Skip to main content
NCBI home page
Renal Failure logoLink to Renal Failure
. 2026 Feb 18;48(1):2628391. doi: 10.1080/0886022X.2026.2628391

Four-organ SOFA score as a potential tool to triage use of continuous kidney replacement therapy in COVID-19 patients with acute kidney injury: a retrospective two-center study

Fatemeh Abbasi a,*, Tomonori Takeuchi b,c,*, Gabriela M Orellana a, David Perez Ingles a, Zachary Bruss d, Chandra Kodavanti a,e, Lisa Saa a, Alisa C Illescas d, Jonathan L Marinaro f,g, Michelle S Harkins a,g, Christos P Argyropoulos e, J Pedro Teixeira a,e,g,✉
PMCID: PMC12918399  PMID: 41708516

Abstract

Acute kidney injury requiring continuous kidney replacement therapy (AKI-CKRT) in the setting of coronavirus disease 2019 (COVID-19) carries a high mortality. In this two-center retrospective cohort study, we analyzed whether a simplified Sequential Organ Failure Assessment (SOFA) score, the 4-organ SOFA score with renal and neurologic sub-scores removed, would perform as well as the full SOFA score in predicting mortality. In our primary cohort, we identified 63 patients with COVID-19 hospitalized in April 2020 to July 2021 with AKI-CKRT and determined their 4-organ and full SOFA scores at CKRT initiation. Thirty-day and in-hospital mortality were 73.0% and 82.5%, respectively. The median 4-organ SOFA score at CKRT initiation among 30-day survivors was 7 (interquartile range 5.5–8.5) versus 8 (7–10) among 30-day non-survivors (p = 0.017). Four-organ SOFA scores for those who survived versus died before discharge were similar (p = 0.071). The full SOFA scores in survivors versus non-survivors were similar (p > 0.05). When comparing receiver operating characteristic curves, the 4-organ SOFA score to predict 30-day mortality performed best (area under curve 0.67). For external validation, we repeated our analysis in 88 patients with COVID-19 and AKI-CKRT from the MIMIC-IV database, and again the 4-organ score performed as well as the full SOFA score to predict 30-day and in-hospital mortality. Though hypothesis-generating and requiring additional validation, these findings suggest that 4-organ SOFA score performs as well as full SOFA score in predicting mortality in patients with COVID-19 and AKI-CKRT. However, these results reinforce prior data that SOFA score has limited prognostic value in COVID-19.

Keywords: COVID-19, CRRT, acute kidney injury, SOFA score, pandemic triage

Introduction

As of May 2025, coronavirus disease 2019 (COVID-19) has caused over 1.2 million deaths in the U.S. since the pandemic began in December 2019 [1]. The risk of death from COVID-19 has been shown to correlate with the degree of organ dysfunction induced, with mortality rates >70% in patients needing mechanical ventilation, vasopressors, and kidney replacement therapy (KRT) [2,3]. In particular, acute kidney injury (AKI), after respiratory failure, represents the second most common form of organ dysfunction in COVID-19 and is independently associated with morbidity and mortality in patients with COVID-19 [2,4]. While the pathophysiology of AKI in COVID-19 is complex, with significant debate surrounding the relative importance of direct viral invasion of the kidney and indirect mechanisms [2,5], the tremendous impact of AKI on the prognosis of COVID-19 is not debatable. In particular, similar to other critically ill patients [6–8], AKI significant enough to require KRT in patients with severe COVID-19 has an especially poor prognosis, with short-term mortality rates typically exceeding 50% [9–14]. In addition to poor patient outcomes, AKI requiring KRT during the pandemic also resulted in a variety of logistical challenges related to supply disruptions and to allocation of KRT machines, intensive care and acute dialysis staff, and other limited resources [15–18]. At the peak of the pandemic, the demand for KRT machines increased 279% over baseline, placing a tremendous strain on hospital systems and leading to the need to ration KRT at some U.S. hospitals [19–21].

The state of New Mexico alone suffered approximately 10,000 COVID-19-related deaths, with two peaks in December 2020 and December 2021, the latter of which coincided with the deadly delta wave and the statewide implementation of crisis standards of care [1,22,23]. Due to high rates of AKI requiring continuous KRT (CKRT) during the initial COVID-19 surge at our institution, the critical care and nephrology leadership in late 2021 were bracing for a possible shortage of CKRT machines. In addition, given an extremely high in-hospital mortality rate of 80% among patients with AKI requiring CKRT at our hospital during our initial COVID-19 surge [12], we sought to use local data to develop a logical and objective approach to rationing CKRT machines, if needed. Specifically, given the extensive usage of Sequential Organ Failure Assessment (SOFA) scores [24] in pandemic triage algorithms developed both locally and throughout the United States [25–27], we sought to determine if SOFA scores or a simplified 4-organ SOFA score, excluding the respiratory and neurologic sub-scores, would reliably predict the short-term mortality of patients with COVID-19 and AKI requiring CKRT.

Methods

Design and setting

This is a two-center retrospective cohort study. The primary cohort included patients admitted to University of New Mexico Hospital (UNMH) during the first 16 months of the pandemic in New Mexico (April 1, 2020 to July, 31 2021), when the alpha variant of SARS-CoV-2 was predominant in the state [22]. UNMH is a publicly funded, tertiary care center serving as the primary safety net hospital for the state of New Mexico, providing care to a largely medically underserved population [28]. It has 618 licensed beds, including 72 beds in three adult intensive care units (ICUs), though the ICUs and the hospital as a whole have been consistently operating above their licensed capacity since early in the pandemic. As of 2022, approximately 250 patients are treated with CKRT over approximately 30,000 therapy hours each year at UNMH [15]. Our CKRT utilizes PrismaSol and PrismaSATE solutions delivered by Prismaflex or PrisMax devices with M100 or HF1000 hemofilters (all from Baxter International, Deerfield, IL). Specifically, at the beginning of the study period we had 11 CKRT devices available for clinical use in both adult and pediatric patients in our hospital, to which another 5 machines were added during the pandemic. Our standard CKRT prescription is continuous venovenous hemodiafiltration at an initial total effluent dose of approximately 25 mL/kg/h with 1-to-1 ratio of replacement fluid and dialysate, with dose adjustment thereafter as needed based on serial laboratory monitoring. For patients with COVID-19 requiring CKRT, unless they are already being prescribed systemic anticoagulation for a separate indication, we prescribe anticoagulation for all patients with either prefilter unfractionated heparin or regional citrate anticoagulation (RCA) using Anticoagulant Citrate Dextrose Solution A (ACD-A), depending on patient characteristics. Our blood flow rate is 150 mL/min for patients treated with RCA and 200 mL/min for all other patients. Apart from the net ultrafiltration rate which is initially set by nephrology and adjusted thereafter as needed on a continual basis by the critical care teams, all aspects of CKRT in our institution are prescribed 24 h per day by nephrology fellows and attendings. Approval to conduct the study with waiver of informed consent was obtained from the UNM Health Sciences Human Research Protections Program (protocol #23-090).

