External validation of the COVID-19 4C mortality score in an urban United States cohort

Riley, Joshua M.; Moeller, Patrick J.; Crawford, Albert; Schaefer, Joseph; Cheney-Peters, Dianna R.; Venkataraman, Chantel M.; Li, Chris J.; Smaltz, Christa; Bradley, Conor G.; Lee, Crystal Y.; Fitzpatrick, Danielle M.; Ney, David B.; Zaret, D.; Chalikonda, Divya; Mairose, Joshua D.; Chauhan, Kashyap; Szot, Margaret V.; Jones, Robert B.; Bashir-Hamidu, Rukaiya; Kubey, Alan

doi:10.1016/j.amjms.2022.04.030

Cited by 5 publications

(5 citation statements)

References 23 publications

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“…Most of the literature considers point-of-admission scores, including the 4C Mortality score,57 which is based on demographic information, comorbidities and blood tests taken on admission. This score performed well in both large development and validation cohorts (AUROC 0.77 and 0.79, respectively) and has now been externally validated in several countries 58–64. Similarly the point-of-admission ISARIC 4C Deterioration score3 has been externally validated 58 60 65 66.…”

Section: Discussionmentioning

confidence: 91%

“…This score performed well in both large development and validation cohorts (AUROC 0.77 and 0.79, respectively) and has now been externally validated in several countries. [58][59][60][61][62][63][64] Similarly the point-of-admission ISARIC 4C Deterioration score 3 has been externally validated. 58 60 65 66 Promising machine-learning alternatives have also been recently proposed, 67 68 although these have not yet been independently validated in contrast to the 4C scores.…”

Section: Sensitivity Analysesmentioning

confidence: 99%

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Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study

et al. 2022

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ObjectivesTo develop a disease stratification model for COVID-19 that updates according to changes in a patient’s condition while in hospital to facilitate patient management and resource allocation.DesignIn this retrospective cohort study, we adopted a landmarking approach to dynamic prediction of all-cause in-hospital mortality over the next 48 hours. We accounted for informative predictor missingness and selected predictors using penalised regression.SettingAll data used in this study were obtained from a single UK teaching hospital.ParticipantsWe developed the model using 473 consecutive patients with COVID-19 presenting to a UK hospital between 1 March 2020 and 12 September 2020; and temporally validated using data on 1119 patients presenting between 13 September 2020 and 17 March 2021.Primary and secondary outcome measuresThe primary outcome is all-cause in-hospital mortality within 48 hours of the prediction time. We accounted for the competing risks of discharge from hospital alive and transfer to a tertiary intensive care unit for extracorporeal membrane oxygenation.ResultsOur final model includes age, Clinical Frailty Scale score, heart rate, respiratory rate, oxygen saturation/fractional inspired oxygen ratio, white cell count, presence of acidosis (pH <7.35) and interleukin-6. Internal validation achieved an area under the receiver operating characteristic (AUROC) of 0.90 (95% CI 0.87 to 0.93) and temporal validation gave an AUROC of 0.86 (95% CI 0.83 to 0.88).ConclusionsOur model incorporates both static risk factors (eg, age) and evolving clinical and laboratory data, to provide a dynamic risk prediction model that adapts to both sudden and gradual changes in an individual patient’s clinical condition. On successful external validation, the model has the potential to be a powerful clinical risk assessment tool.Trial registrationThe study is registered as ‘researchregistry5464’ on the Research Registry (www.researchregistry.com).

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Section: Discussionmentioning

confidence: 91%

Section: Sensitivity Analysesmentioning

confidence: 99%

Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study

et al. 2022

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“…The 4C Mortality score incorporates age, sex, comorbidities, respiratory rate, peripheral oxygen saturation, Glasgow coma score, blood urea nitrogen, and C-reactive protein (CRP). The 4C Mortality Score ranges from 0 to 21 with risk groups de ned by Knight et al as low (0-3), intermediate (4-8), high (9)(10)(11)(12)(13)(14), and very high (≥ 15). ( 7) When more than one value within the rst 24 hours from hospital admission were available, the rst one was used.…”

Section: C Mortality Score Calculationmentioning

confidence: 99%

External validation of the ISARIC 4C Mortality Score for hospitalized patients with COVID-19 in Tunisia

