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 Emerging Infectious Diseases * Volume 3 * Number 1 * January-March 1997
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Dispatches
Knowledge-Based Patient Screening for Rare and Emerging
Infectious/Parasitic Diseases: A Case Study of Brucellosis and Murine
Typhus
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      Many infectious and parasitic diseases, especially those newly
      emerging or reemerging, present a difficult diagnostic
      challenge because of their obscurity and low incidence.
      Important clues that could lead to an initial diagnosis are
      often overlooked, misinterpreted, not linked to a disease, or
      disregarded. We constructed a computer-based decision support
      system containing 223 infectious and parasitic diseases and
      used it to conduct a historical intervention study based on
      field investigation records of 200 cases of human brucellosis
      and 96 cases of murine typhus that occurred in Texas from 1980
      through 1989. Knowledge-based screening showed that the
      average number of days from the initial patient visit to the
      time of correct diagnosis was significantly reduced
      (brucellosisfrom 17.9 to 4.5 days, p = 0.0001, murine
      typhusfrom 11.5 to 8.6 days, p = 0.001). This study
      demonstrates the potential value of knowledge-based patient
      screening for rare infectious and parasitic diseases.
Knowledge-based medical decision support systems allow the user to relate
clinical and laboratory manifestations from which a list of possible
diagnoses can be generated. Studies have demonstrated that such a list is
extremely useful for alerting clinicians and epidemiologists to diseases
seen rarely, if ever, and to unusual clinical manifestations of diseases
(1,2).
This report describes the development and laboratory testing of a medical
decision support system designed to facilitate early recognition of rare
and unusual infectious and parasitic diseases. The assumption is made that
early diagnosis can improve the course of treatment as well as the overall
health of the patient.
              Table 1. Knowledge base of 223 diseases at the
              time of case study
              ------------------------------------------------
                      Information            Disease   Lines
                           axis             entities  of text
              ------------------------------------------------
              Etiology                     410
              Epidemiology(a)                         9724
              Immunodeficiency info                   352
              Diagnostic procedures        306
              Diagnostic procedure         637
              Occupational links(b)        37
              Geographic links
              (countries)(c)               191
              Vectors/Invertebrate hosts   703
              Symptoms/Clinical signs      1422
              Treatment/Prevention                    3162
              Special considerations(d)               308
              Bibliographic citations      838
              Descriptive notes(e)         322
              ------------------------------------------------
              (a)Includes specific etiologic information,
              reservoirs, portal of entry/escape, mode of
              transmission/dissemination, susceptible hosts,
              incubation period, seasonal distribution,
              age/sex/race/religion/occupation relationships,
              mortality and zoonotic classification.
              (b)Lists occupations with increased potential for
              exposure.
              (c)Lists countries where the disease is known to
              occur.
              (d)Describes additional disease attributes not
              fitting other categories.
              (e)Pop-up definitions of medical terms and
              concepts in the knowledge base.
Constructing a Knowledge Base
In this study, the knowledge base was restricted to the 223 diseases common
to humans and animals. Several disease information axes were identified for
inclusion in the medical knowledge base. For each disease, nomenclature
along with any recognized synonyms, etiology, epidemiologic descriptions,
diagnostic procedures, diagnostic procedure findings, and clinical signs
and symptoms were gathered and abstracted from recent editions of standard
medical texts. In addition, occupational risks, food consumption data,
travel history, and animal/insect exposure were included. Finally, basic
treatment, management, and prevention protocols were also recorded (3-10).
Bibliographic citations for all entries into the knowledge base were tied
to each information axis to authenticate the content and provide a quick
reference for additional reading. Glossaries of knowledge base entries
(e.g., signs, symptoms, occupation, diagnostic procedure results,
geography, animal/insect exposure) to be used in generating a differential
diagnosis were created to ensure the consistency of the lexicon. Audits
were performed to validate the knowledge base entries against the
glossaries. The resulting knowledge base contains the entries listed in
Table 1. A batch procedure indexing all findings to diseases for use by the
inference engine resulted in more than 12,000 finding/disease links. The
inference engine is the portion of the decision support system used to
derive useful clinical information (e.g., possible diagnoses). It is
composed of nine program modules containing more than 10,000 lines of C
language code linked into one executable program.
