syndrome differentiation of iga nephropathy based on...

Research ArticleSyndrome Differentiation of IgA Nephropathy Based onClinicopathological Parameters A Decision Tree Model

Yanghui Gu1 YuWang1 Chunlan Ji1 Ping Fan2 Zhiren He3

TaoWang4 Xusheng Liu3 and Chuan Zou3

1Renal Division Second Clinical Medical College Guangzhou University of Traditional Chinese Medicine Guangzhou 510006 China2Renal Division Shanxi Provincial Hospital of Chinese Medicine Xirsquoan 710200 China3Renal Division Guangdong Provincial Hospital of Chinese Medicine Guangzhou 510120 China4Renal Division Peking University Third Hospital Beijing 100191 China

Correspondence should be addressed to Chuan Zou doctorzc541888126com

Received 30 November 2016 Revised 28 February 2017 Accepted 14 March 2017 Published 26 March 2017

Academic Editor Fabio Firenzuoli

Copyright copy 2017 Yanghui Gu et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Background IgA nephropathy is the most common cause of primary glomerulonephritis in China and Traditional ChineseMedicine (TCM) is a vital treatment strategy However not all doctors prescribing TCM medicine have adequate knowledge toclassify the syndrome accurately Aim To explore the feasibility of differentiation of TCM syndrome types among IgA nephropathypatients based on clinicopathological parametersMaterials andMethodsThecross-sectional study enrolled 464 biopsy-proven IgAnephropathy adult patients from 2010 to 2016 The demographic data clinicopathological features and TCM syndrome types werecollected and the decision tree models based on classification and regression tree were built to differentiate between the syndrometypes Results 370 patients of training dataset were 32 years old with serum creatinine of 79 120583molL estimated glomerular filtrationrate (eGFR) of 972mLmin173m2 and proteinuria of 10 gday The scores of Oxford classifications were as follows M1 = 976E1 = 146 S1 = 500 and T1 = 522T2 = 184 The decision trees without or with MEST scores achieved equal precision intraining data However the tree with MEST scores performed better in validation dataset especially in classifying the syndromeof qi deficiency of spleen and kidney Conclusion A feasible method to deduce TCM syndromes of IgA nephropathy patients bycommon parameters in routine clinical practice was proposed The MEST scores helped in the differentiation of TCM syndromeswith clinical data

1 Introduction

IgA nephropathy has been recognized as the most commonform of primary glomerulonephritis [1ndash5] and is a leadingcause of chronic kidney disease (CKD) and end-stage renaldisease (ESRD) [6] In China IgA nephropathy contributedto 32ndash54 of primary glomerulonephritis [3 7] and gt30 ofIgA nephropathy patients would progress to ESRD within 20years after biopsy [7] Although recommended by the guide-lines steroids and immunosuppressive agents are not suitablefor all patients due to side effects [8] especially for those withtheir estimated glomerular filtration rate (eGFR) less than30mLmin173m2 Similar to several other developing coun-tries alternative therapies are considered as pivotal andgeneral treatment strategies for IgA nephropathy in China

especially the decoction and the patent medicine of TCM[9ndash11] In fact IgA nephropathy patients have benefitedfrom TCM treatment and the mechanism has been partiallyunveiled [12ndash15]

The experience of 3000 years and modern researchesshow that Chinese Medicine must be applied with syndromeclassification under the direction of TCM theory Only inthis way could the effectiveness be significant and the adverseevents be avoided [16] Syndrome is mainly identified byelements containing rich information of TCM includingmedical history symptoms and signs which looks a little likesymptom cluster The elements are commonly collected byobservation listeningsmelling questioning and pulse anal-yses Western medicine and TCM have different exploringdimensions but share the same study objects In recent years

HindawiEvidence-Based Complementary and Alternative MedicineVolume 2017 Article ID 2697560 11 pageshttpsdoiorg10115520172697560

2 Evidence-Based Complementary and Alternative Medicine

continued in-depth study has got close connection betweenmodern disease and TCM in multiple levels [17] such asthe syndromes and clinicopathological parameters in IgAnephropathy [18] Thus TCM syndromes have shortcomingsin objectivity and consistency and are hard to be spreadespecially toWestern doctors Is there anymethod to supportthose clinicians with little TCM knowledge in improvingthe ability of syndrome differentiation The TCM syndrometypes have been reported to demonstrate various clinicaland pathological features [19ndash22] The method in the presentstudy described a decision tree as a predictive model whichmaps observations to deduce the target value of an element indetail [23 24] The observations are shown on the branchesof the tree and the target is represented in the leaves Duringanalysis a decision tree can be used to visually and explicitlyaccount for the process of decision-making which makesit one of the predictive modeling approaches most com-monly used in statistics data mining and machine learningTherefore we attempted to use the decision tree in order toexplore the feasibility of syndrome differentiation among IgAnephropathy patients based on clinicopathological parame-ters

2 Materials and Methods

21 Patients 370 biopsy-proven IgA nephropathy patientsfrom 1 January 2010 to 31 December 2014 in GuangdongProvincial Hospital of Chinese Medicine and Shanxi Provin-cial Hospital of ChineseMedicine and 94 from 1 January 2015to 30 June 2016 inGuangdong Provincial Hospital of ChineseMedicine were enrolled in the present study after excludingpatients below 14 years of age and those with Henoch-Schonlein purpura systemic lupus erythematosus and cir-rhosis The patients were categorized into two sets trainingdataset and validation dataset The training dataset included370 patients during 2010ndash2014 while the validation datasetconsisted of 94 patients during 2015-2016

22 Pathological Studies The standard process of inter-preting renopuncture tissues included light microscopyimmunofluorescence and electron microscopy For lightmicroscopy all specimens were stained with hematoxylinand eosin (HampE) Periodic acidndashSchiff Massonrsquos trichromeand Jonesrsquo methenamine silver Renal biopsies were scoredaccording to the Oxford MEST scoring system [25] by twopathologists who were blinded to the patientsrsquo type of syn-dromes An agreement on the definitions and scoring of path-ological features was essential during the pathological review

23 Syndrome Differentiation The evaluation of TCM syn-dromes was performed according to the Guiding Principleof Clinical Research on New Drugs of Traditional ChineseMedicine [26] for the treatment of chronic nephropathy Theinformation on the syndromes was acquired from the recordof latest ward round by the Chinese medical practitionerIn the training dataset 273 patients showed qi deficiency ofspleen and kidney (QDSK) 66 were with both qi and yin(DBQY) 19 exhibited yang deficiency of spleen and kidney8 got yin deficiency of liver and kidney 3 presented lower

energizer damp-heat and 1 had lung wind-heat To increasethe power of statistics the first four syndrome types withsmallest sample sizes were combined in a category of othertypes (OTs) In the validation dataset 80 patients acquiredQDSK and 14 acquired DBQY

24 Statistical Analysis Continuous data with normal distri-bution were presented as the mean and standard deviation(SD) and those with abnormal distributionwere presented asa median and interquartile rangeThe ranked data with equalintervals were expressed as median and interquartile rangeand those with unequal intervals were expressed as countsand percentages of each rank The categorical variables wereexpressed as counts and percentages Comparisons betweengroups were conducted using independent-samples 119905-test orKruskal-Wallis test for continuous variables and chi-squaredtest or Fisherrsquos exact test for categorical variables All thementioned tests were two-tailed and a 119875 value lt 005 wasconsidered statistically significant The analyses were con-ducted by SPSS 170 statistics software (SPSS Inc ChicagoIL) The decision tree was built by R with packages of rpartand rpartplot based on the data from 390 patients biopsiedbetween 1 January 2010 and 31 December 2014 Variablesinvolved in modeling included gender age mean arterialpressure disease course macroscopic hematuria or noteGFR uric acid serum albumin hemoglobin proteinuriaand urinary red blood cell The MEST scores were added tothe model to explore whether pathological parameters wouldincrease the accuracy of the model Positive predictive value(PPV) negative predictive value (NPV) integrated discrimi-nation improvement (IDI) and net reclassification index(NRI) were calculated to evaluate the improvement in pre-dicting QDSK of the model with MEST scores

