Evidence map›Paper›PMID 35414085›Full record

ArticleScientific reports2022

The Polyscore of autonomic parameters predicts mortality and identifies low-risk individuals among diabetic survivors of acute myocardial infarction.

Alexander Steger, Michael Dommasch, Alexander Müller, Petra Barthel, Daniel Sinnecker, Larissa Wieg, Alexander Hapfelmeier, Helene Hildegard Heidegger, Katharina Maria Huster, Eimo Martens and 3 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 36% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
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

13 authors at 3 institutions in 1 country.

Alexander StegerKlinik und Poliklinik für Innere Medizin I, University Hospital, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany. alexander.steger@tum.de.
Michael DommaschKlinik und Poliklinik für Innere Medizin I, University Hospital, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.
Alexander MüllerKlinik und Poliklinik für Innere Medizin I, University Hospital, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.
Petra BarthelKlinik und Poliklinik für Innere Medizin I, University Hospital, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.
Daniel SinneckerKlinik und Poliklinik für Innere Medizin I, University Hospital, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.
Larissa WiegKlinik und Poliklinik für Innere Medizin I, University Hospital, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.
Alexander HapfelmeierSchool of Medicine, Institute for AI and Informatics in Medicine, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.
Helene Hildegard HeideggerDepartment of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistraße 11, 80337, Munich, Germany.
Katharina Maria HusterKlinik und Poliklinik für Innere Medizin I, University Hospital, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.
Eimo MartensKlinik und Poliklinik für Innere Medizin I, University Hospital, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.
Karl-Ludwig LaugwitzKlinik und Poliklinik für Innere Medizin I, University Hospital, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.
Georg SchmidtKlinik und Poliklinik für Innere Medizin I, University Hospital, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.
Ralf DirschingerKlinik und Poliklinik für Innere Medizin I, University Hospital, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.
Technical University of Munich · DEGerman Centre for Cardiovascular Research · DELudwig-Maximilians-Universität München · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Survivors of an acute myocardial infarction with diabetes mellitus retain an increased mortality risk. Reliable assessment of individual risk is required for effective and cost-efficient medical care in these patients. The Polyscore is a previously established risk predictor consisting of seven autonomic tests derived from electrocardiogram, blood pressure, and respiration. The Polyscore allows classification of survivors of myocardial infarction in groups at low, intermediate and high mortality risk. The aim of this study was to investigate the prognostic value of the Polyscore in diabetic survivors of acute myocardial infarction, which may be impaired by the presence of diabetic autonomic neuropathy. Survivors of an acute myocardial infarction were included in a prospective cohort study during hospitalisation due to the index event at two university hospitals in Munich, Germany. The Polyscore was determined from simultaneous non-invasive 30-min recordings of electrocardiogram, continuous arterial blood pressure, and respiration which were performed in all participants. Patients were followed for 5 years. The primary and secondary outcomes were all-cause mortality and cardiac mortality. 184 of 941 enrolled patients (19.6%) suffered from diabetes mellitus. 5-year-mortality was higher in diabetic patients (15.2%) compared to non-diabetic patients (5.8%). A multivariable Cox regression model confirmed the Polyscore as a strong predictor of mortality in diabetic post-MI patients (intermediate risk: HR 6.56, 95% CI 1.61-26.78, p = 0.004, mortality 22.8%; high risk: HR 18.76, 95% CI 4.35-80.98, p < 0.001, mortality 68.8%). There was no interaction between diabetes mellitus and the Polyscore regarding mortality prediction (p = 0.775). Interestingly, in contrast to the groups at intermediate and high risk (73 patients, 39.7%), the Polyscore identified a majority of diabetic patients (111, 60.3%) with a low mortality risk, comparable to that of low-risk non-diabetic patients (3.6% and 2.1%, respectively, p = 0.339). Consistent results were observed for cardiac mortality. This analysis shows that the Polyscore predicts all-cause and cardiac mortality in diabetic survivors of acute myocardial infarction. Within these patients it identifies a large population not affected by the excess mortality associated with diabetes in this setting. Thus, the Polyscore may facilitate risk-adapted follow-up strategies in diabetic survivors of myocardial infarction.

Indexed as

Diabetes MellitusMyocardial InfarctionHumansProspective StudiesRisk FactorsSurvivors

Identifiers

PMID35414085
PMCPMC9005709
OpenAlexW4223601589

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.