Background. Psoriasis is a complex skin disease and difficult to evaluate, and this study aimed to provide an objective and systematic approach for evaluating the efficacy of psoriasis. Methods. We sought to construct a Bayesian network from sixteen indicators in four aspects of psoriasis (skin lesion conditions, laboratory indexes, quality of life, and accompanying symptoms) and obtained weights of each index by combining the analytic hierarchy process with maximum entropy self-learning. Furthermore, we adopted stability analysis to calculate the minimum sample size of the system. The extended set pair analysis was utilized to evaluate the efficacy based on improved weights, which overcomes the limitation of set pair analysis (unable to evaluate the efficacy with uncertain grades and thresholds). Results. A total of 100 psoriasis vulgaris patients were included to evaluate the curative effect by the system. We obtained the weights of each index and the Euclidean distance for efficacy evaluation of 100 patients. The sensitivity analysis proved that the results had no significant change with the variation of single patient’s indexes, which indicated that our results were stable to assess the effectiveness. Conclusions. We provided an available method of comprehensive effective evaluation of various indicators of psoriasis and based on both subjective and objective weights.
National Key Research and Development Program of China2018YFC1705301National Natural Science Foundation of China816719598187447081973860819042148200423582074427Shanghai University of Traditional Chinese MedicineRY411.33.10JY611.02.03.83Natural Science Foundation of Henan ProvinceU1704171Shanghai Pujiang Talent Program2020PJD067Shanghai Development Office of TCMZY(2018-2020)-FWTX-4010Dermatology Department of Traditional Chinese MedicineClinical Key Specialty Construction Project of Shanghaishslczdzk050011. Introduction
Psoriasis is a complex, chronic, immune-mediated inflammatory skin disorder that affects approximately 125 million people worldwide [1]. Moreover, complications are associated with increased exacerbations in subjects with psoriasis, including diabetes, metabolic syndrome, and chronic obstructive pulmonary disease [2–4]. As a refractory systemic disease, psoriasis has a great impact on human health, and some can even be life threatening [5, 6]. Due to the complexity and severity, it is currently difficult to evaluate the efficacy of psoriasis [7]. Although varied evaluation tools and models have been practiced to evaluate the efficacy of diseases, there are still some shortcomings including the inability to integrate multiple indicators to construct an evaluation system and the lack of impartiality in evaluating the weights of various indicators [8–10].
In the Bayesian network, a directed acyclic graph is constructed to intuitively reflect the potential relationship between factors, and a conditional probability distribution table is used to reflect the strength of association [11]. A Bayesian network can reflect the multifactor relationship of psoriasis, so we adopted it to evaluate the efficacy. Meanwhile, the importance of each factor affecting the evaluation of the curative effect is dissimilar, so a reasonable weight needs to be provided [12]. The Analytic Hierarchy Process (AHP) is based on expert experience and was relatively prejudiced [13]. Interestingly, maximum entropy can identify the probability distribution that is most consistent with the cost function and makes the fewest assumptions [14]. To obtain the comparatively actual weight, we combined AHP with maximum entropy to maximize the entropy of the evaluation network through self-learning [15]. On the basis of the evidence mentioned above, we evaluated the efficacy quantitatively through extended set pair analysis (ESPA) based on multifactorial network and reasonable weights [16].
In the current study, we aimed to develop a comprehensive efficacy evaluation system of psoriasis vulgaris, based on Bayesian maximum entropy self-learning and ESPA. A total of 100 patients were included by this system for efficacy evaluation. It is expected to provide a new approach for curative effectiveness evaluation of psoriasis and other complex diseases.
2. Methods2.1. Patients
Adult patients (between 18 and 65 years of age) were eligible to participate if they satisfied the condition of both Western and traditional Chinese medicine (TCM) diagnosis standards for psoriasis vulgaris. The trial was performed in accordance with our previous study [17]. The study was approved by the Ethics Committee of the Yueyang Hospital of Integrated Traditional Chinese and Western Medicine (approval no. 2019-028). All participants provided written informed consent before entering the study.
2.2. Case Study Design
Eligible patients received oral TCM herbal medication tailored to the participant’s disease progression. Medication was administered twice every day during the intervention phase. After 8 weeks of treatments, clinical efficacy was assessed, and blood samples were collected for all eligible patients. The measurement of laboratory indexes was detected in accordance with our previous study [18], which is described in the supplementary materials.
2.3. Bayesian Network Construction
A Bayesian network consists of a directed acyclic graph (DAG) and a series of conditional probability tables (CPTs). The nodes represent random variables, and edges represent the conditional dependences among nodes in a DAG [19]. In our study, the nodes were the indexes related to psoriasis. The conditional probability of each node was obtained according to the relationship between the indexes. An overview of the study flow is shown in Figure 1.