3 Result

31 Demographic and Clinical Data of Training DatasetTable 1 lists the demographic and clinical characteristicsof biopsy of the training dataset with IgA nephropathyand the clinicopathological parameters of different TCMsyndrome types were compared The patients were 32 yearsold (interquartile range 27ndash42 years) including 196 (530)females with a median disease course of 6 months Eventhough the infection was the most common (111) induce-ment the majority of the patients (786) had no obvi-ous inducement Hypertension was present in 136 (368)patients at baseline Microscopic hematuria was found inalmost all patients but only 66 (178) patients showedmacroscopic hematuria The overall level of proteinuria was1 (05ndash22) gday serum albumin was 403 (358ndash438) (gL)The serum creatinine at the time of biopsy was 79(59ndash108)120583molL and eGFR was 972 (672ndash1205)mLmin173m2 The scores of Oxford classification were as followsM1 = 976 E1 = 146 S1 = 500 and T1 = 522T2 =184

32 Comparison of Clinicopathological Features among Differ-ent TCMSyndromes of TrainingDataset 273 (738) patientsgot QDSK 66 (178) patients got DBQY and 31 (84)

Evidence-Based Complementary and Alternative Medicine 3

Table 1 Comparison of demographic and clinicopathological parameters among different TCM syndromes of training dataset

Total (119899 = 370) Qi deficiency of spleenand kidney (119899 = 273)

Deficiency of both qiand yin (119899 = 66) Other types (119899 = 31) 119875 value

Age (years) 32 (27ndash42) 33 (27ndash42) 29 (25ndash36) 37 (28ndash46) 0025a

Female 119899 () 196 (530) 139 (509) 42 (636) 15 (484) 0154Disease course (months) 6 (2ndash23) 5 (1ndash23) 12 (3ndash25) 6 (2ndash13) 0186Inducement 119899 ()

Infection 41 (111) 31 (114) 5 (76) 5 (161)

0780Fatigue 13 (35) 11 (40) 1 (15) 1 (32)Pregnancy 25 (68) 19 (70) 5 (76) 1 (32)No obvious inducement 291 (786) 212 (777) 55 (833) 24 (774)

Systolic BP (mmHg) 120 (110ndash132) 120 (110ndash132) 120 (108ndash130) 125 (118ndash146) 0070Diastolic BP (mmHg) 80 (70ndash86) 80 (70ndash89) 79 (70ndash80) 80 (72ndash100) 0097MAP (mmHg) 93 (86ndash101) 93 (86ndash101) 91 (82ndash97) 94 (88ndash113) 0069Hypertension 119899 () 136 (368) 101 (370) 18 (273) 17 (548) 0031b

Macroscopic hematuria 119899 () 66 (178) 39 (143) 17 (258) 10 (323) 0008c

U-RBC (120583L) 45 (15ndash136) 32 (11ndash134) 60 (30ndash110) 74 (25ndash179) 0015d

Hemoglobin (gL) 1282 plusmn 201 1295 plusmn 202 1261 plusmn 162 1211 plusmn 252 0055Proteinuria (gday) 10 (05ndash22) 10 (05ndash21) 11 (05ndash25) 11 (05ndash27) 0766Uric acid (120583molL) 340 (274ndash429) 344 (277ndash433) 316 (255ndash408) 358 (302ndash460) 0170Serum albumin (gL) 403 (358ndash438) 406 (361ndash440) 402 (364ndash428) 392 (342ndash421) 0405Serum creatinine (120583molL) 79 (59ndash108) 80 (61ndash107) 67 (56ndash88) 123 (63ndash183) 0001e

eGFR (mLmin173m2) 972 (672ndash1205) 971 (663ndash1199) 1153 (820ndash1257) 706 (277ndash1142) lt0001f

eGFR (mLmin173m2)lt15 6 (16) 3 (11) 0 3 (97)

0001ggt15 le30 15 (41) 9 (33) 1 (15) 5 (161)gt30 le60 53 (143) 44 (161) 4 (61) 5 (161)gt60 le90 94 (254) 67 (245) 19 (288) 8 (258)gt90 202 (546) 150 (549) 42 (636) 10 (323)

Serum IgA (gL) 285 (225ndash358) 284 (228ndash358) 281 (219ndash339) 297 (251ndash402) 0246Serum C3 (mgL) 098 (087ndash111) 098 (087ndash112) 098 (085ndash110) 096 (085ndash108) 0611Oxford MEST scores

M1 119899 () 361 (976) 269 (985) 64 (970) 28 (903) 0032h

E1 119899 () 54 (146) 41 (150) 11 (167) 2 (65) 0384S1 119899 () 185 (500) 142 (520) 32 (485) 11 (355) 0210T1 119899 () 193 (522) 146 (535) 30 (455) 17 (548) 0242T2 119899 () 68 (184) 48 (176) 12 (182) 8 (258)

BP blood pressure MAP mean arterial pressure U-RBC urinary red blood cell eGFR estimated glomerular filtration rate QDSK qi deficiency of spleen andkidney DBQY deficiency of both qi and yin OTs other typesa119875QDSK versus DBQY = 0051 119875QDSK versus OTs = 0999 and 119875DBQY versus OTs = 0059

b119875QDSK versus DBQY = 0137 119875QDSK versus OTs = 0053 and 119875DBQY versus OTs = 0008 120572

1015840= (1205723) = 0053 = 0017

c119875QDSK versus DBQY = 0028 119875QDSK versus OTs = 0015 and 119875DBQY versus OTs = 0628 120572

1015840= (1205723) = 0053 = 0017

d119875QDSK versus DBQY = 0053 119875QDSK versus OTs = 0139 and 119875DBQY versus OTs = 0999e119875QDSK versus DBQY = 0019 119875QDSK versus OTs = 0054 and 119875DBQY versus OTs lt 0001

f119875QDSK versus DBQY = 0014 119875QDSK versus OTs = 0012 and 119875DBQY versus OTs lt 0001g119875QDSK versus DBQY = 0231 119875QDSK versus OTs = 0005 and 119875DBQY versus OTs lt 0001

h119875QDSK versus DBQY = 0601 119875QDSK versus OTs = 0025 and 119875DBQY versus OTs = 0323 120572

1015840= (1205723) = 0053 = 0017

patients were categorized as OTs Although some differencewas found in age (119875 = 0025) the pairwise comparison didnot reach a significant level Any statistical difference was notobserved in gender disease course and inducement among

the three types of fundamental symptomsThe systolic bloodpressure diastolic blood pressure and mean arterial pressure(MAP) of different types did not exhibit a significant differ-ence however patients in OTs category showed a higher rate


QDSK007 083 011

50

QDSK018 073 009

3

DBQY018 000 082

3

DBQY006 041 053

9

QDSK004 064 032

7

QDSK005 052 043

16

DBQY038 025 038

2

QDSK009 049 043

18

QDSK010 052 038

21

QDSK010 075 015

29

QDSK010 065 025

50

100

Other types057 043 000

2

QDSK000 062 038

6

Yes No

QDSK005 084 011

48

DBQY000 043 057

4

QDSK000 100 000

2

Proteinuria

DBQY006 044 050

4

QDSK000 092 008

3

Age lt 28

eGFR ge 108

eGFR lt 122

Age ge 46

MAP ge 106

MAP ge 123

URBC ge 108

URBC lt 44

DBQY = 018Other types = 008 QDSK = 074

All = 370

ge 11

Figure 1 Decision tree model without MEST scores

of hypertension than DBQY (119875 = 0008) Although patientsofQDSK seemed less likely to acquiremacroscopic hematuriathanOTs (119875 = 0015) and somedifferencewas seen in urinaryred blood cell (119875 = 0015) a pairwise test could not figure outthe difference Moreover no obvious difference was observedin hemoglobin proteinuria uric acid serum albumin serumIgA and C3 among those three types For the renal functionthe serum creatinine of patients with DBQY was ltQDSK(119875 = 0019) and OTs (119875 lt 0001) The eGFR of those withDBQY was gt QDSK (119875 = 0014) and OTs (119875 lt 0001) andthe eGFR ofOTswasltQDSK (119875 = 0012)When dividing thepatientsrsquo eGFR to different levels according to the 2012KidneyDisease Improving Global Outcomes (KDIGO) ClinicalPractice Guideline for the Evaluation and Management ofCKD [27] the levels of eGFR of OTs were lower than thosewith DBQY (119875 lt 0001) and QDSK (119875 = 0005) primarilybecause the percentage of patients with an eGFR lowerthan 60mLmin173m2 in OTs (419) was significantlylarger than that of those with DBQY (76) and QDSK(205) With respect to the pathological features the threesyndrome types seemed to have some differences inM scores(119875 = 0032) however the pairwise analysis did not showa sufficient difference and a statistical difference was notobserved in E S and T scores and the ratio of crescents

