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Cited by 61Renal Diseases and GlomerulopathiesArtificial Intelligence in HealthcareChronic Kidney Disease and Diabetes

An artificial neural network can select patients at high risk of developing progressive IgA nephropathy more accurately than experienced nephrologists

Geddes, C. C., Fox, J. G., Allison, M. E. M., Boulton-Jones, J. M., Simpson, K.·1998·Nephrology Dialysis Transplantation
DOI 10.1093/ndt/13.1.67
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Abstract

BACKGROUND: The object of the study was to develop an artificial neural network (ANN) to identify patients with IgA nephropathy (IgAN) with a poor prognosis and to compare the predictions of the ANN with the predictions of six experienced nephrologists. METHODS: The following data from the time of renal biopsy were retrieved from the records of 54 patients with IgAN: age, sex, systolic and diastolic blood pressure, number of prescribed antihypertensive drugs, 24-h urine protein excretion, and serum creatinine. Patients aged less than 14 years, or who had serum creatinine > 350 mumol/l at presentation, liver disease or concomitant kidney disease were excluded. Outcome was assigned as 'stable' if serum creatinine was < 150 mumol/l after 7 years and 'non-stable' if serum creatinine was > or = 150 mumol/l. The ANN was trained and tested using a 'jack-knife' sampling technique and performance evaluated in terms of number of correct predictions, sensitivity and specificity. The six nephrologists were asked to predict outcome at 7 years for each patient using the same data as the ANN and their performance was assessed in the same manner. RESULTS: The ANN assigned the correct outcome to 47/54 (87.0%) patients: sensitivity 19/22 (86.4%), specificity 28/32 (87.5%). The mean score for nephrologists was 37.5/54 (69.4%, range 35-40), mean sensitivity 72% and mean specificity 66%. CONCLUSIONS: An ANN trained using routine clinical information obtained at the time of diagnosis can potentially predict 7-year outcome for renal function in IgAN more accurately than experienced nephrologists, and can therefore identify a group of high-risk patients requiring close follow-up.

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  1. [6]6. Floyd CE, Lo JY, Yun AJ, Sullivan DC, Kornguth PJ. Prediction of breast cancer malignancy using an artificial neural network. Cancer 1994; 74: 2944-2948

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Authors
Geddes, C. C., Fox, J. G., Allison, M. E. M., Boulton-Jones, J. M., & Simpson, K.
Affiliations
  • Stobhill Hospital (GB)
  • Glasgow Royal Infirmary (GB)
Corresponding author
Geddes, C. C.
ORCID
Geddes, C. C.
Venue
Nephrology Dialysis Transplantation
Publisher
Oxford University Press (OUP)
Published
1998-01-01
Volume
13
Issue
1
Pages
67-71
ISSN
  • 0931-0509 (ISSN-L)
  • 1460-2385
DOI
10.1093/ndt/13.1.67
OpenAlex
W2140166463
Cited by
61
References
8
Citations by year
2026: 3 · 2025: 5 · 2024: 4 · 2023: 6 · 2022: 3 · 2021: 5 · 2020: 3 · 2019: 3 · 2017: 2 · 2016: 1 · 2015: 3 · 2014: 1 · 2013: 1 · 2012: 2
FWCI
0.56
Type
journal article
Language
en
Open access
closed
Locations
  • Nephrology Dialysis Transplantation (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Nephrology › Renal Diseases and Glomerulopathies
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
Keywords
IgA nephropathy, artificial neural networks, outcome prediction, renal function decline, serum creatinine, high-risk patient identification, clinical risk factors, urinary protein excretion, antihypertensive drugs
MeSH terms
Adolescent, Adult, Aged, Female, Glomerulonephritis, IGA, Humans, Male, Middle Aged, Nephrology, Sensitivity and Specificity, Neural Networks, Computer

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