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Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

邮发代号 80-967

2019 Impact Factor: 3.421

Frontiers of Medicine  2020, Vol. 14 Issue (3): 357-367   https://doi.org/10.1007/s11684-019-0699-3
  本期目录
Symptom network topological features predict the effectiveness of herbal treatment for pediatric cough
Mengxue Huang1, Jingjing Wang2, Runshun Zhang3, Zhuying Ni4,5, Xiaoying Liu4,5, Wenwen Liu2, Weilian Kong2, Yao Chen4,5, Tiantian Huang6, Guihua Li6, Dan Wei4,5(), Jianzhong Liu4,5(), Xuezhong Zhou2()
1. Shanghai Traditional Chinese Medicine-Integrated Hospital, Shanghai 200082, China
2. Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
3. Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
4. Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan 430061, China
5. Hubei Province Academy of Traditional Chinese Medicine, Wuhan 430061, China
6. School of the Clinical Medicine College of Traditional Chinese Medicine, Hubei University of Chinese Medicine, Wuhan 430061, China
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Abstract

Pediatric cough is a heterogeneous condition in terms of symptoms and the underlying disease mechanisms. Symptom phenotypes hold complicated interactions between each other to form an intricate network structure. This study aims to investigate whether the network structure of pediatric cough symptoms is associated with the prognosis and outcome of patients. A total of 384 cases were derived from the electronic medical records of a highly experienced traditional Chinese medicine (TCM) physician. The data were divided into two groups according to the therapeutic effect, namely, an invalid group (group A with 40 cases of poor efficacy) and a valid group (group B with 344 cases of good efficacy). Several well-established analysis methods, namely, statistical test, correlation analysis, and complex network analysis, were used to analyze the data. This study reports that symptom networks of patients with pediatric cough are related to the effectiveness of treatment: a dense network of symptoms is associated with great difficulty in treatment. Interventions with the most different symptoms in the symptom network may have improved therapeutic effects.

Key wordspediatric cough    complex network    symptoms    traditional Chinese medicine    electronic medical records
收稿日期: 2018-08-30      出版日期: 2020-06-08
Corresponding Author(s): Dan Wei,Jianzhong Liu,Xuezhong Zhou   
 引用本文:   
. [J]. Frontiers of Medicine, 2020, 14(3): 357-367.
Mengxue Huang, Jingjing Wang, Runshun Zhang, Zhuying Ni, Xiaoying Liu, Wenwen Liu, Weilian Kong, Yao Chen, Tiantian Huang, Guihua Li, Dan Wei, Jianzhong Liu, Xuezhong Zhou. Symptom network topological features predict the effectiveness of herbal treatment for pediatric cough. Front. Med., 2020, 14(3): 357-367.
 链接本文:  
https://academic.hep.com.cn/fmd/CN/10.1007/s11684-019-0699-3
https://academic.hep.com.cn/fmd/CN/Y2020/V14/I3/357
Number of total patients 496
Number of total cases 1640
Number of patients with efficacy evaluation 254
Number of cases with efficacy evaluation 384
Tab.1  
Interval time Cases
1–7 days 336
8–14 days 44
>14 days 4
Tab.2  
Symptom Abbreviation Symptom Abbreviation Symptom Abbreviation
White-coated tongue wct Pharyngalgia phg Prickly tongue ptt
Pharyngeal red phr Yellow coated tongue yct Abdominal pain abp
Thin tongue coating thc Constipation con Dark red throat drt
Fingerprint in life pass flp Fingerprint in wind pass fiw Fingerprint in qi pass fqp
