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Table 3 Model adjustment and performance

From: Performance of critical care prognostic scoring systems in low and middle-income countries: a systematic review

Study Type of adjustment and changes made Discrimination (original scoring system) Discrimination(after adjustment) Calibration (original scoring system) Calibration (after adjustment)
APACHE II
 Khwannimit and Bhurayanontachai (2011) [51] Recalibration (first-level customisation): customised APACHE II logit = –7.7206 + (APACHE II score × 0.2013) + new diagnostic category weight (Appendix I [51]) 0.936 (0.925–0.947) (entire population n = 2022) 0.936 (0.925–0.947) (validation dataset n = 1011) C statistic χ2 = 104.2 (p = 0.001), H statistic
χ2 = 113.1 (p < 0.001)
C statistic χ2 = 16.1 (p = 0.09),
H statistic χ2 = 14.1 (p = 0.17)
 Eapen et al. (1997) [30] Variable adjustment: GCS excluded Not evaluated 0.6068 Not reported Not reported
 Hashmi et al. (2016) [77] Modelling technique adjustments: APACHE II calculated automatically by software which uses manually entered values using the logit equation = –4.063 + (APACHE II) × 0.181 0.823 (0.76–0.88)
(manual calculation)
0.827 (0.77–0.88)
(software calculation)
χ2 = 11.76 (p = 0.16) χ2 = 5.46 (p = 0.71)
 Nimgaonkar et al. (2004) [35] Modelling technique adjustments: Artificial Neural Network (ANN 22) model
trained on an Indian patient dataset using all 22 APACHE II variables
0.77 0.87 (p < 0.002) H statistic
χ2 = 123.5 (p < 0.05)
H statistic χ2 = 22.4 (p < 0.05)
 Nimgaonkar et al. (2004) [35] Modelling technique adjustments: Artificial Neural Network (ANN 15) model trained on an Indian patient dataset using 15 APACHE II variables 0.77 0.88 (p < 0.001)
ANN 15
H statistic
χ2 = 123.5 (p < 0.05)
H statistic
χ2 = 27.7 (p < 0.05)
SAPS II
 Khwannimit and Bhurayanontachai (2011) [51] Recalibration (first-level customisation): customised SAPS II logit = –10.1779 + 0.0719 (SAPS II score) + 1.4891 × ln(SAPS II score + 1) 0.914 (0.901–0.928) (entire population n = 2022) 0.919 (0.900–0.938) (validation dataset n = 1011) C statistic
χ2 = 124.9 (p < 0.001),
H statistic
χ2 = 97.5 (p < 0.001)
C statistic
χ2 = 8.6 (p = 0.57),
H statistic
χ2 = 9.6 (p = 0.48)
 Zhao et al. (2013) [50] Variable adjustment: 1. Underlying disease variables excluded
2. Admission type variables excluded
0.776 (95% CI 0.750–0.802) at admission, 0.826 (95% CI 0.803–0.850) at 24 hours Not reported: correlation was suggested between the simplified SAPS II score at each time point and outcome with OR of 1.109 (p = 0.000), regardless of the diagnosis Not reported Not reported
SAPS 3
 Khwannimit and Bhurayanontachai (2011) [51] Recalibration (first-level customisation): customised SAPS 3 logit = –33.4249 + ln(SAPS 3 score +1) × 7.8699 0.913 (0.899–0.924) (entire population n = 2022) 0.917 (0.897–0.937)
(validation dataset n = 1011)
C statistic
χ2 = 170 (p < 0.001),
H statistic
χ2 = 79.9 (p < 0.001)
C statistic
χ2 = 8.2 (p = 0.61),
H statistic
χ2 = 79.9 (p < 0.001)
 Riviello et al. (2016) [43] MPM (0) III Exclusion of two patients (0.5%) due to lack of discharge vital status
Normal values attribution details provided in Supplementary Table 3 of the original paper. Highest proportions of missing values were for GCS (36.30%) followed by chronic renal compromise/insufficiency (7.96%)
  1. APACHE Acute Physiology and Chronic Health Evaluation, SAPS Simplified Acute Physiology Score, MPM Mortality Probability Models, GCS Glasgow Coma Score, ICU intensive care unit, CI confidence interval, OR odds ratio