Evidence map›Paper›PMID 32965501›Full record

ArticleJAMA network open2020

Association of Red Blood Cell Distribution Width With Mortality Risk in Hospitalized Adults With SARS-CoV-2 Infection.

Brody H Foy, Jonathan C T Carlson, Erik Reinertsen, Raimon Padros I Valls, Roger Pallares Lopez, Eric Palanques-Tost, Christopher Mow, M Brandon Westover, Aaron D Aguirre, John M Higgins

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in JAMA network open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06755658 (RDW), which is not on this map. Cited by 153 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
153citing papers in PubMed, 5 pooled it
21.6field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06755658 active not recruitingnot on this mapstarted 2021, after this paper: background citation

RDW (red Cell Distribution Width) and What Has Happened to the Platelets. is There an Association of RDW and Platelet Kinetics (PLTs and MPV/PLT Ratio), with Survival Outcome in Covid -19 Hospitalised Patients?

TypeobservationalSponsorSismanoglio General HospitalRan2021 to 2025Enrolled500ConditionsInfection by SARS-COV-2
3 · Its place in the literature

Who cites it

153 citing papers in PubMed, 5 syntheses or guidelines pooled it, 312 citations in OpenAlex.

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  8. Predicting the severity of COVID-19 using machine learning methods.BMC medical informatics and decision making · 2026
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93 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 1 country.

Brody H FoyCenter for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston.
Jonathan C T CarlsonCenter for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston.
Erik ReinertsenCenter for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston.
Raimon Padros I VallsCenter for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston.
Roger Pallares LopezCenter for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston.
Eric Palanques-TostCenter for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston.
Christopher MowCenter for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston.
M Brandon WestoverClinical Data AI Center and Neurology Department, Massachusetts General Hospital, Boston.
Aaron D AguirreCenter for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston.
John M HigginsCenter for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston.
Massachusetts General Hospital · USCenter for Systems Biology · USHarvard University · US

Funding

Integrative Motor Activity Biomarker for the Risk of Alzheimer's RiskRF1AG064312 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI HU, KUN · 2019 to 2019
$3.6M
Multimodal Network Connectivity Architecture (MOCA) of the Brain and its Role in the Recovery of Consciousness in Comatose Cardiac Arrest PatientsR01NS102574 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI GREER, DAVID MATTHEW, WU, ONA · 2018 to 2022
$3.4M
Investigation of Sleep in the Intensive Care Unit (ICU-SLEEP)R01NS102190 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI WESTOVER, MICHAEL BRANDON · 2018 to 2022
$3.2M
Big Data and Deep Learning for the Interictal-Ictal-Injury ContiuumR01NS107291 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI WESTOVER, MICHAEL BRANDON · 2018 to 2022
$2.8M
NIA NIH HHS RF1 AG064312NINDS NIH HHS R01 NS102190NINDS NIH HHS R01 NS102574NINDS NIH HHS R01 NS107291
6 · The paper itself

Abstract

Importance: Coronavirus disease 2019 (COVID-19) is an acute respiratory illness with a high rate of hospitalization and mortality. Biomarkers are urgently needed for patient risk stratification. Red blood cell distribution width (RDW), a component of complete blood counts that reflects cellular volume variation, has been shown to be associated with elevated risk for morbidity and mortality in a wide range of diseases. Objective: To investigate whether an association between mortality risk and elevated RDW at hospital admission and during hospitalization exists in patients with COVID-19. Design, Setting, and Participants: This cohort study included adults diagnosed with SARS-CoV-2 infection and admitted to 1 of 4 hospitals in the Boston, Massachusetts area (Massachusetts General Hospital, Brigham and Women's Hospital, North Shore Medical Center, and Newton-Wellesley Hospital) between March 4, 2020, and April 28, 2020. Main Outcomes and Measures: The main outcome was patient survival during hospitalization. Measures included RDW at admission and during hospitalization, with an elevated RDW defined as greater than 14.5%. Relative risk (RR) of mortality was estimated by dividing the mortality of those with an elevated RDW by the mortality of those without an elevated RDW. Mortality hazard ratios (HRs) and 95% CIs were estimated using a Cox proportional hazards model. Results: A total of 1641 patients were included in the study (mean [SD] age, 62[18] years; 886 men [54%]; 740 White individuals [45%] and 497 Hispanic individuals [30%]; 276 nonsurvivors [17%]). Elevated RDW (>14.5%) was associated with an increased mortality risk in patients of all ages. The RR for the entire cohort was 2.73, with a mortality rate of 11% in patients with normal RDW (1173) and 31% in those with an elevated RDW (468). The RR in patients younger than 50 years was 5.25 (normal RDW, 1% [n = 341]; elevated RDW, 8% [n = 65]); 2.90 in the 50- to 59-year age group (normal RDW, 8% [n = 256]; elevated RDW, 24% [n = 63]); 3.96 in the 60- to 69-year age group (normal RDW, 8% [n = 226]; elevated RDW, 30% [104]); 1.45 in the 70- to 79-year age group (normal RDW, 23% [n = 182]; elevated RDW, 33% [n = 113]); and 1.59 in those ≥80 years (normal RDW, 29% [n = 168]; elevated RDW, 46% [n = 123]). RDW was associated with mortality risk in Cox proportional hazards models adjusted for age, D-dimer (dimerized plasmin fragment D) level, absolute lymphocyte count, and common comorbidities such as diabetes and hypertension (hazard ratio of 1.09 per 0.5% RDW increase and 2.01 for an RDW >14.5% vs ≤14.5%; P < .001). Patients whose RDW increased during hospitalization had higher mortality compared with those whose RDW did not change; for those with normal RDW, mortality increased from 6% to 24%, and for those with an elevated RDW at admission, mortality increased from 22% to 40%. Conclusions and Relevance: Elevated RDW at the time of hospital admission and an increase in RDW during hospitalization were associated with increased mortality risk for patients with COVID-19 who received treatment at 4 hospitals in a large academic medical center network.

Indexed as

Erythrocyte IndicesErythrocytesHospitalizationAdultAgedAged, 80 and overBetacoronavirusBiomarkersBostonCoronavirusCoronavirus InfectionsCOVID-19FemaleHospitalsHumansMaleBiomarkers

Identifiers

PMID32965501
PMCPMC7512057
OpenAlexW3087741580

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.