Prediction of Hospital Mortality in Patients with ST Segment Elevation Myocardial Infarction: Evolution of Risk Measurement Techniques and Assessment of Their Effectiveness (Review).

B I Geltser, I G Domzhalov, K I Shakhgeldyan, N S Kuksin, E A Kokarev, R L Pak, V N Kotelnikov

Abstract readSystematic Review

In one paragraph

Synthesis in Sovremennye tekhnologii v meditsine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it

0cells of the map it votes in

3citing papers in PubMed

–field-weighted citation impact

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

3 citing papers in PubMed.

Machine Learning Approach on Predictive Model Establishment for In-Hospital Mortality in Acute Myocardial Infarction Patients Post-Percutaneous Coronary Intervention: Solutions for Databases With Dimensionality Reduction and Class Imbalance.Reviews in cardiovascular medicine · 2025
Article
Comparative Prognostic Value of Ion Shift Index and Naples Prognostic Score for Predicting In-Hospital Mortality in STEMI Patients: A Single-Center Retrospective Study.Diagnostics (Basel, Switzerland) · 2025
Article
Comparative Performance of the C-reactive Protein-to-Albumin Ratio (CAR) and the Thrombolysis in Myocardial Infarction (TIMI) Score in Predicting Major Adverse Cardiac Events (MACE) Among Patients With ST-Elevation Myocardial Infarction (STEMI).Cureus · 2025
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

7 authors.

B I GeltserMD, DSc, Professor, Corresponding Member of the Russian Academy of Science, Deputy Director for Science of the School of Medicine and Life Sciences; Far Eastern Federal University, 10 Village Ayaks, Island Russkiy, Vladivostok, 690922, Russia.

I G DomzhalovPhD Student, School of Medicine and Life Sciences; Far Eastern Federal University, 10 Village Ayaks, Island Russkiy, Vladivostok, 690922, Russia; Physician, Intensive Care Department, Regional Vascular Surgery Center; Primorsky Regional Clinical Hospital No.1, 57 Aleutskaya St., Vladivostok, 690091, Russia.

K I ShakhgeldyanDSc, Associate Professor, Director of the Institute of Information Technologies; Vladivostok State University, 41 Gogolya St., Vladivostok, 690014, Russia; Head of Laboratory for Big Data Analysis in Medicine and Healthcare, School of Medicine and Life Sciences; Far Eastern Federal University, 10 Village Ayaks, Island Russkiy, Vladivostok, 690922, Russia.

N S KuksinPhD Student, Institute of Mathematics and Computer Technology; Far Eastern Federal University, 10 Village Ayaks, Island Russkiy, Vladivostok, 690922, Russia; Research Assistant, Laboratory for Big Data Analytics in Medicine and Healthcare, School of Medicine and Life Sciences; Far Eastern Federal University, 10 Village Ayaks, Island Russkiy, Vladivostok, 690922, Russia.

E A KokarevMD, PhD, Head of the Intensive Care Department, Regional Vascular Surgery Center; Far Eastern Federal University, 10 Village Ayaks, Island Russkiy, Vladivostok, 690922, Russia.

R L PakPhysician, Intensive Care Department, Regional Vascular Surgery Center; Far Eastern Federal University, 10 Village Ayaks, Island Russkiy, Vladivostok, 690922, Russia.

V N KotelnikovMD, DSc, Professor, Department of Clinical Medicine, School of Medicine and Life Sciences; Far Eastern Federal University, 10 Village Ayaks, Island Russkiy, Vladivostok, 690922, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Risk stratification of hospital mortality in patients with ST segment elevation myocardial infarction on the electrocardiogram is an important part of the specialized medical care provision. The systematic review presents scientific literature data characterizing the predictive value of both classical prognostic scales (GRACE, CADDILLAC, TIMI risk score for STEMI, RECORD, etc.) and new risk measurement tools developed on the basis of modern machine learning techniques. Most studies on this issue are often focused on the search for new predictors of adverse events, which allow to detail the relations between indicators of the clinical and functional status of patients and the end point of the study. Here, an important task is to develop hospital mortality prognostic algorithms characterized by explainable artificial intelligence and trusted by doctors.

Indexed as

Hospital MortalityST Elevation Myocardial InfarctionAlgorithmsElectrocardiographyHumansMachine LearningPrognosisRisk Assessmenthospital mortalitymachine learningprognostic modelsST segment elevation myocardial infarction

Identifiers

PMID39881833

DOI10.17691/stm2024.16.4.07

PMCPMC11773138

What Socratic holds

Textmetadata

LicenceCC BY

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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 Socratic graph.