To externally validate our results, we repeated our analysis using publicly available data from the MIMIC (Medical Information Mart for Intensive Care) IV database [29], as described in additional detail below.

Patient population: primary cohort

All adults with COVID-19 and AKI treated with CKRT in the medical ICU at UNMH during the study period were included in the primary cohort. COVID-19 diagnosis was confirmed in all cases by PCR from a respiratory sample, most commonly from a nasopharyngeal swab (less commonly from endotracheal tube aspirate or bronchoalveolar lavage). Patients were excluded if initiated on KRT prior to admission, including patients with end-stage kidney disease (ESKD) on outpatient dialysis and those initiated on KRT for AKI an outside hospital prior to transfer to UNMH. Patients with ESKD were excluded as these patients have been consistently shown to have a substantially better prognosis when admitted to the ICU than critically ill patients with AKI requiring KRT [30–34]. In addition, those felt to be incidentally infected, as clinically determined and documented by the treating ICU teams, were excluded. Patients with kidney transplants were included. CKRT treatment sessions separated by >24 h were considered to be separate runs of CKRT, but only the 4-organ SOFA and full SOFA scores at the time of initiation of the first CKRT run were considered in the analysis.

Outcomes

The outcomes of interest were 30-day mortality and in-hospital mortality.

Data sources

All data for the UNMH cohort were retrospectively collected from the electronic medical record. The variables used to compute the SOFA scores included the most abnormal values from the 24-h period prior to initiation of CKRT whenever available. If a variable was not available in the prior 24 h (as was the case for 3 total bilirubin measurements), the most recent value was utilized. For a minority of patients, an arterial blood gas was not performed prior to CKRT initiation and the SOFA respiratory sub-score was derived from the oxygen saturation as measured by pulse oximetry as previously described [35]. Patients requiring extracorporeal membrane oxygenation (ECMO) or extracorporeal carbon dioxide removal (ECCO2R) at CKRT initiation were assigned a maximal SOFA respiratory sub-score of 4 regardless of their blood gas values.

To evaluate the performance of the 4-organ and full SOFA scores in an external dataset, we conducted a validation analysis using the MIMIC-IV database, version 3.1 [29,36,37]. Similar inclusion and exclusion criteria were applied as in the UNMH derivation cohort, with one exception being that—because COVID-19 PCR results were not available—COVID-19 cases were identified using the ICD-10 code (U07.1), as has been previously done in prior COVID-19-related analyses of the MIMIC-IV database [38]. The database contains comprehensive clinical data on patients admitted to the ICUs at Beth Israel Deaconess Medical Center, an academic medical center in Boston, MA, with 743 total beds and 77 ICU beds serving a diverse but racially and socioeconomically distinct population than that served by UNMH [39,40]. The database contains data from patients admitted to the ICU in 2008 through 2022, with the most recent updates including data from the COVID-19 pandemic.

Statistical analysis

Categorical variables are presented as frequencies and proportions. Continuous variables are presented by median, interquartile range (IQR), and range. To evaluate whether these scores have a statistically significant association with mortality, the medians of the 4-organ SOFA scores and the full SOFA scores were compared between survivors and non-survivors at 30 days and at discharge using Mann–Whitney U-tests. Statistical significance was defined as a one-sided p value of ≤0.05. To quantify the predictive value of these scores, receiver operating characteristic (ROC) curves were generated and the areas under the curves (AUROCs) with 95% confidence intervals (CIs) were computed. Data compilation and statistical analyses were performed using Excel (Microsoft, Redmond, WA) and R version 4.2.2 (R Foundation for Statistical Computing, Vienna, Austria).

Results

Patient inclusion, characteristics, and overall patient outcomes: primary cohort

Of the total 82 patients with COVID-19 treated with CKRT at UNMH during the time period, 11 patients with ESKD, 6 initiated on KRT at an outside hospital, and 2 felt to have incidental COVID-19 infection were excluded. Thus, a total of 63 patients were included in the final analysis (Figure 1). The demographics, baseline characteristics, and details of the CKRT treatment are presented in Table 1. The median age was 52 years, 71% of the cohort was male, and most subjects were requiring mechanical ventilation and vasopressor support at the time of CKRT initiation. The median Glasgow Coma Score (GCS) at CKRT initiation was 3, with the majority of subjects receiving intravenous sedatives and 18 (28.6%) subjects requiring paralytic agents. Overall, 17 patients survived to 30 days and 11 survived to discharge, yielding a 30-day mortality of 73.0% and in-hospital mortality of 82.5%.

Figure 1.

Figure 1.

Open in a new tab

Patient flow diagram for the primary UNMH cohort.

Abbreviations: CKRT, continuous kidney replacement therapy; COVID-19, coronavirus disease 2019; ESKD, end-stage kidney disease; KRT, kidney replacement therapy; UNMH, University of New Mexico Hospital.

Table 1.

Characteristics of the UNMH and MIMIC-IV cohorts.

  UNM (n = 63) MIMIC-IV (n = 88)
Demographics and Baseline Characteristics

  • Age (years)

 52 (39–66, 26–81) 62 (54–73, 27–91)

  • Female

 18 (29%) 26 (29.5%)

  • Body mass index (kg/m2)

 33 (26–37, 19.5–55)* 33 (29–38, 20.5–67)†

  • Prior COVID-19 vaccination

 0 –

  • Comorbidities

  • Diabetes

 31 (49%) 37 (42%)

  • Hypertension

 29 (46%) 41 (47%)

  • Chronic kidney disease

 17 (27%) 36 (41%)

  • Kidney transplant recipient

 2 (3.2%) 3 (3.4%)

  • Coronary artery disease or heart failure

 8 (13%) 35 (40%)

  • Prior respiratory disease‡

 12 (19%) 30 (34%)

  • Liver disease§

 11 (17%) 4 (4.5%)

  • Malignancy‖

 3 (4.8%) 7 (8.0%)
Characteristics at CKRT initiation

  • Use of Organ Support

  • Invasive mechanical ventilation

 59 (94%) 83 (94%)

  • Noninvasive ventilation

 1 (1.6%) 0 (0.0%)

  • ECMO

 9 (14%) 2 (2.3%)

  • ECCO2R

 2 (3.2%) –

  • Vasopressor therapy

 54 (86%) 80 (91%)

  • Renal variables

  • Pre-CKRT peak serum creatinine (mg/dL)

 3.31 (2.28–5.06, 0.44–9.29) 4.95 (3.62–6.38, 1.90–9.70)†

  • Pre-CKRT peak blood urea nitrogen (mg/dL)

 49 (36–81, 13–192) 80 (59.5–110, 26–219)†

  • Urine output in prior 24 hours (mL)

 150 (0–723, 0–2920) 178.5 (55–940, 0–3705)

  • Neurologic variables

  • GCS

 3 (3–7, 3–15) 3 (3–3, 3–14)

  • Intravenous sedative therapy

 58 (92%) 86 (99%)

  • Paralytic therapy

 18 (29%) 56 (64%)

  • Other baseline laboratory values

  • White blood cell count (× 109/L)

