ArticleIntensive care medicine2024
Prediction of post-traumatic stress disorder in family members of ICU patients: a machine learning approach.
Article in Intensive care medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 24 citations in OpenAlex.
- Anxiety, Depression, and Post-traumatic Stress Symptoms in Relatives of Patients Admitted to Intensive and Cardiac Intensive Care Units.Journal of clinical psychology in medical settings · 2026Article
- "It was torture for him, and it was torture for us to see him like that": understanding multiple ICU-related triggers of family PTSD symptoms-a qualitative thematic study.Intensive care medicine · 2026Article
- Artificial intelligence in intensive care units: a scoping review addressing the translational gap to clinical practice.Korean journal of anesthesiology · 2026Article
- Satisfaction with end-of-life care and self-rated health among bereaved family members; a descriptive cross-sectional study in an intensive care context.BMC palliative care · 2026Article
- Development and external validation of a machine learning model for predicting chronic critical illness in ICU patients with acute pancreatitis.BMC medical informatics and decision making · 2026Article
- Development and external validation of a machine learning model for predicting the 28-day mortality risk in patients with sepsis complicated by acute respiratory failure in the ICU.Journal of intensive medicine · 2026Article
- Morphological subphenotypes of acute pancreatitis-related acute respiratory distress syndrome.Critical care (London, England) · 2026Article
- An interpretable machine learning model for preoperative prediction of renal mass malignancy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Comparison Study of Multiple Machine Learning Models for Predicting Anxiety Among Neurosurgical ICU Family Members Using Critical Care Family Needs Inventory.Journal of nursing management · 2026Article
- Post-COVID-19 Consequences in Relatives of Severely Ill Patients: Results of the Prospective Multicenter NeNeSCo Study.Clinical nursing research · 2025Article
- Temporal stability of phenotypes of acute respiratory distress syndrome: clinical implications for early corticosteroid therapy and mortality.Intensive care medicine · 2025Article
- Development and validation of an explainable machine learning model for mortality prediction among patients with infected pancreatic necrosis.EClinicalMedicine · 2025Article
- Towards accurate screening and prevention for PTSD (2-ASAP): protocol of a longitudinal prospective cohort study.BMC psychiatry · 2024Article
- Post-Intensive Care Syndrome as a Burden for Patients and Their Caregivers: A Narrative Review.Journal of clinical medicine · 2024Review
- Enhancing Performance of the National Field Triage Guidelines Using Machine Learning: Development of a Prehospital Triage Model to Predict Severe Trauma.Journal of medical Internet research · 2024Article
- The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis.NPJ digital medicine · 2024Review
- Association between SOFA score and risk of acute kidney injury in patients with diabetic ketoacidosis: an analysis of the MIMIC-IV database.Frontiers in endocrinology · 2024Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposePost-traumatic stress disorder (PTSD) can affect family members of patients admitted to the intensive care unit (ICU). Easily accessible patient's and relative's information may help develop accurate risk stratification tools to direct relatives at higher risk of PTSD toward appropriate management.
methodsPTSD was measured 90 days after ICU discharge using validated instruments (Impact of Event Scale and Impact of Event Scale-Revised) in 2374 family members. Various supervised machine learning approaches were used to predict PTSD in family members and evaluated on an independent held-out test dataset. To better understand variables' contributions to PTSD predicted probability, we used machine learning interpretability methods on the best predictive algorithm.
resultsNon-linear ensemble learning tree-based methods showed better predictive performances (Random Forest-area under curve, AUC = 0.73 [0.68-0.77] and XGBoost-AUC = 0.73 [0.69-0.78]) than regularized linear models, kernel-based models, or deep learning models. In the best performing algorithm, most important features that positively contributed to PTSD's predicted probability were all non-modifiable factors, namely, lower patient's age, longer duration of ICU stay, relative's female sex, lower relative's age, relative being a spouse/child, and patient's death in ICU. A sensitivity analysis in bereaved relatives did not alter the algorithm's predictive performance.
conclusionWe propose a machine learning-based approach to predict PTSD in relatives of ICU patients at an individual level. In this model, PTSD is mostly influenced by non-modifiable factors.
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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 OpenQuestion graph.