Schematic of the process.
2.4. Calculation of the Initial Weight (IW) by the AHP
We invited experts to score according to the significance of the indexes (Supplementary Table S1). Then, the IWs of indexes were calculated by the AHP according to the following steps [20]. The scores were regarded by the expert, and the IWs were back in calculation if they failed to pass the consistency test.
2.5. Calculation of Maximum Entropy and the Final Weight (FW) by Self-Learning
In the process of self-learning, the maximum entropy was taken as the output condition by the gradient descent [21]. The IWs were input into the system to self-learn. Finally, the FW of the indexes was calculated according to the Bayesian network.
2.6. Efficacy Evaluation by ESPA
The efficacy of patients was evaluated by ESPA as described in the previous studies [16]. For the data of included patients in the index k, the greatest and smallest value of the data represented the upper threshold uk and lower threshold vk, respectively. The assuming arbitrary value xkl belongs to vk,uk. In this study, if the smaller values mean a better level of the effect, the connection degree (CD) of the patient l with respect to the index k could be calculated by equation (1). Conversely, if the greater values of data mean a lower level of efficacy, then the CD could be calculated by equation (2) [22].(1)μkl=xklxkl−vkukuk−vk+2uk−xklxkl−vkukuk−vki+uk−xkluk+vk−xklukuk−vkj,(2)μkl=uk−xkluk+vk−xklukuk−vk+2uk−xklxkl−vkukuk−vki+xklxkl−vkukuk−vkj,where μkl denotes CD of the patient l with respect to the index k, i indicates the uncertainty coefficient of discrepancy and its value range is −1,1, and j connotes the contradictory coefficient with the value defined as −1.
After that, the similarity degree (SD) was proposed to reflect a couple of patients’ similarities, and the range of SD was 0,1. Then, the ideal patient denoted a patient with the optimal response and was availed to make a comparison with the patients who are evaluated for the efficacy. As set pair analysis (SPA) defines [23], the CD of an ideal patient was calculated by the following equation:(3)u∗=1+0⋅i+0⋅j,where u∗ denotes the CD of an ideal patient.
Moreover, the Euclidean distance (ED) was set to evaluate the SD of each patients, and the specific calculation method was mentioned in Yan’s study [16]. The comprehensive evaluation was confirmed, and the weights were also considered in the calculation of the ED [3]. The formula is given as follows:(4)dμl,u∗=∑k=1mωk1−Sμkl,u∗1/2,where dμl,u∗ denotes the ED between the assessed patient l and the ideal patient, μkl connotes the CD of the assessed patient l in index k, Sμkl,u∗ represents the SD of the CD for patient l and the ideal patient, and index k is the FW of index k.
2.7. Stability and Sensitivity Analysis
The stability analysis was practiced to test the rationality of the sample size. When the sample size changed, the corresponding entropy and self-learning times were determined by the Bayesian maximum entropy self-learning model. The sample size was set from 5 to 100, and the interval was 5. This process was repeated 20 times.
As inputs of some indexes are uncertain, the results were affected by the uncertainties. Consequently, a sensitivity analysis was performed to check the consistency. Each patient was set as the variable sample in turn, and data in each index were set as the variable inputs. Assuming an error of ±10% in the inputs determined, that is to say, the range of input values was between 90% and 110% of the reference values [24]. Herein, the interval of input values was set as 1%. Then, ED with different input values were made by ESPA.
3. Results3.1. Results of the Index Weights
A total of 100 psoriasis vulgaris patients were included from outpatients of 20 clinical centers. The characteristics of the patients and 16 indicators are shown in Table 1. The details are provided in Supplementary Table S2.
The characteristics of the patients and the indexes.
Characteristics
n or means
Percentages or stds
Minimums
Maximums
Male
59
59%
—
—
Ages, yrs
45.04
10.65
21
65
PASI
7.91
2.96
1.30
17.60
BSA (%)
9.57
2.84
3.00
17.00
SCC-Ag (ng/mL)
20.94
28.78
1.17
143.69
TNF-α (pg/mL)
9.38
4.49
3.70
41.70
C3 (g/L)
0.91
0.20
1.71
0.48
C4 (g/L)
0.47
2.38
0.13
24.00
IL-23 (pg/mL)
912.43
210.94
509.99
1446.99
IL-22 (pg/mL)
5.73
3.96
2.62
27.93
IL-17 (pg/mL)
5.07
1.77
1.93
9.11
IL-10 (pg/mL)
4.77
0.86
2.90
7.90
DLQI
5.58
3.18
0
15.00
SAS
36.75
9.13
25.00
68.75
SDS
32.44
10.10
25.00
80.00
XQ
16.20
9.31
0
43.00
CCS
9.74
4.97
0
21.00
PSQI
9.28
5.67
0
21.00
Stds, standard deviations; PASI, psoriasis area and severity index; BSA, body surface area; SCC-Ag, squamous cell carcinoma antigen; TNF-α, tumor necrosis factor-α; C3, complement 3; C4, complement 4; IL, interleukin; DLQI, Dermatology Life Quality Index; SAS, Self-Rating Anxiety Scale; SDS, Self-Rating Depression Scale; XQ, Xerostomia Questionnaire; CCS, Cleveland Clinic Score; PSQI, Pittsburgh Sleep Quality Index.