Moderate pulse mop Loose stool lst Rapid pulse rpp
Anorexia ano Sneezing snz Yellow sputum ysp
Red tongue rtg Pulmonary rales pmr Fine fingerprints fif
Thready pulse thp Nasal secretions nas White sputum wsp
Expectoration exp Nasal mucus nam Vomiting vom
Slippery pulse slp Thin tongue coating ttc Nasal discharge nsd
Antiadoncus ant Wheeze whz Purulent snot psn
Nasal obstruction nao Sleep disturbed sld Postnasal drip pod
Pink tongue pkt Throat secretions ths Barking cough bcg
Fever fev Poor complexion pcp Deep red tongue dpg
Conjunctival congestion cjc Purple red fingerprint prf Lavender fingerprints lvf
Pulmonary breath sounds rough pbs Inflamed lymph follicles of the throat ilt Swollen lymph follicles of the throat slft
Unconspicuous fingerprint ucf Night crying ncr
Thick coated tongue tct Erythra eth
Tab.3  
Age group Group A (cases) Group B (cases)
Newborn baby (1 day<age≤28 days) 0 2
Infancy (28 days<age≤1 year) 2 37
Toddler period (1 year<age≤3 years) 17 94
Pre-school age (3 years<age≤7 years) 17 176
School age (7 years<age≤13 years) 4 35
Tab.4  
Gender Group A (cases) Group B (cases)
Boys 25 209
Girls 15 135
Tab.5  
Symptom Mean SD Statistic P value
Group A Group B Group A Group B
phr 0.85 0.81 0.36 0.40 7188 0.50
wct 0.78 0.79 0.42 0.41 6792 0.85
nsd 0.65 0.61 0.48 0.49 7172 0.60
ahc 0.58 0.52 0.50 0.50 7256 0.51
ano 0.50 0.48 0.51 0.50 7000 0.84
thp 0.23 0.39 0.42 0.49 5748 0.04
rtg 0.43 0.38 0.50 0.49 7204 0.56
mop 0.43 0.37 0.50 0.48 7244 0.51
exp 0.43 0.30 0.50 0.46 7724 0.11
slp 0.15 0.29 0.36 0.46 5892 0.06
pkt 0.15 0.22 0.36 0.41 6412 0.32
nao 0.13 0.21 0.33 0.41 6300 0.21
pmr 0.05 0.20 0.22 0.40 5824 0.02
lvf 0.30 0.19 0.46 0.40 7604 0.12
fiw 0.28 0.19 0.45 0.39 7452 0.21
tct 0.13 0.19 0.33 0.39 6420 0.30
con 0.30 0.18 0.46 0.38 7704 0.07
ant 0.18 0.17 0.38 0.38 6884 0.99
pbs 0.15 0.16 0.36 0.37 6792 0.84
fev 0.10 0.16 0.30 0.37 6468 0.32
nam 0.15 0.13 0.36 0.34 7012 0.74
yct 0.10 0.12 0.30 0.32 6748 0.72
snz 0.08 0.10 0.27 0.30 6716 0.63
Lst 0.05 0.09 0.22 0.29 6584 0.37
ucf 0.05 0.09 0.22 0.29 6604 0.39
psn 0.05 0.06 0.22 0.24 6804 0.78
whz 0.05 0.06 0.22 0.23 6824 0.84
prf 0.13 0.05 0.33 0.22 7400 0.05
nas 0.08 0.05 0.27 0.22 7056 0.49
ttc 0.08 0.04 0.27 0.20 7116 0.32
ths 0.05 0.03 0.22 0.18 6984 0.63
cjc 0.03 0.03 0.16 0.17 6852 0.89
pcp 0.05 0.03 0.22 0.17 7024 0.47
drt 0.05 0.02 0.22 0.15 7064 0.32
sld 0.03 0.02 0.16 0.15 6892 0.95
eth 0.03 0.02 0.16 0.15 6892 0.95
ysp 0.03 0.02 0.16 0.14 6912 0.85
rpp 0.03 0.02 0.16 0.14 6912 0.85
phg 0.03 0.02 0.16 0.14 6912 0.85
ptt 0.03 0.02 0.16 0.13 6932 0.74
vom 0.03 0.02 0.16 0.13 6932 0.74
fqp 0.05 0.02 0.22 0.13 7104 0.17
abp 0.03 0.02 0.16 0.13 6932 0.74
ilt 0.03 0.01 0.16 0.12 6952 0.62
slft 0.03 0.01 0.16 0.11 6972 0.48
dpg 0.03 0.01 0.16 0.11 6972 0.48
bcg 0.05 0.01 0.22 0.11 7144 0.06
wsp 0.03 0.01 0.16 0.11 6972 0.48
fif 0.05 0.01 0.22 0.11 7144 0.06
pod 0.03 0.01 0.16 0.08 7012 0.19
ncr 0.03 0.01 0.16 0.08 7012 0.19
flp 0.03 0.00 0.16 0.05 7032 0.07
Tab.6  
Fig.1  
Group A (cases) Group B (cases)
Positive 395 931
Negative 515 811
Tab.7  
Invalid group Valid group
Symptom Symptom Weight Symptom Symptom Weight
slft dpg 1.00 fiw lvf 0.67
ths bcg 1.00 prf lvf 0.40
cjc phg 1.00 fqp fif 0.40
ysp flp 1.00 thc wct 0.38
sld ncr 1.00 thp slp 0.35
ths fif 1.00 drt dpg 0.34
bcg fif 1.00 nam nsd 0.31
ptt drt 0.70 tct yct 0.28
ysp fqp 0.70 cjc fev 0.26
fqp flp 0.70 drt pod 0.24
nsd thp −0.36 fiw slp −0.31
rtg pkt −0.36 lvf slp −0.32
ptt phr −0.38 fiw mop −0.38
