key: cord-0921506-ckd44x1w
authors: Reddy, Yuvaram N.V.; Walensky, Rochelle P.; Mendu, Mallika L.; Green, Nathaniel; Reddy, Krishna P.
title: Estimating Shortages in Capacity to Deliver Continuous Kidney Replacement Therapy During the COVID-19 Pandemic in the United States
date: 2020-07-28
journal: Am J Kidney Dis
DOI: 10.1053/j.ajkd.2020.07.005
sha: baa082cab0db8ed330245e275addb4922a3f2ee5
doc_id: 921506
cord_uid: ckd44x1w

RATIONALE OBJECTIVE: During the coronavirus disease 2019 (COVID-19) pandemic, New York encountered shortages in continuous kidney replacement (CKRT) capacity for critically ill patients with acute kidney injury stage 3 requiring dialysis (AKI 3D). 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 (IHME) model, the Harvard Global Health Institute model, and published literature. SETTING: Population: US patients hospitalized during the initial wave of the COVID-19 pandemic (02/06/2020 to 08/04/2020). INTERVENTION: CKRT. OUTCOMES: CKRT demand and capacity at peak resource utilization; 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 utilization. In sensitivity analyses, varying assumptions around (1) the number of pre-COVID-19 surplus CKRT machines available and (2) the incidence of AKI 3D requiring CKRT among hospitalized patients with COVID-19 resulted in projected shortages in 3-8 states (933-1,282 machines) and 4-8 states (945-1,723 machines), respectively. In the best-case 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 on CKRT capacity and by the IHME model’s limitations. CONCLUSIONS: Several US states are projected to encounter CKRT shortages during the COVID-19 pandemic. These findings – while based on limited data on 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. INDEX WORDS: Continuous renal replacement therapy (CKRT), coronavirus disease 2019 (COVID-19), acute kidney injury (AKI), acute kidney injury stage 3 requiring dialysis (AKI 3D), shortages, mathematical model.

The coronavirus disease 2019 (COVID-19) pandemic, with over 2,800,000 confirmed cases in the US as of July 6, 2020, has created a surge in patients requiring intensive care. 1, 2 Among critically ill patients with COVID-19, 4.8%-6.9% develop acute kidney injury stage 3 requiring dialysis (AKI 3D) -a condition routinely managed with continuous kidney replacement therapy (CKRT) in the intensive care unit (ICU). [3] [4] [5] [6] [7] [8] [9] Anticipating this surge, health care systems underwent crisis capacity activation for inpatient kidney replacement therapy (KRT), constituting a substantial adjustment to standards of care. 4, 7, 10, 11 Nephrologists employed various strategies to improve KRT capacity, including procuring additional CKRT machines from manufacturers, decreasing the dose and duration of CKRT, and expanding the use of intermittent dialysis modalities such as hemodialysis (HD) and peritoneal dialysis (PD). [10] [11] [12] Despite these efforts, New York hospital systems encountered CKRT shortages during the initial wave of the COVID-19 pandemic. 11, 13, 14 In this time of uncertainty, mathematical models have informed capacity planning for ICU beds and ventilators, enabling increased ventilator production and distribution across the US to mitigate shortages. [15] [16] [17] [18] Similarly, mathematical models could improve CKRT capacity planning. The objective of this study was to develop mathematical models of CKRT demand and capacity to inform emergency planning, to identify areas where more data are needed, and to mitigate CKRT shortages during the current COVID-19 pandemic and future health care crises. 2, [17] [18] [19] 

We developed mathematical models to project CKRT demand due to COVID-19, non-COVID-19 CKRT demand, and CKRT capacity during the initial wave of the COVID-19 pandemic. Model results were used to estimate nationwide and statewide CKRT shortages.

Given uncertainty in many of the model parameters, we first applied base-case parameter estimates and then varied them in sensitivity analysis.

