Table 1.
Prospective models
Paper | Design | Target condition | Patient encounters | Machine learning model | Comparators | |
---|---|---|---|---|---|---|
Validation | ||||||
ED | Brown et al. | Prospective validation | Severe sepsis and Septic shock | 93,773 (15 months) |
Cut05 Primary outcome Sensitivity: 0.764 False positive rate: 0.47 Secondary outcome AUC: 0.859 |
Nurse triage Primary outcome Sensitivity: 0.543 False positive rate: 0.31 Secondary outcome AUC: 0.756 |
SIRS Primary outcome Sensitivity: 0.216 False positive rate: 0.004 Secondary outcome AUC: 0.606 | ||||||
In-hospital | Thiel et al. | Prospective validation | Septic shock | 27,674 (24 months) |
RPARTa 2006 Primary outcome Misclassification rate: 8.4% |
None |
RPARTa 2007 Primary outcome Misclassification rate: 8.8% |
Paper | Design | Target condition | Patient encounters | Machine learning group | Control group | |
---|---|---|---|---|---|---|
Interventional | ||||||
In-hospital | Giannini et al. | Pre-post implementation | Severe sepsis and septic shock | 54,464 (6 pre-months, 1 post-month) |
EWS 2.0 Primary/secondary outcome Hospital LOS: 9 days Time to ICU transfer after alert: 8 he In-hospital mortality: 10.3% |
Unclear Primary/secondary outcome Hospital LOS: 9 days Time to ICU transfer after alert: 16 he In-hospital mortality: 10.6% |
McCoy et al. | Pre-post implementationb | Severe sepsis | 611 (3 pre-months, 2 post-months) |
Linear model (Insight) Primary outcome In-hospital mortality: 2.94% Secondary outcome Hospital LOS: 2.92 days Readmission rate: 7.84% |
Manual nurse scoringc Primary outcome In-hospital mortality: 7.37% Secondary outcome Hospital LOS: 3.35 days Readmission rate: 46.19% |
|
ICU | Shimabukuro et al. | RCT | Severe sepsis | 142 (3 months) |
Elastic net reg.d (Insight) Primary outcome Hospital LOS: 10.3 dayse Secondary outcome ICU LOS: 6.3 dayse In-hospital mortality: 8.96%e |
SIRS detector Primary outcome Hospital LOS: 13.0 dayse Secondary outcome ICU LOS: 8.4 dayse In-hospital mortality: 21.3%e |
aRecursive partitioning and regression tree (RPART) analysis
bOnly baseline and steady state are reported
cNurses scored patient twice daily to see if they met the SIRS criteria
dElastic net regularization (generalized linear model)
eSignificant results