33 Decision Trees The decision tree without MEST scoresfor TCM syndromes is shown in Figure 1 The patients were

classified according to the values of the variables at each nodePatients would fall into the nodes on the left if they fit thejudgment in rhombuses And patients would fall into thenodes on the right if they did not fit the judgment The pre-dicted syndrome typeswould be shown in the final nodes Forpatients with a urinary red blood cell lt 44120583L at biopsy theTCM fundamental syndrome was more likely to be QDSKnevertheless OTswould bemore possible in those combininga MAP not less than 123mmHg For patients with urinaryred blood cell not less than 108120583L the TCM syndrome wasmore likely to beQDSK For patients with a urinary red bloodcell between 44120583L and 107120583L additional information of ageeGFR MAP and proteinuria was required for the classifi-cation of the syndromes Table 2 and Figure 1 summarizedthe predicting syndromes by the decision tree grouped bydifferent recorded syndromes The precision of QDSK was934 that of DBQY was 576 and that of OTs was 226The decision tree model without MEST scores achieved totalprecision of 776

We also attempted to build a decision model with MESTscores (Figure 2) The tree was quite similar to the modeldescribed above but the condition of the judgment forpatients below 46 years of age with a urinary red blood cellbetween 44120583L and 107120583L S score of MEST eGFR and dis-ease course was essential for decision-makingThe predictingresult of the model with MEST is presented in Table 3 andFigure 2 The precision of QDSK was 960 that of DBQY


QDSK007 083 011

50

QDSK018 073 009

QDSK000 083 017

DBQY000 046 054

8

QDSK000 057 042

11

DBQY021 036 043

8

QDSK009 049 043

18

QDSK010 052 038

21

QDSK010 075 015

29

QDSK010 065 025

50


2

Yes No

QDSK005 084 011

48

Disease

QDSK000 067 033

333

DBQY000 031 069

QDSK031 054 015

DBQY013 020 067

444

100DBQY = 018

Other types = 008 QDSK = 074All = 370

MAP ge 123

URBC lt 44

URBC ge 108

S = 1

Age ge 46

eGFR ge 121

course lt 9

eGFR lt 111

Figure 2 Decision tree model with MEST scores

Table 2 Patientsrsquo distribution of predicting syndromes based on the decision tree without MEST scores

Predicted syndromesRecorded syndromes

Qi deficiency of spleen and kidney(119899 = 273)

Deficiency of both qi and yin(119899 = 66) Other types (119899 = 31)

Qi deficiency of spleen and kidney 255 (934) 38 (576) 21 (677)Deficiency of both qi and yin 13 (48) 25 (379) 3 (97)Other types 5 (18) 3 (45) 7 (226)Note the accuracy of the model was 776

was 318 and that of OTs was 129 rendering the totalprecision similar to the model without MEST scores

34 Validation of Decision Trees Data from94 biopsy-provenIgA nephropathy adult patients from 1 January 2015 to 30June 2016 in Guangdong Provincial Hospital of ChineseMedicinewas collected to validate themodels with orwithoutMEST scores 80 patients were differentiated as QDSK and 14patients were differentiated as DBQY and the clinicopatho-logical parameters are shown in Table 4 The systolic bloodpressure (119875 lt 0001) diastolic blood pressure (119875 = 0001)and MAP (119875 lt 0001) of the validation dataset were largercompared to the training dataset (Table 5) The higher uricacid (119875 lt 0001) and serum creatinine (119875 lt 0001) andthe lower eGFR (119875 lt 0001) implied that kidney injurywas more severe in the validation dataset However the M

score (119875 lt 0001) and T score (119875 lt 0001) were smallerin the validation dataset We analyzed the eGFR of eachT score (Table 6) and found that it declined from T0 andT1 to T2 in both training and validation datasets In otherwords the larger T score corresponded to less eGFR in bothdatasets In the model without MEST (Table 7) 70 (875)patients were accurately predicted as QDSK leading to totalprecision of 755 The total precision of the model withMEST was 809 as 75 patients were accurately predicted asQDSK (Table 8) Because the precision of predicting DBQYwas identical additional indexes were evaluated to determinewhether MEST scores supported the prediction of QDSK(Table 9)ThePPVandNPVbecame larger after addingMESTscores to the clinical data The values of IDI and NRI were gt0 Thus adding MEST scores to the clinical data seemed toslightly improve the precision in predicting syndrome types


Table 3 Patientsrsquo distribution of predicting syndromes based on the decision tree with MEST scores


Qi deficiency of spleen andkidney (119899 = 273)


Qi deficiency of spleen and kidney 262 (960) 45 (682) 25 (806)Deficiency of both qi and yin 8 (29) 21 (318) 2 (65)Other types 3 (11) 0 4 (129)Note the accuracy of the model was 776

Table 4 Comparison of demographic and clinicopathological parameters among different TCM syndromes of validation dataset

Total (119899 = 94) Qi deficiency of spleen andkidney (119899 = 80)

Deficiency of both qi and yin(119899 = 14) 119875 value

Age (years) 31 (25ndash42) 33 (27ndash42) 27 (22ndash34) 0015Female 119899 () 47 (500) 40 (500) 7 (500) 0999Disease course (months) 9 (1ndash21) 12 (2ndash24) 15 (1ndash75) 0018Inducement 119899 ()

Infection 16 (170) 12 (150) 4 (286)

0327Fatigue 3 (32) 2 (25) 1 (71)Pregnancy 1 (11) 1 (12) 0No obvious inducement 74 (787) 65 (812) 9 (643)

Systolic BP (mmHg) 127 (117ndash143) 129 (117ndash143) 122 (116ndash132) 0183Diastolic BP (mmHg) 84 (75ndash93) 85 (76ndash94) 81 (72ndash85) 0056MAP (mmHg) 98 (90ndash107) 100 (91ndash110) 93 (87ndash99) 0079Hypertension 119899 () 39 (415) 36 (450) 3 (214) 0099Macroscopic hematuria 119899 () 9 (96) 8 (100) 1 (71) 0999U-RBC (120583L) 40 (13ndash172) 41 (18ndash172) 23 (3ndash122) 0128Hemoglobin (gL) 1282 plusmn 201 1247 plusmn 214 1419 plusmn 212 0006Proteinuria (gday) 10 (05ndash24) 10 (05ndash24) 08 (04ndash18) 0644Uric acid (120583molL) 410 (328ndash519) 410 (335ndash521) 400 (306ndash489) 0425Serum albumin (gL) 394 (353ndash429) 386 (345ndash422) 429 (369ndash452) 0061Serum creatinine (120583molL) 96 (72ndash151) 104 (78ndash154) 72 (64ndash95) 0012eGFR (mLmin173m2) 788 (505ndash1093) 733 (466ndash958) 1098 (908ndash1238) 0002eGFR (mLmin173m2)lt15 5 (53) 5 (62) 0

0006gt15 le30 9 (96) 8 (100) 1 (71)gt30 le60 20 (213) 19 (238) 1 (71)gt60 le90 22 (234) 21 (262) 1 (71)gt90 38 (404) 27 (338) 11 (786)

Serum IgA (gL) 299 (223ndash357) 298 (222ndash362) 299 (223ndash345) 0866Serum C3 (mgL) 105 (087ndash120) 104 (085ndash116) 120 (108ndash126) 0005Oxford MEST scores

