Table 7.

A comparison of our model and several previous models for predicting emergency department disposition decisions for bronchiolitis.

Model	EDa visits (n)	Method for building the model	Features included in the final model	Accuracy (%)	Sensitivity (%)	Specificity (%)	AUCb	PPVc (%)	NPVd (%)
Our model	7599	Random forest	As listed in the Results section	90.66	92.09	89.96	0.960	81.80	95.87
Walsh et al [27]	119	Neural network ensemble	Age, respiratory rate after initial treatment, heart rate before initial treatment, oxygen saturation before and after initial treatment, dehydration, maternal smoking, increased work of breathing, poor feeding, wheezes only without associated crackles, entry temperature, and presence of both crackles and wheezes	81	78	82	—e	68	89
Marlais et al [7]	449	Scoring system	Age, respiratory rate, heart rate, oxygen saturation, and duration of symptoms	—	74	77	0.81	67	83
Destino et al [28]	195	Single variable	The Children’s Hospital of Wisconsin respiratory score	—	65	65	0.68	—	—
Laham et al [8]	101	Logistic regression	Age, need for intravenous fluids, hypoxia, and nasal wash lactate dehydrogenase concentration	80	81	77	0.87	88	66
Corneli et al [9]	598	Decision tree	Oxygen saturation, the Respiratory Distress Assessment Instrument score computed from wheezing and retractions, and respiratory rate	—	56	74	—	—	—
Walsh et al [29]	300	Logistic regression	Age, dehydration, increased work of breathing, and heart rate	—	91	83	—	62	—

^aED: emergency department

^bAUC: area under the receiver operating characteristic curve

^cPPV: positive predictive value

^dNPV: negative predictive value

^eThe performance metric is unreported in the original paper describing the model.