wct pcp −0.43 mop lvf −0.38
thc tct −0.44 fiw thp −0.39
wct ttc −0.53 lvf thp −0.39
fiw mop −0.53 wct ttc −0.40
drt phr −0.55 rtg pkt −0.41
mop lvf −0.56 thc tct −0.51
wct yct −0.62 wct yct −0.71
Tab.8  
Fig.2  
Network metric P value
Global strength <0.05
Average shortest path length >0.05
Diameter <0.05
Density <0.05
Tab.9  
Fig.3  
Symptom Effect size Symptom Effect size Symptom Effect size
exp 3.21 ths 1.79 ysp 0.97
con 3.03 fev 1.70 vom 0.91
nsd 2.88 pcp 1.60 sld 0.90
pbs 2.81 whz 1.47 ncr 0.88
ano 2.61 psn 1.38 cjc 0.86
ant 2.51 abp 1.16 slft 0.86
phr 2.30 wsp 1.10 ilt 0.85
nao 2.20 drt 1.05 pod 0.84
snz 2.03 lst 1.04 phg 0.80
nas 1.97 eth 1.00
Tab.10  
1 PG Gibson, AB Chang, NJ Glasgow, PW Holmes, P Katelaris, AS Kemp, LI Landau, S Mazzone, P Newcombe, P Van Asperen, AE Vertigan; CICADA. CICADA: Cough in Children and Adults: Diagnosis and Assessment. Australian cough guidelines summary statement. Med J Aust 2010; 192(5): 265–271
pmid: 20201760
2 AB Chang, LI Landau, PP Van Asperen, NJ Glasgow, CF Robertson, JM Marchant, CM Mellis; Thoracic Society of Australia and New Zealand. Cough in children: definitions and clinical evaluation. Med J Aust 2006; 184(8): 398–403
pmid: 16618239
3 AB Chang, WB Glomb. Guidelines for evaluating chronic cough in pediatrics: ACCP evidence-based clinical practice guidelines. Chest 2006; 129(1 Suppl): 260S–283S
https://doi.org/10.1378/chest.129.1_suppl.260S pmid: 16428719
4 FJ Gilchrist, WD Carroll. Assessing chronic cough in children. Paediatr Child Health 2016; 26(6): 273–275
https://doi.org/10.1016/j.paed.2016.03.002
5 G Feng, H Zheng, QZ Zheng. Experience of ZHENG Qi-zhong in using Puji Xiaodu Decoction. China J Tradit Chin Med Pharm (Zhonghua Zhong Yi Yao Za Zhi) 2016; 31(7): 2615–2617 (in Chinese)
6 S Wang, JM Qiao-Wong, X Zhao. Pediatrics in Chinese Medicine: PMPH-USA, 2012
7 AL Barabási, N Gulbahce, J Loscalzo. Network medicine: a network-based approach to human disease. Nat Rev Genet 2011; 12(1): 56–68
https://doi.org/10.1038/nrg2918 pmid: 21164525
8 D Borsboom, AO Cramer. Network analysis: an integrative approach to the structure of psychopathology. Annu Rev Clin Psychol 2013; 9: 91–121
https://doi.org/10.1146/annurev-clinpsy-050212-185608 pmid: 23537483
9 L Boschloo, CD van Borkulo, M Rhemtulla, KM Keyes, D Borsboom, RA Schoevers. The network structure of symptoms of the diagnostic and statistical manual of mental disorders. PLoS One 2015; 10(9): e0137621
https://doi.org/10.1371/journal.pone.0137621 pmid: 26368008
10 C van Borkulo, L Boschloo, D Borsboom, BW Penninx, LJ Waldorp, RA Schoevers. Association of symptom network structure with the course of depression. JAMA Psychiatry 2015; 72(12): 1219–1226
https://doi.org/10.1001/jamapsychiatry.2015.2079 pmid: 26561400
11 C D van Borkulo. NetworkComparisonTest: Statistical comparison of two networks based on three invariance measures. R package version 2.0.1. Retrieved from the website of CRAN-NetworkComparisonTest. 2016
12 RDC Team. R: a language and environment for statistical computing. Computing 2013; 1: 12–21
13 X Zhou, S Chen, B Liu, R Zhang, Y Wang, P Li, Y Guo, H Zhang, Z Gao, X Yan. Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support. Artif Intell Med 2010; 48(2-3): 139–152
https://doi.org/10.1016/j.artmed.2009.07.012 pmid: 20122820
14 Q Lu. Consensus of experts on standardized diagnosis and treatment of common cold in Chinese children (2013). Chin J Prac Pediatr (Zhongguo Shi Yong Er Ke Za Zhi) 2013; 28(9): 680–681 (in Chinese)