 16.4 (9.7–24.0, 1.1–63.1) 16.9 (13.4–24.1, 3.4–70.0)†

  • Platelet count (× 109/L)

 225 (125–339, 30–567) 246 (159.5–322.5, 25–543)†

  • Serum albumin (g/dL)

 2.0 (1.6–2.4, 0.7–3.3) 2.4 (2.0–2.7, 1.2–3.5)†

  • C-reactive protein (mg/dL)

 17.5 (8.3–22.9, 0.7–37.5)* 14.3 (8.2–21.1, 0.1–29.3)†

  • Lactate (mmol/L)

 2.0 (1.35–3.4, 0.4–21.9)* 1.60 (1.2–2.7, 0.5–25.0)†

  • Total bilirubin (mg/dL)

 0.9 (0.5–1.8, 0.1–28.8) 0.8 (0.5–2.1, 0.2–18.4)†
CKRT treatment characteristics

  • Time from admission to CKRT initiation (days)

 10 (3–19, 0–79) 5 (1 − 10.25, 0–45)

  • Duration of CKRT (hours)

 87.7 (31.0–283, 1.6–1510) 133.2 (65.9–218.7, 1.6–962)

  • Initial CKRT modality

  • Continuous venovenous hemodiafiltration

 62 (98%) –

  • Slow continuous ultrafiltration

 1 (1.6%) –

  • Indication(s) for CKRT initiation

  • Anuria or persistent oliguria

 50 (79%) –

  • Acidosis

 39 (62%) –

  • Volume overload/hypoxemia

 30 (48%) –

  • Hyperkalemia

 17 (27%) –

  • Number of CKRT runs

  • 1

 54 (86%) 63 (72%)

  • 2

 7 (11%) 18 (21%)

  • 3

 1 (1.6%) 5 (5.7%)

  • 4

 1 (1.6%) 2 (2.3%)
Outcomes

  • Hospital length of stay (days)

 24 (9–48, 1–142) 21 (12–38, 1–98)

  • Survival to day 30

 17 (27%) 37 (42.0%)

  • Survival to hospital discharge

 11 (17.5%) 25 (28.4%)
Open in a new tab

Categorical variables are presented as count (percentage). Continuous variables are presented as median (interquartile range, range). CKRT treatment sessions are considered distinct runs if separated by >24 h.

Variables marked with a dash (–) were not available in the MIMIC-IV database. *In the UNM cohort, body mass index was missing in 1 subject, C-reactive protein in 42, and lactate in 3. †In the MIMIC-IV cohort, body mass index was missing in 1 subject, serum creatinine in 10, blood urea nitrogen in 9, white blood cell in 9, platelets in 9, albumin in 21, C-reactive protein in 48, lactate in 18, and total bilirubin in 18. ‡Includes asthma, pulmonary hypertension, and obstructive sleep apnea. §Includes cirrhosis and alcoholic hepatitis. ‖Excludes non-melanoma skin cancer. Most subjects had multiple indications for CKRT.

Abbreviations: CKRT, continuous kidney replacement therapy; ECCO2R, extracorporeal carbon dioxide removal; ECMO, extracorporeal membrane oxygenation; GCS, Glasgow Coma Score.

Analysis of 4-organ SOFA and full SOFA scores

The 4-organ SOFA and full SOFA scores according to survival status are displayed in Figure 2. The median 4-organ SOFA score at CKRT initiation among patients who survived to day 30 was 7 (IQR 5.5–8.5, range 1–10) compared to 8 (IQR 7–10, range 5–13) among 30-day non-survivors, a difference which was statistically significant (p = 0.017), though the overlap between the distribution of scores was substantial. While a similar trend was observed for survival to discharge, the difference was no longer statistically significant, with a median 4-organ SOFA score of 8 (IQR 4–9, range 1–9) among those who survived to discharge versus 8 (IQR 7 − 9.75, range 5–13) among those who died before discharge (p = 0.071). Those with 4-organ SOFA score of 10 or greater (n = 13, 20.6%) had 100% in-hospital mortality.

Figure 2.

Figure 2.

Open in a new tab

Distributions of 4-organ SOFA scores and full SOFA scores at CKRT initiation among survivors and non-survivors at 30 days and at hospital discharge from the UNMH cohort.

The mean of each series is indicated by an X, while the boxes are labeled with the median and IQR. The whiskers denote the maximum and minimum values with any outliers (which lie greater than 1.5 * IQR from the median) indicated as separate points beyond the whiskers. P values are for difference in medians using the Mann Whitney U test and are one-sided.

Abbreviations: CKRT, continuous kidney replacement therapy; IQR, interquartile range; SOFA, Sequential Organ Failure Assessment; UNMH, University of New Mexico Hospital.

When comparing the full SOFA scores between survivors and non-survivors, the differences were not statistically significant. Among 30-day survivors the median full SOFA score at CKRT initiation was 14 (IQR 12–16, range 5–17) versus 15 (IQR 14–17, range 9–20) among 30-day non-survivors (p = 0.092). When considering survival to discharge, the median full SOFA score was 14 (IQR 10–16, range 5–17) among survivors while the median full SOFA was 15 (IQR 13.25–16.75, range 9–20) among those who died before discharge (p = 0.15).

The ROC curves for all 4 analyses are shown in Figure 3. Consistent with the comparison of medians, the 4-organ SOFA score performed best at predicting 30-day mortality, with an AUROC of 0.67 (95% CI 0.52–0.83). Though the 4-organ SOFA score outperformed the full SOFA score at both timepoints, the 95% CI of the AUROC of the 4-organ SOFA score to predict in-hospital mortality included 0.5 (i.e. did not exclude the possibility that 4-organ SOFA score has no value in predicting in-hospital mortality). Likewise, when considering the ability of the full SOFA score to predict mortality at both timepoints, the 95% CIs for the AUROCs also included 0.5.

Figure 3.

Figure 3.

Open in a new tab

Receiver operating characteristic (ROC) curves for 4-organ SOFA and full SOFA scores at CKRT initiation to predict 30-day and in-hospital mortality from the UNMH cohort. Areas under the ROC curves (AUCs) are shown with 95% confidence intervals.

Additional Abbreviations: CKRT, continuous kidney replacement therapy; SOFA, Sequential Organ Failure Assessment; UNMH, University of New Mexico Hospital.

External validation cohort

In the MIMIC-IV database, we identified another 88 subjects with a diagnosis code of COVID-19 that were treated CKRT admitted during the same date range as the UNMH cohort. Overall, the patient characteristics, treatments, and outcomes were similar to that of the UNMH cohort, though the median age was somewhat higher, the frequency of several baseline comorbidities differed somewhat, the rate of use of ECMO was lower, the rate of use of paralytic agents was higher, and the mortality was somewhat lower (Table 1). The overall 30-day mortality of the cohort was 58.0% and the in-hospital mortality was 71.6%.

