ArticleCritical care (London, England)2022
External validation of a machine learning model to predict hemodynamic instability in intensive care unit.
Article in Critical care (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 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
12 citing papers in PubMed, 22 citations in OpenAlex.
- Correction: External validation of a machine learning model to predict hemodynamic instability in intensive care unit.Critical care (London, England) · 2026Article
- Ensemble Machine Learning Model for Real-Time Valproic Acid Prediction in Epilepsy Treatment.Pharmacopsychiatry · 2026Article
- Development and external validation of a machine learning model for predicting severe immediate postoperative complications in adults undergoing cardiac surgery.BMC anesthesiology · 2026Article
- Stratifying mortality risk in hypotension using outcome-oriented clustering of vital sign trajectories.iScience · 2026Article
- Machine Learning Algorithms to Predict Venous Thromboembolism in Patients With Sepsis in the Intensive Care Unit: Multicenter Retrospective Study.JMIR medical informatics · 2026Article
- Noninvasive predictive value of preoperative perfusion index for intraoperative hemodynamic fluctuations in patients undergoing lower-extremity orthopedic surgery under regional anesthesia: a retrospective cohort study.Frontiers in surgery · 2026Article
- ChronoSynthNet: a dual-task deep learning model development and validation study for predicting real-time norepinephrine dosage and the early detection of hypotension in patients with septic shock.Cardiovascular diagnosis and therapy · 2025Article
- Development and validation of a dynamic early warning system with time-varying machine learning models for predicting hemodynamic instability in critical care: a multicohort study.Critical care (London, England) · 2025Article
- Developing technologies to assess vascular ageing: a roadmap from VascAgeNet.Physiological measurement · 2024Review
- Extension of an ICU-based noninvasive model to predict latent shock in the emergency department: an exploratory study.Frontiers in cardiovascular medicine · 2024Article
- Optimizing artificial intelligence in sepsis management: Opportunities in the present and looking closely to the future.Journal of intensive medicine · 2024Review
- Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly prediction model of hemodynamic instability has the potential to improve the critical care, whereas limited external validation on the generalizability. We aimed to independently validate the Hemodynamic Stability Index (HSI), a multi-parameter machine learning model, in predicting hemodynamic instability in Asian patients.
methodHemodynamic instability was marked by using inotropic, vasopressor, significant fluid therapy, and/or blood transfusions. This retrospective study included among 15,967 ICU patients who aged 20 years or older (not included 20 years) and stayed in ICU for more than 6 h admitted to Taipei Veteran General Hospital (TPEVGH) between January 1, 2010, and March 31, 2020, of whom hemodynamic instability occurred in 3053 patients (prevalence = 19%). These patients in unstable group received at least one intervention during their ICU stays, and the HSI score of both stable and unstable group was calculated in every hour before intervention. The model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and was compared to single indicators like systolic blood pressure (SBP) and shock index. The hemodynamic instability alarm was set by selecting optimal threshold with high sensitivity, acceptable specificity, and lead time before intervention was calculated to indicate when patients were firstly identified as high risk of hemodynamic instability.
resultsThe AUROC of HSI was 0.76 (95% CI, 0.75-0.77), which performed significantly better than shock Index (0.7; 95% CI, 0.69-0.71) and SBP (0.69; 95% CI, 0.68-0.70). By selecting 0.7 as a threshold, HSI predicted 72% of all 3053 patients who received hemodynamic interventions with 67% in specificity. Time-varying results also showed that HSI score significantly outperformed single indicators even up to 24 h before intervention. And 95% unstable patients can be identified more than 5 h in advance.
conclusionsThe HSI has acceptable discrimination but underestimates the risk of stable patients in predicting the onset of hemodynamic instability in an external cohort.
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.