15 The Subspecialty Group of Respiratory Diseases, The Society of Pediatrics, Chinese Medical Association. Guidelines for the diagnosis and treatment of bronchial asthma in children (2016). Chin J Pediatr (Zhonghua Er Ke Za Zhi) 2016; 54(3): 168–173 (in Chinese)
16 The Subspecialty Group of Respiratory Diseases, The Society of Pediatrics, Chinese Medical Association. Guidelines for the management of community-acquired pneumonia in children (2013). Chin J Pediatr (Zhonghua Er Ke Za Zhi) 2013; 51(10): 745–749 (in Chinese)
pmid: 24406226
17 X Zhou, J Menche, AL Barabási, A Sharma. Human symptoms-disease network. Nat Commun 2014; 5(1): 4212
https://doi.org/10.1038/ncomms5212 pmid: 24967666
18 HB Mann, DR Whitney. On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 1947; 18(1): 50–60
https://doi.org/10.1214/aoms/1177730491
19 M Maass, J Gieffers, E Krause, PM Engel, C Bartels, W Solbach. Qgraph: network visualizations of relationships in psychometric data. J Stat Softw 2012; 48(4): 1–18
20 A Barrat, M Barthélemy, R Pastor-Satorras, A Vespignani. The architecture of complex weighted networks. Proc Natl Acad Sci USA 2004; 101(11): 3747–3752
https://doi.org/10.1073/pnas.0400087101 pmid: 15007165
21 DJ Watts, SH Strogatz. Collective dynamics of ‘small-world’ networks. Nature 1998; 393(6684): 440
22 G Caldarelli. Introduction to complex network. AIP Conf Proc 2003; 661: 17–23
https://doi.org/10.1063/1.1571286
23 P Good. Permutation tests: a practical guide to resampling methods for testing hypotheses. Springer Science & Business Media, 2013
24 S Boccaletti, V Latora, Y Moreno, M Chavez, DU Hwang. Complex networks: structure and dynamics. Phys Rep 2006; 424(4–5): 175–308
https://doi.org/10.1016/j.physrep.2005.10.009
25 T Opsahl, F Agneessens, J Skvoretz. Node centrality in weighted networks: generalizing degree and shortest paths. Soc Networks 2010; 32(3): 245–251
https://doi.org/10.1016/j.socnet.2010.03.006
26 P Bonacich. Power and centrality: a family of measures. Am J Sociol 1987; 92(5): 1170–1182
https://doi.org/10.1086/228631
27 B Efron, R J Tibshirani. An introduction to the bootstrap. CRC press, 1994
28 M Jiang, C Lu, C Zhang, J Yang, Y Tan, A Lu, K Chan. Syndrome differentiation in modern research of traditional Chinese medicine. J Ethnopharmacol 2012; 140(3): 634–642
https://doi.org/10.1016/j.jep.2012.01.033 pmid: 22322251
29 C Miaskowski, M Dodd, K Lee. Symptom clusters: the new frontier in symptom management research. JNCI Monographs 2004; 2004(32): 17–21
30 X Zhang, X Wang, W Wei, X Zhang. Analysis on regulation of Wang Xuefeng’s prescriptions on children children chronic cough by data mining technology. Liaoning J Traditi Chin Med (Liaoning Zhong Yi Za Zhi) 2017; 44(9): 1793–1795 (in Chinese)
31 QX Zhang. Experience of professor NIE Hui-min in treating exogenous cough of children. China J Tradit Chin Med Pharm (Zhonghua Zhong Yi Yao Za Zhi) 2008; 23(4): 335–337 (in Chinese)
32 XZ Zhou, RS Zhang, J Shah, D Rajgor, YH Wang, R Pietrobon, BY Liu, J Chen, JG Zhu, RL Gao. Patterns of herbal combination for the treatment of insomnia commonly employed by highly experienced Chinese medicine physicians. Chin J Integr Med 2011; 17(9): 655–662
https://doi.org/10.1007/s11655-011-0841-9 pmid: 21910065
33 JZ Liu, MX Huang, XY Liu, Y Chen. The data mining analysis of Professor Ni Zhuying’s treatment of pediatric cough disease. World Sci Technol (Shi Jie Ke Xue Ji Shu—Zhong Yi Yao Xian Dai Hua Za Zhi) 2017; 19(9): 1527–1532 (in Chinese)
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