The potential of real-time analytics to improve care for mechanically ventilated patients in the intensive care unit: an early economic evaluation

Table 1 Input parameters for the cost-effectiveness model

Parameter	Base case estimate	Lowest estimate	Highest estimate	Distribution	Source
Discount rate costs (%)	3.5	3	5	–
Discount rate health benefits (%)	3.5	1	5	–
Sensitivity prototype monitor (%)	88	79	94	Beta	[10]
Specificity prototype monitor (%)	99	80	100	Beta pert	[10]
Prior probability of IEEVs (%)	38	10	50	Beta pert	[5]
Treatment’s effectiveness (%)	30	0	50	–	[Expert opinion]
ICU survival (%)
With IEEVs	63	48	77	Beta	Hospital data
Without IEEVs	75	63	84	Beta	Hospital data
Hospital survival (%)
With IEEVs	41	27	57	Beta	Hospital data
Without IEEVs	67	55	77	Beta	Hospital data
Hazard ratio of death after ICU admission vs no. admission	2.01	1.64	2.46	Normal	[11]
Quality of Life (utilities)
ICU	0.297	0.24	0.36	Beta	Assumed
Hospital	0.6	0.53	0.67	Beta	[14]
Year 1 post discharge	0.67	0.62	0.71	Beta	[15]
Year 2–10 post discharge	0.70	0.65	0.75	Beta	[15]
Year > 10 post discharge	0.68	0.62	0.74	Beta	[15]
Resource Use
ICU LOS (days)
With IEEVs	28	23	34	Gamma	Hospital data
Without IEEVs	22	18	27	Gamma	Hospital data
Time on MV (days)
With IEEVs	21	17	27	Gamma	Hospital data
Without IEEVs	15	12	17	Gamma	Hospital data
Hospital LOS post-ICU discharge (days)	17.3	14	21	Gamma	[20]
Unit costs (in 2019 Euros)
Analytics licensing (per bed, per year)	1918	100	20,000	–	[16]
Treatment	100	57	155	Gamma	[Expert opinion]
ICU day	686	392	1060	Gamma	[17]
Hospital day	298	170	460	Gamma	[18]
Reduction in ICU costs when patients no longer receive MV (%)	10	0	35	Beta pert	[Expert opinion]

IEEVs ineffective effort events, ICU intensive care unit, LOS length of stay, MV mechanical ventilation, Hospital data Patient level data from the intensive care unit of PAGNI in Greece

ISSN: 1478-7547