Differential Diagnosis Methods
The goal of the system is to remind the user of relationships between
clinical signs, symptoms, and findings and the diseases themselves.
Therefore, a simple, intuitive pattern-matching technique was employed. The
user sets a "closeness-of-fit" parameter in the system that determines how
tight the relationship between case signs/findings and diseases must be
before a disease can become a candidate for the differential diagnosis
list. A simple ratio is created with signs/findings entered that fit each
disease as the numerator and the total number of signs/findings entered as
the denominator. The ratio ranges from zero to 1.00 with 1.00 implying a
perfect fit. If this value is equal to or greater than the
"closeness-of-fit" parameter set by the user (also ranging from zero to
1.00), the disease is entered in the differential diagnosis list as a
candidate. The ratio for each disease shows how close the clinical
manifestations are to previously published descriptions of the disease. For
example, if seven clinical signs and findings are collected on a case and
six fit a particular disease by the pattern-matching algorithm, the case
fit would be 0.86. If the case fit parameter in the system is set to 0.86
or greater, that disease would be added to the differential diagnosis list.
If the case fit parameter is set to 1.00, only diseases that fit the
clinical manifestations perfectly would become differential diagnosis
candidates. Signs and findings are not weighted by the system.
Decision Support System Trial on Murine Typhus Cases
The purpose of this trial was to determine if the intervention of the
support system at the time of the first physician visit would significantly
alter the mean number of days to the correct diagnosis. Investigation
records of 96 cases of murine typhus occurring in Texas from 1979 to 1987
were obtained from the Texas Department of Health. All records were
complete and were used in the study. Each record contained the date of the
first contact with a physician (dp). The date on which the indirect
fluorescence antibody test for Rickettsia typhi was ordered was defined as
the date the correct diagnosis was suspected (ds). Therefore, the number of
days from the first contact with a physician (dp) to the correct diagnosis
without decision support system intervention (dc (subscript ni)) was
defined as follows: dc (subscript ni) = ds - dp.
The number of days till the correct diagnosis was suspected (dc (subscript
ni)) was calculated for all cases without support system intervention. A
case was classified as missed if dc (subscript ni) >13 days. This number
was derived from the worst-case scenario for a murine typhus test
turnaround from the Texas Department of Health. During 1979-1987, the
department had the only laboratory in Texas that used the indirect
fluorescence antibody test for confirming murine typhus. On the basis of
this criterion, 23 of the 96 original cases fell into the missed category.
The clinical history, symptoms, physical findings, and some preliminary
laboratory findings as noted in the investigation records for the 23 missed
patients were entered in the support system and processed at case fit
parameter values of 0.25, 0.50, 0.75, and 1.00. Specific historical data
such as occupation, travel, and animal/insect exposure, if available, were
also entered in the support system. The support system was said to have
suggested the correct diagnosis of murine typhus if the disease was
included in a differential diagnosis list of five or fewer diseases at a
case fit parameter of 0.50 or greater.
Testing for murine typhus was assumed to be ordered immediately if the
disease was suggested by the support system. This effectively set the
number of days to suspect the correct diagnosis with intervention (dc
(subscript wi)) to 1 day under these conditions.
The distributions of dc (subscript ni) and dc (subscript wi) were found to
be Gaussian (PROC UNIVARIATE, SAS). Therefore, parametric statistical
techniques were used for data analysis. A paired t-test was run on
(subscript ni) and (subscript wi) (mean time to suspecting murine typhus
with and without the system, respectively) to see if there was a
significant difference in the means (PROC MEANS, SAS) (14).
In 11 (48%) of the 23 cases defined as missed, murine typhus was clearly
suggested by the support system in a differential list of five or fewer
possibilities by using only clinical history, signs, symptoms, and
preliminary laboratory data if available. In six (26%) of these cases,
murine typhus was the only disease suggested by the support system. The
support system did not suggest the correct disease in 12 (52%) of the cases
classified as missed. Table 2 lists the most common diseases that also
appeared in the differential diagnosis lists for murine typhus cases.
