Evidence map›Paper›PMID 34976472›Full record

ArticleCureus2021

Mortality Prediction of COVID-19 Patients at Intensive Care Unit Admission.

Rajarajan Ganesan, Varun Mahajan, Karan Singla, Sushant Konar, Tanvir Samra, Senthil K Sundaram, Vikas Suri, Mandeep Garg, Naveen Kalra, Goverdhan D Puri

Open access · diamondAbstract read
In one paragraph

Article in Cureus, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 20% 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

  1. Pooled it
  2. Admission biomarkers and COVID-19 mortality: A retrospective study during Vietnam's pandemic peak.Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures) · 2026
    Article
  3. Article
  4. Article
  5. Observational
  6. Article
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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 1 institution in 1 country.

Rajarajan GanesanAnaesthesia and Intensive Care, Postgraduate Institute of Medical Education and Research, Chandigarh, IND.
Varun MahajanAnaesthesia and Intensive Care, Postgraduate Institute of Medical Education and Research, Chandigarh, IND.
Karan SinglaAnaesthesia and Intensive Care, Postgraduate Institute of Medical Education and Research, Chandigarh, IND.
Sushant KonarAnaesthesia and Intensive Care, Postgraduate Institute of Medical Education and Research, Chandigarh, IND.
Tanvir SamraAnaesthesia and Intensive Care, Postgraduate Institute of Medical Education and Research, Chandigarh, IND.
Senthil K SundaramAnaesthesia and Intensive Care, Postgraduate Institute of Medical Education and Research, Chandigarh, IND.
Vikas SuriInternal Medicine, Postgraduate Institute of Medical Education and Research, Chandigarh, IND.
Mandeep GargRadiodiagnosis, Postgraduate Institute of Medical Education and Research, Chandigarh, IND.
Naveen KalraRadiodiagnosis, Postgraduate Institute of Medical Education and Research, Chandigarh, IND.
Goverdhan D PuriAnaesthesia and Intensive Care, Postgraduate Institute of Medical Education and Research, Chandigarh, IND.
Post Graduate Institute of Medical Education and Research · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Coronavirus-2019 (COVID-19) patients admitted to the intensive care unit (ICU) have mortality rates between 30%-50%. Identifying patient factors associated with mortality can help identify critical patients early and treat them accordingly. Patients and methods In this retrospective study, the records of patients admitted to the COVID-19 ICU in a single tertiary care hospital from April 2020 to September 2020 were analysed. The clinical and laboratory parameters between patients who were discharged from the hospital (survival cohort) and those who died in the hospital (mortality cohort) were compared. A multivariate logistic regression model was constructed to identify parameters associated with mortality.  Results A total of 147 patients were included in the study. The age of the patients was 55 (45, 64), median (IQR), years. At admission, 23 (16%) patients were on mechanical ventilation and 73 (50%) were on non-invasive ventilation. Sixty patients (40%, 95% CI: 32.8 to 49.2%) had died. Patients who died had a higher Charlson comorbidity index (CCI): 3 (2, 4) vs. 2 (1, 3), p = 0.0019, and a higher admission sequential organ failure assessment (SOFA) score: 5 (4, 7) vs. 4 (3, 4), p < 0.001. Serum urea, serum creatinine, neutrophils on differential leukocyte count, neutrophil to lymphocyte ratio (N/L ratio), D-dimer, serum lactate dehydrogenase (LDH), and C-reactive protein were higher in the mortality cohort. The ratio of partial pressure of arterial oxygen to fraction of inspired oxygen, platelet count, lymphocytes on differential leukocyte count, and absolute lymphocyte count was lower in the mortality cohort. The parameters and cut-off values used for the multivariate logistic regression model included CCI > 2, SOFA score > 4, D-dimer > 1346 ng/mL, LDH > 514 U/L and N/L ratio > 27. The final model had an area under the curve of 0.876 (95% CI: 0.812 to 0.925), p < 0.001 with an accuracy of 78%. All five parameters were found to be independently associated with mortality.  Conclusions CCI, SOFA score, D-dimer, LDH, and N/L ratio are independently associated with mortality. A model incorporating the combination of these clinical and laboratory parameters at admission can predict COVID-19 ICU mortality with good accuracy.

Indexed as

acute respiratory distress syndrome [ards]covid-19covid-19 ardsmortalitymortality predictionsevere covid-19

Identifiers

PMID34976472
PMCPMC8681888
OpenAlexW3214883768

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.