To assess the efficacy of psoriasis vulgaris, we sought to construct the Bayesian network with four layers (Figure 2). The top layer was efficacy evaluation, that is, the target value of the network. The intermediate layers corresponded to the attribute values for the upper layer, and the target values for the next layer. The second layer consisted of four aspects of psoriasis vulgaris, including skin lesion conditions, laboratory indexes, quality of life, and accompanying symptoms. The third layer contained psoriasis area and severity index (PASI), body surface area (BSA) [25], squamous cell carcinoma antigen (SCC-Ag), tumor necrosis factor-α (TNF-α), interleukin (IL) and complement levels [26], Dermatology Life Quality Index (DLQI), Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) [27], the scales of Xerostomia Questionnaire (XQ), Cleveland Clinic Score (CCS), and Pittsburgh Sleep Quality Index (PSQI) [28–31]. Of these, IL comprised of IL-10, IL-17, IL-22, and IL-23, and the complement involved complement 3 (C3) and complement 4 (C4) [26]. Each node of the bottom layer was the attribute value of assessment, which was as well the outcome measure of this study.
The Bayesian network of efficacy evaluation of psoriasis vulgaris.
Each abovementioned index contributed variously to the efficacy evaluation. Therefore, we confirmed the IW by the AHP according to expert score (Supplementary Table S3). Then, we performed consistency checks (Supplementary Table S4). The results passed the consistency check, so the weights were available for the efficacy evaluation, whereas the AHP required repeated artificial modification of the judgment matrix, and the evaluation of each index with different experts tended to be various [13]. Therefore, AHP was combined with the Bayesian network and self-learning to obtain more unbiased and accurate results. The Bayesian maximum entropy self-learning weights were obtained when the entropy values were the maximum. Furthermore, the FW was calculated according to the hierarchical relationships of the Bayesian network (Table 2).
Comparison of the weight by the AHP and FW.
Indexes
Weights by AHP
FW
Skin lesion conditions
PASI
0.3126
0.1362
BSA
0.1839
0.1182
SCC-Ag
0.0392
0.0544
TNF-α
0.0682
0.0545
Laboratory indexes
Complement
C3
0.0119
0.0385
C4
0.0059
0.0385
Interleukin
IL-10
0.0134
0.0171
IL-17
0.0483
0.0171
IL-22
0.0409
0.0171
IL-23
0.0380
0.0171
Quality of life
DLQI
0.0290
0.0845
SAS
0.0209
0.0846
SDS
0.0330
0.0844
Accompanying symptoms
XQ
0.0593
0.0912
CCS
0.0274
0.0421
PSQI
0.0681
0.1047
AHP, analytic hierarchy process; FW, final weight.
To further demonstrate the availability of our method, we compared the weights from two methods (AHP and AHP with maximum entropy self-learning). The results revealed that the weight orderings of recognized indexes of psoriasis (PASI and BSA) were same, which confirmed our method was reliable to an extent. Interestingly, the weight orderings of complement (C3 and C4), quality of life (DLQI, SAS, and SDS), and accompanying symptoms (PSQI, XQ, and CCS) ascended after maximum entropy self-learning, which was consistent with the latest research that psoriasis is a systemic disease [1–4, 7]. Unexpectedly, the weights of IL-17, IL-22, and IL-23 were decreased that were correlated to the interaction between the interleukin family, or the specificity of interleukins in psoriasis is actually not high.
3.2. Stability Analysis
Considering the influence of sample size, stability analysis was carried out. The weights and entropy gradually altered and finally tended to be stable in the course of self-learning (Figures 3 and 4(a)). It indicated the results are relatively stable when the sample size is 100. On this basis, we explored the relationship among sample size, weights, self-learning times, and entropy values. When sample size input was increased, the entropy altered. In addition, the error bars of the entropy and weights were shorter gradually, indicating that the larger the sample size is, the more stable the entropy and weights are (Figures 4(b) and 5). Besides, the self-learning times showed a linear increase (r = 0.987) with the growth of the sample size (Figure 4(c)), revealing that the computing cost was enhanced with increasing sample size. Next, we showed the relations between the three. The vertical error bars and horizontal error bars varied with the numbers of self-learning increasing. With the increase of sample size, the entropy was more stable, and the numbers of self-learning increased, which reminded the three-way interaction (Figure 4(d)). Above, the stability analysis demonstrated that entropy increases from starting levels and becomes gradually stable with the growth of sample size and self-learning times. Approximately 50 patients were needed to make the model stable.
