title: Predicting cardiovascular risk using creatinine clearance and an artificial neural network in primary hypertension.
abstract: OBJECTIVE
  A slight reduction in estimated creatinine clearance is a predictor of unfavorable outcome in patients with primary hypertension. We evaluated how well an artificial neural network (ANN) can assess cardiovascular risk profile on the basis of estimated creatinine clearance and routine, low-cost clinical data, as compared with thorough clinical work-up, which includes an accurate assessment of target organ damage.
  
  
  METHODS
  A group of 404 untreated patients with essential hypertension (250 men, 154 women; mean age, 47 +/- 9 years) were studied. We compared two different approaches that can be used to allocate patients into different risk classes according to the European Society of Hypertension-European Society of Cardiology guidelines: thorough clinical work-up, including cardiac and vascular ultrasound scan and microalbuminuria; and prediction by an ANN on the basis of estimated creatinine clearance and routine clinical data.
  
  
  RESULTS
  Thorough evaluation, as recommended by the guidelines, showed that 6% (n = 24) of our patients were at low risk, 20% (n = 81) were at medium risk, 45% (n = 182) were at high risk, and 29% (n = 117) were at very high risk. The ANN approach yielded almost superimposable results (sensitivity, 94%; positive predictive value, 96%; r = 0.95).
  
  
  CONCLUSIONS
  An ANN can accurately identify the patient's risk status using low-cost, clinical data and estimated creatinine clearance. These results emphasize the value of even a mild reduction in creatinine clearance for the stratification of cardiovascular risk in primary hypertension.
authors.affiliation: Department of Internal Medicine and Cardionephrology, Azienda Universitaria Ospedale San Martino, Italy.
authors.forename: Francesca
authors.initials: F
authors.lastname: Viazzi
authors.forename: Giovanna
authors.initials: G
authors.lastname: Leoncini
authors.forename: Giorgio
authors.initials: G
authors.lastname: Sacchi
authors.forename: Denise
authors.initials: D
authors.lastname: Parodi
authors.forename: Elena
authors.initials: E
authors.lastname: Ratto
authors.forename: Valeria
authors.initials: V
authors.lastname: Falqui
authors.forename: Angelica
authors.initials: A
authors.lastname: Parodi
authors.forename: Valentina
authors.initials: V
authors.lastname: Vaccaro
authors.forename: Cinzia
authors.initials: C
authors.lastname: Tomolillo
authors.forename: Giacomo
authors.initials: G
authors.lastname: Deferrari
authors.forename: Roberto
authors.initials: R
authors.lastname: Pontremoli
mesh_terms: D000328:Adult; D015415:Biomarkers / Q000378:metabolism; D002318:Cardiovascular Diseases / Q000175:diagnosis / Q000209:etiology*; D003401:Creatine / Q000097:blood; D003404:Creatinine / Q000378:metabolism*; D005260:Female; D005919:Glomerular Filtration Rate / Q000502:physiology; D006801:Humans; D006973:Hypertension / Q000150:complications* / Q000378:metabolism; D007677:Kidney Function Tests; D008297:Male; D008403:Mass Screening / Q000379:methods; D008875:Middle Aged; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity
chemical_list: D015415:Biomarkers; D003404:Creatinine; D003401:Creatine