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Table 2 Baseline characteristics and clinical/laboratory parameters in the training and testing cohorts

From: Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study

Variable Training cohorts (n = 236) Testing cohorts (n = 60) P value
Sex (male) 167 (70.8%) 36 (60%) 0.121
Age (year) 65.23 ± 18.02 66.03 ± 18.68 0.761
Minimum respiratory rate 23.14 ± 4.75 23.62 ± 5.08 0.494
Maximum respiratory rate 32.18 ± 6.08 31.65 ± 6.25 0.551
Minimum haematocrit 29.81 ± 6.62 30.52 ± 6.72 0.461
Minimum systolic pressure 99.40 ± 20.47 91.82 ± 19.50 0.010*
Minimum MAP 75.33 ± 60.14 67.25 ± 15.10 0.303
Maximum heart rate 122.33 ± 21.22 126.18 ± 21.37 0.211
Minimum glucose 135.00 ± 92.24 124.03 ± 54.68 0.378
Minimum white blood cell count 11.90 ± 6.24 12.88 ± 7.29 0.295
Minimum heart rate 90.00 ± 17.57 93.88 ± 17.85 0.129
Minimum temperature 36.70 ± 0.88 36.66 ± 0.84 0.738
Minimum sodium 140.06 ± 6.35 139.87 ± 6.82 0.834
APACHE II 19.96 ± 5.81 20.78 ± 5.51 0.322
PH 7.38 ± 0.10 7.37 ± 0.10 0.283
Bacteraemia 4 (1.69%) 3 (5%) 0.150
Diabetes 42 (17.8%) 17 (28.3%) 0.073
Tobacco 85 (36.0%) 24 (40%) 0.653
  1. The binary variables are described as counts and percentages and were evaluated by the Chi-squared test or Fisher’s exact test. Continuous variables of each group are presented as the mean ± SEM. Student’s t-test was used to compare the normally distributed continuous variables. *P < 0.05, ARDS compared with non-ARDS
  2. MAP mean arterial pressure, APACHE II Acute Physiology and Chronic Health Evaluation II