Ali,

Abdullah,

Sekma

et al. 2023

Preprint

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Estimating mortality risk in hospitalized patients with COVID-19 infection may help clinicians to early triage patients with poor prognostic outcome. The Coronavirus Clinical Characterization Consortium Mortality Score (4C Score) is one of the predictive models that was externally validated in large cohorts. However, its use may be limited in population with quite different demographic and epidemiologic features. Objective To externally validate the 4 C score in a large Tunisian population Methods Multicenter retrospective cohort study of patients aged ≥ 14 years, hospitalized with the diagnosis of COVID-19. The primary outcome was in-hospital mortality, need for ICU admission and combined outcome (in-hospital mortality and/or ICU admission). We calculated the area under the receiver operating characteristic (ROC) curve (C statistics) for the 4C Mortality Score to assess the discriminatory power of the 4C Mortality Score for predicting outcomes. To assess calibration of the model, we used the Hosmer-Lemeshow goodness-of-fit test. Results 2327 patients with diagnosis of COVID-19 based on positive RT-PCR assay or rapid antigen test of a nasopharyngeal swab were included for final analysis. Median time between symptoms start and hospital admission was 4 days [2-7], and 69.2% needed oxygen therapy at hospital admission. In-hospital mortality was 15.4% (n=358); most deaths (11%, n=257) occurred in the ICU. Mortality rates within the 4C Mortality Score risk groups were 0.6% (Low), 8.7% (Intermediate), 53.1% (High), and 37.7% (Very High). The score achieved a good estimated discrimination when predicting death (C-statistic:0.86; 95%, CI [0.84-0.88]), ICU admission (C-statistic: 0.69; 95%, CI [0.65-0.72]) and the combined outcome (C-statistic:0.79; 95%, CI [0.77-0.81]). The calibration plot indicated good calibration for both in-hospial mortality and combined outcome (HosmerLemeshow goodness-of-fit test p value of 0.86 and 0.28 respectively). Our study represents a new external validation of the 4C score in COVID-19 patients with high reliability in predicting disease severity. These findings imply that the 4C Mortality Score may be generalized to patients with COVID-19 regardless of ethnicity and healthcare system.

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“…All risk scores were calculated as recommended in the derivation and validation studies, using the appropriate patient-level data [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. Elementary data about risk scores are shown in Supplementary Table S1.…”

Section: Risk Scores Of Interestsmentioning

confidence: 99%

“…The risk score ranges from 0 to 20 and divides patients into 4 risk groups based on the inhospital mortality [16]. Several external validation studies showed satisfactory performance of this risk score in assessing mortality risk [30,31].…”

Section: Risk Scores Of Interestsmentioning

confidence: 99%

Association of Different Risk Scores and 30-Day Mortality in Kidney Transplant Recipients with COVID-19

2023

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Background and Objectives: Clinical risk scores were poorly examined in kidney transplant recipients (KTR) with COVID-19. Materials and Methods: This observational study compared the association and discrimination of clinical risk scores (MEWS, qCSI, VACO, PSI/PORT, CCI, MuLBSTA, ISTH-DIC, COVID-GRAM and 4C) with 30-day mortality in 65 hospitalized KTRs with COVID-19. Cox regression was used to derive hazard ratios (HR) and 95% confidence intervals (95%CI), and discrimination was assessed by Harrell’s C. Results: A significant association with 30-day mortality was demonstrated for MEWS (HR 1.65 95%CI 1.21–2.25, p = 0.002); qCSI (HR 1.32 95%CI 1.15–1.52, p < 0.001); PSI/PORT (HR 1.04 95%CI 1.02–1.07, p = 0.001); CCI (HR 1.79 95%CI 1.13-2.83, p = 0.013); MuLBSTA (HR 1.31 95%CI 1.05–1.64, p = 0.017); COVID-GRAM (HR 1.03 95%CI 1.01–1.06, p = 0.004); and 4C (HR 1.79 95%CI 1.40–2.31, p < 0.001). After multivariable adjustment, significant association persisted for qCSI (HR 1.33 95% CI 1.11–1.59, p = 0.002); PSI/PORT (HR 1.04 95% CI 1.01–1.07, p = 0.012); MuLBSTA (HR 1.36 95% CI 1.01–1.85, p = 0.046); and 4C Mortality Score (HR 1.93 95% CI 1.45–2.57, p < 0.001) risk scores. The best discrimination was observed with the 4C score (Harrell’s C = 0.914). Conclusions: Risk scores such as qCSI, PSI/PORT and 4C showed the best association with 30-day mortality amongst KTRs with COVID-19.

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External validation of the COVID-19 4C mortality score in an urban United States cohort

Cited by 5 publications

References 23 publications

Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study

Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study

External validation of the ISARIC 4C Mortality Score for hospitalized patients with COVID-19 in Tunisia

Association of Different Risk Scores and 30-Day Mortality in Kidney Transplant Recipients with COVID-19

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