Estimating Shortages in Capacity to Deliver Continuous Kidney Replacement Therapy During the COVID-19 Pandemic in the United States

Abstract

Rationale & objective: During the coronavirus disease 2019 (COVID-19) pandemic, New York encountered shortages in continuous kidney replacement therapy (CKRT) capacity for critically ill patients with acute kidney injury stage 3 requiring dialysis. To inform planning for current and future crises, we estimated CKRT demand and capacity during the initial wave of the US COVID-19 pandemic.

Study design: We developed mathematical models to project nationwide and statewide CKRT demand and capacity. Data sources included the Institute for Health Metrics and Evaluation model, the Harvard Global Health Institute model, and published literature.

Setting & population: US patients hospitalized during the initial wave of the COVID-19 pandemic (February 6, 2020, to August 4, 2020).

Intervention: CKRT.

Outcomes: CKRT demand and capacity at peak resource use; number of states projected to encounter CKRT shortages.

Model, perspective, & timeframe: Health sector perspective with a 6-month time horizon.

Results: Under base-case model assumptions, there was a nationwide CKRT capacity of 7,032 machines, an estimated shortage of 1,088 (95% uncertainty interval, 910-1,568) machines, and shortages in 6 states at peak resource use. In sensitivity analyses, varying assumptions around: (1) the number of pre-COVID-19 surplus CKRT machines available and (2) the incidence of acute kidney injury stage 3 requiring dialysis requiring CKRT among hospitalized patients with COVID-19 resulted in projected shortages in 3 to 8 states (933-1,282 machines) and 4 to 8 states (945-1,723 machines), respectively. In the best- and worst-case scenarios, there were shortages in 3 and 26 states (614 and 4,540 machines).

Limitations: Parameter estimates are influenced by assumptions made in the absence of published data for CKRT capacity and by the Institute for Health Metrics and Evaluation model's limitations.

Conclusions: Several US states are projected to encounter CKRT shortages during the COVID-19 pandemic. These findings, although based on limited data for CKRT demand and capacity, suggest there being value during health care crises such as the COVID-19 pandemic in establishing an inpatient kidney replacement therapy national registry and maintaining a national stockpile of CKRT equipment.

Keywords: Continuous renal replacement therapy (CRRT); acute care; acute kidney injury (AKI); acute kidney injury stage 3 requiring dialysis (AKI 3D); acute renal failure (ARF); continuous kidney replacement therapy (CKRT); coronavirus disease 2019 (COVID-19); mathematical model; pandemic; resource allocation; resource shortage; shortages.

Graphical abstract

Figure 1

Continuous kidney replacement therapy (CKRT) shortages by state during the initial wave of the coronavirus disease 2019 (COVID-19) pandemic; base-case scenario. Estimates were model-generated. Group (1) represents all states projected to encounter a CKRT shortage, where CKRT capacity is below the 95% uncertainty interval (UI) of CKRT demand; group (2), states that may encounter a CKRT shortage, where CKRT capacity is within the 95% UI of CKRT demand; group (3), states not anticipated to encounter a CKRT shortage, where CKRT capacity is above the 95% UI of CKRT demand.

Figure 2

One-way sensitivity analysis of the number of states projected to encounter continuous kidney replacement therapy (CKRT) shortage during the initial wave of the coronavirus disease 2019 (COVID-19) pandemic. The horizontal axis of this tornado diagram shows the number of states projected to encounter a CKRT shortage. The vertical axis shows key input parameters. The base-case value for each input parameter is listed in parentheses before the semicolon. The range across which we varied each parameter is listed after the semicolon. The number on the left in the range corresponds to the left end of the horizontal bar, and the number on the right in the range corresponds to the right end of the horizontal bar. The dashed vertical line represents the base-case scenario. As shown, the CKRT capacity multiplier has the greatest impact on the outcome of number of states projected to encounter CKRT shortage during the initial wave of the COVID-19 pandemic. Abbreviations: AKI 3D, acute kidney injury stage 3 requiring dialysis; ICU, intensive care unit; IHME, Institute for Health Metrics and Evaluation.