         Table 2. Other diseases commonly listed in the differential
        diagnosis of murine typhus
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        1. Lyme borreliosis            7. Relapsing fever
        2. Tularemia                   8. Avian chlamydiosis
        3. Brucellosis                 9. Leptospirosis
        4. Zoonotic epidemic typhus    10. Salmonellosis
        5. Rocky Mountain              11. Babesiosis
             spotted fever             12. Malaria
        6. Rat bite fever              13. Q fever
        ------------------------------------------------------------
The mean number of days to suspect the correct diagnosis without support
system intervention ((subscript wi)) was 11.5 days. The mean number of
days to suspect the correct diagnosis with support system intervention
((subscript wi)) was 8.6 days, an improvement of 2.9 days (p = 0.001).
Decision Support System Trial on Brucellosis Cases
Investigation records of 342 cases of brucellosis occurring in Texas from
1980 to 1989 were also obtained from the Texas Department of Health. Of
these records, 13 were incomplete, and 129 involved cases of recrudescence
of the disease or had no specific date of onset of illness and were
excluded from the study. The remaining 200 records contained the date of
onset of illness. However, the day of the first physician visit was not
available and had to be extrapolated from the day of onset of illness. The
average number of days from onset of illness to the first physician visit
was assumed to be 4 days, on the basis of a study in Texas for patients
with murine typhus (12).
The day that the correct diagnosis was suspected was defined as the day on
which the serum agglutination test or a bacterial culture for brucellosis
was ordered. The number of days from the first contact with a physician to
the day the correct diagnosis was suspected without support system
intervention (dc (subscript ni)) was calculated as indicated above.
A case was classified as missed if dc (subscript ni) >11 days. This number
was derived from the worst-case scenario for a brucellosis test turnaround
from the Texas Department of Health. By this liberal criterion, 98 of the
200 original cases still fell into the missed category.
The clinical history, symptoms, physical findings, and some preliminary
laboratory findings as noted in the investigation records for the 98 missed
patients were entered in the support system and processed at case fit
parameter values of 0.25, 0.50, 0.75, and 1.00. Specific historical data
such as occupation, travel, and animal/insect exposure, if available, were
also entered in the support system. By definition, the system gave the
correct diagnosis of brucellosis if the disease was included in a
differential diagnosis list of five or fewer diseases at a case fit
parameter of 0.50 or greater.
It was assumed that the testing for brucellosis would be ordered
immediately if the disease was suggested by the support system. This
effectively sets the number of days to suspect the correct diagnosis with
intervention (dc (subscript wi)) to 1 day under these conditions. The
distributions of dc (subscript ni) and dc (subscript wi) were also Gaussian
and analyzed by paired t-tests on (subscript ni) and (subscript wi) (14).
In 86 (88%) of the 98 cases defined as missed, brucellosis was clearly
suggested by the support system in a differential list of five or fewer
possibilities by using only clinical history, signs, symptoms, and
preliminary laboratory data if available. In 69 (70%) of the cases,
brucellosis was the only disease suggested. The support system did not
suggest the correct disease in only 12 (12%) of the cases classified as
missed. Table 3 lists the most common diseases that also appeared in the
differential diagnosis of brucellosis.
               Table 3. Other diseases commonly listed in
               the differential diagnosis of brucellosis
               ---------------------------------------------
               1. Toxoplasmosis       6. Rat bite fever
               2. Q fever             7. Listeriosis
               3. Viral hepatitis A   8. Blastomycosis
               4. Salmonellosis       9. Trichinellosis
               5. Histoplasmosis      10. Lyme borreliosis
               ---------------------------------------------
The mean number of days to suspect the correct diagnosis without support
system intervention ((subscript wi)) was 17.9 days. The mean number of
days to suspect the correct diagnosis with support system intervention was
4.5 days, an improvement of 12.9 days (p = 0.0001). However, data were
derived by using an extrapolated day of first physician visit, and that may
have affected these comparisons.
The brucellosis investigation records screened showed that of nine cases,
three involved apparently healthy pregnancies, one a premature delivery,
one a miscarriage, one seizures, one weakness and chronic headaches, and
two deaths. In eight of the nine cases, the support system correctly
suggested brucellosis. In addition, 59 (30%) of the patients with
brucellosis were hospitalized, some for extended periods during the
diagnostic phase.
