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Plasma D-dimer Can Effectively Predict the Prospective Occurrence of Ascites in Advanced Schistosomiasis Japonica Patients
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Plasma D-dimer Can Effectively Predict the Prospective Occurrence of Ascites in Advanced Schistosomiasis Japonica Patients

The Korean Journal of Parasitology 2017;55(2):167-174.
Published online: April 30, 2017

1School of Public Health, Fudan University, Shanghai, China

2Health Department of Guard Bureau of General Office of the Communist Party of China, Beijing, P. R. China

3Jiangxi Provincial Institute of Parasitic Diseases, Nanchang, P. R. China

4Schistosomiasis Control Station of Yugan County, Shangrao, P. R. China

5Key Laboratory of Tropical Diseases and Control of the Ministry of Education, Guangzhou, P. R. China

6Department of Parasitology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, P. R. China

7School of Community and Global Health, Claremont Graduate University, Claremont, California, USA

*Corresponding authors (07046262@163.com; zhongzheng.niu@cgu.edu)
• Received: September 19, 2016   • Revised: February 13, 2017   • Accepted: February 14, 2017

Copyright © 2017 by The Korean Society for Parasitology and Tropical Medicine

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • Machine learning algorithms to predict the 1 year unfavourable prognosis for advanced schistosomiasis
    Honglin Jiang, Weicheng Deng, Jie Zhou, Guanghui Ren, Xinting Cai, Shengming Li, Benjiao Hu, Chunlin Li, Ying Shi, Na Zhang, Yingyan Zheng, Yue Chen, Qingwu Jiang, Yibiao Zhou
    International Journal for Parasitology.2021; 51(11): 959.     CrossRef
  • Derivation and external validation of a model to predict 2-year mortality risk of patients with advanced schistosomiasis after discharge
    Guo Li, Shanshan Huang, Lifei Lian, Xiaoyan Song, Wenzhe Sun, Jinfeng Miao, Bohan Li, Yong Yuan, Shengfan Wu, Xiaoyan Liu, Zhou Zhu
    EBioMedicine.2019; 47: 309.     CrossRef

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Plasma D-dimer Can Effectively Predict the Prospective Occurrence of Ascites in Advanced Schistosomiasis Japonica Patients
Korean J Parasitol. 2017;55(2):167-174.   Published online April 30, 2017
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Plasma D-dimer Can Effectively Predict the Prospective Occurrence of Ascites in Advanced Schistosomiasis Japonica Patients
Korean J Parasitol. 2017;55(2):167-174.   Published online April 30, 2017
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Plasma D-dimer Can Effectively Predict the Prospective Occurrence of Ascites in Advanced Schistosomiasis Japonica Patients
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Fig. 1 (A) A patient of advanced schistosomiasis with ascites in the endemic area (2014, photo by one of the authors). (B) Excised spleen of a patient which developed to collateral circulation with splenomegaly (2009, photo by one of the authors).
Plasma D-dimer Can Effectively Predict the Prospective Occurrence of Ascites in Advanced Schistosomiasis Japonica Patients

Comparison of the general, infective, and hepatic characteristics in baseline (2013) between the cases of advanced schistosomiasis with/without ascites in 2014

Baseline measurement Ascites occurrence in 2014 P-value

Yes (n=44) No (n=247)
Age (mean±SD, year) 62.83±9.68 61.80±9.71 0.443

Gender 0.026
 Male 33 (75.0) 141 (57.1)
 Female 11 (25.0) 106 (42.9)

Occupation 0.483
 Fisher 27 (61.4) 163 (66.0)
 Farmer 15 (34.1) 65 (26.3)
 Others 2 (4.5) 19 (7.7)

HBV infection in 2013 0.996
 Negative 11 (25.0) 61 (25.4)
 Antibody 26 (59.1) 142 (59.2)
 Positive 7 (15.9) 37 (15.4)

Hepatic fibrosis level in 2013 0.075
 Level 1 4 (9) 36 (15)
 Level 2 13 (30) 104 (42)
 Level 3 27 (61) 105 (43)

Portal hypertension in 2013 0.337
 Yes 10 (22.7) 41 (16.7)
 No 34 (77.3) 204 (83.3)

Treatment in 2014 0.107
 Yes 33 (75.0) 154 (62.3)
 No 11 (25.0) 93 (37.7)

D-dimer (mg/L) 0.71±2.44 0.48±2.12 0.005

ELISA (SEA IgG, OD value) 0.34 (0.15–0.58) 0.27 (0.17–0.47) 0.248

ALB (g/L) 44.5 (40.8–45.8) 46.2 (44.0–48.6) <0.001

GLB (g/L) 29.3 (24.8–31.8) 27.6 (24.7–30.3) 0.101

A/G 1.5 (1.4–1.8) 1.7 (1.5–1.9) 0.006

HA (μg/L) 186.5 (122.8–260.8) 151.8 (105.4–234.6) 0.109

PC-III (μg/L) 83.5 (71.3–113.5) 77.6 (63.8–93.4) 0.053

Type IV collagen (μg/L) 50.0 (43.2–73.3) 44.5 (27.1–57.6) 0.006

LN (μg/L) 124.7 (108.7–152.3) 125.9 (102.6–147.2) 0.453

The area under curve of each individual predictor for the occurrence of ascites in 2014

Indicators Area Std. error P-value Asymptotic 95% confidence interval
Lower bound Upper bound
D-dimer 0.635 0.050 0.004 0.537 0.733
Gender 0.591 0.045 0.058 0.537 0.733
Hepatic fibrosis level 0.595 0.046 0.045 0.505 0.685
ALB 0.697 0.042 <0.001 0.615 0.779
Type IV collagen 0.641 0.046 0.006 0.550 0.730

The final inclusive prediction model of ascites occurrence in 2014

Beta Standardized error Wald P-value OR (95% CI)
Constant −2.516 0.444 32.060 0.000 0.081 (NA)

Gender Female Reference - - - -
Male 0.942 0.450 4.376 0.036 2.57 (1.06–6.20)

D-dimer ≤0.81 ng/mL Reference - - - -
>0.81 ng/mL 0.82 0.399 4.219 0.040 2.27 (1.04–4.96)

Hepatic Class 2 Reference - - - -

Fibrosis level Class 3 0.29 0.394 0.540 0.462 1.34 (0.62–2.90)

ALB 34–48 g/L (normal) Reference - - - -
<34 g/L (lower) 0.609 1.461 0.174 0.677 1.84 (0.11–32.25)
≥48 g/L (higher) −1.551 0.638 5.906 0.015 0.21 (0.06–0.74)

Type IV Collagen <75 μg/L Reference - - - -
≥75 μg/L 0.879 0.512 2.950 0.086 2.41 (0.88–6.57)

Probability of ascites occurrence in combinations of risk factors

Combinations of risk factors Gender Higher level of D-dimer Level 3 hepatic fibrosis Higher ALB Higher TIC Probability (%)
1 Male Yes Yes Yes Yes 73.5
2 Male Yes No Yes Yes 67.5
3 Male Yes Yes No Yes 60.2
4 Male No Yes Yes Yes 55.0
5 Male Yes Yes Yes No 53.6
6 Male Yes No No Yes 53.1
7 Female Yes Yes Yes Yes 52.0
Table 1 Comparison of the general, infective, and hepatic characteristics in baseline (2013) between the cases of advanced schistosomiasis with/without ascites in 2014
Table 2 The area under curve of each individual predictor for the occurrence of ascites in 2014
Table 3 The final inclusive prediction model of ascites occurrence in 2014
Table 4 Probability of ascites occurrence in combinations of risk factors