The 4-organ SOFA and full SOFA scores according to survival status are displayed in Figure 4. The median 4-organ SOFA score at CKRT initiation among patients who survived to day 30 was 6 (IQR 5–8, range 0–11) compared to 7 (IQR 5.5–9, range 1–14) among 30-day non-survivors, a difference which was not statistically significant (p = 0.059). When assessing survival to discharge, the difference became statistically significant, with a median 4-organ SOFA score of 6 (IQR 4–7, range 0–9) among survivors versus 7 (IQR 6–9, range 1–14) among those who died before discharge (p = 0.00061). As with the UNMH cohort, those with 4-organ SOFA score of 10 or greater (n = 11, 12.5%) had 100% in-hospital mortality.

Figure 4.

Figure 4.

Open in a new tab

Distributions of 4-organ SOFA scores and full SOFA scores at CKRT initiation among survivors and non-survivors at 30 days and at hospital discharge from the MIMIC-IV cohort.

The medians are represented by the horizontal bold lines with the upper and lower extent of the boxes representing the IQR. The vertical lines denote the maximum and minimum values with any outliers indicated as separate points beyond. P values are for difference in medians using the Mann Whitney U test and are one-sided.

Abbreviations: CKRT, continuous kidney replacement therapy; IQR, interquartile range; Medical Information Mart for Intensive Care; SOFA, Sequential Organ Failure Assessment.

When comparing the full SOFA scores between survivors and non-survivors, the results were similar. Among 30-day survivors the median full SOFA score at CKRT initiation was 14 (IQR 13–15, range 6–17) versus 15 (IQR 13–16, range 8–22) among 30-day non-survivors, a difference which was not statistically significant (p = 0.11). When considering survival to discharge, the median full SOFA score was 13 (IQR 12–15, range 6–17) among survivors while the median full SOFA was 15 (IQR 13–16, range 8–22) among those who died before discharge (p = 0.0034).

The ROC curves for all 4 analyses are shown in Figure 5. As with the UNMH cohort, the AUROCs were overall similar but numerically slightly higher with the 4-organ SOFA scores compared to the full SOFA scores for both 30-day and in-hospital mortality. In contrast to the UNMH cohort, both the 4-organ SOFA and the full SOFA scores performed better for in-hospital mortality than 30-day mortality.

Figure 5.

Figure 5.

Open in a new tab

Receiver operating characteristic (ROC) curves for 4-organ SOFA and full SOFA scores at CKRT initiation to predict 30-day and in-hospital mortality from the MIMIC-IV cohort. Areas under the ROC curves (AUCs) are shown with 95% confidence intervals.

Additional Abbreviations: CKRT, continuous kidney replacement therapy; SOFA, Sequential Organ Failure Assessment.

Discussion

Primary findings

In this two-center retrospective analysis of 151 critically ill patients with COVID-19 and AKI requiring CKRT, we found that the 4-organ SOFA score performed as well as the full SOFA score in predicting short-term mortality. If anything, though the improvements were marginal, in all four comparisons performed (i.e. the prediction of both 30-day and in-hospital mortality for both the UNMH and the MIMIC-IV cohorts), the 4-organ SOFA scores performed slightly better with numerically higher AUROCs. However, importantly, regardless of which SOFA score was used, the level of discrimination achieved (i.e., AUCs ranging from 0.58 to 0.72) would be considered moderate at best [41]. Notably, the patients in our cohorts with 4-organ SOFA scores of 10 or greater (n = 13 [20.6%] from UNMH and n = 11 [12.5%] from MIMIC-IV) had 100% in-hospital mortality, potentially signifying a cutoff above which provision of CKRT may be nonbeneficial. Though these preliminary data are purely hypothesis-generating and require additional external multicenter validation and comparison to other triage tools, notably no other measure of severity of illness has been demonstrated to better predict short-term mortality specifically in patients with COVID-19 and AKI requiring CRKT. Therefore, in the absence of a superior metric, these findings suggest that 4-organ SOFA scores may potentially be a reasonable alternative to the full SOFA score in triaging CKRT machines and supplies in the setting of pandemic-related shortages.

Rationale for 4-organ SOFA score

The rationale for evaluating a 4-organ SOFA score to predict mortality in patients with COVID-19 and AKI requiring CKRT included the desire to maintain maximal objectivity when triaging resource allocation, our suspicion that the renal and neurologic sub-scores would not provide much independent prognostic value, and the practical consideration that a simpler score—if similarly predictive—would be clinically easier to use. We considered the importance of objectivity in any prognostic tool paramount to mitigating the risk of healthcare disparities during crisis standards of care, as we serve a minority-majority state and this pandemic disproportionately affected minorities [42,43].

First, the neurologic component of the SOFA score consists of the GCS which—though broadly applied to other patient populations and critical illness scenarios—was originally developed and validated to evaluate patients with traumatic brain injury (TBI). However, even in the setting of TBI, GCS has significant limitations [44]. Furthermore, in the setting of severe COVID-19, assessment of neurologic status is heavily confounded by frequent use of sedatives and paralytics to manage severe hypoxemia and facilitate lung-protective mechanical ventilation. Indeed, in our primary cohort, >90% of the patients were receiving intravenous sedatives at the time of CKRT initiation and 25–30% were being treated with neuromuscular blockade, with even higher rates of sedative and paralytic use in our external validation cohort, yielding a median GCS of 3 for both cohorts. However, this low median GCS score is likely more reflective of the patients’ respiratory status than neurologic status and therefore may not add much independent prognostic value beyond the respiratory sub-score. Though we only tabulated paralytic use at the time of CKRT initiation, if anything the rate of use of neuromuscular blockade in our primary cohort was lower than most other centers, with other observational studies of patients with COVID-19 requiring mechanical ventilation early in the pandemic reporting rates of paralytic use similar to that of the MIMIC-IV cohort at 60-88% [45–50]. These rates of paralytic use are substantially higher than the rate of 22% reported among the 2,377 mechanically ventilated patients described in the international multicenter LUNG SAFE study, which was the largest study describing ARDS epidemiology and practice patterns before the pandemic [51]. This dramatic increase in paralytic use during the pandemic would be expected to significantly erode the prognostic utility of GCS among patients with COVID-19 requiring mechanical ventilation. We suspect that the confounding effect of sedative and paralytic medications on GCS is the primary reason why the 4-organ SOFA score outperformed the full SOFA score in our cohort. Moreover, as each of the components of the GCS (eye, verbal, and motor response) is evaluated via physical examination using a continuous scale from no response to normal response, scoring involves some degree of subjectivity, especially in patients with borderline findings or inconsistent response to stimuli.

The decision to exclude the renal sub-score was felt conceptually appropriate given the device being allocated is a kidney support device. To some degree, the renal sub-score is unlikely to provide much prognostic value in a cohort defined by severe AKI. Indeed, in our primary cohort 50 subjects (nearly 80%) had a renal sub-score of 3 or 4. Furthermore, use of the renal sub-score in triaging KRT use could potentially allow for provider manipulation of the allocation system. For example, in a patient with COVID, AKI, and underlying advanced age, disability, or other characteristics that may make a provider subjectively less enthusiastic about providing aggressive care, barring severe hyperkalemia or other obvious life-threatening complication of AKI, the provider could theoretically opt to wait to consult nephrology for consideration of KRT until the patient’s serum creatinine rises sufficiently to up-stage their renal sub-score to a point that they no longer qualify for CKRT.