Twenty-one of 23 murine typhus patients were hospitalized, some for
extended periods during the diagnostic phase of the case. Furthermore, many
patients had to undergo expensive diagnostic testing, including 19
electrocardiograms, four computer-aided tomography scans, and a various
other procedures such as bone scans, liver scans, barium enemas, and renal
and pelvic sonograms. Earlier diagnoses of brucellosis and murine typhus
could have eliminated the need for many of the diagnostic procedures,
shortened hospital stays, and improved the course of treatment.
On the average, it took approximately 3 minutes of interaction with the
decision support system to construct a differential diagnosis. Therefore,
this type of screening could easily become part of the routine
history-taking and physical exam of a patient. Furthermore, this study
underlines the discriminatory power of the clinical history and physical
signs and symptoms in suspecting the presence of a certain disease in a
patient. Factors such as the patient's occupation, travel history,
animal/insect exposure, and unusual dietary habits can be especially
important in helping to diagnose many rare infectious and parasitic
illnesses.
The results of this evaluation indicate that for two rare diseases
(brucellosis and murine typhus), the decision support system appears to
perform well with high sensitivity) in suggesting the correct diagnosis in
a patient. However, the specificity of the system has not been evaluated. A
prospective double-blinded study in a general clinical or hospital setting
would make that determination.
Craig N. Carter,* Norman C. Ronald, James H. Steele, Ed Young, Jeffery
P. Taylor, Leon H. Russell, Jr.,* A. K. Eugster,* Joe E. West*
*Texas A&M University, College Station, Texas, USA; Texas Medical
Informatics, Inc., College Station, Texas, USA; University of Texas School
of Public Health, Houston, Texas, USA; Veteran Affairs Medical Center,
Houston, Texas, USA; and Texas Department of Health, Austin, Texas, USA
References
  1. Elkin PL, Barnett GO, Famigletti KT, Kim RJ. Closing the loop on
     diagnostic decision support systems. Proc Annu Symp Comput Appl Med
     Care, 1990;589-93.
  2. Johnston ME, Langton KB, Haynes RB, Mathieu A. Effects of
     computer-based clinical decision support systems on clinician
     performance and patient outcome. Yearbook of Medical Informatics
     1995:486-93.
  3. Acha PN, Szyfres B. Zoonoses and communicable diseases common to
     animals and man. 2nd ed. Washington (DC): Pan American Health
     Organization, 1987.
  4. Steele JH. CRC handbook series in zoonoses. 1st ed. Boca Raton (FL):
     CRC Press, 1981.
  5. Young EJ. Human brucellosis. Review of Infectious Diseases
     1983;5:821-41.
  6. Benenson AS. Control of communicable diseases in man. 15th ed.
     Washington (DC): American Public Health Association, 1990.
  7. Mandell GL, Douglas RG, Bennett JE. Principles and practice of
     infectious diseases. 3rd ed. New York: Churchill Livingstone, 1990.
  8. Greene CE. Infectious diseases of the dog and cat. 1st ed.
     Philadelphia: Saunders, 1990.
  9. Blood DC, Radostits OM. Veterinary medicine. 7th ed. London: Billiere
     Tindall, 1989.
 10. Smith BP. Large animal internal medicine. 1st ed. St. Louis: Mosby,
     1990.
 11. Hughes, C. The representation of uncertainty in medical expert
     systems. Med Inf 1989;14:269-79.
 12. Dumler JS, Taylor JP, Walker DH. Clinical and laboratory features of
     murine typhus in Texas, 1980-1987. JAMA 1991;266:1365-70.
 13. Buck C, Donner A. The design of controlled experiments in the
     evaluation of nontherapeutic interventions. Journal of Chronic
     Diseases 1982;35:531-8.
 14. Ott L. An introduction to statistical methods and data analysis 3rd
     ed. Boston: PWS-Kent Publishing, 1988.
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Emerging Infectious Diseases
National Center for Infectious Diseases
Centers for Disease Control and Prevention
Atlanta, GA