ArticleJournal of clinical medicine2021
Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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.
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.
Who cites it
17 citing papers in PubMed, 33 citations in OpenAlex.
- Implementation of infection prevention and control strategies in Italian intensive care units: results from the SPIN-UTI network.Antimicrobial resistance and infection control · 2025Article
- Key Concepts in Machine Learning and Clinical Applications in the Cardiac Intensive Care Unit.Current cardiology reports · 2025Review
- Public and Research Interest in Telemedicine From 2017 to 2022: Infodemiology Study of Google Trends Data and Bibliometric Analysis of Scientific Literature.Journal of medical Internet research · 2024Article
- Development and evaluation of a model for predicting the risk of healthcare-associated infections in patients admitted to intensive care units.Frontiers in public health · 2024Article
- Interpretability-based machine learning for predicting the risk of death from pulmonary inflammation in Chinese intensive care unit patients.Frontiers in medicine · 2024Article
- Association between quality of governance, antibiotic consumption, and antimicrobial resistance: an analysis of Italian regions.Antimicrobial resistance and infection control · 2023Article
- Article
- Artificial intelligence for COVID-19 mortality prediction: improvement of risk stratification and clinical decision-making.Internal and emergency medicine · 2023Article
- An observational study on factors associated with ICU mortality in Covid-19 patients and critical review of the literature.Scientific reports · 2023Review
- Machine Learning Identification of Obstructive Sleep Apnea Severity through the Patient Clinical Features: A Retrospective Study.Life (Basel, Switzerland) · 2023Article
- Chinese experts' consensus on the application of intensive care big data.Frontiers in medicine · 2023Article
- Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure.Journal of clinical medicine · 2022Article
- Diabetic Retinopathy Detection from Fundus Images of the Eye Using Hybrid Deep Learning Features.Diagnostics (Basel, Switzerland) · 2022Article
- Machine Learning and Antibiotic Management.Antibiotics (Basel, Switzerland) · 2022Article
- Predictive Analysis of Healthcare-Associated Blood Stream Infections in the Neonatal Intensive Care Unit Using Artificial Intelligence: A Single Center Study.International journal of environmental research and public health · 2022Article
- U-Shape Relationship between Plasma Leucine Level and Mortality in the Intensive Care Unit.Disease markers · 2022Article
- Disentangling the Association of Hydroxychloroquine Treatment with Mortality in Covid-19 Hospitalized Patients through Hierarchical Clustering.Journal of healthcare engineering · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Patients in intensive care units (ICUs) were at higher risk of worsen prognosis and mortality. Here, we aimed to evaluate the ability of the Simplified Acute Physiology Score (SAPS II) to predict the risk of 7-day mortality, and to test a machine learning algorithm which combines the SAPS II with additional patients' characteristics at ICU admission. We used data from the "Italian Nosocomial Infections Surveillance in Intensive Care Units" network. Support Vector Machines (SVM) algorithm was used to classify 3782 patients according to sex, patient's origin, type of ICU admission, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II, presence of invasive devices, trauma, impaired immunity, antibiotic therapy and onset of HAI. The accuracy of SAPS II for predicting patients who died from those who did not was 69.3%, with an Area Under the Curve (AUC) of 0.678. Using the SVM algorithm, instead, we achieved an accuracy of 83.5% and AUC of 0.896. Notably, SAPS II was the variable that weighted more on the model and its removal resulted in an AUC of 0.653 and an accuracy of 68.4%. Overall, these findings suggest the present SVM model as a useful tool to early predict patients at higher risk of death at ICU admission.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.