The weights of each index altered in self-learning progress.
The stability and sensitivity analysis results of the evaluation system of the psoriasis curative effect. (a) The entropy was incremental and tended to be stable in increasing with numbers of self-learning when n is 100. (b) The maximum entropy varied when the sample size was from 5 to 100. (c) The self-learning times showed a linear increase (r = 0.987) with the growth of the sample size. (d) The relationship among entropy, self-learning times, and sample size.
The weight altered when the sample size was from 5 to 100.
3.3. Results of Efficacy Evaluation
We included 100 patients for efficacy evaluation based on the results of stability analysis.
Table 3 shows means and standard deviations (Stds) of all patients with respect to each index (complete results are in the Supplementary Table S5). The SD reflected the similarities between the patient and the ideal patient. When the value of SD was closer to 1, it indicated that a single index of the patient being tested was more similar to that of an ideal patient.
The characteristics of SD of each index.
Indexes
PASI
BSA
SCC
TNF
C3
C4
IL-23
IL-22
IL-17
IL-10
DLQI
SAS
SDS
XQ
CCS
PSQI
Means
0.5512
0.5085
0.8359
0.8104
0.6274
0.9859
0.5832
0.8511
0.5620
0.4328
0.5792
0.7254
0.8555
0.5761
0.3095
0.5377
Stds
0.1497
0.1503
0.1904
0.1106
0.1336
0.0742
0.1753
0.1517
0.1971
0.1258
0.1929
0.1770
0.1738
0.2003
0.1007
0.2085
SD, similarity degree.
Aggregating SD of each index to obtain ED: the ED from small to large corresponded to the efficacy from superior to inferior (Table 4). The results showed that the patients with preferable curative effect were L1 (0.3196), L18 (0.3713), L17 (0.3722), L37 (0.3906), and L21 (0.4126), whereas those with unfavorable effect were L100 (0.7904), L90 (0.7749), L61 (0.7656), L82 (0.7469), and L99 (0.7400). Interestingly, ESPA was feasible for efficacy evaluation, overcoming the major limitation of uncertain grades and thresholds.
The ED of 100 patients.
Serial number
Patients
ED
1
L1
0.3196
2
L18
0.3713
3
L17
0.3722
4
L37
0.3906
5
L21
0.4126
6
L6
0.4328
7
L16
0.4449
8
L14
0.4507
9
L32
0.4563
10
L3
0.4591
11
L10
0.4791
12
L29
0.4813
13
L35
0.4843
14
L26
0.4851
15
L5
0.4872
16
L15
0.4939
17
L38
0.4966
18
L8
0.4974
19
L19
0.5016
20
L62
0.5059
21
L51
0.5070
22
L28
0.5123
23
L31
0.5132
24
L46
0.5152
25
L2
0.5168
26
L57
0.5237
27
L27
0.5249
28
L7
0.5250
29
L50
0.5287
30
L11
0.5290
31
L53
0.5293
32
L23
0.5318
33
L9
0.5348
34
L59
0.5354
35
L25
0.5363
36
L40
0.5411
37
L78
0.5411
38
L41
0.5423
39
L4
0.5452
40
L73
0.5468
41
L64
0.5581
42
L52
0.5611
43
L36
0.5616
44
L42
0.5642
45
L75
0.5765
46
L76
0.5837
47
L44
0.5852
48
L92
0.5905
49
L45
0.5915
50
L33
0.5918
51
L34
0.5921
52
L63
0.5951
53
L22
0.5971
54
L58
0.6017
55
L56
0.6055
56
L72
0.6070
57
L83
0.6080
58
L66
0.6082
59
L71
0.6091
60
L97
0.6136
61
L20
0.6148
62
L77
0.6192
63
L55
0.6212
64
L49
0.6218
65
L86
0.6225
66
L30
0.6231
67
L70
0.6242
68
L95
0.6297
69
L87
0.6385
70
L47
0.6454
71
L13
0.6520
72
L74
0.6609
73
L80
0.6618
74
L88
0.6664
75
L65
0.6691
76
L94
0.6696
77
L91
0.6725
78
L96
0.6753
79
L39
0.6772
80
L81
0.6785
81
L69
0.6801
82
L79
0.6807
83
L68
0.6823
84
L24
0.6829
85
L67
0.6846
86
L54
0.6895
87
L89
0.6953
88
L12
0.7016
89
L48
0.7036
90
L60
0.7108
91
L43
0.7213
92
L85
0.7264
93
L84
0.7330
94
L94
0.6696
95
L95
0.6297
96
L96
0.6753
97
L97
0.6136
98
L98
0.7360
99
L99
0.7400
100
L100
0.7904
ED, Euclidean distance.
