We developed a simulation model to illustrate the downstream consequences of different triage rules under varying conditions of patient volume, patient severity of illness, and ventilator availability. The model is available online for users to test with varying numbers of patients, ventilators and different allocation strategies:
https://wparker-uchicago.shinyapps.io/ventilator_allocation/.
For this study, we set the conditions to 1,000 patients with 500 available ventilators to simulate severe scarcity across a regional area (i.e., multiple hospitals across a city). The model created a simulated dataset of 1,000 patients and their characteristics (age, comorbidity burden and SOFA score). The age of these patients were sampled from the age distribution of COVID-19-infected patients as reported by the Centers for Disease Control.8
The relationships between age, comorbidities and SOFA score for these simulated patients were derived from a large tertiary care hospital system.9 Based on general impracticability and minimal harm, waivers of consent were granted by the University of Chicago Institutional Review for this study. As the majority of ICU patients during this pandemic would be patients with respiratory failure secondary to COVID-19 infection, the clinical data was sampled from patients with evidence of infection and respiratory failure requiring intubation. Of the 262,937 inpatient admissions, 2,661 patients met this criteria and were included in the analyses. Clinical characteristics of this cohort are presented in Methods Table 1. The comorbidity burden for these patients was categorized as none, major or severe based on a combination of currently utilized priority scores2,10 and the value of the AHRQ Elixhauser Co-morbidity Index Score.11 A multinomial logistic regression was performed on age to comorbidity burden, and then linear regression was done to regress the SOFA score on the age and comorbidity burden of the patients. The coefficients from these regressions are presented in Methods Table 2. These relationships between age, comorbidity burden and SOFA were used to simulate patient data for the simulation model.
We applied eight different ventilator allocation rules to the simulated population, and these allocation rules are described in further detail in Table 1 in the manuscript. If the allocation rule assigned a patient to a ventilator, the model then determined patient survival based on the calibration of the SOFA score to survival from a multi-center cohort of over 180,000 critically ill patients with infections.12 If the patient survived, the life-years saved was determined by subtracting the patient’s age from one hundred (maximum lifespan). Life-years saved was discounted by 50% if a major chronic disease is present. Since the definition of severe chronic condition in allocation scores is an expected lifespan of one year, the life-years saved was reduced to one if such a condition was present. If the allocation rule did not assign a patient to a ventilator, the patient did not survive. The simulation was repeated for 10,000 runs. Lives saved and life-years saved were averaged over the 10,000 runs and compared across the different allocation strategies.
The simulated data used age, comorbidity and SOFA score relationships based on clinical data from a single medical center. In order to make the simulation more generalizable and widely available, we have released the open-source code. The open-source nature of the annotated code allows users to tailor the age, comorbidity, and SOFA distributions of the patient population based on local institutional data. Our simulation model is not intended to be a comprehensive representation of allocation, but rather an open-source tool to illustrate the potential effects of different triage rules.
Methods Table 1. Clinical characteristics of mechanically ventilated patients used to simulate patient data.
|
Total population (n=2,661)
|
Age mean (SD)
|
61 (16)
|
Gender n (%)
|
|
Female
|
1359 (51)
|
Male
|
1302 (49)
|
Race n (%)
|
|
White
|
465 (17)
|
Black
|
2025 (76)
|
Other
|
171 (6)
|
Number of co-morbidities median (IQR)
|
2 (0-6)
|
Elixhauser co-morbidities n (%)
|
|
Congestive heart failure
|
590 (22)
|
Valvular disease
|
184 (7)
|
Pulmonary circulatory disorders
|
189 (7)
|
Peripheral vascular disease
|
245 (9)
|
Hypertension, uncomplicated
|
1047 (39)
|
Hypertension, complicated
|
447 (17)
|
Paraplegia
|
116 (4)
|
Other neurological disorders
|
260 (10)
|
Chronic pulmonary disease
|
489 (18)
|
Diabetes, uncomplicated
|
537 (20)
|
Diabetes, complicated
|
212 (8)
|
Hypothyroidism
|
211 (8)
|
Renal failure
|
521 (20)
|
Liver disease
|
197 (7)
|
Peptic ulcer disease
|
26 (1)
|
AIDS/HIV
|
14 (0.5)
|
Lymphoma
|
77 (3)
|
Metastatic cancer
|
188 (7)
|
Solid tumor without metastasis
|
334 (13)
|
Rheumatoid arthritis/collagen vascular
disease
|
104 (4)
|
Coagulopathy
|
374 (14)
|
Obesity
|
242 (9)
|
Weight loss
|
400 (15)
|
Fluid and electrolyte disorders
|
804 (30)
|
Blood loss anemia
|
111 (4)
|
Deficiency anemia
|
95 (4)
|
Alcohol abuse
|
148 (6)
|
Drug abuse
|
135 (5)
|
Psychosis
|
178 (7)
|
Depression
|
268 (10)
|
SOFA at time of intubation median (IQR)
|
7 (6-9)
|
Maximum SOFA score median (IQR)
|
10 (8-13)
|
Duration of intubation, hours median (IQR)
|
38 (14-90)
|
LOS, days median (IQR)
|
9 (5-17)
|
Mortality n (%)
|
833 (31)
|
Methods Table 2. Regression coefficients for relationships between age, comorbidity burden, and SOFA scores.
|
Coefficient
|
Description
|
Comorbidity ~ Age
|
|
|
None
|
-
|
Reference
|
Major intercept
|
-0.783942
|
Log odds of having major comorbidity compared to none
|
slope
|
.0066036
|
Increase in log odds of having major comorbidity compared to none with each year increase in age
|
Severe intercept
|
-2.398532
|
Log odds of having severe comorbidity compared to none
|
slope
|
.0128016
|
Increase in log odds of having severe comorbidity compared to none with each year increase in age
|
SOFA ~ Age + Comorbidity
|
|
|
Intercept
|
7.008651
|
Intercept of SOFA score
|
Age
|
.0066036
|
Increase in SOFA score per year increase in age
|
Comorbidity
|
|
|
None
|
-
|
Reference
|
Major
|
.0603057
|
Increase in SOFA score for major comorbidity compared to none
|
Severe
|
.6066374
|
Increase in SOFA score for severe comorbidity compared to none
|