development and evaluation of a urine protein - clinical chemistry

1214

Clinical Chemistry 42:8

1214-1222 (1996)

Development and evaluation of a urine proteinexpert system

MIRosiv IVANDIC,* WALTER HOFMANN, and WALTER G. GUDER

Based on the quantitative determination of creatmine, totalprotein, albumin, a1-microglobulin, IgG, a2-macroglobu-

liii, and N-acetyl-I3,n-glucosaininidase in urine in combina-tion with a test strip screening, the findings of hematuria,leukocyturia, and proteinuria can be assigned to prerenal,

renal, or postrenal causes. Using this graded diagnostic

strategy as a knowledge base, we developed a computer-based expert system for urine protein differentiation(“UPES”) as a decision-supporting tool. The knowledge

base was implemented as a combination of “Wthen” rulesand two-step bivariate distance classffication of marker

proteins. The knowledge for this form of pattern recogni-tion was derived from the results for a set of 267 patientswith clinically and histologically documented nephropa-thies. To determine the diagnostic value of UPES, we

tested another set of data: results for 129 urine analyses

from 94 patients. Using these data, the system reached 98%

concordance with the clinical diagnoses for the patients andwas superior to the diagnostic interpretations of four hu-

man experts. UPES has been successfully integrated into

the laboratory routine process, including automated data

import.

INDEXING TERMS: knowledge-based system . decision-support-

ing system #{149}albumin . a,-microglobulin #{149}a2-macroglobulin

proteinuria #{149}kidney diseases #{149}hematuria #{149}leukocyturia

nephropathy

Continuously changing medical knowledge has resulted in in-

creasing specialization in medicine. Providing optimal medical

care requires experts who can keep up with the enormous

information flow; however, such experts are not always available.

To conserve the knowledge of a specialist and to widely

distribute this knowledge, software tools called expert systems

Institut f#{252}rKlinische Chemie, St#{228}dt.Krankenhaus Munchen-Bogenhausen,

Englschalkinger Str. 77, D-81925 Munchen, Germany.Author for correspondence. Fax +49 89 9270 2113; e-mail wguho@pc-

labor.uni-bremen.de.Dedicated to H. Keller of ZUrich (Switzerland), on the occasion of his 70th

birthday. This paper contains part of the results of the doctoral thesis of MI.

Received November 7, 1995; accepted April I, 1996.

or, better, knowledge-based systems have been developed and

are being used with increasing frequency. Laboratory medicine,

given its high degree of specialization and its use of objective

quantitative findings, seems especially suited to benefit from

these computer programs [1, 2].

Here we describe such a decision-supporting system, the

Urine Protein Expert System (UPES), developed for the inter-

pretation of urine protein differentiation.’ As with electro-

phoretic techniques [3-5], quantitative analysis of urine marker

proteins has been successfully applied to detect and differentiate

nephropathies [5-7]. The multivariate evaluation of the excre-

tion pattern allows differentiation of prerenal from glomerular,

tubular, and postrenal causes of proteinuria and hematuria

[8-11].

Knowledge for describing and interpreting complex urine

protein patterns has accumulated in recent years, a result of

collaboration between nephrologists and clinical chemists. We

have tried to implement this knowledge in the form of “if/then”

rules in the knowledge base of UPES, a knowledge base that

contains facts and strategies drawn from literature as well as

from heuristics and empirical guidelines. The rules have been

worked out in close collaboration with specialists in the field of

urine protein differentiation.

Because various nephropathies could not be sufficiently

identified by interpretation of excretion patterns when based on

rules alone, we have used another method of knowledge repre-

sentation, geometric distance classification, to extract and apply

the knowledge of this multivariate pattern recognition. Using

this hybrid model of a knowledge base, UPES is able to process

the laboratory results provided and to propose a medical report

generated from 36 text elements. Twenty-four of those elements

(all the ones used in this paper) are listed in the Appendix.

Matenais and MethodsAnalytical procedures. Test strip screening was performed with

test strips from Behring (Marburg, Germany). Quantitative

determinations of total protein, albumin, ce,-microglobulmn,

IgG, a2-macroglobulmn (turbidimetrically), N-acetyi-/3,n-glu-

Nonstandard abbreviations: UPES, Urine Protein Expert System; /3-NAG,N-acervl-(3,o-glucosaminidase; and GFR, glomerular filtration rate.

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ClinicalChemisliy 42, No. 8, 1996 1215

B. C.

cosaminidase (p-NAG) (kinetically and photometrically), and

creatinine in urine as well as serum concentrations of creatinine

and a1 -microglobulin were performed as described elsewhere

[12]. The reference values used are from previous publications

[7, 9].

Hardware and software. The knowledge-based system for urine

protein analysis was developed by using an IBM-compatible PC

(80386 CPU) with 1 MB RAM and DOS. A Turbo C Compiler

(V. 2.0; Borland, Munich, Germany) and a BGI Printer Toolkit

(Ryle Design, Mt. Pleasant, MI) served as programing tools.

The statistical software package SAS (V. 6.10; SAS Institute,

Cary, NC) was used to perform discriminant analysis.