Use of SOFA score during the COVID-19 pandemic and other studies of SOFA score in COVID-19

SOFA scores were broadly incorporated into ventilator-allocation guidelines proposed by experts both prior to [52] and during [53] the pandemic and implemented in New Mexico and many other state departments of health [25–27]. For example, Piscitello et al. published in 2020 a systematic review of all publicly available U.S. state guidelines for ventilator allocation and found that, among the 26 states, which had such guidelines, 21 utilized SOFA or modified SOFA scores as their primary adult triage tool [25].

However, our findings are consistent with multiple other studies indicating that the ability of SOFA score to predict mortality in patients with COVID-19 is relatively poor. For example, Raschke et al. retrospectively evaluated 675 patients with COVID-19 requiring mechanical ventilation across 18 ICUs and found that SOFA scores, tabulated using the worst values available in the 48 h prior to endotracheal intubation, achieved an AUC for prediction of in-hospital mortality of only 0.59 [54]. Likewise, a prospective single-center cohort study from Iran analyzed over 1,000 patients admitted to the ICU with COVID-19, of which nearly 75% required mechanical ventilation upon admission, and found that—though the predictive value of SOFA score improved with serial assessment—SOFA score upon admission achieved an AUC of only 0.57 in predicting 28-day mortality [55]. Similarly, a prospective study of 166 patients admitted to the ICU with COVID-19 at a single U.S. center found that SOFA score was associated with hospital mortality on univariable analysis but not on multivariable analysis [56]. Finally, in a retrospective study of over 2,500 patients admitted with COVID-19 to a single U.S. academic center, the maximum SOFA recorded in the first 24 h after admission predicted in-hospital mortality with an AUC of 0.68, which was substantially lower than the AUC of 0.74 achieved using SOFA in a multicenter historical cohort of over 75,000 patients without COVID-19 admitted to over 300 U.S. ICUs [57].

Overall, SOFA score appears to perform better in general ICU populations with AKI, with several studies suggesting SOFA independently predicts mortality, outperforms other disease severity scores (e.g. Acute Physiology and Chronic Health Evaluation [APACHE] II score), and can achieve AUCs as high as >0.8 in predicting ICU mortality [58–61]. This is not entirely surprising, as SOFA score was originally developed for use in sepsis and sepsis is the most common etiology of or contributor to AKI in the ICU [24,62].

Prior data specific to patients with AKI or patients requiring CKRT in the setting of COVID-19 are limited [13,14,63]. Within this context, the finding that SOFA score overall performed poorly in both of our cohorts is noteworthy. However, two other studies also suggest that SOFA scores are less useful in patients with AKI and COVID-19. In a similar retrospective cohort of 45 patients with COVID-19 and AKI requiring KRT in Italy, median SOFA score at KRT initiation was the same among patients who survived or died by day 90 and SOFA score was not associated with either ICU or 90-day mortality on univariate or multivariable analyses [14]. Similarly, in a multicenter Korean cohort of 640 patients with COVID-19 requiring both mechanical ventilation and CKRT, SOFA score at the time of initiation of mechanical ventilation was higher among patients who required CKRT and suffered ICU mortality, but the association between SOFA score and mortality was lost upon multivariable analysis [13].

Studies of other disease severity scores in COVID-19

We did not compare full SOFA or 4-organ SOFA to more detailed illness severity scores, such as APACHE or Simplified Acute Physiology Score (SAPS), but notably not a single state included in the systematic review published by Piscitello et al. utilized APACHE or SAPS [25], likely reflecting the need to balance comprehensiveness of triage tools with the practical requirement for efficient and timely triage during pandemic surges. While the full SOFA score is composed of six organ-specific sub-scores, APACHE II includes 15 variables [64] while SAPS 3 includes over 30 [65]. Furthermore, while several studies suggest that APACHE or SAPS scores may predict prognosis of COVID-19 in general better than SOFA [66–70], SOFA outperformed APACHE or SAPS in other studies [71,72]. Moreover, very few studies analyzed APACHE or SAPS specifically in patients with COVID-19 and severe AKI, and these studies yielded mixed results. For example, one retrospective study of 215 patients found that APACHE II score independently predicts the risk of AKI in the patients with COVID-19 in the ICU [73], while another retrospective study of 465 patients with COVID-19 in the ICU failed to find an independent association between SAPS score and risk of AKI or risk of death among the AKI patients [74].

Study limitations

Our study has many important limitations. First, given its observational and retrospective nature, the study is subject to unmeasured confounding and other biases, and thus, these findings are considered only hypothesis-generating. Second, as a two-center study, these findings may have limited generalizability. Therefore, our finding that the 4-organ SOFA score performs as well as the full SOFA score in predicting mortality in patients with COVID-19 and AKI requiring CKRT score requires additional external validation in larger multicenter studies. Likewise, these results from early in the pandemic may not apply to other phases of the pandemic when other variants of COVID-19 (e.g. delta or omicron) were predominant. This is especially true given that the rates of AKI and overall mortality decreased substantially over the course of the pandemic [2]. Furthermore, given the limited sample size of both cohorts, the certainty of our findings is reduced as reflected in the wide confidence intervals for the calculated AUCs. In addition, the data obtained for this analysis are limited in both their comprehensiveness and granularity, precluding comparison of 4-organ SOFA scores to more sophisticated models of prognosis in patients with COVID-19 and AKI requiring CKRT. Moreover, our limited sample sizes would have significantly limited the number of variables we could potentially include if we attempted multiple regression or other multivariable modeling. That said, the purpose of this analysis was not to determine whether 4-organ SOFA score was independently associated with mortality. Rather, the purpose was to assess a simplified version—4-organ SOFA score—of a relatively simple bedside tool—full SOFA score—that was already being calculated in all patients admitted to our adult ICUs and many other institutions across the country during the pandemic. In addition, though our CKRT program did suffer major disruptions related to supply of CKRT solutions [15], fortunately we never exhausted our supply of CKRT machines and never had to implement a structured algorithm to triage CKRT machines. As a result, our report provides no information or practical wisdom about the real-life performance of the use of 4-organ SOFA score in CKRT device allocation. Finally, though the full SOFA was de facto the most commonly utilized triage tool in ICUs across the U.S. during the pandemic [25], we would caution against the use of either the full SOFA score or the 4-organ SOFA score—given their poor overall predictive ability—as triage tools outside the context of a pandemic, crisis standards of care, or similar resource-limited circumstances.

Conclusions

In this two-center retrospective analysis of 151 adults treated with CKRT for AKI in the setting of COVID-19, 4-organ SOFA score performed as well as the traditional full SOFA score in predicting short-term mortality. These findings are only hypothesis-generating and require external validation in larger multicenter studies and involving comparisons with alternative triage or severity-scoring systems. Nonetheless, these data suggest that—given the absence of a superior validated metric—this simplified version of the SOFA score may potentially be a reasonable alternative to use instead of the full SOFA score in triaging the allocation of CKRT during crisis standards of care during similar future pandemics. However, the discriminant value of either version of the SOFA score is modest at best, reinforcing prior studies that SOFA score overall has limited prognostic value in COVID-19 and underlying the need for future research to develop and validate superior triage tools for use in respiratory viral pandemics.