3.4. Sensitivity Analysis
As the inputs of some indexes of efficacy evaluation are uncertain, efficacy evaluation results were affected by their uncertainties. Thus, a sensitivity analysis was performed to check the consistency of the obtained efficacy evaluation ranking. If the change of a patient’s data affected the thresholds of the indexes, the ED of other patients changed accordingly. Sensitivity analysis was performed on 100 patients, where 20 patients are randomly shown in Figure 6. The other patients’ ED had no noticeable changes when a patient’s indicators were changed. This result illustrated that the ESPA efficacy evaluation model is relatively stable.
The other patients’ ED changed when the input data of a patient were modified.
4. Discussion
Psoriasis is an immune-mediated chronic inflammatory skin disease with a high incidence. Till now, the question of how to comprehensively evaluate the treatment for psoriasis is a major clinical issue [32]. In this study, we first constructed a novel evaluation system of psoriasis by adopting Bayesian maximum entropy self-learning and ESPA.
The most obvious finding to emerge from the analysis was that PASI and BSA, as the most common evaluation tools of psoriasis, were the core indicators with the highest weights either before or after self-learning, which is consistent with expert knowledge and clinical experience. Guidelines of European Association of Dermatology and venereal Diseases recommended that PASI score and BSA should be the first choice of an objective indicator [33]. Even though the United States guidelines indicated that PASI score is cumbersome, it is still employed as a criterion for assessing the severity of psoriasis patients [34]. One previous study showed that compared with Patients Global Assessment, BSA and PASI have higher weights, which is consistent with our result [35].
One interesting finding was that the weights of C3 and C4 increased significantly in the self-learning system. Previous studies showed that the levels of C3 and C4 in patients with psoriasis were significantly higher, and C3 could be considered as a reliable marker of cardiac metabolic risk in psoriasis [36, 37]. Animal experiments have demonstrated that psoriasiform dermatitis was significantly alleviated and a marked reduction in C3 has been observed throughout when the S100A9 gene was deleted, in an imiquimod-induced mouse psoriasis model [38]. These studies indicate that there is a correlation between psoriatic lesions and C3 level, and the complement factor should be monitored as a considerable indicator of evaluation in various therapeutic interventions. Combining the AHP and maximum entropy criterion, we acquired more subjective and accurate results than the AHP on the weights of C3 and C4. However, C3 and C4 have not been integrated into the existing system, and the current study will fill this gap.
Besides, accompanying symptoms and quality of life received higher weights after self-learning. Previous research suggested that DLQI should be recognized as a major indicator and has even assisted in deciding patient-specific treatment strategies [27, 39]. In addition, the current study showed that the weight of SAS is higher than that of the AHP. One study has revealed that the psoriasis patients are more prone to anxiety than normal people, even though there is no obvious correlation between anxiety and the severity of psoriasis lesion, suggesting that doctors should not ignore the anxiety level of patients with mild psoriasis [40].
What is curious about this result is that the weight of interleukin was significantly lower than that by the AHP, and there were few differences among IL-10, IL-17, IL-22, and IL-23. In contrast, these indicators were considered as essential indicators in one of our earlier studies [26]. On the one hand, the reason may be related to the interactions among these indicators, and the evidence of the complex interplay has been proven. Previous studies reported that IL-23 stimulates the process of IL-17 secretion by Th17 cells, while the secretion of IL-22 needs to be encouraged by Th17 [41]. Moreover, enhancing IL-22 could lead to inhibit the production of IL-17 or stimulate the production of IL-10 [42], indicating that there may be a mechanism of interaction among IL-17, IL-23, and IL-22, which needs to be further unraveled. Another possible explanation for this is that it likely relates to specificity of interleukin. The occurrence and progression of psoriasis is the result of the combined force of a series of inflammatory cytokines and signaling pathways, including IL-6, IL-17, IL-23, IL-27, TNF-α, and INF-γ, but only IL-17 is the core mediator directly involved in the inflammation and disease progression of psoriasis. However, it has been proved that the IL-17 family has been found to play a causative role in tumor progression and autoimmune disease [43]. Therefore, this phenomenon may be correlated with the complex downstream signaling pathways resulting in less specificity.