Geometric distanceclassification.Geometric distance classification

is a method for describing and separating multidimensionalpattern classes. Patterns are defined by complete quantitative or

qualitative data. Patterns of distinguishable classes form distinct

clusters in multidimensional spaces. In geometric distance clas-

sification, groups of geometric figures such as spheroids and

ellipsoids are used to represent these clusters (Fig. 1).

The distance classifier GEODICLA [13] was developed

separately to determine the position and size of such spheroids

and ellipsoids automatically. The program selects random mem-

bers from each class from a training set of typical examples and

defines their geometric “region of influence” [14]. This is doneby taking their coordinates as the centers of the figures and

extending a user-defined minimal radius until reaching either a

maximum radius or the “nearest” example of a different class. Ifan example is picked that is already covered by a figure

belonging to the same class, then this example can be classified

already and does not need its own region of interest. When

every training example is covered, the training is stopped.

Reclassifying the training set now always results in a 100%

classification rate. The resulting geometric shapes can be

adapted manually after this automatic “learning process.”

Using the software tool GEODICLA, we have generalized

the information contained in the urine protein patterns of two

training sets and condensed them into two sets of figures: circlesand ellipses. To classify an unknown urine pattern, we compare

it with these representatives sets in UPES: The geometric

distance from this pattern to the centers of each of the circles!

ellipses is calculated and compared with the radius of each

circle/ellipse. If the pattern lies within a circle/ellipse, the

associated class is stored. Comparison of the stored classes leads

UPES to its diagnostic conclusion.

Training sets. Protein patterns of 503 second morning urines

from 267 patients with clinically or histologically diagnosednephropathies were used to train the distance classifier GEO-

DICLA. The urines were collected from patients of the II.Medical Department of the Hospital Munchen-Harlaching and

of the III. Medical Department and the Department of Neuro-

surgery of the Hospital Munchen-Bogenhausen. Depending on

their clinical diagnoses, patients were assigned to the following

diagnostic groups:

primary glomerulopathy-different forms of glomerulone-

phritis, histologically documented

secondary glomerulopathy- diabetic nephropathies, clinical

diagnoses

interstitial nephropathy-e.g., acute tubulo-toxic nephropa-

thy, chronic interstitial nephropathy, partly histologically doc-

umented diagnoses

renal dysfunction-protein excretion patterns ranging from

normal values to as much as twice the upper reference limit from

patients from any of these three diagnostic groups

Diagnoses that were not histologically documented were

based on clinical criteria (e.g., anamnesis, clinical examination,

laboratory results,medical imaging, clinicalcourse) and made by

the physician treating the patient. Table 1 summarizes the

composition of the training set.

Validationset.To evaluate the diagnostic interpretation of urine

protein patterns, we used data from 129 urine analyses. These

test data were collected from 94 patients of the II. Medical

Department of the Hospital Munchen-Harlaching and the III.

Medical Department of the Hospital Munchen-Bogenhausen.

As in the training set, the urines were assigned to the diagnostic

groups primary glomerulopathy, secondary glomerulopathy,

and interstitial nephropathy, according to their diagnoses (Table

1).

Discriminant analysis. To compare the diagnostic performance of

the distance classifier with the performance of a statistical

method, we performed classificatory linear and quadratic dis-

criminant analysis. We used the training set to compute the

parameters (coefficients and constants) of the linear and qua-

dratic functions. Equal prior probabilities were assumed for all

four diagnostic groups. The same validation data were used to

evaluate the results of discriminant analysis as were used with

geometric distance classification (Table 1).

#{149}#{149}#{149}S

#{149}a. #{149}#{149}#{149}#{149}.#{149} a

#{149}%#{149}#{149}.#{149}a. #{149}S . #{149}

#{149}SS U at#{149} U #{149} #{149}

#{149}I. .R#{149}1

U... #{149}aU #{149}#{149}#{149}#{149}

Fig. 1. Use of circles to describe clusters of twodifferent classes: (A) individual examples of two dif-ferent classes forming two distinct clusters; (B) anoptimal characterization of the clusters by using sixcircles (GEODICLA; see text); (C) the resulting sixrepresentatives of the two classes.

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1216 Ivandi#{233}et al.: Urine protein expert system (UPES)

Table 1. Compos ition of the trainTraining collective

ing and the valid ation collective.Validation collective

Urine samples Patients Urine sampies Patients

Diagnostic groups

Primary glomerulopathy

Secondary glomerulopathy

Interstitial nephropathy

Renal dysfunctionTotal

n

285

123

66

29503

n

57 117

24 97

13 33

6 20267

%

44

36

12

7

n

46

76

7

-

129

%

36

59

5

-

n

27

62

5

-

94

%

29

66

5

-

ResultsINPUT DATA

For interpretation of a urine protein pattern, UPES requires at

least the data for urine creatinine, total protein, albumin, and

a,-microglobulin. For differential diagnosis during the decision

process, the system asks for data on IgG, a2-macroglobulin, and

p-NAG if necessary. The program refers all quantitative mea-

surements to the urine creatinine content to take into account

the concentration of the urine sample [7]. These quantitative

data are processed together with the results of the urine teststrips for assessing leukocytes (granulocyte esterase), hemoglo-

bin (pseudoperoxidase), protein, and glucose. As an option, the

glomerular filtrationrate (GFR) can be considered by providing

the data for serum creatinine and serum a,-microglobulin.

