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Table 4 Comparative summary of the published studies

From: Would artificial neural networks implemented in clinical wards help nephrologists in predicting epoetin responsiveness?

Authors

Number of patients/centres included

Epoetin isoform

Variables explored (inputs)

Predictions (outputs)

Gabutti et al (present study)

432/29

Beta

Sex; age; weight, presence or absence of a diabetes mellitus and/or a cardiomyopathy with EF<50%; haemoglobin; creatinine; BUN; pH; ionized calcium; albumin; CRP; ferritin; PTH; epoetin and iron dose; epoetin administration route sc vs. iv; Kt/V

Epoetin dose and follow-up haemoglobin

Bellazzi [24]

10/1

n.s.

Sex; age; haemoglobin; calcium; PTH; epoetin dose and others non specified

Follow-up haemoglobin

Martin Guerriero et al [20]

110/1

Alpha and beta

Age; weight; haemoglobin; ferritin; epoetin dose; isoform and number of administrations weekly; iron dose

Epoetin dose

Gaweda et al [25]

209/1

n.s.

Haematocrit; albumin; ferritin; iron saturation; PTH; epoetin and iron dose; Kt/V

Follow-up haematocrit

Jacobs et al [26]

166/n.s.

n.s.

Haematocrit; albumin; ferritin; iron saturation; PTH; epoetin and iron dose; Kt/V

Follow-up haematocrit

  1. n.s.: not stated
  2. Comparative table summarizing the characteristics of the studies already published predicting the epoetin dose and/or the follow-up haemoglobin/haematocrit with ANNs.