In order to guarantee the accuracy of unsupervised self-learning, we verified the stability and sensitivity of the system. The large sample sizes increased statistical power that makes the results reliable. With the increase of the sample size, the entropy values were gradually stable, and the numbers of self-learning were also gradually increasing. Moreover, we conducted sensitivity analysis arbitrarily changing the clinical data of patients one by one and found that EPSA results are almost unaffected by the sample size, which indicated that our results were stable.
Compared with the AHP, the present system contains the following characteristic and advantages. The main strength of this study is that we applied a Bayesian network to construct a comprehensive measurement of treatment effectiveness among a variety of symptom indicators. Secondly, the judgment matrix needs to repeatedly modify in the weight’s calculation process by the AHP, and the scores assessed by a variety of experts have considerable variation by subjective factors. Therefore, on the basis of the known weights and self-learning method, the optimal weights were output according to the maximum entropy criterion, which has higher objectivity [44]. In addition, SPA or the set pair cloud model all relied on hierarchical classification so that the symptom grades and thresholds were needed during calculation of the CD. In the study, ESPA was an improvement over the SPA, which is applicable at uncertain grades and thresholds.
Despite these promising results, limitations remain. Firstly, the current study only included patients with psoriasis vulgaris, so that one should be cautious when generalizing to other types of psoriasis and the indicators may need to be adjusted. Secondly, a larger sample for model testing may be needed before practical application. Thirdly, considering the large numbers of indicators for psoriasis, other indicators could be included to evaluate the efficacy of psoriasis.
5. Conclusions
Given diversified indicators of psoriasis, we proposed a system that considers multiple indicators and uses more objective weights to evaluate the efficacy, while overcoming the application limitations of SPA. In addition, 100 patients were included to confirm the stability and effectiveness of the system through stability analysis and sensitivity analysis. Theoretically, the system can be applied to other diseases, which requires further research for clarification.
AbbreviationsAHP:
Analytic hierarchy process
ESPA:
Extended set pair analysis
TCM:
Traditional Chinese medicine
DAG:
Directed acyclic graph
CPTs:
Probability tables
IW:
Initial weight
FW:
Final weight
CD:
Connection degree
SD:
Similarity degree
SPA:
Set pair analysis
ED:
Euclidean distance
PASI:
Psoriasis area and severity index
BSA:
Body surface area
SCC-Ag:
Squamous cell carcinoma antigen
TNF-a:
Tumor necrosis factor-α
IL:
Interleukin
DLQI:
Dermatology Life Quality Index
SAS:
Self-Rating Anxiety Scale
SDS:
Self-Rating Depression Scale
XQ:
Xerostomia Questionnaire
CCS:
Cleveland Clinic Score
PSQI:
Pittsburgh Sleep Quality Index
C3/4:
Complement 3/4
Stds:
Standard deviations.
Data Availability
All of the data used to support the findings of this study are available from the corresponding author upon request.
Ethical Approval
The data from preclinical studies [17] have been approved by the Ethics Committee of the Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, protocol 2019-028.
Consent
All clinical study participants provided written informed consent before the study.
Disclosure
Le Kuai, Xiao-ya Fei, and Jing-si Jiang are the co-first authors.
Conflicts of Interest
The authors declare that there are no conflicts of interest.
Authors’ Contributions
Dr and Bin Li had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Le Kuai and Xiao-ya Fei conceptualized the study;Xin Li curated data; Le Kuai and Xin Li conducted formal analysis; Bin Li, Le Kuai, and Xin Li acquired funding; Jing-si Jiang, Ying Zhang, Ying Luo, and Yi Ru conducted investigation; Jing-si Jiang and Yi Ru formulated the methodology; Wei Li and Jian-kun Song were involved in project administration; Wei Li, Jian-kun Song, and Shuang-yi Yin obtained resources; Xiao-ya Fei and Shuang-yi Yin were responsible for software; Jing-si Jiang, Shuang-yi Yin, Jian-kun Song, and Bin Li supervised the work; Xin Li and Ying Luo performed validation; Shuang-yi Yin performed visualization; Xiao-ya Fei, Jing-si Jiang, and Ying Zhang prepared the original draft; Le Kuai, Xiao-ya Fei, Jing-si Jiang, Xin Li, Ying Zhang, Ying Luo, Yi Ru, Jian-kun Song, Wei Li, Shuang-yi Yin, and Bin Li reviewed and edited the manuscript.