Apart from the results of the serum and urine analysis,

additional data concerning the patient and the request of the

urine protein differentiation data can be entered into UPES by

using an input screen or can be imported automatically by

retrieving a file.

KNOWLEDGE EASE

The knowledge base of UPES is divided into five modules-

Plausibility and consistency check, Hematuria, Leukocyturia,

Proteinuria, and GFR-which are considered if necessary. Theimplemented strategy is represented as if/then rules. The geo-

metric distance classification is used only in the Proteinuria

module to interpret the marker protein patterns.

Plausibility and consistemy check. All data are checked for plausi-

bility during the input or import process; formats and thresholds

are used to exclude values that exceed medical and analytical

ranges. For analytical validation, this module considers thevalues for total protein, albumin, test strip protein, and the two

serum measurements (creatinine and a,-microglobulin). A

warning appears on the screen (“Discrepancy between test strip,

albumin, and total protein!”) if the comparison of the test strip

result and the quantitative measurements fulfills one of the

following conditions:protein test strip positive and total protein �200 mg/L

protein test strip negative and albumin >300 mg/L

albumin > total protein and albumin >50 mg/L

These rules take into account that the detection limit of the test

strip is -300 mg/L albumin and thus detect false-positive and

false-negative test strip results.If the value for urine protein excretion is normal and one of

the serum values indicates a decreased GFR (see next section),

the user is asked to check the input data.

MedicalassessmentofGFR. The GFR module isconsidered only

if the concentrations of the optional serum analytes creatinine

and a,-microglobulin are provided. a,-Microglobulin partially

fills the diagnostic gap associated with creatinine, by sometimes

detecting a decrease of GFR earlier than creatinine does [15-

17].A major restriction of the GFR is unlikely if both serum

analytes are within their reference ranges (text element I; see

Appendix).The GFR is assumed to be decreased if concentra-

tions of both analytes are increased (text element 2). In combi-

nation with a normal urine excretion pattern, this is interpreted

as a lossof functioning nephrons that iscompletely compensated

by the remaining nephrons (text element 3).

An increase of only a,-microglobulin in serum indicates a

possible restriction in glomerular clearance (diagnostic gap ofcreatinine). In this case, determination of creatinine clearance is

recommended to confirm or to exclude this suspicion (text

element 4). If only creatinine is increased, this more likely

indicates the presence of pseudocreatinines or increased muscle

mass (text element 5), given the greater diagnostic sensitivity of

a, -microglobulin.

Medical assessment of hematuria. Whenever the test strip result for

blood is positive, the Hematuria module is considered, to

distinguish prerenal from glomerular, tubular, and postrenal

causes.Prerenal causes of the test strip result are assumed if the

criteria for prerenal proteinuria are met (i.e., a “protein gap”; see

text element 6) [18]. If albumin excretion is <100 mg/L,

differentiationof renal and postrenal hematuria by urine protein

analysis is not possible [9]. In such cases, UPES suggests using

phase-contrast microscopy to look for dysmorphic ervthrocytes

[19, 20] (text element 7).

At higher albumin concentrations,the system considersthe

ratios of albumin with a2-macroglobulin, IgG, and a,-micro-globulin to assign the hematuria to a renal (glomerular or

tubulo-interstitial)or postrenal bleeding [9, 10]. If a2-macro-globulin and lgG results have not yet been provided, UPES asks

fortheirmanual input.

Because of their molecular size, only small amounts of

a,-macroglobulin(250 kDa) and IgG (125 kDa) usually pass theglomerular filter,and those are reabsorbed in the tubule. When

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mg/g creatinine

14-20

20-50

50-100>100

20-3030-100

100-10001000-3000

>3000

ClinicalCbemisy 42, No. 8, 1996 1217

albumin ratios with these proteins in urine are similar to those

in plasma, therefore, a postrenal lesion is indicated (a2-macro-

globulinlalbumin >0.02 and IgG/albumin >0.2). In this case,

the system proposes that the clinicianrepeat the urine protein

differentiation to exclude additional renal hematuria after

postrenal hematuria has ceased (text element 8).

In renalhematuria (a2-macroglobulinlalbumin<0.02), gb-

merular and tubulo-interstitial causes can be distinguished by

the concentrations of IgG: In tubular hematuria, even small

amounts of filtered IgG cannot be reabsorbed (IgG/albumin

>0.2). Increased excretion of the tubular marker a,-micro-

globulin is taken as additional confirmation of the tubulo-

interstitial lesion (text element 9).

Medical assessment of leukoyturia. The Leukocyturia module is

considered whenever the leukocyte esteraseteststripshows a

positive result. An isolated leukocyturia in combination with a

normal urineproteinpatternindicateseithera contamination of

the urine sample or an inflammation of the lower urinarytract

(textelement 10).Leukocyturia with a slightglomerular pro-

teinuria(totalprotein <150 mg/g creatinine,albumin <100

mg/g creatinine, a,-microglobulin <14 mg/g creatinine) can

have both renaland postrenalcauses(textelement 11),whereas

substantial glomerular or tubular proteinuria indicates renal

involvement in an inflammatory process (text element 12).