Disclosures statement

CK reports having ownership interest via current or previous stocks or options in AbbVie, Bristol Myers Squibb, Coherus BioSciences, Medtronic, and Pfizer. CPA reports consultancy agreements with Otsuka and Quanta and serving as a Medical Director of a DCI Inc. outpatient dialysis unit in Cuba, NM. JPT reports receiving research funding from or serving as a PI for studies by La Jolla Pharmaceutical Company/Innoviva, Sentien Biotechnologies, Rediscovery Life Sciences, Pfizer, and Astute Medical/bioMérieux; having ownership interest via current or previous stocks or options in Eli Lilly, Novo Nordisk A/S, and Pfizer; having a consultancy agreement with Outset Medical; and serving as a Medical Director of a DCI Inc. outpatient dialysis unit in Albuquerque, NM. None of these disclosures have relevance to this study or the content of this manuscript. The remaining authors have no conflicts of interest.

Data availability statement

The data for the primary cohort are available upon reasonable request and are subject to regulatory approval by the University of New Mexico Health Sciences Center Human Research Review Committee. The data from the MIMIC-IV cohort are publicly available at https://physionet.org/content/mimiciv/3.1/.

References

  • 1.United States Centers for Disease Control and Prevention . COVID Data Tracker: Trends in United States COVID-19 Deaths, Emergency Department (ED) Visits, and Test Positivity by Geographic Area. Accessed May 21; 2025. https://covid.cdc.gov/covid-data-tracker/#trends_totaldeaths.
  • 2.Teixeira JP, Barone S, Zahedi K, et al. Kidney injury in COVID-19: epidemiology, molecular mechanisms and potential therapeutic targets. Int J Mol Sci. Feb 17. 2022;23(4):2242. doi: 10.3390/ijms23042242. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Leazer S, Collen J, Alcover K, et al. Outcomes associated with intensive care and organ support among patients with COVID-19: a systematic review and meta-analysis. Mil Med. 2023;188(3-4):541–546. doi: 10.1093/milmed/usac143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Lin L, Wang X, Ren J, et al. Risk factors and prognosis for COVID-19-induced acute kidney injury: a meta-analysis. BMJ Open. 2020;10(11):e042573. doi: 10.1136/bmjopen-2020-042573. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Legrand M, Bell S, Forni L, et al. Pathophysiology of COVID-19-associated acute kidney injury. Nat Rev Nephrol. 2021;17(11):751–764. doi: 10.1038/s41581-021-00452-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Uchino S, Kellum JA, Bellomo R, et al. Acute renal failure in critically ill patients: a multinational, multicenter study. JAMA. 2005;294(7):813–818. doi: 10.1001/jama.294.7.813. [DOI] [PubMed] [Google Scholar]
  • 7.Wald R, McArthur E, Adhikari NK, et al. Changing incidence and outcomes following dialysis-requiring acute kidney injury among critically ill adults: a population-based cohort study. Am J Kidney Dis. 2015;65(6):870–877. doi: 10.1053/j.ajkd.2014.10.017. [DOI] [PubMed] [Google Scholar]
  • 8.Rewa OG, Ortiz-Soriano V, Lambert J, et al. Epidemiology and outcomes of AKI treated with continuous kidney replacement therapy: the multicenter CRRTnet study. Kidney Med. 2023;5(6):100641. doi: 10.1016/j.xkme.2023.100641. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Mohamed MMB, Lukitsch I, Torres-Ortiz AE, et al. Acute kidney injury associated with coronavirus disease 2019 in urban New Orleans. Kidney 360. 2020;1(7):614–622. doi: 10.34067/KID.0002652020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Chan L, Chaudhary K, Saha A, et al. AKI in hospitalized patients with COVID-19. J Am Soc Nephrol. 2021;32(1):151–160. doi: 10.1681/ASN.2020050615. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Gupta S, Coca SG, Chan L, et al. AKI treated with renal replacement therapy in critically ill patients with COVID-19. J Am Soc Nephrol. 2021;32(1):161–176. doi: 10.1681/ASN.2020060897. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Perez Ingles D, Illescas A, Perryman Collins N, et al. Impact of COVID-19 pandemic on crude mortality rates associated with acute kidney injury requiring continuous renal replacement therapy: a single-center study [abstract]. Am J Respir Crit Care Med. 2021;203: A2582–A2582. American Thoracic Society International Conference Abstracts. doi: 10.1164/ajrccm-conference.2021.203.1_MeetingAbstracts.A2582. [DOI] [Google Scholar]
  • 13.Choi DE, Kim DK, Park S, et al. Clinical characteristics and prognosis of patients with COVID-19 on mechanical ventilation undergoing continuous renal replacement therapy. PLoS One. 2024;19(4):e0297344. doi: 10.1371/journal.pone.0297344. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Godi I, Pasin L, Ballin A, et al. Long-term outcome of COVID-19 patients with acute kidney injury requiring kidney replacement therapy. J Anesth Analg Crit Care. May 9. 2024;4(1):32. doi: 10.1186/s44158-024-00163-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Teixeira JP, Saa L, Kaucher KA, et al. Rapid implementation of an emergency on-site CKRT dialysate production system during the COVID-19 pandemic. BMC Nephrol. Aug 22. 2023;24(1):245. doi: 10.1186/s12882-023-03260-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Goldfarb DS, Benstein JA, Zhdanova O, et al. Impending shortages of kidney replacement therapy for COVID-19 patients. Clin J Am Soc Nephrol. 2020;15(6):880–882. doi: 10.2215/CJN.05180420. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Lumlertgul N, Tunstell P, Watts C, et al. In-house production of dialysis solutions to overcome challenges during the coronavirus disease 2019 pandemic. Kidney Int Rep. 2021;6(1):200–206. doi: 10.1016/j.ekir.2020.10.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Division of Nephrology Columbia University Vagelos College of Physicians . Disaster response to the COVID-19 pandemic for patients with kidney disease in New York City. J Am Soc Nephrol. 2020;31(7):1371–1379. doi: 10.1681/ASN.2020040520. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Anger MS, Mullon C, Ficociello LH, et al. Meeting the demand for renal replacement therapy during the COVID-19 pandemic: a manufacturer’s perspective. Kidney360. 2021;2(2):350–354. doi: 10.34067/KID.0006192020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Abelson R, Fink S, Kulish N, et al. An overlooked, possibly fatal coronavirus crisis: a dire need for kidney dialysis. New York Times. April 19 2020;169:1; Updated November 1, 2021; Accessed June 11, 2025. https://www.nytimes.com/2020/04/18/health/kidney-dialysis-coronavirus.html?smid=url-share [Google Scholar]
  • 21.Kulish N. A life and death battle: 4 days of kidney failure but no dialysis. New York Times. Updated May 1,. 2020; Accessed June 11, 2025. [Google Scholar]
  • 22.New Mexico Department of Health . COVID-19 Variant of Concern Annual Reports. Accessed June 11, ; 2025. https://cv.nmhealth.org/epidemiology-reports/.