Acknowledgments
This work was supported by a grant from the National Key Research and Development Program of China (No. 2018YFC1705301); the National Natural Science Foundation of China (Nos. 81671959, 81874470, 81973860, 81904214, 82004235, and 82074427); the Xinglin Young Scholar, Shanghai University of Traditional Chinese Medicine (No. RY411.33.10); the School-level Postgraduate Innovation Training Program, Shanghai University of Traditional Chinese Medicine (No. JY611.02.03.83); the Joint Funds of the NSFC and Henan Province (No. U1704171); the Shanghai Pujiang Talent Program (No. 2020PJD067); the Shanghai Development Office of TCM (No. ZY(2018-2020)-FWTX-4010); and Dermatology Department of Traditional Chinese Medicine, Clinical Key Specialty Construction Project of Shanghai (No. shslczdzk05001). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Supplementary Materials
Supplementary Table S1: experts’ judgment on importance of each index. Supplementary Table S2: the data of each index of 100 patients. Supplementary Table S3: expert scores of the indexes. Supplementary Table S4: results of the weights calculated by the AHP and the consistency test. Supplementary Table S5: the SD of 100 patients.
ArmstrongA. W.ReadC.Pathophysiology, clinical presentation, and treatment of psoriasis2020323191945196010.1001/jama.2020.4006ArmstrongA. W.HarskampC. T.ArmstrongE. J.Psoriasis and metabolic syndrome: a systematic review and meta-analysis of observational studies201368465466210.1016/j.jaad.2012.08.0152-s2.0-84875442130FangN.JiangM.FanY.Association between psoriasis and subclinical atherosclerosis: a meta-analysis20169520e357610.1097/md.00000000000035762-s2.0-85013188051LiX.KongL. J.LiF. L.Association between psoriasis and chronic obstructive pulmonary disease: a systematic review and meta-analysis201510e014522110.1371/journal.pone.01452212-s2.0-84956915533SimoesJ. F.RibeiroJ.FerreiraB. R.The role of tonsillectomy in psoriasis treatment20152015bcr201420689910.1136/bcr-2014-2068992-s2.0-84964292231TakeichiT.AkiyamaM.Generalized pustular psoriasis: clinical management and update on autoinflammatory aspects202021222723610.1007/s40257-019-00492-0MichalekI. M.LoringB.JohnS. M.2016Geneva, SwitzerlandWorld Health OrganizationBrugnaraG.IsenseeF.NeubergerU.Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis20203042356236410.1007/s00330-019-06593-yXuW.ZhaoY.NianS.Differential analysis of disease risk assessment using binary logistic regression with different analysis strategies20184693656366410.1177/03000605187771732-s2.0-85053707674CodoñerF. M.Ramírez-BoscaA.ClimentE.Gut microbial composition in patients with psoriasis20188381210.1038/s41598-018-22125-y2-s2.0-85043286299PanJ.RenZ.LiW.Prevalence of hyperlipidemia in Shanxi Province, China and application of Bayesian networks to analyse its related factors20188375010.1038/s41598-018-22167-22-s2.0-85042914327LiuJ.ChenM.YangT.WuJ.IoT hierarchical topology strategy and intelligentize evaluation system of diesel engine in complexity environment2018187222410.3390/s180722242-s2.0-85050096968ZouQ.ZhouJ.ZhouC.SongL.GuoJ.Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP201327252554610.1007/s00477-012-0598-52-s2.0-84872492356KimuraY. T.TakahashiD. Y.HolmesP.Vocal development in a Waddington landscape20176e2078210.7554/e.life.20782XuS.ChengJ.QuanZ.Reconstructing all-weather land surface temperature using the bayesian maximum entropy method over the Tibetan plateau and Heihe river basin2017129910.1109/JSTARS.2019.29219242-s2.0-85073157989YanF.XuK. L.LiD. S.A novel hazard assessment method for biomass gasification stations based on extended set pair analysis201712e018500610.1371/journal.pone.01850062-s2.0-85029799385LuoY.RuY.SunX.Characteristics of psoriasis vulgaris in China: a prospective cohort study protocol201972269410.21037/atm.2019.10.46KuaiL.SongJ.-K.ZhangR.-X.Uncovering the mechanism of Jueyin granules in the treatment of psoriasis using network pharmacology202026211321410.1016/j.jep.2020.113214BryoisJ.BuilA.EvansD. M.Cis and trans effects of human genomic variants on gene expression201410e100446110.1371/journal.pgen.10044612-s2.0-84905484562JiangX.XuS.LiuY.WangX.River ecosystem assessment and application in ecological restorations: a mathematical approach based on evaluating its structure and function20157615115710.1016/j.ecoleng.2014.04.0272-s2.0-85027922428ZhangH. B.ChenW. J.YanM.The Bayesian maximum entropy weight self-learning method in the multimodal transport path optimization model2018352844YangF.