Medical assessment of proteinuria. In contrastto the previoustwo

modules, the Proteinuria module is used in all cases to interpret

the various urine protein ratios.Active renal diseasecan be

excludedifteststrip results are negative and the concentrations

ofurinetotalprotein,albumin,and a,-microgbobulinarewithin

their reference ranges (text element 13). Normal excretion of

both marker proteinsbut increasedIgG in urine may indicate

(e.g.)monoclonal gammopathies (textelement 14).

If totalprotein excretionis >300 mg/L and the sum of

albumin, a1-microglobulin, and IgG is <30% of the total

protein excretion, prerenal causes such as Bence Jones protein-

uria might account for this disproportion [18]. ImmunofIxation

is suggested for further confirmation. This finding initiates a

temporary report (text element 15), and the decision process is

stopped.Renal proteinuria can be quantitatively and qualitatively

described and assigned to different kinds of nephropathies by

analysis of the excretion pattern of albumin, a,-microglobulin,

and IgG. Using albumin as a glomerular marker and a1-

microglobulin as a tubular marker, UPES describes the extent of

glomerular and tubularproteinuriaasborderline,slight,signif-

icant, distinct, and nephrotic, according to the thresholds given

in Table 2. The IgG/albumin ratio helps to distinguish “selec-

tive” (<0.03) from “nonselective” (>0.03) proteinuria in gb-merulopathies with albuminuria >500 mg/g creatinine. An

example of a description of a renal proteinuria is text element 16

(albumin 1100 mg/g creatinine, a,-microgbobulin 25 mg/gcreatinine, IgG 15 mg/g creatinine).

Apart from thisquantitativeand qualitativedescription,a

renal proteinuria can also be assigned to different diagnostic

classes of renal diseases.

Table 2. DescrIption of tubular and glomerular proteinurlaaccording to the excretion of the marker proteins albumin

and a1-microglobulin.a1.Microglobuiin Albumin

Description

Borderline

Slight

Significant

Distinct

Njephrotic

The training sets of patients with clinically or histologically

documented diagnoses show that tububo-interstitial nephropa-

thies and primary and secondary glomerubopathies are each

characterized by a specific urine protein pattern. The clusters of

these disease groups can be defined and separated in logarith-mic coordinates, with the marker proteins albumin and a,-mi-

croglobulin making up the x- and y-axes, respectively (Fig. 2,

top).

A renalproteinuriawith albumin <40 mg/g creatinineand

a,-microgbobulin <28 mg/g creatinine cannot be clearly as-

signed to only one of the three disease classes because of the

overlapping zones of the clusters. Such a slight proteinuria can

be interpretedas“renaldysfunction,”which can have renaland

extrarenal causes: e.g., metabolic disorder, fever, intense physi-

cal exercise (see text element 17). In the overlapping zone

between primary and secondary glomerubopathies as well as

interstitial nephropathies, further diagnostic information might

be achievedby takingIgG intoconsideration(Fig.2, bottom).

An excretion pattern from an unknown patient can be

assigned to any of the diagnostic groups by comparing it with

the position of the different clusters in both coordinates.

To implement thisvisualclassificationin the knowledge-

based system UPES, we used the geometric distance classifier

GEODICLA [13]. After five fictitious patterns had been added

to the training samples to detect implausible marker constella-

tions (Fig. 2, top), and the learning and abstracting process of

GEODICLA had been performed, the information contained in

these 508 single urine protein patterns of the training sets was

specifically condensed into some representative examples: Theclusters of the different classes were now described with 60

circles (albumin-a,-microglobulin patterns) and 15 ellipses (al-

bumin-IgG patterns).For diagnostic interpretation of urine findings of an un-

known patient, UPES calculates the logarithm of the patient’s

concentrations of albumin and a1-microglobulin. The geomet-

ric distance of this pattern to the centers of each of the 60 circles

is calculated and compared with their radii. If the pattern lieswithin a circle, the corresponding class is stored.

According to the classes stored after this first classification,

thesystem identifiestheexcretionpatternasbelonging with one

of the following diagnostic groups: renal dysfunction (text

element 17),primary glomerubopathy (textelement 18),second-

ary glomerubopathy (element 18), primary or secondary gb-

merulopathy (text element 19),tubulo-interstitialnephropathy

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Fig. 2. Albumin-a1-microglobulin patterns(top) and albumin-lgG patterns (bottom)ofthe diagnosticclassesofthe trainingcollective: tubulo-interstitial nephropa-thies (A), primary () and secondary(#{149})glomerulopathies,the dysfunctionalcol-lective (*) and the plausibility collective

(element 18), glomerulopathy with interstitial involvement or

interstitial nephropathy with secondary glomerulopathy (text

element 20),or implausiblemarker proteinpattern.

Only ifthisfirstclassificationbased on the a,-microgbobulinl

albumin ratiorevealsan ambiguous diagnosis(elements19 and

20) does UPES consider IgG in a second step: One of a pair of

diagnoses in an ambiguous diagnosis is more probable if the

albumin/IgG pattern of the patient is covered by ellipses of only

one class(unambiguous classification;textelement 21).This

two-step pattern identification in UPES reflects that the diag-

nostic discrimatory power of IgG is less than that of a1-

microgbobulin.

