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. 2022 Mar 16;90(6):691–699. doi: 10.1227/neu.0000000000001895

TABLE 4.

Performance of the Quantitative GLM Model vs the CHIIDA Model in Testing Data set

Testing data set (n = 374) GLM RP KIIDS-TBI
<1 <3 <5 <1 <3 <5 <1 <3 <5
High acuity disposition (%)
  Composite outcome 26 25 24 24 24 24 27 26 25
  No composite outcome 85 43 36 73 73 46 255 114 62
Low acuity disposition
  Composite outcome 1 2 3 3 3 3 0 1 2
  No composite outcome 262 304 311 274 274 301 92 233 285
Sensitivity (95% CI) 0.96 (0.81-1.0) 0.93 (0.76-0.99) 0.89 (0.71-0.98) 0.89 (0.71-0.98) 0.89 (0.71-0.98) 0.89 (0.71-0.98) 1.0 (0.87-1.0) 0.96 (0.81-1.0) 0.93 (0.76-0.99)
Specificity (95% CI) 0.76 (0.71-0.80) 0.88 (0.84-0.91) 0.90 (0.86-0.93) 0.79 (0.74-0.83) 0.79 (0.74-0.83) 0.87 (0.83-0.90) 0.27 (0.22-0.31) 0.67 (0.62-0.72) 0.82 (0.78-0.86)
PPV (95% CI) 0.23 (0.16-0.32) 0.37 (0.25-0.49) 0.40 (0.28-0.53) 0.25 (0.17-0.35) 0.25 (0.17-0.35) 0.34 (0.23-0.47) 0.10 (0.06-0.14) 0.19 (0.13-0.26) 0.29 (0.20-0.39)
NPV (95% CI) 0.996 (0.98-1.0) 0.99 (0.98-1.0) 0.99 (0.97-1.0) 0.99 (0.97-1.0) 0.99 (0.97-1.0) 0.99 (0.97-1.0) 1.0 (0.96-1.0) 0.996 (0.98-1.0) 0.99 (0.98-1.0)

CHIIDA, Children's Intracranial Injury Decision Aid; GLM, generalized linear modeling; KIIDS-TBI, kids intracranial injury decision support tool for traumatic brain injury; NPV, negative predictive value; PPV, positive predictive value; RP, recursive partitioning.