-G.LiangY.SinghV. P.Debris flow hazard assessment using set pair analysis models: take Beichuan county as an example20141141015102210.1007/s11629-013-2495-x2-s2.0-84904197802WangT.ChenJ.-S.WangT.WangS.Entropy weight-set pair analysis based on tracer techniques for dam leakage investigation201576274776710.1007/s11069-014-1515-72-s2.0-84925503649ScarponiG. E.GuglielmiD.Casson MorenoV.CozzaniV.Assessment of inherently safer alternatives in biogas production and upgrading20166282713272710.1002/aic.152242-s2.0-84963782374HenselerT.Schmitt-RauK.A comparison between BSA, PASI, PLASI and SAPASI as measures of disease severity and improvement by therapy in patients with psoriasis200847101019102310.1111/j.1365-4632.2008.03753.x2-s2.0-54749137827LiX.XiaoQ. Q.LiF. L.Immune signatures in patients with psoriasis vulgaris of blood-heat syndrome: a systematic review and meta-analysis2016201611950365210.1155/2016/95036522-s2.0-84971268828HäggD.SundströmA.ErikssonM.Schmitt-EgenolfM.Decision for biological treatment in real life is more strongly associated with the psoriasis area and severity index (PASI) than with the dermatology life quality index (DLQI)201529345245610.1111/jdv.125762-s2.0-84923070112FengL.KanZ.CaoX. X.To establish the syndrome-typing model of psoriasis vulgaris blood-heat syndrome based on the set pair analysis method20123213081312JiangN.WeiS.MrtenssonJ.Assessment of radiation-induced xerostomia: validation of the xerostomia questionnaire in Chinese patients with head and neck cancer20194410.1097/NCC.0000000000000751HeilmannC.RuhB.GallC.Preoperative prediction of survival for ventricular assist device (VAD) patients200956110.1055/s-0029-1191495PilzL. K.KellerL. K.LenssenD.Time to rethink sleep quality: PSQI scores reflect sleep quality on workdays20184110.1093/sleep/zsy0292-s2.0-85047207912FinkC.UhlmannL.KloseC.Automated, computer-guided PASI measurements by digital image analysis versus conventional physicians’ PASI calculations: study protocol for a comparative, single-centre, observational study201885e01846110.1136/bmjopen-2017-0184612-s2.0-85053106795NastA.SpulsP. I.van der KraaijG.European S3-guideline on the systemic treatment of psoriasis vulgaris-update apremilast and secukinumab-EDF in cooperation with EADV and IPC201731121951196310.1111/jdv.144542-s2.0-85034955985SinghJ. A.GuyattG.OgdieA.Special article: (2018) American college of rheumatology/national psoriasis foundation guideline for the treatment of psoriatic arthritis20197153210.1002/acr.237892-s2.0-85057837574BożekA.ReichA.The reliability of three psoriasis assessment tools: psoriasis area and severity index, body surface area and physician global assessment201726585185610.17219/acem/698042-s2.0-85028669359ÖztürkG.ErbaşD.GelirE.GülekonA.İmirT.Natural killer cell activity, serum immunoglobulins, complement proteins, and zinc levels in patients with psoriasis vulgaris200130318119010.1081/imm-1001050632-s2.0-0034839752TorresT.BettencourtN.MendonçaD.VasconcelosC.SilvaB. M.SeloresM.Complement C3 as a marker of cardiometabolic risk in psoriasis2014306765366010.1007/s00403-014-1467-52-s2.0-84906790693SchonthalerH. B.Guinea-ViniegraJ.WculekS. K.S100A8-S100A9 protein complex mediates psoriasis by regulating the expression of complement factor C320133961171118110.1016/j.immuni.2013.11.0112-s2.0-84890192032LiluashviliS.KituashviliT.Dermatology life quality index and disease coping strategies in psoriasis patients201936441942410.5114/ada.2018.758102-s2.0-85072230042YuS.TuH.-P.HuangY.-C.LanC.-C. E.The incidence of anxiety may not be correlated with severity of psoriasis: a prospective pilot study201913010925410.1016/j.mehy.2019.1092542-s2.0-85067170732WuM.DengY.LiS.The immunoregulatory effects of traditional Chinese medicine on psoriasis via its action on interleukin: advances and considerations201846473975010.1142/s0192415x185003862-s2.0-85046534298KandaN.KamataM.TadaY.IshikawaT.SatoS.WatanabeS.Human β-defensin-2 enhances IFN-γ and IL-10 production and suppresses IL-17 production in T cells201189693594410.1189/jlb.01110042-s2.0-79957809530TanJ.LiuH.HuangM. H.Small molecules targeting RORγt inhibit autoimmune disease by suppressing Th17 cell differentiation20201169710.1038/s41419-020-02891-2KimballA. B.GladmanD.GelfandJ. M.National Psoriasis Foundation clinical consensus on psoriasis comorbidities and recommendations for screening20085861031104210.1016/j.jaad.2008.01.0062-s2.0-43449137849