  • 23.New Mexico Department of Health . New Mexico Crisis Standards of Care Plan. Accessed May 25; 2025. https://nmhealth.org/publication/view/plan/4877/.
  • 24.Vincent JL, Moreno R, Takala J, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med. 1996;22(7):707–710. doi: 10.1007/BF01709751. [DOI] [PubMed] [Google Scholar]
  • 25.Piscitello GM, Kapania EM, Miller WD, et al. Variation in ventilator allocation guidelines by US state during the coronavirus disease 2019 pandemic: a systematic review. JAMA Netw Open. 2020;3(6):e2012606. doi: 10.1001/jamanetworkopen.2020.12606. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.White DB, Lo B.. A framework for rationing ventilators and critical care beds during the COVID-19 pandemic. JAMA. 2020;323(18):1773–1774. doi: 10.1001/jama.2020.5046. [DOI] [PubMed] [Google Scholar]
  • 27.New York State Department of Health and New York State Task Force on Life and the Law . Ventilator Allocation Guidelines. Accessed February 25, ; 2025. https://www.health.ny.gov/press/releases/2015/2015-11-25_ventilator_allocation_guidelines.htm.
  • 28.US Department of Health and Human Services, Health Resources and Services Administration . MUA (Medically Underserved Areas) Find. Accessed February 20, ; 2025. http://muafind.hrsa.gov/index.aspx.
  • 29.Johnson AEW, Bulgarelli L, Shen L, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023;10(1):219. doi: 10.1038/s41597-022-01899-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Clermont G, Acker CG, Angus DC, et al. Renal failure in the ICU: comparison of the impact of acute renal failure and end-stage renal disease on ICU outcomes. Kidney Int. 2002;62(3):986–996. doi: 10.1046/j.1523-1755.2002.00509.x. [DOI] [PubMed] [Google Scholar]
  • 31.Ostermann M, Chang R, Riyadh I, Riyadh ICU Program Users Group . Renal failure in the intensive care unit: acute kidney injury compared to end-stage renal failure. Crit Care. 2008;12(5):432. doi: 10.1186/cc7085. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Hussain S, Piering W, Mohyuddin T, et al. Outcome among patients with acute renal failure needing continuous renal replacement therapy: a single center study. Hemodial Int. 2009;13(2):205–214. doi: 10.1111/j.1542-4758.2009.00342.x. [DOI] [PubMed] [Google Scholar]
  • 33.Rocha E, Soares M, Valente C, et al. Outcomes of critically ill patients with acute kidney injury and end-stage renal disease requiring renal replacement therapy: a case-control study. Nephrol Dial Transplant. 2009;24(6):1925–1930. doi: 10.1093/ndt/gfn750. [DOI] [PubMed] [Google Scholar]
  • 34.Walcher A, Faubel S, Keniston A, et al. In critically ill patients requiring CRRT, AKI is associated with increased respiratory failure and death versus ESRD. Ren Fail. 2011;33(10):935–942. doi: 10.3109/0886022X.2011.615964. [DOI] [PubMed] [Google Scholar]
  • 35.Pandharipande PP, Shintani AK, Hagerman HE, et al. Derivation and validation of Spo2/Fio2 ratio to impute for Pao2/Fio2 ratio in the respiratory component of the Sequential Organ Failure Assessment score. Crit Care Med. 2009;37(4):1317–1321. doi: 10.1097/CCM.0b013e31819cefa9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Johnson A, Bulgarelli L, Pollard T, et al. MIMIC-IV (version 3.1). PhysioNet. Accessed June 11, ; 2025. https://physionet.org/content/mimiciv/3.1/.
  • 37.Goldberger AL, Amaral LA, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101(23):E215–20. doi: 10.1161/01.cir.101.23.e215. [DOI] [PubMed] [Google Scholar]
  • 38.Zhao J, Jiang HH, Wan HH, et al. The impact of dexamethasone on short- and long-term mortality in hospitalized COVID-19 patients: a retrospective study. BMC Infect Dis. 2024;24(1):1343. doi: 10.1186/s12879-024-10216-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Staff Report to the Commissioner for a Determination of Need . Accessed June 11, ; 2025. https://www.mass.gov/doc/beth-israel-lahey-health-inc-don-required-equipment-staff-report/download.
  • 40.Patterson B, Scharmen TN, Steimel LUNM Hospitals: A Community Perspective on Access and Spending, January ‐ December, et al. ; 2008. Accessed June 11, 2025. https://hsc.unm.edu/about/community/docs/2008communityperspectives.pdf.
  • 41.de Hond AAH, Steyerberg EW, van Calster B.. Interpreting area under the receiver operating characteristic curve. Lancet Digit Health. 2022;4(12):e853–e855. doi: 10.1016/S2589-7500(22)00188-1. [DOI] [PubMed] [Google Scholar]
  • 42.Gershengorn HB, Holt GE, Rezk A, et al. Assessment of disparities associated with a crisis standards of care resource allocation algorithm for patients in 2 US hospitals during the COVID-19 pandemic. JAMA Netw Open. 2021;4(3):e214149. doi: 10.1001/jamanetworkopen.2021.4149. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.White DB, Lo B.. Mitigating inequities and saving lives with ICU triage during the COVID-19 pandemic. Am J Respir Crit Care Med. 2021;203(3):287–295. doi: 10.1164/rccm.202010-3809CP. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Bodien YG, Barra A, Temkin NR, et al. Diagnosing level of consciousness: the limits of the Glasgow Coma Scale total score. J Neurotrauma. 2021;38(23):3295–3305. doi: 10.1089/neu.2021.0199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Schenck EJ, Hoffman K, Goyal P, et al. Respiratory mechanics and gas exchange in COVID-19-associated respiratory failure. Ann Am Thorac Soc. 2020;17(9):1158–1161. doi: 10.1513/AnnalsATS.202005-427RL. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Gupta S, Hayek SS, Wang W, et al. Factors associated with death in critically ill patients with coronavirus disease 2019 in the US. JAMA Intern Med. 2020;180(11):1436–1447. doi: 10.1001/jamainternmed.2020.3596. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Ferrando C, Suarez-Sipmann F, Mellado-Artigas R, et al. Clinical features, ventilatory management, and outcome of ARDS caused by COVID-19 are similar to other causes of ARDS. Intensive Care Med. 2020;46(12):2200–2211. doi: 10.1007/s00134-020-06192-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.COVID-ICU Group on behalf of the REVA Network and the COVID-ICU Investigators . Clinical characteristics and day-90 outcomes of 4244 critically ill adults with COVID-19: a prospective cohort study. Intensive Care Med. 2021;47(1):60–73. doi: 10.1007/s00134-020-06294-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Dres M, Hajage D, Lebbah S, et al. Characteristics, management, and prognosis of elderly patients with COVID-19 admitted in the ICU during the first wave: insights from the COVID-ICU study: prognosis of COVID-19 elderly critically ill patients in the ICU. Ann Intensive Care. 2021;11(1):77. doi: 10.1186/s13613-021-00861-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Roger C, Collange O, Mezzarobba M, et al. French multicentre observational study on SARS-CoV-2 infections intensive care initial management: the FRENCH CORONA study. Anaesth Crit Care Pain Med. 2021;40(4):100931. doi: 10.1016/j.accpm.2021.100931. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Bellani G, Laffey JG, Pham T, et al. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. JAMA. 2016;315(8):788–800. doi: 10.1001/jama.2016.0291. [DOI] [PubMed] [Google Scholar]
