# Machine Learning for Early Outcome Prediction in Septic Patients in the Emergency Department
## 3 Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Total**} & \multicolumn{2}{c}{**Outcome**} \\ \hline \multirow{2}{*}{**Characteristic**} & \multicolumn{2}{c}{**Alive**} & \multicolumn{2}{c}{**Dead**} \\ \cline{2-5}  & **N = 425** & **N = 360** & **N = 6** & \(p\)**-Value** \\ \hline White blood cell count & 13 (10, 18) & 13 (10, 18) & 15 (10, 19) & 0.4 \\ Haemoglobin & 12.10 (10.60, 13.80) & 12.30 (10.90, 13.90) & 11.40 (10.00, 12.60) & 0.001 \\ Neutrophiles & 14 (9, 18) & 14 (9, 18) & 13 (8, 18) & 0.5 \\ Platelet count & 204 (141, 260) & 207 (142, 258) & 191 (136, 267) & 0.5 \\ Glucose & 163 (119, 277) & 165 (119, 289) & 153 (111, 204) & 0.008 \\ Bilirubin & 0.96 (0.55, 1.61) & 1.02 (0.58, 1.77) & 0.75 (0.47, 1.24) & 0.011 \\ Creatinine & 1.51 (1.03, 2.13) & 1.50 (1.03, 2.10) & 1.67 (1.04, 2.36) & 0.3 \\ Serum sodium & 136 (130, 140) & 136 (129, 139) & 140 (134, 146) & \(<\)0.001 \\ Serum potassium & 4.20 (3.70, 4.70) & 4.20 (3.70, 4.70) & 4.20 (3.70, 4.90) & 0.9 \\ D-dimer & 1124 (538, 2510) & 1125 (568, 2523) & 837 (493, 2081) & 0.3 \\ Fibrinogen & 554 (424, 785) & 555 (424, 800) & 540 (407, 738) & 0.4 \\ C-reactive protein & 13 (6, 21) & 13 (6, 20) & 17 (8, 25) & 0.008 \\ INR & 1.34 (1.18, 1.83) & 1.34 (1.18, 1.83) & 1.34 (1.15, 1.71) & 0.5 \\ Blood gas analysis & & & & \\ PaO2 & 83 (79, 104) & 84 (79, 104) & 80 (61, 90) & 0.007 \\ Paco2/FiO2 ratio & 390 (367, 495) & 395 (367, 495) & 381 (286, 410) & \(<\)0.001 \\ bicarbonate pH & 22 (16, 27) & 21 (14, 26) & 25 (20, 29) & \(<\)0.001 \\ PH & 7.44 (7.37, 7.49) & 7.44 (7.37, 7.49) & 7.41 (7.36, 7.47) & 0.14 \\ Lactate & 2.6 (1.5, 7.2) & 2.6 (1.5, 7.8) & 2.3 (1.5, 4.3) & 0.4 \\ Clinical scores & & & & \\ APACHE II & 12.0 (9.0, 16.0) & 12.0 (8.8, 16.0) & 15.0 (11.0, 17.0) & \(<\)0.001 \\ SOFA score & 2.00 (0.00, 4.00) & 1.00 (0.00, 3.00) & 3.00 (2.00, 5.00) & \(<\)0.001 \\ qSOFA score & 0.00 (0.00, 1.00) & 0.00 (0.00, 1.00) & 1.00 (0.00, 1.00) & \(<\)0.001 \\ Need for vasopressors in ED & 52.0 (12.2\%) & 35.0 (9.7\%) & 17.0 (26.2\%) & \(<\)0.001 \\ \hline \end{tabular} Data are presented as frequency (percent) or median (IQR). APACHE-II = Acute Physiology and Chronic Health Disease Classification System II. SOFA = Sequential Organ Failure Assessment score. qSOFA = quick Sequential Organ Failure Assessment score. ED = emergency department.  
\end{table} 
Table 2: Laboratory values and clinical scores. \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Blood Culture** & **Uninary Culture** \\ \hline _E. coli_ & 38 (21.5\%) & 34 (21.7\%) \\ _C. albicans_ & 0 & 5 (3.2\%) \\ _Enterobacteriaceae_ & 3 (1.7\%) & 4 (2.5\%) \\ _K. pneumoniae_ & 7(4\%) & 9 (5.7\%) \\ _E. faecalis_ & 11 (6.2\%) & 7 (4.5\%) \\ _E. facetum_ & 10 (10.6\%) & 1 (0.6\%) \\ _P. nirabilis_ & 4 (2.3\%) & 11 (7\%) \\ _P. aeruginosa_ & 3 (1.7\%) & 3 (1.9\%) \\ _S. aureus_ & 9 (5.1\%) & 2 (1.3\%) \\ Contamination & 13 (7.3\%) & 4 (2.5\%) \\ Other & 17 (9.6\%) & 2 (1.3\%) \\ Negative results & 71 (40.1\%) & 75 (47.8\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Blood culture and urinary culture microbiological data. All ML scores achieved a higher predictive performance after inclusion of SOFA and APACHE-II scored as a feature. Random forest achieved higher discrimination power (AUC 0.863) than balanced (AUC 0.811) or unbalanced (AUC 0.813) logistic regression, but lower overall accuracy (0.76 vs. 0.77 vs. 0.84). Unbalanced logistic regression achieved the highest F1 score, at the expense of a lower precision (0.87). Figure 2 reports feature importance for random forest and logistic regression models. APACHE II, qSOFA, and SOFA scores had lower sensitivity and discrimination ability compared to both logistic and random forest models, but were superior compared to the dummy classifier.