CKRT demand due to COVID-19

The model simulated a US cohort of patients hospitalized due to COVID-19 between 02/06/2020 and 08/04/2020, reflecting the initial wave of the COVID-19 pandemic. We estimated new daily cases of AKI 3D from COVID-19 requiring CKRT and daily CKRT demand as follows: 

We obtained estimates of the daily number of hospitalized patients with COVID-19 from the Institute for Health Metrics and Evaluation (IHME) model (version 06/10/2020) -a multistage hybrid model that utilizes COVID-19 death rates, viral transmission characteristics, and the impact of social interventions to provide daily estimates of hospitalizations and deaths due to COVID-19. 16 The IHME model accounts for uncertainty in the number of hospitalizations in each state from fixed and random effect estimations influenced by state characteristics. 16 This range of uncertainty is used to produce several iterations of model-generated results, which are aggregated to create 95% uncertainty intervals (UI). Wherever appropriate, we present estimates derived from the IHME model as means with 95% UI.

For this simulated cohort, we determined the incidence of AKI 3D requiring CKRT, the time from hospitalization to development of AKI 3D requiring CKRT, and the duration of CKRT from published literature (Table 1) . [3] [4] [5] 7, 8, 20, 21 We estimated an incidence of AKI 3D requiring CKRT from the largest New York study of patients with COVID-19. 8 Within this study, 5.2% of hospitalized patients with COVID-19 developed AKI 3D requiring KRT in the ICU. Although only 46% of these patients received CKRT, this was likely due to the expanded use of HD in the ICU from CKRT capacity constraints. 4, 7, 10, 11 As most of these critically ill patients would have preferentially received CKRT instead of HD in the pre-COVID-19 era, we assumed an incidence of AKI 3D requiring CKRT among hospitalized patients with COVID-19 of 5.2%. 8 For the time from hospitalization to development of AKI 3`D, the same study reported a median of 2 hours with an interquartile range of -1.63 to +141 hours. 8 As the range included a negative value for time, these data were unsuitable for the model value. With insufficient US data, we estimated the time from hospitalization to development of AKI 3D requiring CKRT from data by Zhou et al. in China. 19 21 To account for a high mortality rate (55%) 8 among patients with COVID-19 who require CKRT, we assumed that CKRT duration for non-survivors was 50% that of survivors, and the adjusted CKRT duration used in the model was estimated as follows: (4 days * 0.55) + (8 days * 0.45) ≈ 6 days. 8 Non-COVID-19 CKRT demand

We developed a second model to estimate non-COVID-19 CKRT demand and CKRT capacity. Within this model, we simulated the average number of occupied ICU beds across the US between 2011 and 2016, prior to the COVID-19 pandemic. We estimated pre-COVID-19

CKRT demand and daily non-COVID-19 CKRT demand as follows: 

We obtained estimates of occupied ICU beds across the US from the Harvard Global 

Our literature review revealed no publicly available data on the number of CKRT machines in the US. Therefore, we assumed capacity was 1.50 times the pre-COVID-19 (or historical) CKRT demand. That is, for every 2 CKRT machines in use in a health care system, we assumed there was 1 additional CKRT machine available prior to the COVID-19 pandemic.

This assumption was based on clinical experience informed by local capacity in Boston. We confirmed face validity of this assumption with nephrologists at two hospitals.

CKRT shortages during the COVID-19 pandemic

Using the above models, we estimated daily total CKRT demand as the sum of daily CKRT demand due to COVID-19 and daily non-COVID-19 CKRT demand. We compared daily total CKRT demand to daily CKRT capacity by US state to estimate CKRT shortages as follows:

(1) CKRT shortage: if CKRT capacity was below the 95% UI of CKRT demand.

(2) Possible CKRT shortage: if CKRT capacity was within the 95% UI of CKRT demand.

(3) No CKRT shortage: if CKRT capacity was above the 95% UI of CKRT demand.

Accordingly, the models projected the number of states encountering CKRT shortages, the number of states encountering possible CKRT shortages, the magnitude of shortage at peak resource utilization during the initial wave of the COVID-19 pandemic, and the initial date of shortage in each state. As peak resource utilization occurs at different times in different states, the projected nationwide combined shortage in CKRT machines at peak resource utilization refers to the sum of these statewide shortages that occur at different times.