  • 52.Daugherty Biddison EL, Faden R, Gwon HS, et al. Too many patients…a framework to guide statewide allocation of scarce mechanical ventilation during disasters. Chest. 2019;155(4):848–854. doi: 10.1016/j.chest.2018.09.025. [DOI] [PubMed] [Google Scholar]
  • 53.Ehmann MR, Zink EK, Levin AB, et al. Operational recommendations for scarce resource allocation in a public health crisis. Chest. 2021;159(3):1076–1083. doi: 10.1016/j.chest.2020.09.246. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Raschke RA, Agarwal S, Rangan P, et al. Discriminant accuracy of the SOFA score for determining the probable mortality of patients with COVID-19 pneumonia requiring mechanical ventilation. JAMA. 2021;325(14):1469–1470. doi: 10.1001/jama.2021.1545. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Esmaeili Tarki F, Afaghi S, Rahimi FS, et al. Serial SOFA-score trends in ICU-admitted COVID-19 patients as predictor of 28-day mortality: a prospective cohort study. Health Sci Rep. 2023;6(5):e1116. doi: 10.1002/hsr2.1116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Burke E, Haber E, Pike CW, et al. Outcomes of renal replacement therapy in the critically ill with COVID-19. Med Intensiva. 2021;45(6):325–331. doi: 10.1016/j.medine.2021.02.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Sherak RAG, Sajjadi H, Khimani N, et al. SOFA score performs worse than age for predicting mortality in patients with COVID-19. PLoS One. 2024;19(5):e0301013. doi: 10.1371/journal.pone.0301013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Chang CH, Fan PC, Chang MY, et al. Acute kidney injury enhances outcome prediction ability of sequential organ failure assessment score in critically ill patients. PLoS One. 2014;9(10):e109649. doi: 10.1371/journal.pone.0109649. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Wang H, Kang X, Shi Y, et al. SOFA score is superior to APACHE-II score in predicting the prognosis of critically ill patients with acute kidney injury undergoing continuous renal replacement therapy. Ren Fail. 2020;42(1):638–645. doi: 10.1080/0886022X.2020.1788581. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Li L, Bai M, Zhang Q, et al. Characteristics and predictors of ICU-mortality in critically ill patients with hyperlactatemia requiring CRRT: a retrospective cohort study. Int J Artif Organs. 2022;45(12):973–980. doi: 10.1177/03913988221126728. [DOI] [PubMed] [Google Scholar]
  • 61.Medina-Liabres KRP, Jeong JC, Oh HJ, et al. Mortality predictors in critically ill patients with acute kidney injury requiring continuous renal replacement therapy. Kidney Res Clin Pract. 2021;40(3):401–410. doi: 10.23876/j.krcp.20.205. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Griffin BR, Liu KD, Teixeira JP.. Critical Care Nephrology: core Curriculum 2020. Am J Kidney Dis. 2020;75(3):435–452. doi: 10.1053/j.ajkd.2019.10.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Patel D, Cama-Olivares A, Tamhane A, et al. Clinical factors associated with hospital mortality in critically ill adult COVID patients with AKI requiring CRRT [abstract. ]. American Journal of Kidney Diseases. 2024;83(4):S3. doi: 10.1053/j.ajkd.2024.01.011. [DOI] [Google Scholar]
  • 64.Knaus WA, Draper EA, Wagner DP, et al. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13(10):818–829. doi: 10.1097/00003246-198510000-00009. [DOI] [PubMed] [Google Scholar]
  • 65.Moreno RP, Metnitz PG, Almeida E, et al. SAPS 3–From evaluation of the patient to evaluation of the intensive care unit. Part 2: development of a prognostic model for hospital mortality at ICU admission. Intensive Care Med. 2005;31(10):1345–1355. doi: 10.1007/s00134-005-2763-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Wilfong EM, Lovly CM, Gillaspie EA, et al. Severity of illness scores at presentation predict ICU admission and mortality in COVID-19. J Emerg Crit Care Med. 2021;5:7–7. doi: 10.21037/jeccm-20-92. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Vicka V, Januskeviciute E, Miskinyte S, et al. Comparison of mortality risk evaluation tools efficacy in critically ill COVID-19 patients. BMC Infect Dis. 2021;21(1):1173. doi: 10.1186/s12879-021-06866-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Vedovati MC, Barbieri G, Urbini C, et al. Clinical prediction models in hospitalized patients with COVID-19: A multicenter cohort study. Respir Med. 2022;202:106954. doi: 10.1016/j.rmed.2022.106954. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Zhang S, Huang S, Liu J, et al. Identification and validation of prognostic factors in patients with COVID-19: A retrospective study based on artificial intelligence algorithms. J Intensive Med. 2021;1(2):103–109. doi: 10.1016/j.jointm.2021.04.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Bartenschlager CC, Brunner JO, Kubiciel M, et al. Evaluation of score-based tertiary triage policies during the COVID-19 pandemic: simulation study with real-world intensive care data. Med Klin Intensivmed Notfmed. 2025;120(4):307–315. Beurteilung von scorebasierten Strategien tertiarer Triage wahrend der COVID-19-Pandemie: simulationsstudie mit realen Daten aus der Intensivpflege. doi: 10.1007/s00063-024-01162-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Monk M, Torres J, Vickery K, et al. A comparison of ICU mortality scoring systems applied to COVID-19. Cureus. 2023;15(2):e35423. doi: 10.7759/cureus.35423. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Nogueira MCA, Nobre V, Pires MC, et al. Assessment of risk scores to predict mortality of COVID-19 patients admitted to the intensive care unit. Front Med (Lausanne). 2023;10:1130218. doi: 10.3389/fmed.2023.1130218. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Anumas S, Chueachinda S, Tantiyavarong P, et al. The prediction score of acute kidney injury in patients with severe COVID-19 infection. J Clin Med. 2023;12(13):4412. doi: 10.3390/jcm12134412. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Bouguezzi N, Ben Saida I, Toumi R, et al. Clinical features and outcomes of acute kidney injury in critically ill COVID-19 patients: a retrospective observational study. J Clin Med. 2023;12(15):5127. doi: 10.3390/jcm12155127. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data for the primary cohort are available upon reasonable request and are subject to regulatory approval by the University of New Mexico Health Sciences Center Human Research Review Committee. The data from the MIMIC-IV cohort are publicly available at https://physionet.org/content/mimiciv/3.1/.

Articles from Renal Failure are provided here courtesy of Taylor & Francis

RESOURCES