Evidence map›Paper›PMID 32671649›Full record

ArticleNeurocritical care2021

A Model for Prediction of In-Hospital Mortality in Patients with Subarachnoid Hemorrhage.

Mónica Mourelo-Fariña, Sonia Pértega, Rita Galeiras

Abstract read
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In one paragraph

Article in Neurocritical care, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
0.9field-weighted citation impact, top 21% 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

14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 citations in OpenAlex.

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  8. Development of a predictive nomogram for 28-day mortality risk in non-traumatic or post-traumatic subarachnoid hemorrhage patients.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024
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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

3 authors at 1 institution in 1 country.

Mónica Mourelo-FariñaCritical Care Unit, Complejo Hospitalario Universitario de A Coruña (CHUAC), Instituto de Investigación Biomédica de A Coruña (INIBIC), SERGAS, Universidade de A Coruña (UDC), As Xubias, 15006, A Coruña, Spain. monica.mourelo@gmail.com.ORCID http://orcid.org/0000-0003-3690-0265
Sonia PértegaClinical Epidemiology and Biostatistics Unit, Complejo Hospitalario Universitario de A Coruña (CHUAC), Instituto de Investigación Biomédica de A Coruña (INIBIC), SERGAS, Universidade de A Coruña (UDC), As Xubias, 15006, A Coruña, Spain.
Rita GaleirasCritical Care Unit, Complejo Hospitalario Universitario de A Coruña (CHUAC), Instituto de Investigación Biomédica de A Coruña (INIBIC), SERGAS, Universidade de A Coruña (UDC), As Xubias, 15006, A Coruña, Spain.
Complexo Hospitalario Universitario A Coruña · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite being a rare cause of stroke, spontaneous subarachnoid hemorrhage (SAH) is associated with high mortality rates. The prediction models that are currently being used on SAH patients are heterogeneous, and few address premature mortality. The aim of this study was to develop a mortality risk stratification score for SAH.

methodsA retrospective study was carried out with 536 patients diagnosed with SAH who had been admitted to the intensive care unit (ICU) at the University Hospital Complex of A Coruña (Spain) between 2003 and 2013. A multivariate logistic regression model was developed to predict the likelihood of in-hospital mortality, adjusting it exclusively for variables present on admission. A predictive equation of in-hospital mortality was then computed based on the model's coefficients, along with a points-based risk-scoring system. Its discrimination ability was also tested based on the area under the receiver operating characteristics curve and compared with previously developed scores.

resultsThe mean age of the patients included in this study was 56.9 ± 14.1 years. Most of these patients (73.9%) had been diagnosed with aneurysmal SAH. Their median length of stay was 7 days in the ICU and 20 days in the general hospital ward, with an overall in-hospital mortality rate of 28.5%. The developed scales included the following admission variables independently associated with in-hospital mortality: coma at onset [odds ratio (OR) = 1.87; p = 0.028], Fisher scale score of 3-4 (OR = 2.27; p = 0.032), Acute Physiology and Chronic Health Evaluation II (APACHE II) score within the first 24 h (OR = 1.10; p < 0.001), and total Sequential Organ Failure Assessment (SOFA) score on day 0 (OR = 1.19; p = 0.004). Our predictive equation demonstrated better discrimination [area under the curve (AUC) = 0.835] (bootstrap-corrected AUC = 0.831) and calibration properties than those of the HAIR scale (AUC = 0.771; p ≤ 0.001) and the Functional Recovery Expected after Subarachnoid Hemorrhage scale (AUC = 0.814; p = 0.154).

conclusionsIn addition to the conventional risk factors for in-hospital mortality, in our study, mortality was associated with the presence of coma at onset of the condition, the physiological variables assessed by means of the APACHE II scale within the first 24 h, and the total SOFA score on day 0. A simple prediction model of mortality was developed with novel parameters assessed on admission, which also assessed organ failure and did not require a previous etiological diagnosis.

Indexed as

Subarachnoid HemorrhageAPACHEHospital MortalityHumansInfant, NewbornIntensive Care UnitsPrognosisRetrospective StudiesROC CurveIn-hospital mortalityPrediction modelScoreSpontaneous subarachnoid hemorrhage

Identifiers

PMID32671649
OpenAlexW3042784105

What OpenQuestion holds

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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.