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Table 1 Logistic regression models for NETS and SENSS from derivation study

From: External validation of inpatient neonatal mortality prediction models in high-mortality settings

SENSS:

 Linear predictor (LPSENSS) =  − 3.8583 + 5.7580 * ELBW + 3.7082 * VLBW + 0.9232 * LBW − 0.4918 * macrosomia − 0.1336 * Male + 1.3596 * difficulty feeding + 1.3977 * convulsion + 1.9790 * indrawing + 0.9584 * cyanosis + 1.6266 * floppy unable to suck

NETS:

 Linear predicator (LPNETS) =  − 4.1521 + 5.6836 * ELBW + 4.5359 * VLBW + 1.4186 * LBW − 0.2927 * macrosomia − 0.3125 * male + 1.3695 * antibiotics + 1.3256 * fluids − 1.9135 * feeds + 0.6142 * oxygen + 2.5947 * phenobarbital

  1. For each variable, the presence of the indicator takes a value of 1, and the absence takes a value of 0. The coefficients are summated to give the linear predictor, which is then converted to the predicted probability of in-hospital mortality [13]
  2. ELBW Extremely low birth weight, LBW Low birth weight, LP Linear predictor, NETS Neonatal Essential Treatment Score, SENSS Score of Essential Neonatal Symptoms and Signs, VLBW Very low birth weight
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BMC Medicine

ISSN: 1741-7015

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