ArticleJournal of emergency and critical care medicine (Hong Kong, China)2021
Predicting open wound mortality in the ICU using machine learning.
Article in Journal of emergency and critical care medicine (Hong Kong, China), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
- The Role of Cold Atmospheric Plasma in Wound Healing Processes in Critically Ill Patients.Journal of personalized medicine · 2023Review
- The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.Frontiers in public health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundOpen wounds have a significant impact on the health of patients causing pain, loss of function, and death. Labeled as a comorbid condition, open wounds represent a "silent epidemic" that affect a large portion of the US population. Due to their burden of care, open wound patients face an increased risk of ICU stay and mortality. There is a dearth of studies that investigate mortality among wound patients in the ICU. We sought to develop a model that predicts the risk of mortality among wound patients in the ICU.
methodsRandom forest and binomial logistic regression models were developed to predict the risk of mortality among open wound patients in the Medical Information Mart for Intensive Care III (MIMIC-III) database. MIMIC-III includes de-identified data for patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. Six variables were used to develop the model (wound location, gender, age, admission type, minimum platelet count and hyperphosphatemia). The Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index were used to assess model strength.
resultsA total of 3,937 patients were included with a mean age of 76.57. Of those, 3,372 (85%) survived and 565 (15%) died during their ICU stay. The random forest model achieved an area under the curve (AUC) of 0.924. The CCI and Elixhauser models resulted in AUC of 0.528 and 0.565, respectively.
conclusionsMachine learning models may allow clinicians to provide better care and management to open wound patients in the ICU.
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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.