M1 119899 () 79 (868) 69 (896) 10 (714) 0085E1 119899 () 11 (121) 10 (130) 1 (71) 0690S1 119899 () 55 (604) 48 (623) 7 (500) 0554T1 119899 () 23 (253) 22 (286) 1 (71) 0133T2 119899 () 16 (176) 14 (182) 2 (143)

BP blood pressure MAP mean arterial pressure U-RBC urinary red blood cell eGFR estimated glomerular filtration rate


Table 5 Comparison of demographic and clinicopathological parameters between training and validation datasets

Training dataset (119899 = 370) Validation dataset (119899 = 94) 119875 valueAge (years) 32 (27ndash42) 31 (25ndash42) 0899Female 119899 () 196 (530) 47 (500) 0606Disease course (months) 6 (2ndash23) 9 (1ndash21) 0946Inducement 119899 ()

Infection 41 (111) 16 (170)

0089Fatigue 13 (35) 3 (32)Pregnancy 25 (68) 1 (11)No obvious inducement 291 (786) 74 (787)

Systolic BP (mmHg) 120 (110ndash132) 127 (117ndash143) lt0001Diastolic BP (mmHg) 80 (70ndash86) 84 (75ndash93) 0001MAP (mmHg) 93 (86ndash101) 98 (90ndash107) lt0001Hypertension 119899 () 136 (368) 39 (415) 0285Macroscopic hematuria 119899 () 66 (178) 9 (96) 0052U-RBC (120583L) 45 (15ndash136) 40 (13ndash172) 0798Hemoglobin (gL) 1282 plusmn 201 1282 plusmn 201 0693Proteinuria (gday) 10 (05ndash22) 10 (05ndash24) 0764Uric acid (120583molL) 340 (274ndash429) 410 (328ndash519) lt0001Serum albumin (gL) 403 (358ndash438) 394 (353ndash429) 0238Serum creatinine (120583molL) 79 (59ndash108) 96 (72ndash151) lt0001eGFR (mLmin173m2) 972 (672ndash1205) 788 (505ndash1093) lt0001eGFR (mLmin173m2)lt15 6 (16) 5 (53)

0001gt15 le30 15 (41) 9 (96)gt30 le60 53 (143) 20 (213)gt60 le90 94 (254) 22 (234)gt90 202 (546) 38 (404)

Serum IgA (gL) 285 (225ndash358) 299 (223ndash357) 0736Serum C3 (mgL) 098 (087ndash111) 105 (087ndash120) 0045Oxford MEST scores

M1 119899 () 361 (976) 79 (868) lt0001E1 119899 () 54 (146) 11 (121) 0538S1 119899 () 185 (500) 55 (604) 0074T1 119899 () 193 (522) 23 (253)

lt0001T2 119899 () 68 (184) 16 (176)


Table 6 The eGFR of different T scores in training and validationdatasets

Training dataset Validation datasetT0 1191 (1027ndash1261) 948 (784ndash1188)T1 941 (715ndash1188) 573 (389ndash893)T2 534 (353ndash724) 276 (173ndash479)Note the unit used in the form was mLmin173m2

4 Discussion

TCM syndromes of kidney diseases indeed have shortcom-ings in objectivity and consistency leading to the difficulty

in repeating syndrome classifications But in recent yearswith the gradual deepening of TCM researches in the field ofkidney diseases some exact relationship between TCM syn-dromes and clinicopathological parameters was subsequentlyfound With appropriate TCM treatment based on syndromedifferentiation patients with kidney diseases can have theirgeneral condition and clinical indicators improved Amongthe series of kidney diseases IgA nephropathy is the mostdeeply explored one [28 29] A kidney disease research teamled by academician explored the correlation between TCMsyndromes and clinicopathological parameters among 1016IgA nephropathy patients They found that TCM syndromeswere closely related to prognostic factors like proteinuria


Table 7 Validating the decision tree without MEST scores



Deficiency of both qi and yin(119899 = 14)

Qi deficiency of spleen and kidney 70 (875) 13 (929)Deficiency of both qi and yin 4 (50) 1 (71)Other types 6 (75) 0Note the accuracy of the model was 755

Table 8 Validating the decision tree with MEST scores




Qi deficiency of spleen and kidney 75 (9375) 13 (929)Deficiency of both qi and yin 0 1 (71)Other types 5 (625) 0Note the accuracy of the model was 809

Table 9 Discrimination in performance of different models predicting syndrome of qi deficiency of spleen and kidney

Model without MEST Model with MESTIDI NRIPrediction QDSK

(119899 = 80)nQDSK(119899 = 14) PPV NPV Prediction QDSK

(119899 = 80)nQDSK(119899 = 14) PPV NPV

QDSK 70 13 084 009 QDSK 75 13 085 017 008 013nQDSK 10 1 nQDSK 5 1Note QDSK qi deficiency of spleen and kidney nQDSK no qi deficiency of spleen and kidney PPV positive predictive value NPV negative predictive valueIDI integrated discrimination improvement NRI net reclassification index

hypertension and renal tissue injury [30 31] Under theguidance of the above studies a randomized controlled trialwas conducted which found that treatments based on syn-drome differentiation can decrease urine protein and serumcreatinine without obvious adverse events [32 33] Soon afterthat Chinese Association of Integrative Medicine issued theguideline to spread the evidence of treating IgA nephropathybased on syndrome differentiation However the diagnosis ofa syndrome is largely dependent on the clinicianrsquos educationalbackground and experience and not all the renal physicianscan master the skill in a short time As the syndromes havesuch a close correlation with clinicopathological parametersit is feasible to assist clinicians to classify TCM syndromeswith clinicopathological parameters and help apply TCMtreatments The model could promote the precision of syn-drome differentiation and make the syndromes more objec-tive and repeatable The promoted syndrome differentiationcould benefit the consistency of the therapeutic effect of TCMas well as the performing of a large-scale prospective RCT inthe future aiming to further verify the effect of TCM on IgAnephropathy All these works will contribute to the spreadingand development of Chinese Medicine

In the present study demographic data clinicopatho-logical parameters and TCM fundamental syndromes ofIgA nephropathy patients were retrospectively collected Thesyndromes could be classified by decision tree models using

five or six parameters For those patients without biopsy fivevariables were needed to classify the syndromes includingurinary red blood cell MAP age eGFR and proteinuria Forthe biopsied patients additional details were supplementedthrough the renal tissueThen the syndromes could be differ-entiated by urinary red blood cell MAP age disease courseeGFR and S score Although the models with or withoutpathological features achieved identical precision in the train-ing data the model with MEST scores showed an advantagein validation especially in predicting the syndrome types ofQDSK

When we compared the baseline clinicopathological fea-tures of the three syndrome types in training dataset OTsperformed poorly in multiple clinical indexes OTs showeda higher proportion of hypertension than DBQY and weremore likely to obtainmacroscopic hematuria In addition rel-evant variables of renal function showed an extremely severekidney injury in patients with OTs As the OTs encompassedfour syndrome typeswith small sample sizes it was difficult todecipher the responsible factor however 27 (871) patientshad yin deficiency of liver and kidney or yang deficiency ofspleen and kidney A cross-sectional study surveyed 1148Chi-nese IgA nephropathy patientsrsquo clinicopathological features[34]The study found out that the renal function of thosewithyin deficiency of liver and kidney or yang deficiency of spleenand kidney was worse than those with DBQY or qi deficiency


of lung and kidney Moreover the MAP of patients with yindeficiency of liver and kidney or yang deficiency of spleenand kidney was higher than that of those with DBQY or qideficiency of lung and kidney Therefore the inferior valueof OTs in multiple clinical indexes may be attributed to thetypes of yin deficiency of liver and kidney or yang deficiencyof spleen and kidney