To assess the impact of uncertainty in model input parameters on model outcomes, we conducted one-way and multi-way deterministic sensitivity analysis. In one-way sensitivity analysis, key parameters influencing our estimates of CKRT demand and capacity were varied across a range of plausible values (Table 1) . For example, we varied the incidence of AKI 3D requiring CKRT among hospitalized patients with COVID-19 between 4.8%-6.9% based on data from three hospital systems in New York. 4-8 Based on expert opinion, we varied the non-COVID-19 CKRT demand multiplier and the CKRT capacity multiplier between 0.25-0.75 and 1.25-1.75, respectively. We also conducted a sensitivity analysis of the IHME model by projecting outcomes using the 06/10/2020 IHME model (base-case) and the 04/22/2020 IHME model. Additionally, in multi-way deterministic sensitivity analyses, all input parameters influencing CKRT demand and capacity were simultaneously varied to examine the best-case (lowest demand, highest capacity) and worst-case (highest demand, lowest capacity) scenarios.

The models projected that from 02/06/2020 to 08/04/2020 cumulatively, 28,479 (95% UI, 21,974-39,338) patients with COVID-19 in the US would require CKRT. We estimated a nationwide daily capacity of 7,032 CKRT machines (Table S1) Table S2 .

Sensitivity analysis of the CKRT demand input parameters demonstrated shortages in 4-8 states (945-1,723 machines) when the incidence of AKI 3D requiring CKRT among hospitalized patients with COVID-19 was varied between 4.8%-6.9%, shortages in 4-8 states (986-1,388 machines) when the non-COVID-19 CKRT demand multiplier during the COVID-19 pandemic was varied between 0.25-0.75, shortages in 6-8 states (1,088-2,067 machines) when the duration of CKRT among hospitalized patients with COVID-19 was varied between 6-9 days, and no change in the number of states with shortages (or the number of machines in shortage) when the time from hospitalization to AKI 3D requiring CKRT among hospitalized patients with COVID-19 was varied between 5-10 days (Tables S3-S6) .

Similarly, sensitivity analysis demonstrated shortages in 3-8 states (919-1,302 machines) when the prevalence of AKI 3D among ICU patients pre-COVID-19 (influencing CKRT demand and capacity) was varied between 11.0%-6.6%, shortages in 3-8 states (933-1,282 machines) when the CKRT capacity multiplier (influencing CKRT capacity) was varied between 1.75-1.25 and shortages in 6-7 states (1,088-1,239 machines) when the IHME model estimates used were varied between the 06/10/2020 (base-case) and the 04/22/2020 version (Table S7-S9). The impact of uncertainty in these input parameters on the outcome of number of states encountering CKRT shortages is summarized in Figure 2 .

In the best-case scenario (lowest demand, highest capacity), projections demonstrated that from 02/06/2020 to 08/04/2020, 26,053 (95% UI, 20,229-35,523) patients with COVID-19 in the US would require CKRT. We estimated a nationwide daily capacity of 10,254 CKRT machines (Table S10) . A state-by-state comparison demonstrated a combined shortage of 614 (95% UI, 498-834) machines, with shortages projected in 3 states -Connecticut, New Jersey, and New York -at peak resource utilization during the initial wave of the COVID-19 pandemic.

Additionally, there were possible shortages in 2 states -Arizona and Colorado (Figure 3 ).