Depending on the result of the two-step classification, addi-

tional rules are considered. If the diagnostic pattern classifica-

tion reveals a glomerulopathy, the system takes into account the

possibilitythatthe tubularcomponent of a proteinuriamight

resultfrom tubularoverload caused by an excessiveglomerular

proteinuria. In nephrotic proteinuria (albumin >3 g/g creati-nine),therefore,the extentof the tubularshare iscorrectedby

using the followingequation,derived from urine findingsin

selectedpatientswith glomerulonephritiswhose renalintersti-

tialspace was devoid of major histopathologicalfindings[21]:

a1-microgbobulin (corr.) = a,-microglobulin - 4.7

e#{176}#{176}#{176}#{176}22. .,lbumin

This equation approximately describes the lower margin of

the cluster of primary gbomerulopathies in Fig. 2 and allows

estimation of the amount of tububo-interstitial involvement in

gbomerular diseases:Tubular proteinuriaisassumed to result

from tubularoverloadifthecorrectedvalueofa1-microglobulin

is <14 mg/g creatinine (text element 22). a,-Microgbobulin

concentrations >14 mg/g creatinine are interpreted as showing

an involvement of the renal interstitialspace in gbomerulone-

phritis (text element 23).

To differentiateacute from chronic tubular disordersin

interstitial nephropathies, UPES requests data for the catalytic

concentrationof the tubularenzyme p-NAG. In acute lesions

(e.g., caused by nephrotoxic antibiotics), the excretion of

p-NAG usuallyexceeds20 U/L ifa1-microgbobulinexcretionis

>40 mg/g creatinine (text element 24) [12]. Chronic tububo-

interstitial diseases are described by increased a,-microgbobulin

excretion without a major increase of p-NAG.

OUTPUT (FINAL REPORT)

UPES composes the finalreport from the selectedtextitems

afterthe urineand serum proteinfindingshave been medically

assessed.

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PrimaryGP Secondary GP TP

Table 3. Diagnostic interpretation of urine protein differentiatIons of 46 prImary glomerulopathles, 76 secondaryglomerulopathies, and 7 interstitIal nephropathies by UPES, linear and quadratic discrimlnant functions, and four experts.

Clinical diagnosis

Expertise Pulm.GP GP GP/TP Dys Others Sec.GP GP GP/TP Dys Others TP GP/TP Dys Others

UPES 9 31 1 3 2” 0 46 1 27 2” 6 0 1 0

Expert 1 2 41 1 2 0 0 43 0 32 1 5 0 1 1

Expert 2 19 18 6 3 0 0 32 0 34 10 5 0 0 2Expert 3 0 38 5 3 0 11 30 1 32 2 6 0 1 1Expert 4 22 8 9 5 2 5 17 9 39 6 5 2 0 0LDF 22 12 1 5 6 11 15 0 33 17 7 0 0 0

QDF 22 12 0 2 10 13 11 0 35 17 5 2 0 0a Correct” diagnoses are listed (prim./sec.) GP = (primary/secondary) glomerulopathy, TP = tubulo-interstitial nephropathy, or Dys = renal dysfunction, whereas

patterns interpreted as implausible constellations and urines not classified or misclassified are summarizedas Others.b 2 patterns not classified by UPES.

“2 patterns classified by UPES as a primary glomerulopathy.

LDF, linear discriminated function; QDF, quadratic discriminant function.

ClinicalChemistry42, No. 8, 1996 1219

EVALUATING THE KNOWLEDGE BASE WITH THE

VALIDATION SET

To compare the medical interpretation of proteinuria by UPES,

statistical methods and human expertise, we assessed the results

of urine proteindifferentiationof the validationset(129 urines

from 94 patients)as classifiedby IJPES, linear and quadratic

discrimination fimctions, and four experts in our laboratory who

were familiarwith thismethod of urine analysis.The resultsof

these evaluationsare given in Table 3; misclassifIcationsare

summarized as “others.”

Because there are no gold standards for evaluating urinary

protein patterns,it was difficult to define correct and false

interpretations. Patients with a documented diabeticnephropa-

thy, for example, showed many different patterns of protein

excretion. The patterns were describedby human experts and by

the system asreflectinggbomerularand (or)tubulardysfunction,

secondary gbomerulopathy, primary or secondary gbomerubopa-thy, or mixed (gbomerubar and tubular) nephropathy, and allof

these diagnostic groups were assumed to be a correct interpre-

tation. Only the description “primary gbomerubopathy” would

be judged a clearmisclassificationof these patients.

UPES. Of 46 urines from patients with gbomerubonephritis,

UPES identified 9 primary gbomerubopathies (20%) by first-step

classification. The correct but more global diagnosis “primary

or secondary glomerulopathy” was chosen in the majorityof

cases(31 of46 urines,67%) because of theoverlappingzones of

the albuminla,-microgbobulin patterns. Using the IgG excre-

tion in a second-stage pattern classification correctly assigned 6

of these 31 ambiguous cases to the primary gbomerulopathygroup. Two patientswith gbomerubonephritisand albuminuria

>10 g/g creatininecould not be interpretedby UPES.