To our knowledge this is the first report differentiatingTCM syndromes by building models with objective indexesin IgA nephropathy In other diseases some model has beendeveloped to diagnose patientsrsquo syndromes [35ndash40] Some ofthese studies were based on syndrome factors which com-prised subjective indexes encountering challenges of extrap-olation and repeatability [36 40] Cluster analysis can groupthe similar clinical features together which may be related todifferent syndrome types however the similar features didnot indicate cooccurrence of clinical features or symptoms[39] The questionnaire is good for quantifying the variablessaving time andmoney and enlarging the sample size rapidly[37] Nevertheless it is difficult to design the questionnaire soas to target reliability and validity among large crowds Themethods of classifying the TCM syndromes with objectivevariables were demonstrated by decision tree models usingclinicopathological parameters of 370 IgA patients and thenvalidating the models by 94 patients The package of rpart isbased on the method of Classification and Regression Trees[41] and classification or regression models of a very generalstructure using a two-stage procedure [42] The resultingmodels of rpart have been represented as binary trees Judg-ments of yes or no were the only requirement in the processof decision-making which simplifies the interpretation Thedecision-making processes are entirely visible thus makingit convenient to conduct validation promotion and applica-tionThenodes of the trees are determined by the values of thevariables and the values of the cut-off points are based on thestatistical probability The variables that do not contribute todecision-making will be ignored Unlike random forest thismethod is less affected by partially missing data Thus thedecision tree is sufficiently simple for use in clinical practice

The classification and regression tree has several advan-tages of its own irrespective of the management decisionand statistical method One obvious advantage is that thelogic and operation are easily understood making it intuitiveand clear The advantage is prominent when compared to themethodwith an opaquemechanismofweight and predictionsuch as neural networks Another advantage is the way ofhandling the missing data that can make full use of the dataThe most common approach to handling missing values isdeleting missing observations but classification and regres-sion tree only deletes the observationswithmissed dependentvariables while the observations with missed independentvariables would be reserved This method also exhibitsadequate stability and versatility because the dividing basisof variables is dependent on the rank of value instead of thenumeric size and thus even some outliers appearing in thedataset would have little influence on the result The result isless affected by choice of variables than multiple regressionanalysis as the addition and elimination of variables wouldnot affect the result unless the variables were involved in the

tree Compared to the simple regressions the classificationand regression tree also eliminates the trouble in selectingvariables because during the process of constructing the treethe optimal cut-off point of each involved variable will beautomatically selected

Several limitations should be considered while interpret-ing our study This is a retrospective study thus the types ofTCM syndromes were not strictly classified by identical crite-ria and necessary postreclassification was performed Exceptfor the combined type of OTs only 2 syndromes QDSK andDBQY entered into the statistical analysis making the gener-alization of the findings to other TCM syndromes uncertainTheprecision of classifyingOTswasmuch less than 13 whichmight be due to the type mixed with several types and thecharacteristic of each original type was confused The clini-copathological parameters of training and validation datasetswere quite different especially in renal function and patholog-ical features The limited sample size limitedly representedthe population of all IgA nephropathy patients

In conclusion the decision trees on the basis of clinicalindexes can help classify the types of TCM syndromes andthe pathological features may slightly improve the precisionSimilar studies based on larger sample size or prospectivestudies would be useful for further exploration

Ethical Approval

The study was approved by the Ethics Committee of Guang-dong Provincial Hospital of Chinese Medicine

Conflicts of Interest

The authors declare that they have no conflicts of interest thatmay influence the results

Acknowledgments

The study was funded by Research Project for Practice Devel-opment ofNational TCMClinical ResearchBases (Project noJDZX2015202)

References

[1] R JWyatt andBA Julian ldquoIgAnephropathyrdquoTheNewEnglandJournal of Medicine vol 368 no 25 pp 2402ndash2414 2013

[2] GDrsquoAmico ldquoNatural history of idiopathic IgAnephropathy andfactors predictive of disease outcomerdquo Seminars in Nephrologyvol 24 no 3 pp 179ndash196 2004

[3] L-S Li and Z-H Liu ldquoEpidemiologic data of renal diseasesfrom a single unit in China analysis based on 13519 renalbiopsiesrdquoKidney International vol 66 no 3 pp 920ndash923 2004

[4] P Simon M-P Ramee R Boulahrouz et al ldquoEpidemiologicdata of primary glomerular diseases in western Francerdquo KidneyInternational vol 66 no 3 pp 905ndash908 2004

[5] S V Jargin ldquoRenal biopsy research in the former soviet unionprevention of a negligent customrdquo ISRN Nephrology vol 2013Article ID 980859 5 pages 2013

[6] K Kiryluk Y Li S Sanna-Cherchi et al ldquoGeographic dif-ferences in genetic susceptibility to IgA nephropathy GWAS


replication study and geospatial risk analysisrdquo PLoS Geneticsvol 8 no 6 Article ID e1002765 2012

[7] F-D Zhou M-H Zhao W-Z Zou G Liu and H WangldquoThe changing spectrum of primary glomerular diseases within15 years a survey of 3331 patients in a single Chinese centrerdquoNephrology Dialysis Transplantation vol 24 no 3 pp 870ndash8762009

[8] Kidney Disease Improving Global Outcomes Glomeruloneph-ritis Work Group ldquoKDIGO clinical practice guideline forglomerulonephritisrdquo Kidney International Supplements vol 2no 2 pp 139ndash274 2012

[9] A Francis Y Cho andDW Johnson ldquoHoney in the preventionand treatment of infection in the CKD population a narra-tive reviewrdquo Evidence-Based Complementary and AlternativeMedicine vol 2015 Article ID 261425 8 pages 2015

[10] S Wang J Zhang M Guo X Lian M Sun and L GuoldquoThe efficacy of Shen shuaining capsule on chronic kidneydisease a systematic review andmeta-analysisrdquo Evidence-BasedComplementary and Alternative Medicine vol 2016 Article ID7515413 11 pages 2016

[11] L Nie ldquoTCM differential treatment of IgA nephrosisrdquo Journalof Traditional Chinese Medicine vol 25 no 1 pp 37ndash41 2005

[12] Y Yuwen N-N Shi L-Y Wang Y-M Xie X-J Han andA-P Lu ldquoDevelopment of clinical practice guidelines in 11common diseases with Chinese medicine interventions inChinardquo Chinese Journal of Integrative Medicine vol 18 no 2pp 112ndash119 2012

[13] P-C Shen and L-Q He ldquoEffect of lsquogubentongluo formularsquo onthe IgA class switch recombination of B lymohocytes in Peyerrsquospatches inmicewith IgAnephropathyrdquo SichuanDaXueXueBaoYi Xue Ban vol 47 no 3 pp 337ndash341 2016

[14] P Shen J Shen C Sun X Yang and L He ldquoA systembiology approach to understanding the molecular mechanismsof Gubentongluo decoction acting on IgA Nephropathyrdquo BMCComplementary and Alternative Medicine vol 16 no 1 article312 2016

[15] Z Y Hu Y P Chen and P Zha ldquoAnalysis of the treatment withtraditional Chinese medicine in chronic glomerulonephritisbased on histopathologic typerdquo Zhongguo Zhong Xi Yi Jie He ZaZhi vol 12 no 8 pp 455ndash457 1992

[16] M Liu C Zhang Q Zha et al ldquoAttitudes to personalisedversus standardised practice in traditional Chinese medicinea national cross-sectional survey of practitioners in ChinardquoTheLancet vol 388 supplement 1 p S59 2016

[17] J-L Tang B-Y Liu and K-W Ma ldquoTraditional ChinesemedicinerdquoThe Lancet vol 372 no 9654 pp 1938ndash1940 2008

[18] X-M Chen ldquoPlay superior role of integrative medicine toimprove clinical efficacy of IgA nephropathyrdquo Zhongguo ZhongXi Yi Jie He Za Zhi vol 27 no 6 pp 555ndash556 2007

[19] T Ying S Pei-cheng Z Wen-jun and H Li-qun ShuguangldquoCorrelativity between Chinese Medical Syndromes and Clin-ical Prognosis of Iga Nephropathyrdquo Shanghai Journal of Tradi-tional Chinese Medicine vol 44 no 5 pp 27ndash30 2010

[20] S Jing and M Hongzhen ldquoThe relationship between renalpathological classifications and TCM syndromes of IgAnephropathyrdquo Journal of Jiangxi University of TCM vol 21 no3 2009

[21] J-Z Wang P Liu C-Y He et al ldquoAnalysis on TCM syndromesand pathological grading of 488 patients with IgAnephropathyrdquoPractical Clinical Journal of Integrated Traditional Chinese andWestern Medicine vol 15 no 2 2015