In the worst-case scenario (highest demand, lowest capacity), projections demonstrated that from 02/06/2020 to 08/04/2020, 38,013 (95% UI, 29,208-52,978) patients with COVID-19 in the US would require CKRT. We estimated a nationwide daily capacity of 4,395 CKRT machines (Table S11) pandemic. Concordant with model findings, hospital systems in New York, Massachusetts, and Louisiana encountered shortages in CKRT machines, solutions, cartridges, and/or trained personnel which were managed through the expansion of intermittent dialysis modalities and a decrease in CKRT dose and duration. 8, 13, 25, 26 However, while individual hospital systems reported shortages, due to a lack of consistent reporting of CKRT demand and capacity, it is unclear if these shortages occurred throughout each state with a projected shortage in our models. Apart from anecdotal data from the press, webinars, and social media, little is otherwise known about the actual state of CKRT demand and capacity in the US. 13, 25, 27 Within these models, limited US data led to uncertainty. In sensitivity analysis, uncertainty in CKRT demand input parameters (such as the incidence of AKI 3D and the duration of CKRT among hospitalized patients with COVID-19) had the largest impact on the model outcome of the number of machines in shortage at peak resource utilization during the COVID-19 pandemic. For example, the range of the incidence of AKI 3D requiring CKRT among hospitalized patients with COVID-19 in the models (4.8%-6.9%) was derived from three New York counties -where the incidence was considerably higher than other regions, such as China (1.45-2.3%). [4] [5] [6] [7] [8] [9] 28 In the absence of data from other US states, it is unclear if this high incidence is reflective of the remainder of the US. Similarly, uncertainty in CKRT capacity input parameters (such as the CKRT capacity multiplier) had the largest impact on the model outcome of number of states projected to encounter CKRT shortages. This is not unexpected, as a lack of data on the number of CKRT machines in each state forced the use of assumptions to estimate CKRT capacity as a multiple of pre-COVID-19 (or historical) CKRT demand. Varying this parameter between 1.25 and 1.75 predictably resulted in a lower or higher surplus of CKRT machines, changing the threshold at which a state may encounter a CKRT shortage. More data from US states on the number of CKRT machines available (capacity) and in use (demand), would allow future model-based analyses to provide more precise estimates of CKRT shortages.

Although the assumptions made on CKRT demand and capacity allowed projections of plausible results at a nationwide and statewide level, these projections are insufficiently granular to hold true at the county, health care system, and hospital levels. As such, these models may not be useful for county-level or hospital-level decision making. Instead, these models provide highlevel projections of CKRT shortages and highlight the need for reliable nationwide and local data on the number of CKRT machines available and in use in each system.

In the absence of reliable data on CKRT machine availability, recommendations during the COVID-19 pandemic have been for all systems to conserve KRT (CKRT, HD, and PD) supplies and standardize lower dialysate patient prescriptions in fear of an imminent shortage. 10, 11 This has led hospitals to race to purchase more KRT machines and supplies, creating a competition for machines. 11, 27, 29 If publicly available data on KRT capacity existed, hospitals could collaborate during health care crises to mitigate shortages while continuing to provide the standard of care. While this analysis focused on CKRT machines, estimates of CKRT demand and capacity could be further improved if data were available for all inpatient KRT machines, supplies, and personnel. 11 The current lack of standardized reporting of data on inpatient KRT machines, supplies, and personnel is an impediment to emergency preparedness; strategies to improve data collection are urgently needed. Creating a national, multi-disciplinary taskforce comprising key stakeholders -the federal government, the nephrology community, industry, and patients -could improve data collection and emergency preparedness planning for KRT. Considerations for a taskforce include i) developing a national registry of inpatient KRT machines, supplies, and personnel, ii) creating a national stockpile of KRT machines and supplies, and iii) adding questions about the number of CKRT, HD, and PD machines in each hospital to the American Hospital Association annual hospital survey. Notably, as hospitals return to standard capacities towards the eventual end of the COVID-19 pandemic, many will be left with surplus CKRT machines. This creates a unique opportunity to improve emergency preparedness, as the federal government could repurpose these surplus machines to provide relief for future waves of the COVID-19 pandemic and other health care crises. 11, 29 With these strategies in place to collect data on the number and availability of KRT machines, subsequent iterations of mathematical models could help determine the optimal number of KRT machines needed for a national stockpile, inform triage of machines to areas of need, and prompt early manufacturing of KRT supplies for future health care crises.