Only 2 of 76 urines(3%) with secondary glomerulopathies

were misclassified as primary glomerulopathies by UPES. Both

of these urines showed substantial albuminuria (844 and 552

mg/g creatinine) and IgG excretion (63 and 59 mg/g creatinine)

but no significanttubularproteinuria. Again, UPES assigned

most (46, or 61%) of the 76 urines to the diagnosticgroup

“primary or secondary glomerulopathy.” The excretion ratio for

IgG/albumin misled the system in 3 of these 46 decisions to

favor the primary type of glomerulopathy.The remaining 27urines (36%) were classified as “renal dysfunction” because of

the low quantitiesof marker proteinsexcreted.

Finally, UPES interpreted the urine patterns of all 7 inter-

stitial nephropathies correctly.

Discriminant functions. As an alternative classification method, weused the discriminantfunctionsestimated from the albumin,

a,-microglobulin, and IgG patternsof the trainingset (no

implausible constellations were included). Each protein pattern

was classified to the diagnostic group having the highest group

probability, as computed with linear and quadratic discriminant

functions. Resubstitution of the trainingsetresultedina reclas-

sificationrateof 75% by lineardiscriminantfunctionsand 79%

by quadratic discriminant functions.

To allow consideration of an ambiguous classification, as in

UPES, we took into account the differencebetween the two

highest group probabilities.If this differencewas <0.3, the

pattern was assigned to both classes(ambiguous classification).

Linear discriminant functions described 38 samples (29%) ofthe validationsetas caused by “renaldysfunction”;68 patterns

(53%) were interpretedcorrectlyasbelonging toother diagnos-

tic groups matching the known diagnosis. By quadratic discrimi-nant functions,37 cases(29%) were classifiedas“renaldysfunc-

tion,” whereas other, correct diagnostic classes were chosen for

65 samples (50%). In total,there were 23(18%) vs 27 (21%)

misclassifications by linear and quadratic discriminant functions,respectively (Table 3).

Human experts. The qualityof the human expertisevaried

greatly, depending on the experience of each expert with urine

protein differentiation. Generally, the humans interpreted moreproteinuriasas being “renaldysfunction”than did UPES. Two

experts more oftendecidedon an unambiguous diagnosis,atthe

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risk of increasing their misclassifications; the other two experts

preferred the more general diagnosis “primary or secondary

glomerulopathy,” to be on the safe side. Notably, one expert

reliedon a positiveglucoseteststripresultto classify a glomeru-

bopathy as the secondary type. In contrast to UPES, he and two

other expertsfailedto identifythe primary gbomerubopathy in a

32-year-old woman with IgA nephropathy and familial glucos-

uria.

DiscussionEvaluation with the validation data set showed that noninvasive

urineproteindifferentiationmay be a usefuldiagnosticstrategy

in nephrology. The knowledge-based system UPES performed

well in diagnostic interpretation of urine protein patterns,

correctly distinguishing all interstitial nephropathies from gb-merulopathies. It misclassified only 2 of 129 urines (2%),

incorrectlyconcluding that patternsof significantglomerular

proteinuria had instead indicated a primary gbomerubopathy.

Discriminantfunctionswere not able to deal properlywith

the overlapping zones of allclinicalclasses.The four human

experts also had problems correctly classifying primary and

secondary gbomerulopathies-which are difficult to distinguish

by clinicalchemistrymeans.

After the evaluation,we adjusted the knowledge base of

UPES to improve the medical assessment. We added one circle

to the secondary glomerulopathy class so that this diagnostic

group would be considered in cases of significant glomerular

proteinuria.Another circlewas also added to the primary

gbomerubopathy class to ascertain the identification of cases ofexcessive proteinuria. The addition of these two circles will help

prevent misclassificationin similarcases.

Knowledge-based systems, as means of rationalization, accel-

erate the time-consuming process of medical assessment and

increase the economic efficiency of a clinical laboratory. Such

programs make possible consistent and standardized medical

assessmentof constantand high quality,especiallywhen dealing

with the highly complex data produced in increasingly special-

ized areas [22-24]. Apart from learning effects, transparent data

interpretation rather than simple “data intoxication” [25] may

provide clinical physicians with useful additional information

[26].

The knowledge-based system we designed provides for the

first time a concise decision-supporting system to exclude and

differentiate proteinuria, hematuria, and leukocyturia. Working

with the complex excretion pattern of different marker proteins,

UPES can distinguish prerenal, glomerular, tubulo-interstitial,

and postrenal causes of pathological urine findings. By using two

differentmethods forknowledge representation,we essentially

implemented the strategyand experienceof a specialistinurine

protein differentiation as a knowledge base.

Modelling the framework of the knowledge base with if/then

rules makes itpossibleto integratethe heuristicsthatguide a

human expert in the diagnosticdecisionprocess.Rulesallowthe

designofa modular knowledge basetomaintaina clearstructure

and facilitate regular update. Furthermore, the user can easily

retrace the decisions formulated by the system. Diagnostic

pattern classification in urine protein differentiation can be

implemented in a rule bae by using constant thresholds to

describe the different clusters by squares bike a mosaic. How-

ever,good resolutionforsufficientrepresentationoftheclusters

isobtained only by using a largenumber of thresholds.Thus,

the quality of classification is limited by the number of rules

needed to compare the patternof the patientwith the margins

of allclusters.Although thisiseasilydone in a two-dimensional

pattern recognition, more dimensions increase the number and

complexity of rules exponentially.Because rules, therefore, did not appear to be the optimal

solution,we booked foralternativeways of knowledge represen-

tation. Classificatory discriminant analysis [27], for example, can

designate and separate the different diagnostic groups in a

statistical way, but several assumptions are necessary that are not

always met (e.g.,mubtivariatenormal distributionof data).