[22] M Honezhen L He H Chen and B Lu ldquoAnalysis on labora-tory indices and pathological characteristics of 123 IgA neph-ropathy cases with different TCM syndromerdquo Journal of Tradi-tional Chinese Medicine vol 52 no 17 pp 1483ndash1485 2011

[23] A Holzinger Data Mining with Decision Trees Theory andApplications 2015

[24] J R Quinlan ldquoInduction of decision treesrdquo Machine Learningvol 1 no 1 pp 81ndash106 1986

[25] D C Cattran R Coppo H T Cook et al ldquoThe Oxfordclassification of IgA nephropathy rationale clinicopathologicalcorrelations and classificationrdquo Kidney International vol 76no 5 pp 534ndash545 2009

[26] X Y Zheng Guiding Principle of Clinical Research on NewDrugs of Traditional Chinese Medicine (Trial) Chinese MedicalScience and Technology Press 2002

[27] CKD Work Group ldquoKDIGO 2012 clinical practice guidelinefor the evaluation and management of chronic kidney diseaserdquoKidney International Supplements vol 3 no 1 pp 1ndash150 2013

[28] P-H Hou J Fang and S Li ldquoDistribution features of Chinesemedicine syndrome types in IgA nephropathy patients compli-cated with hypertension and analysis of its correlated factorsrdquoZhongguo Zhong Xi Yi Jie He Za Zhi vol 31 no 8 pp 1080ndash1084 2011

[29] J-J Li X-M Chen and R-BWei ldquoPotential of renal pathologyon refining syndrome typing of chinese medicine in IgAnephropathyrdquo Chinese Journal of Integrative Medicine vol 19no 2 pp 92ndash97 2013

[30] X-M Chen Y-P Chen and P Li ldquoA multi-centeric epidemi-ological survey on TCM syndrome in 1016 patients with IgAnephropathy and analysis of its relevant factorsrdquoChinese Journalof Integrated Traditional amp Western Medicine vol 26 no 3 pp197ndash201 2006

[31] X-M Chen Y-P Chen and Y-P Chen ldquoMulticenter prospec-tive study on relationship of TCM syndrome type and renalpathology in 286 patients with IgA nephropathyrdquo ZhongguoZhong Xi Yi Jie He Za Zhi vol 24 no 2 pp 101ndash105 2004

[32] X-M Chen Y-P Chen and Z-L Zhou ldquoProspective multi-centered randomized and controlled trial on effect of ShenleCapsule in treating patients with IgA nephropathy of Fei-Pi qi-deficiency syndromerdquo Chinese Journal of Integrated Traditionaland Western Medicine vol 26 no 12 pp 1061ndash1065 2006

[33] X-M Chen Y-P Chen and J Chen ldquoMulticentered random-ized controlled clinical trial on patients with IgA nephropathyof Qi-yin deficiency syndrome typerdquo Chinese Journal of Inte-grated Traditional and Western Medicine vol 27 no 2 pp 101ndash105 2007

[34] Y Wang H Chen and C Zhu ldquoStudy on syndrome-manifestation of traditional Chinese medicine in 1148 patientswith IgA nephropathyrdquo Chinese Journal of Integrated Tradi-tional 2009

[35] F Lin Z Zhang S-F Lin J-S Zeng and Y-F Gan ldquoStudy ofTCM clinical records based on LSA and LDA SHTDT modelrdquoExperimental and Therapeutic Medicine vol 12 no 1 pp 288ndash296 2016

[36] W Dai X Liu Z Zhang et al ldquoA two-level model for the analy-sis of syndrome of acute ischemic stroke fromdiagnosticmodelto molecular mechanismrdquo Evidence-Based Complementary andAlternativeMedicine vol 2013 Article ID 293010 15 pages 2013

[37] K-F Chung W-F Yeung F Y-Y Ho et al ldquoComparison ofscoring methods for the Brief Insomnia Questionnaire in ageneral population samplerdquo Journal of Psychosomatic Researchvol 78 no 1 pp 34ndash38 2015


[38] W-F Yeung K-F Chung N L-W Zhang et al ldquoIdentificationof Chinese medicine syndromes in persistent insomnia asso-ciated with major depressive disorder a latent tree analysisrdquoChinese Medicine vol 11 no 1 pp 1ndash11 2016

[39] N N Jin ldquoStudy of traditional Chinese medicine syndromefactors of dysfunctional uterine bleeding based on clusteranalysis and factor analysisrdquo China Journal of Chinese MateriaMedica vol 33 no 13 pp 1622ndash1625 2008

[40] J Dai J Fang S Sun et al ldquoZHENG-omics applicationin ZHENG classification and treatment Chinese personal-izedmedicinerdquo Evidence-Based Complementary and AlternativeMedicine vol 2013 Article ID 235969 9 pages 2013

[41] L Breiman Classification and Regression Trees WadsworthInternational Group Belmont Calif USA 1984

[42] TMTherneau andE J Atkinson ldquoAn introduction to recursivepartitioning using the RPART routinerdquo Tech Rep 61 MayoFoundation Rochester NY USA 1997

Submit your manuscripts athttpswwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


MEDIATORSINFLAMMATION

of


Behavioural Neurology

EndocrinologyInternational Journal of



Disease Markers


BioMed Research International

OncologyJournal of



Oxidative Medicine and Cellular Longevity


PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of



Computational and Mathematical Methods in Medicine

OphthalmologyJournal of


Diabetes ResearchJournal of



Research and TreatmentAIDS


Gastroenterology Research and Practice


Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom


continued in-depth study has got close connection betweenmodern disease and TCM in multiple levels [17] such asthe syndromes and clinicopathological parameters in IgAnephropathy [18] Thus TCM syndromes have shortcomingsin objectivity and consistency and are hard to be spreadespecially toWestern doctors Is there anymethod to supportthose clinicians with little TCM knowledge in improvingthe ability of syndrome differentiation The TCM syndrometypes have been reported to demonstrate various clinicaland pathological features [19ndash22] The method in the presentstudy described a decision tree as a predictive model whichmaps observations to deduce the target value of an element indetail [23 24] The observations are shown on the branchesof the tree and the target is represented in the leaves Duringanalysis a decision tree can be used to visually and explicitlyaccount for the process of decision-making which makesit one of the predictive modeling approaches most com-monly used in statistics data mining and machine learningTherefore we attempted to use the decision tree in order toexplore the feasibility of syndrome differentiation among IgAnephropathy patients based on clinicopathological parame-ters

2 Materials and Methods

21 Patients 370 biopsy-proven IgA nephropathy patientsfrom 1 January 2010 to 31 December 2014 in GuangdongProvincial Hospital of Chinese Medicine and Shanxi Provin-cial Hospital of ChineseMedicine and 94 from 1 January 2015to 30 June 2016 inGuangdong Provincial Hospital of ChineseMedicine were enrolled in the present study after excludingpatients below 14 years of age and those with Henoch-Schonlein purpura systemic lupus erythematosus and cir-rhosis The patients were categorized into two sets trainingdataset and validation dataset The training dataset included370 patients during 2010ndash2014 while the validation datasetconsisted of 94 patients during 2015-2016

22 Pathological Studies The standard process of inter-preting renopuncture tissues included light microscopyimmunofluorescence and electron microscopy For lightmicroscopy all specimens were stained with hematoxylinand eosin (HampE) Periodic acidndashSchiff Massonrsquos trichromeand Jonesrsquo methenamine silver Renal biopsies were scoredaccording to the Oxford MEST scoring system [25] by twopathologists who were blinded to the patientsrsquo type of syn-dromes An agreement on the definitions and scoring of path-ological features was essential during the pathological review

23 Syndrome Differentiation The evaluation of TCM syn-dromes was performed according to the Guiding Principleof Clinical Research on New Drugs of Traditional ChineseMedicine [26] for the treatment of chronic nephropathy Theinformation on the syndromes was acquired from the recordof latest ward round by the Chinese medical practitionerIn the training dataset 273 patients showed qi deficiency ofspleen and kidney (QDSK) 66 were with both qi and yin(DBQY) 19 exhibited yang deficiency of spleen and kidney8 got yin deficiency of liver and kidney 3 presented lower

energizer damp-heat and 1 had lung wind-heat To increasethe power of statistics the first four syndrome types withsmallest sample sizes were combined in a category of othertypes (OTs) In the validation dataset 80 patients acquiredQDSK and 14 acquired DBQY