In the interim, pragmatic research is needed to study new practices borne out of necessity from the COVID-19 pandemic. For example, concerns of CKRT shortages led to recommendations to standardize CKRT dosing and duration. 9 Prior studies have shown a benefit to adopting standardized criteria for initiation of KRT. 30 If the outcome of these CKRT recommendations during the COVID-19 pandemic suggest no harm, this standardization of dosing can help conserve dialysis solutions. Similarly, due to shortages, urgent-start PD has also expanded in the inpatient setting. 12,31-33 While short-term outcomes of urgent-start PD during the COVID-19 pandemic suggest safety, longer-term results on peritonitis, technique failure, and mortality are needed to assess the benefit of this program. 12, 31, 32 Successful practices from the COVID-19 pandemic, if studied appropriately, could help avoid shortages and improve patient outcomes during future health care crises.

There are limitations to this analysis. First, the model results are subject to simplifications and assumptions. Sensitivity analysis demonstrates the influence of these assumptions on the results. The models utilize IHME model estimates and are subject to that model's limitations. 34 In particular, early versions of the IHME model did not account for viral transmission characteristics -traditionally done with a susceptible, exposed, infectious, recovered (SEIR) framework. This study used estimates from the 06/10/2020 IHME model, which is an improved multi-stage hybrid model that incorporates an SEIR framework. The impact of this SEIR framework on model outcomes can be seen in the sensitivity analyses, where the absence of this framework in the 04/22/2020 IHME model resulted in 1 additional state (Louisiana) encountering a CKRT shortage, with 151 additional machines in shortage at peak resource utilization during the initial wave of the COVID-19 pandemic. Second, due to the dynamic nature of the COVID-19 pandemic, subtle characteristics of model results from IHME such as the exact date of peak resource utilization should be interpreted cautiously. 35 Fortunately, as the IHME model is updated periodically, we anticipate future IHME iterations will allow for more precise projections over time. 16 Third, we assumed all AKI 3D patients in the ICU receive CKRT. As hospitals are faced with a surge in AKI 3D, the use of intermittent dialysis modalities in the ICU have expanded. 34 To the extent that supplies and personnel for these modalities are available, results may underestimate total KRT capacity in the ICU. 10 Finally, although we conducted a deterministic multi-way sensitivity analysis, this approach tends to over-weight extreme values compared with probabilistic sensitivity analysis. 36 Given the evolving nature of COVID-19 and the limited data on these input parameters, we were unable to generate more specific distributions for the model input parameters at the time of manuscript submission.

Policymakers are cautioned to avoid over-valuing the likelihood of the best-case and worst-case scenarios presented in this paper.

In conclusion, several US states could encounter CKRT shortages at peak resource utilization during the initial wave of the COVID-19 pandemic. More complete and reliable data on CKRT demand and capacity would improve the estimates of future model-based analyses.

Strategies such as the creation of an inpatient RRT national registry and a national stockpile to bolster state capacity should be considered to mitigate CKRT shortages during the COVID-19 pandemic and future health care crises. Within this company, he also works on AllTraq -a tracking technology product which locates equipment and personnel in GPS-denied environments. His company, American Biomedical Group, Inc. had no role in study design; collection, analysis, and interpretation of data; writing the report; funding for the study (apart from employment of Mr. Green); or the decision to submit the report for publication. Specifically, the company did not impose any limits on authors' access to the study data, or the content of the manuscript. Table S1 . Model-generated statewide estimates of occupied ICU beds and CKRT demand and capacity prior to the pandemic. Table S2 . Model-generated initial CKRT shortage date in the base-case, best-case, and worst-case scenarios during the initial wave of the pandemic. Table S3 . Effect of varying the incidence of AKI 3D requiring CKRT among hospitalized patients with COVID-19 on a) nationwide CKRT shortage at peak resource utilization in each state and b) number of states projected to encounter CKRT shortage during the initial wave of the pandemic. Table S4 . Effect of varying the non-COVID-19 CKRT demand multiplier during the pandemic on model-generated a) nationwide CKRT shortage at peak resource utilization in each state and b) number of states projected to encounter CKRT shortage during the initial wave of the pandemic. Table S5 . Effect of varying the duration of CKRT among hospitalized patients with COVID-19 on a) nationwide CKRT shortage at peak resource utilization in each state and b) number of states projected to encounter CKRT shortage during the initial wave of the pandemic. Table S6 . Effect of varying the time from hospitalization to AKI 3D requiring CKRT among hospitalized patients with COVID-19 on a) nationwide CKRT shortage at peak resource utilization in each state and b) number of states projected to encounter CKRT shortage during the initial wave of the pandemic. Table S7 . Effect of varying the prevalence of AKI 3D among ICU patients pre-COVID-19 on model-generated a) nationwide CKRT shortage at peak resource utilization in each state and b) number of states projected to encounter CKRT shortage during the initial wave of the pandemic. Table S8 . Effect of varying the CKRT capacity multiplier on a) nationwide CKRT shortage at peak resource utilization in each state and b) number of states projected to encounter CKRT shortage during the initial wave of the pandemic. Table S9 . Effect of varying the IHME model version on a) nationwide CKRT shortage at peak resource utilization in each state and b) number of states projected to encounter CKRT shortage during the initial wave of the pandemic. Table S10 . Multi-way sensitivity analysis assessing CKRT demand, capacity, and shortage at peak resource utilization during the initial wave of the pandemic -best-case scenario. Table S11 . Multi-way sensitivity analysis assessing CKRT demand, capacity, and shortage at peak resource utilization during the initial wave of the pandemic -worst-case scenario.