Moreover, a largersetof examples of allclassesisnecessaryto

finddiscriminatingfunctionsthatare generallyvalid,and every

change in this collective (e.g., adding a new patient not yet

correctly classified) requires a complete recalculation. Never-

theless,we used the trainingset of urine proteinpatternsto

estimateassociatedlinearand quadraticdiscriminantfunctions.

Using these functionsto classifythe validationset,however,

revealedmajor difficulties in dealingwith overlappingzones of

the diagnostic groups.

Another flexible tool used successfully in laboratory medicine

forrobustpatternrecognitionisneuralnetworks [28-30].Thesemodels forknowledge-processingand representationareabbeto

deal with complex, uncertain, and even incomplete data. In a

self-organizing process they use the information contained in

training data to build up and adjust their “knowledge.” After thisdynamic learning process, the adapted network structure itself

incorporates the knowledge base [31]. Quasi-parallel processing

of data enables fastclassificationin neural networks but also

makes it difficultfor users to influenceand understand their

behavior. The successful training of these “black boxes” depends

on theirarchitecture(i.e.,number of neurons and layers).The

lack of general rulesfor constructionmeans that findingthe

rightconfigurationofa neuralnetwork requiresmuch empirical

testing.

In designing UPES, we chose another way to simulate the

diagnostic identification of marker patterns as an important part

of the expert’s considerations. Geometric distance classification

allows the system to recognize and separate quantitative multi-

dimensional patterns [14]. Implemented in the flexible software

tool GEODICLA 113], thisclassificationmethod can describe

and separatecomplex clustersin terms of spheroidsand ellip-

soidswith straightand obliqueaxes.Informationcontainedina

trainingsetisspecificallyintegratedand generalizedin a rapid

automated learningprocess.In contrasttomost neuralnetworks

and statisticalclassificationmethods, the resultingrepresenta-

tivesalwaysguaranteea 100% reclassificationratewhen cbassi-

fying the training collective. Multiple features such as mathe-

matical preprocessing, several learning modes, and differentways of distance calculation help influence the self-organizing

process and optimize its results. The geometric figures and their

parameters can easily be adjusted and extended. A simple localmodification of the geometric knowledge base, e.g., if a pattern

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isnot yet correctlyclassifiedor isnot classifiedatall,adds new

knowledge to a classification system. Updates of the knowledge

base are thus facilitated.

Geometric distance classification enables UPES to make

robust and nonparamen-ic pattern recognitions. Further, the

diagnostic classification can be elucidated by showing the cir-

cles/ellipses on the screen together with a symbol representing

the patient’spattern.Thus, a user does not have to acceptthe

UPES interpretation of the marker proteins as if it were a Greek

oracle.

The quality of a decision-supporting system for daily routine

assessmentsuch as UPES depends on easy and comfortableuse

of the system as well as the knowledge integrated being highly

accurate and sufficiently extensive. Because widespread use of

the system depends on its acceptanceby users,considerationsof

comfort and safety have played a major role in its development.

The complete integration of the knowledge-based system in the

computer network structure of our laboratoryas webb as the

automated data import and export minimizes errors during data

transfer and contributes greatly to the comfortable and prob-

lem-free use of UPES in daily routine. Use of programming

language C guaranteesthatmedical evaluationof the dataisnot

time consuming: UPES takes2 s to compose the reports from a

filecontaining data for 30 patients(the estimated average

number of dailyrequests),using a Model 486 PC (33 MHz).

Actually, >90% of the reports created by UPES are not

modified. In the remaining cases, additional clinical information

(e.g.,known renaltransplant)isconsidered.

Apart from these practicalaspects,the credibilityand reli-

ability of a decisionby implemented knowledge are an essential

condition for the widespread use ofan expertsystem,especially

in medical fields.Evaluationwith the validationsetconfirmed

thatinterpretationof urine proteindifferentiationisa complex

and difficult task sufficiently solved by UPES. Moreover, the

evaluation results provided evidence that even experts can learn

from a continually growing knowledge base of an expert system.

Given that gold standards have yet to be defined for many of the

observed protein patterns (e.g., “dysfunction”), future prospec-

tive studies may help improve the predictive qualities of the

system. Consideration of additional clinical information, imple-

mentation of other urine results (e.g., microscopy), and exten-

sion to previous urine protein patternsare currentlyunder

development.

We conclude that urine protein differentiationin itspresent

form issuperior to traditional urine analysis as a mirror of renal

function [32] and isa valuableadditionto the morphological

information provided by histopathobogy and medical imaging.

Use of the decision-supportingsystem UPES for medical as-

sessment of urine proteindifferentiationprovidesa standardof

high and constant quality. A graduated and transparent decision

process is implemented in a hybrid knowledge base that uses

both production rules and geometric distance classification as

complementary methods of knowledge representation.In the

hands of a responsible physician, UPES can be a useful tool for

increasing the efficiencyand qualityof a laboratory.

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5. Petersen A, Evrin PE, Berggard I. Differentiation of glomerular,tubular and normal proteinuria: determinations of urinary excretionof $32-microglobulin, albumin and total protein. J Clin Invest1969;48:1189-98.