24 Statistical Analysis Continuous data with normal distri-bution were presented as the mean and standard deviation(SD) and those with abnormal distributionwere presented asa median and interquartile rangeThe ranked data with equalintervals were expressed as median and interquartile rangeand those with unequal intervals were expressed as countsand percentages of each rank The categorical variables wereexpressed as counts and percentages Comparisons betweengroups were conducted using independent-samples 119905-test orKruskal-Wallis test for continuous variables and chi-squaredtest or Fisherrsquos exact test for categorical variables All thementioned tests were two-tailed and a 119875 value lt 005 wasconsidered statistically significant The analyses were con-ducted by SPSS 170 statistics software (SPSS Inc ChicagoIL) The decision tree was built by R with packages of rpartand rpartplot based on the data from 390 patients biopsiedbetween 1 January 2010 and 31 December 2014 Variablesinvolved in modeling included gender age mean arterialpressure disease course macroscopic hematuria or noteGFR uric acid serum albumin hemoglobin proteinuriaand urinary red blood cell The MEST scores were added tothe model to explore whether pathological parameters wouldincrease the accuracy of the model Positive predictive value(PPV) negative predictive value (NPV) integrated discrimi-nation improvement (IDI) and net reclassification index(NRI) were calculated to evaluate the improvement in pre-dicting QDSK of the model with MEST scores

3 Result

31 Demographic and Clinical Data of Training DatasetTable 1 lists the demographic and clinical characteristicsof biopsy of the training dataset with IgA nephropathyand the clinicopathological parameters of different TCMsyndrome types were compared The patients were 32 yearsold (interquartile range 27ndash42 years) including 196 (530)females with a median disease course of 6 months Eventhough the infection was the most common (111) induce-ment the majority of the patients (786) had no obvi-ous inducement Hypertension was present in 136 (368)patients at baseline Microscopic hematuria was found inalmost all patients but only 66 (178) patients showedmacroscopic hematuria The overall level of proteinuria was1 (05ndash22) gday serum albumin was 403 (358ndash438) (gL)The serum creatinine at the time of biopsy was 79(59ndash108)120583molL and eGFR was 972 (672ndash1205)mLmin173m2 The scores of Oxford classification were as followsM1 = 976 E1 = 146 S1 = 500 and T1 = 522T2 =184

32 Comparison of Clinicopathological Features among Differ-ent TCMSyndromes of TrainingDataset 273 (738) patientsgot QDSK 66 (178) patients got DBQY and 31 (84)







Infection 41 (111) 31 (114) 5 (76) 5 (161)







eGFR (mLmin173m2)lt15 6 (16) 3 (11) 0 3 (97)



M1 119899 () 361 (976) 269 (985) 64 (970) 28 (903) 0032h

E1 119899 () 54 (146) 41 (150) 11 (167) 2 (65) 0384S1 119899 () 185 (500) 142 (520) 32 (485) 11 (355) 0210T1 119899 () 193 (522) 146 (535) 30 (455) 17 (548) 0242T2 119899 () 68 (184) 48 (176) 12 (182) 8 (258)



1015840= (1205723) = 0053 = 0017


1015840= (1205723) = 0053 = 0017




1015840= (1205723) = 0053 = 0017




QDSK007 083 011

50

QDSK018 073 009

3

DBQY018 000 082

3

DBQY006 041 053

9

QDSK004 064 032

7

QDSK005 052 043

16

DBQY038 025 038

2

QDSK009 049 043

18

QDSK010 052 038

21

QDSK010 075 015

29

QDSK010 065 025

50

100


2

QDSK000 062 038

6

Yes No

QDSK005 084 011

48

DBQY000 043 057

4

QDSK000 100 000

2

Proteinuria

DBQY006 044 050

4

QDSK000 092 008

3

Age lt 28

eGFR ge 108

eGFR lt 122

Age ge 46

MAP ge 106

MAP ge 123

URBC ge 108

URBC lt 44


All = 370

ge 11







QDSK007 083 011

50

QDSK018 073 009

QDSK000 083 017

DBQY000 046 054

8

QDSK000 057 042

11

DBQY021 036 043

8

QDSK009 049 043

18

QDSK010 052 038

21

QDSK010 075 015

29

QDSK010 065 025

50


2

Yes No

QDSK005 084 011

48

Disease

QDSK000 067 033

333

DBQY000 031 069

QDSK031 054 015

DBQY013 020 067

444

100DBQY = 018


MAP ge 123

URBC lt 44

URBC ge 108

S = 1

Age ge 46

eGFR ge 121

course lt 9

eGFR lt 111




















Infection 16 (170) 12 (150) 4 (286)



0006gt15 le30 9 (96) 8 (100) 1 (71)gt30 le60 20 (213) 19 (238) 1 (71)gt60 le90 22 (234) 21 (262) 1 (71)gt90 38 (404) 27 (338) 11 (786)


M1 119899 () 79 (868) 69 (896) 10 (714) 0085E1 119899 () 11 (121) 10 (130) 1 (71) 0690S1 119899 () 55 (604) 48 (623) 7 (500) 0554T1 119899 () 23 (253) 22 (286) 1 (71) 0133T2 119899 () 16 (176) 14 (182) 2 (143)





Infection 41 (111) 16 (170)



0001gt15 le30 15 (41) 9 (96)gt30 le60 53 (143) 20 (213)gt60 le90 94 (254) 22 (234)gt90 202 (546) 38 (404)


M1 119899 () 361 (976) 79 (868) lt0001E1 119899 () 54 (146) 11 (121) 0538S1 119899 () 185 (500) 55 (604) 0074T1 119899 () 193 (522) 23 (253)

lt0001T2 119899 () 68 (184) 16 (176)




4 Discussion

















(119899 = 80)nQDSK(119899 = 14) PPV NPV













Ethical Approval




Acknowledgments


References



















































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of






















Infection 41 (111) 31 (114) 5 (76) 5 (161)







eGFR (mLmin173m2)lt15 6 (16) 3 (11) 0 3 (97)



M1 119899 () 361 (976) 269 (985) 64 (970) 28 (903) 0032h

E1 119899 () 54 (146) 41 (150) 11 (167) 2 (65) 0384S1 119899 () 185 (500) 142 (520) 32 (485) 11 (355) 0210T1 119899 () 193 (522) 146 (535) 30 (455) 17 (548) 0242T2 119899 () 68 (184) 48 (176) 12 (182) 8 (258)



1015840= (1205723) = 0053 = 0017


1015840= (1205723) = 0053 = 0017




1015840= (1205723) = 0053 = 0017




QDSK007 083 011

50

QDSK018 073 009

3

DBQY018 000 082

3

DBQY006 041 053

9

QDSK004 064 032

7

QDSK005 052 043

16

DBQY038 025 038

2

QDSK009 049 043

18

QDSK010 052 038

21

QDSK010 075 015

29

QDSK010 065 025

50

100


2

QDSK000 062 038

6

Yes No

QDSK005 084 011

48

DBQY000 043 057

4

QDSK000 100 000

2

Proteinuria

DBQY006 044 050

4

QDSK000 092 008

3

Age lt 28

eGFR ge 108

eGFR lt 122

Age ge 46

MAP ge 106

MAP ge 123

URBC ge 108

URBC lt 44


All = 370

ge 11







QDSK007 083 011

50

QDSK018 073 009

QDSK000 083 017

DBQY000 046 054

8

QDSK000 057 042

11

DBQY021 036 043

8

QDSK009 049 043

18

QDSK010 052 038

21

QDSK010 075 015

29

QDSK010 065 025

50


2

Yes No

QDSK005 084 011

48

Disease

QDSK000 067 033

333

DBQY000 031 069

QDSK031 054 015

DBQY013 020 067

444

100DBQY = 018


MAP ge 123

URBC lt 44

URBC ge 108

S = 1

Age ge 46

eGFR ge 121

course lt 9

eGFR lt 111




















Infection 16 (170) 12 (150) 4 (286)