Supplementary File (PDF). Tables S1-S11. We assumed an unadjusted duration of CKRT of 8 days based on the Acute Renal Failure Trial Network Study. 8 Assuming patients who died had an average CKRT duration of 4 days, we adjusted this duration to 6 days to account for the high mortality rate among patients with COVID-19 (55%). 16 This model has been criticized as it did not specifically account for COVID-19 infection transmission characteristics -traditionally modeled under a susceptible, exposed, infectious, recovered (SEIR) framework. 34 IHME updated its model and the 06/10/2020 IHME version utilizes a multi-stage hybrid model, incorporating COVID-19 transmission characteristics, death rates, and the impact of social interventions. To assess the impact of this SEIR framework and other IHME updates to the model on outcomes, we varied the IHME version between the 04/22/2020 and 06/10/2020 versions. e This assumption was based on clinical experience informed by local capacity. We confirmed face validity of this assumption with nephrologists at two hospitals. f Data were obtained from a meta-analysis including 17 US studies and over 415,000 patients with acute kidney injury in medical and surgical ICUs. 3 Although this sample size provided a very narrow confidence interval, we chose a range of 6.6-11.0% based on expert opinion. In this chart, orange dots represent model-generated CKRT capacity estimates, blue dots represent modelgenerated CKRT demand estimates, and blue bars represent the 95% UI for CKRT demand estimates.

Group (1) Red represents all states projected to encounter a CKRT shortage, where CKRT capacity is below the 95% UI of CKRT demand.

Orange represents states that may encounter a CKRT shortage, where CKRT capacity is within the 95% UI of CKRT demand.

Yellow represents states not anticipated to encounter a CKRT shortage, where CKRT capacity is above the 95% UI of CKRT demand.

Panel A reflects the base-case scenario with input parameters listed in the base-case value column of Table 1 .

Panel B reflects the best-case scenario with 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 Figure3.

Panel C reflects the worst-case scenario with 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 Figure 4 . 

No CKRT shortage (CKRT capacity is above the 95% UI of CKRT demand) Possible CKRT shortage (CKRT capacity is within/included in the 95% UI of CKRT demand) CKRT shortage (CKRT capacity is below the 95% UI of CKRT demand)

No CKRT shortage (CKRT capacity is above the 95% UI of CKRT demand) Possible CKRT shortage (CKRT capacity is within/included in the 95% UI of CKRT demand) CKRT shortage (CKRT capacity is below the 95% UI of CKRT demand)

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This analysis uses the base-case values for the input parameters listed in Table 1 . The bolded rows represent states that are projected to encounter a shortage (where CKRT capacity is below the 95% UI of CKRT demand). The italicized rows represent states that could possibly encounter a shortage (where CKRT capacity is within the 95% UI of CKRT demand). Minor discrepancies in numerical values in the table are due to rounding. b We derived these estimates from the Institute for Health Metrics and Evaluation model and present them as means with 95% UI. 16 (2) Possible CKRT shortage