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9. Hofmann W, Schmidt 0, Guder WG, Edel H. Differentiation ofhematuria by quantitative determination of urinary marker pro-teins. Kim Wochenschr 1991;69:68-75.

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11. Hofmann W, Sedlmeir-Hofmanrr C, lvandi M, Schmidt 0, GuderWG, Edel H. Assessment of urinary.protein patterns on the basis

of clinically characterized patients. Typical examples with reports.Lab Med 1993;17:502-12.

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Befundungssystems zur Urineiwei8differenzierung [Dissertationl.M#{252}nchen:Ludwig-Maximilians-Universitat, 1995.

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tion of Bence-Jones proteinuria by automated nephelometricscreening. J dIm Chem Clin Biochem 1990;28:37-42.

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23. Bepperling C, Hehrmann R, Haas H, Hotz G, Olbricht T, Schmidt R,et al. A knowledge-based system for the interpretationofthyroidhormone measurements: evaluation and optimisation of thesystem Pro.M.D.-SD in five clinical laboratories. Lab Med 1994;18:564-71.

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Appendix: Text Elements Selected by UPESto Compose aLahOFatOIY Report

1. Based on the serum findings,a major decrease of

glomerular filtration rate is not likely.

2. The glomei-ular filtration rate is reduced.

3.An isolatedincreaseof both serum valuesincombination

with normal urine protein excretion might reflect a boss of

functioning nephrons. The protein reabsorption is fully com-

pensated by the remaining nephrons. An active renal disease is

unlikely.

4.To excludethe possibilityof a reduced GFR, gbomerular

clearanceshould be investigated.

5. The isolatedincreaseof serum creatininemight indicate

so-called pseudocreatinines.Alternatively,increased muscle

mass or a meat diet may be involved.

6. Discrepancy between the sum of albumin, IgG, and

a, -microglobubin and the concentration of total protein (single

proteins/total protein <0.3) in combination with a positive

test-strip result for blood indicates a prerenab hematuria. Defi-nite report follows after additional tests to exclude myogbobin-

uriaor hemogbobinuria.

7. Differentiationof renal and postrenal hematuria by

proteinanalysisisimpossibleat albumin concentrations<100

mgfL. Phase-contrast microscopy of a fresh morning urine may

allow the differentiation of renal and postrenal causes of hema-

tuna (acanthocytes?).

8. Most likely,postrenab hematuria is present.Because

additionalrenal excretionof proteinscannot be excluded,a

control after disappearance of hematuria issuggested.

9.Most likely,renal(glomerular/tububo-interstitial)hema-

tuna ispresent.A slightadditionalpostrenalsource of erythro-

cytes cannot be excluded.

10. The detection of beukocyte esterase may indicate a

possible postrenal inflammation or contamination of the urine

with leukocytes.

11.The urineproteindifferentiationshouldbe repeatedafter

leukocyturia has stopped, because inflammations in the lower

urinary tract can also cause a slight proteinuria.

12. The detection of leukocyte esterase may indicate inflam-

mation with renal involvement, if there was no leukocyte

contamination during sampling.

13. Analyses of the marker proteinsin the urine do not

indicateany dysfunctionof gbomerular protein filtrationand

tubular reabsorption. No signs of hematuria or granulocyturia

are present.

14.An isolatedincreaseof IgG may indicate,e.g.,monocbo-

nab gammopathies.

15. Discrepancy between the sum of albumin, IgG, and

a, -microglobulin and the concentration of total protein (single

proteins/totalprotein <0.3) indicatesa prerenal proteinuria.

Immunofixation will be performed to exclude Bence Jones

proteinunia.The definitivereportwillfollowafterthisinvesti-

gation.

16.A distinctselectiveglomerular proteinuriawith simulta-

neous slight tubular proteinuria is found.

17. The findings are consistent with impaired gbomerular

permeability or a tububo-interstitial dysfunction (or both). A

slight increase of the marker proteins does not necessarily

indicatea renaldisease.If clinicalcues are missing,a control

measurement made under standardizedconditions(no intense

physical stress before investigation, optimal metabolic and hy-

pertonic equilibrium of diabetic and hypertonic patients) is

recommended.

18. The findingsare consistentwith a tubulo-interstitial

nephropathy/primary glomerubopathy/secondary glomerubopa-

thy.

19.The findingsare consistentwith a primary or secondary

glomerubopathy (e.g., diabetes mebbitus, hypertension).

20. The findings are consistent with either (a) a glomeru-.

bopathy with impaired tubulo-interstitiab reabsorption or (b) an

interstitial nephropathy with secondary glomerulopathy.

21. The IgC excretion indicates a primary glomerulopathy/

secondary glomerubopathy/tubulo-interstitiab nephropathy.

22. The increased excretion of the tubular marker a,-

microglobubinisthe resultof a tubularoverload(exhaustionof

the tubularreabsorptivecapacity).

23. The extent of interstitial fibrosis correlates with the

excretionof the tubularmarker proteina,-microglobubin.

24. The increased excretion of the tubular enzyme (3-NAG

indicates a possible acute disorder of proximal tubular cells.

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development and evaluation of a urine protein - clinical chemistry

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