Evidence map›Paper›PMID 29765580›Full record

ArticleElectronic physician2018

Predictive factors associated with mortality and discharge in intensive care units: a retrospective cohort study.

Mohammad Ghorbani, Haleh Ghaem, Abbas Rezaianzadeh, Zahra Shayan, Farid Zand, Reza Nikandish

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Electronic physician, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
0.3field-weighted citation impact, top 38% 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.

  1. Pooled it
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

6 authors at 2 institutions in 1 country.

Mohammad GhorbaniPh.D. of Epidemiology, Assistant Professor, Department of Public Health, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran.
Haleh GhaemPh.D. of Epidemiology, Assistant Professor, Research Center for Health Sciences, Institute of Health, Department of Epidemiology, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
Abbas RezaianzadehMD, MPH, Ph.D. of Epidemiology, Professor, Colorectal Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Zahra ShayanPh.D. of Biostatistics, Assistant Professor, Trauma Research Center, Department of Community Medicine, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
Farid ZandM.D., Professor of Anesthesia and Critical Care Medicine, Anesthesiology and Critical Care Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Reza NikandishM.D., Associate Professor of Anesthesia and Critical Care, Department of Emergency Medicine, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
Shiraz University of Medical Sciences · IRTorbat Heydarieh University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimAccurate prediction of prognosis of patients admitted to intensive care units (ICUs) is very important for the clinical management of the patients. The present study aims to identify independent factors affecting death and discharge in ICUs using competing risk modeling.

methodsThis retrospective cohort study was conducted on enrolling 880 patients admitted to emergency ICU in Namazi hospital, Shiraz University of Medical Sciences, Shiraz, Iran during 2013-2015. The data was collected from patients' medical records using a researcher-made checklist by a trained nurse. Competing risk regression models were fitted for the factors affecting the occurrence of death and discharge in ICU. Data analysis was conducted using STATA 13 and R 3.3.3 software.

resultsAmong these patients, 682 (77.5%) were discharged and 157 (17.8%) died in the ICU. The patients' mean ± SD age was 48.90±19.52 yr. Among the study patients, 45.57% were female and 54.43% were male. In the competing risk model, age (Sub-distribution Hazard Ratio (SHR)) =1.02, 95% CI: 1.007-1.032), maximum heart rate (SHR=1.009, 95% CI: 1.001-1.019), minimum sodium level (SHR=1.035, 95% CI: 1.007-1.064), PH (SHR=7.982, 95% CI: 1.259-50.61), and bilirubin (SHR=1.046, 95% CI: 1.015-1.078) increased the risk of death, while maximum sodium level (SHR=0.946, 95% CI: 0.908-0.986) and maximum HCT (SHR=0.938, 95% CI: 0.882-0.998) reduced the risk of death.

conclusionIn conclusion, the results of this study revealed several variables that were effective in ICU length of stay (LOS). The variables that independently influenced time-to-discharge were age, maximum systolic blood pressure, minimum HCT, maximum WBC, and urine output, maximum HCT and Glasgow coma score. The results also showed that age, maximum heart rate, maximum sodium level, PH, urine output, and bilirubin, minimum sodium level and maximum HCT were the predictors of death. Furthermore, our findings indicated that the competing risk model was more appropriate than the Cox model in evaluating the predictive factors associated with the occurrence of death and discharge in patients hospitalized in ICUs. Hence, this model could play an important role in managers' and clinicians' decision-making and improvement of the standard of care in ICUs.

Indexed as

Competing riskDischargeIntensive care unitsMortality

Identifiers

PMID29765580
PMCPMC5942576
OpenAlexW2801805064

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.