Figure 3

Continuous kidney replacement therapy (CKRT) shortages by state during the initial wave of the coronavirus disease 2019 (COVID-19) pandemic; best-case scenario. Estimates were model-generated. Group (1) represents all states projected to encounter a CKRT shortage, where CKRT capacity is below the 95% uncertainty interval (UI) of CKRT demand; group (2), states that may encounter a CKRT shortage, where CKRT capacity is within the 95% UI of CKRT demand; group (3), states not anticipated to encounter a CKRT shortage, where CKRT capacity is above the 95% UI of CKRT demand. The best-case scenario projected by the model is obtained when the input parameters are varied simultaneously as follows: (1) incidence of acute kidney injury stage 3 requiring dialysis (AKI 3D) requiring CKRT among hospitalized patients with COVID-19: 4.8%; (2) time from hospitalization to AKI 3D: 10 days; (3) duration of CKRT: 6 days; (4) non–COVID-19 CKRT demand multiplier during the initial wave of the COVID-19 pandemic: 0.25; (5) prevalence of AKI 3D among intensive care unit patients pre–COVID-19: 11.0%; and (6) CKRT capacity multiplier: 1.75.

Figure 4

Continuous kidney replacement therapy (CKRT) shortages by state during the initial wave of the coronavirus disease 2019 (COVID-19) pandemic; worst-case scenario. Estimates were model-generated. Group (1) represents all states projected to encounter a CKRT shortage, where CKRT capacity is below the 95% uncertainty interval (UI) of CKRT demand; group (2), states that may encounter a CKRT shortage, where CKRT capacity is within the 95% UI of CKRT demand; group (3), states not anticipated to encounter a CKRT shortage, where CKRT capacity is above the 95% UI of CKRT demand. The worst-case scenario projected by the model is obtained when the input parameters are varied simultaneously as follows: (1) incidence of acute kidney injury stage 3 requiring dialysis (AKI 3D) requiring CKRT among hospitalized patients with COVID-19: 6.9%; (2) time from hospitalization to AKI 3D: 5 days; (3) duration of CKRT: 9 days; (4) non–COVID-19 CKRT demand multiplier during the initial wave of the COVID-19 pandemic: 0.75; (5) prevalence of AKI 3D among intensive care unit patients pre–COVID-19: 6.6%; and (6) CKRT capacity multiplier: 1.25.

Figure 5

Heat maps demonstrating states with continuous kidney replacement therapy (CKRT) shortages during the initial wave of the coronavirus disease 2019 (COVID-19) pandemic in the base-case, best-case, and worst-case scenario. The base-case scenario uses input parameters listed in the base-case value column of Table 1. The best-case scenario uses the highest CKRT capacity estimate and lowest CKRT demand estimate, which is obtained when the input parameters are varied simultaneously as detailed in the legend to Figure 3. The worst-case scenario uses the lowest CKRT capacity estimate and highest CKRT demand estimate, which is obtained when the input parameters are varied simultaneously as detailed in the legend to Fig 4. Abbreviation: UI, uncertainty interval.

Figure 5

Heat maps demonstrating states with continuous kidney replacement therapy (CKRT) shortages during the initial wave of the coronavirus disease 2019 (COVID-19) pandemic in the base-case, best-case, and worst-case scenario. The base-case scenario uses input parameters listed in the base-case value column of Table 1. The best-case scenario uses the highest CKRT capacity estimate and lowest CKRT demand estimate, which is obtained when the input parameters are varied simultaneously as detailed in the legend to Figure 3. The worst-case scenario uses the lowest CKRT capacity estimate and highest CKRT demand estimate, which is obtained when the input parameters are varied simultaneously as detailed in the legend to Fig 4. Abbreviation: UI, uncertainty interval.