知识和研究热点,为预测该领域的研究特征及发展趋势提供依据[2].

目的 调查分析河南省新型冠状病毒肺炎(新冠肺炎)患者的临床特征和中医证候分布规律,为进一步优化新冠肺炎中医辨证治疗方案提供依据.方法 基于文献分析,并结合专家意见,制定“河南省新冠肺炎中医临床调查表”,收集524例新冠肺炎患者一般资料、临床症状、舌脉等信息,并进行辨证分析,探讨不同年龄段(≤45岁、46～ 60岁、＞60岁)及不同临床分型(轻型/普通型、重型、危重型)患者的中医证型分布情况. 结果 524例患者中男性304例,女性220例;轻型/普通型349例,重型144例,危重型31例;中位年龄45 (33,55)岁;中位症状持续时间10(6,15)天.患者主要临床症状(百分比＞20％)包括发热、恶寒、乏力、呼吸困难、气短、喘息、气促.舌苔以苔腻(175次)、苔白(149次)和苔黄(127次)为主,舌质以舌质淡(170次)和舌质红(151次)常见,脉象方面以数脉(112次)和滑脉(100次)常见.中医证型主要以寒湿郁肺证(1 19例,22.71％)、湿热蕴肺证(113例,21.56％)、疫毒闭肺证(76例,14.50％)、湿阻肺胃证(70例,13.36％)、湿遏肺卫证(63例,12.02％)为主.各年龄段患者中医证型分布差异均无统计学意义(P＞0.05).寒湿郁肺证、湿热蕴肺证、疫毒闭肺证、湿阻肺胃证、湿遏肺卫证、内闭外脱证在不同临床分型的分布差异均有统计学意义(P＜0.05). 结论 河南省新冠肺炎患者以青中年居多,以普通型和重型为主;中医证型以寒湿郁肺证、湿热蕴肺证、疫毒闭肺证、湿阻肺胃证、湿遏肺卫证为主.

OBJECTIVE:To explore the correlation between symptoms and their contribution to syndrome based on syndrome of lung damp-heat accumulation in coronavirus disease 2019 (COVID-19), thus to provide methodological basis for the syndrome diagnosis.METHODS:Based on 654 clinical investigation questionnaires data of COVID-19 patients, a model based on syndrome of lung damp-heat accumulation was set. Using SPSS Modeler 14.1 software, association rules and Bayesian network were applied to explore the correlation between symptoms and their contribution to syndrome.RESULTS:There were 121 questionnaires referring to syndrome of lung damp-heat accumulation in total 654 questionnaires. The symptoms with frequency > 40% were fever (53.72%), cough (47.93%), red tongue (45.45%), rapid pulse (43.80%), greasy fur (42.15%), yellow tongue (41.32%), fatigue (40.50%) and anorexia (40.50%). Association rule analysis showed that the symptom groups with strong binomial correlation included fever, thirst, chest tightness, shortness of breath, cough, yellow phlegm, etc. The symptom groups with strong trinomial correlation included cough, yellow phlegm, phlegm sticky, anorexia, vomiting, heavy head and body, fever, thirst, fatigue, etc. Based on SPSS Modeler 14.1 software, with syndrome of lung damp-heat accumulation (yes = 1, no = 0) as target variable, and the selected symptoms with frequency > 15.0% as input variables, the Bayesian network model was established to obtain the probability distribution table of symptoms (groups), in which there was only one parent node (the upper node of each input variable) of fever, and the conditional probability was 0.54. The parent node of cough had yellow phlegm and syndrome of lung damp-heat accumulation, indicating that there was a direct causal relationship between cough and yellow phlegm in syndrome of lung damp-heat accumulation, and the conditional probability of cough was 0.99 under the condition of yellow phlegm. The common symptom groups and their contribution to syndrome were as follows: fever and thirsty (0.47), cough and yellow phlegm (0.49), chest tightness and polypnea (0.46), anorexia and heavy cumbersome head and body (0.61), yellow greasy fur and slippery rapid pulse (0.95).CONCLUSIONS:It is feasible and objective to analyze the correlation between symptoms and their contribution to syndromes by association rules combined with Bayesian network. It could provide methodological basis for the syndrome diagnosis.