0006gt15 le30 9 (96) 8 (100) 1 (71)gt30 le60 20 (213) 19 (238) 1 (71)gt60 le90 22 (234) 21 (262) 1 (71)gt90 38 (404) 27 (338) 11 (786)


M1 119899 () 79 (868) 69 (896) 10 (714) 0085E1 119899 () 11 (121) 10 (130) 1 (71) 0690S1 119899 () 55 (604) 48 (623) 7 (500) 0554T1 119899 () 23 (253) 22 (286) 1 (71) 0133T2 119899 () 16 (176) 14 (182) 2 (143)





Infection 41 (111) 16 (170)



0001gt15 le30 15 (41) 9 (96)gt30 le60 53 (143) 20 (213)gt60 le90 94 (254) 22 (234)gt90 202 (546) 38 (404)


M1 119899 () 361 (976) 79 (868) lt0001E1 119899 () 54 (146) 11 (121) 0538S1 119899 () 185 (500) 55 (604) 0074T1 119899 () 193 (522) 23 (253)

lt0001T2 119899 () 68 (184) 16 (176)




4 Discussion

















(119899 = 80)nQDSK(119899 = 14) PPV NPV













Ethical Approval




Acknowledgments


References



















































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of

















QDSK007 083 011

50

QDSK018 073 009

3

DBQY018 000 082

3

DBQY006 041 053

9

QDSK004 064 032

7

QDSK005 052 043

16

DBQY038 025 038

2

QDSK009 049 043

18

QDSK010 052 038

21

QDSK010 075 015

29

QDSK010 065 025

50

100


2

QDSK000 062 038

6

Yes No

QDSK005 084 011

48

DBQY000 043 057

4

QDSK000 100 000

2

Proteinuria

DBQY006 044 050

4

QDSK000 092 008

3

Age lt 28

eGFR ge 108

eGFR lt 122

Age ge 46

MAP ge 106

MAP ge 123

URBC ge 108

URBC lt 44


All = 370

ge 11







QDSK007 083 011

50

QDSK018 073 009

QDSK000 083 017

DBQY000 046 054

8

QDSK000 057 042

11

DBQY021 036 043

8

QDSK009 049 043

18

QDSK010 052 038

21

QDSK010 075 015

29

QDSK010 065 025

50


2

Yes No

QDSK005 084 011

48

Disease

QDSK000 067 033

333

DBQY000 031 069

QDSK031 054 015

DBQY013 020 067

444

100DBQY = 018


MAP ge 123

URBC lt 44

URBC ge 108

S = 1

Age ge 46

eGFR ge 121

course lt 9

eGFR lt 111




















Infection 16 (170) 12 (150) 4 (286)



0006gt15 le30 9 (96) 8 (100) 1 (71)gt30 le60 20 (213) 19 (238) 1 (71)gt60 le90 22 (234) 21 (262) 1 (71)gt90 38 (404) 27 (338) 11 (786)


M1 119899 () 79 (868) 69 (896) 10 (714) 0085E1 119899 () 11 (121) 10 (130) 1 (71) 0690S1 119899 () 55 (604) 48 (623) 7 (500) 0554T1 119899 () 23 (253) 22 (286) 1 (71) 0133T2 119899 () 16 (176) 14 (182) 2 (143)





Infection 41 (111) 16 (170)



0001gt15 le30 15 (41) 9 (96)gt30 le60 53 (143) 20 (213)gt60 le90 94 (254) 22 (234)gt90 202 (546) 38 (404)


M1 119899 () 361 (976) 79 (868) lt0001E1 119899 () 54 (146) 11 (121) 0538S1 119899 () 185 (500) 55 (604) 0074T1 119899 () 193 (522) 23 (253)

lt0001T2 119899 () 68 (184) 16 (176)




4 Discussion

















(119899 = 80)nQDSK(119899 = 14) PPV NPV













Ethical Approval




Acknowledgments


References



















































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of

















QDSK007 083 011

50

QDSK018 073 009

QDSK000 083 017

DBQY000 046 054

8

QDSK000 057 042

11

DBQY021 036 043

8

QDSK009 049 043

18

QDSK010 052 038

21

QDSK010 075 015

29

QDSK010 065 025

50


2

Yes No

QDSK005 084 011

48

Disease

QDSK000 067 033

333

DBQY000 031 069

QDSK031 054 015

DBQY013 020 067

444

100DBQY = 018


MAP ge 123

URBC lt 44

URBC ge 108

S = 1

Age ge 46

eGFR ge 121

course lt 9

eGFR lt 111




















Infection 16 (170) 12 (150) 4 (286)



0006gt15 le30 9 (96) 8 (100) 1 (71)gt30 le60 20 (213) 19 (238) 1 (71)gt60 le90 22 (234) 21 (262) 1 (71)gt90 38 (404) 27 (338) 11 (786)


M1 119899 () 79 (868) 69 (896) 10 (714) 0085E1 119899 () 11 (121) 10 (130) 1 (71) 0690S1 119899 () 55 (604) 48 (623) 7 (500) 0554T1 119899 () 23 (253) 22 (286) 1 (71) 0133T2 119899 () 16 (176) 14 (182) 2 (143)





Infection 41 (111) 16 (170)



0001gt15 le30 15 (41) 9 (96)gt30 le60 53 (143) 20 (213)gt60 le90 94 (254) 22 (234)gt90 202 (546) 38 (404)


M1 119899 () 361 (976) 79 (868) lt0001E1 119899 () 54 (146) 11 (121) 0538S1 119899 () 185 (500) 55 (604) 0074T1 119899 () 193 (522) 23 (253)

lt0001T2 119899 () 68 (184) 16 (176)




4 Discussion

















(119899 = 80)nQDSK(119899 = 14) PPV NPV













Ethical Approval




Acknowledgments


References



















































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of


























Infection 16 (170) 12 (150) 4 (286)



0006gt15 le30 9 (96) 8 (100) 1 (71)gt30 le60 20 (213) 19 (238) 1 (71)gt60 le90 22 (234) 21 (262) 1 (71)gt90 38 (404) 27 (338) 11 (786)


M1 119899 () 79 (868) 69 (896) 10 (714) 0085E1 119899 () 11 (121) 10 (130) 1 (71) 0690S1 119899 () 55 (604) 48 (623) 7 (500) 0554T1 119899 () 23 (253) 22 (286) 1 (71) 0133T2 119899 () 16 (176) 14 (182) 2 (143)





Infection 41 (111) 16 (170)



0001gt15 le30 15 (41) 9 (96)gt30 le60 53 (143) 20 (213)gt60 le90 94 (254) 22 (234)gt90 202 (546) 38 (404)


M1 119899 () 361 (976) 79 (868) lt0001E1 119899 () 54 (146) 11 (121) 0538S1 119899 () 185 (500) 55 (604) 0074T1 119899 () 193 (522) 23 (253)

lt0001T2 119899 () 68 (184) 16 (176)




4 Discussion

















(119899 = 80)nQDSK(119899 = 14) PPV NPV













Ethical Approval




Acknowledgments


References



















































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of



















Infection 41 (111) 16 (170)



0001gt15 le30 15 (41) 9 (96)gt30 le60 53 (143) 20 (213)gt60 le90 94 (254) 22 (234)gt90 202 (546) 38 (404)


M1 119899 () 361 (976) 79 (868) lt0001E1 119899 () 54 (146) 11 (121) 0538S1 119899 () 185 (500) 55 (604) 0074T1 119899 () 193 (522) 23 (253)

lt0001T2 119899 () 68 (184) 16 (176)




4 Discussion

















(119899 = 80)nQDSK(119899 = 14) PPV NPV













Ethical Approval




Acknowledgments


References



















































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of






























(119899 = 80)nQDSK(119899 = 14) PPV NPV













Ethical Approval




Acknowledgments


References



















































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of























Ethical Approval




Acknowledgments


References



















































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of




























































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of
















syndrome differentiation of iga nephropathy based on...

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