ArticleFrontiers in medicine2021
Dynamic APACHE II Score to Predict the Outcome of Intensive Care Unit Patients.
Article in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 1 of them a synthesis that pooled 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.
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
70 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Early Prediction of Mortality Risk in Acute Respiratory Distress Syndrome: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- STREAM: A data-driven framework for physiological state monitoring in ICU patients.PLOS digital health · 2026Article
- Integrating frailty and acute illness severity scoring for risk stratification in acute geriatric care: Review and practical guide.World journal of critical care medicine · 2026Review
- Development of an Explainable Machine Learning Model to Predict Mortality Risk in Sepsis Patients: Insights From a Real-World Clinical Data.Shock (Augusta, Ga.) · 2026Article
- Development and Validation of the Composite Renal-Inflammatory-Nutritional-Metabolic Index for Predicting 28-Day Mortality in Critically Ill Patients.Diagnostics (Basel, Switzerland) · 2026Article
- Effects of daily bathing with 2% chlorhexidine compared to bathing with soap and water on risk of death, clinical severity, and intensive care unit length of stay in critically ill patients: an updated systematic review and meta-analysis.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026Review
- Eravacycline in Critically Ill Patients with Multidrug-Resistant Gram-Negative Infections: A Real-World Cohort Study.Journal of clinical medicine · 2026Article
- Outcomes of acute kidney injury in critically ill adults with tropical acute febrile illness: Prospective observational study.World journal of critical care medicine · 2026Article
- Prognostic Value of mNUTRIC and CONUT Scores in ICU Patients with Intracranial Hemorrhage.Journal of clinical medicine · 2026Article
- Albumin, mNUTRIC and NRS-2002: Predicting Mortality in Elderly ICU Fracture Patients.Healthcare (Basel, Switzerland) · 2026Article
- Prognostic Value of Nutritional Risk Scores in Septic ICU Patients: A Survival Analysis Using mNUTRIC, PNI, and CONUT.Diagnostics (Basel, Switzerland) · 2026Article
- Development and Validation of a Multivariable Machine Learning Model for Mortality Prediction among Intensive Care Unit Patients.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2026Article
- Nottingham Hip Fracture Score and Acute Physiology and Chronic Health Evaluation-II: Predicting 30-day mortality in elderly hip fracture.World journal of methodology · 2026Article
- Review
- The role of the nutritional screening tools in predicting clinical outcomes among older adults with hemorrhagic cerebrovascular disease.BMC neurology · 2026Article
- Development and temporal validation of SCRAPS scoring tool to predict poor prognosis in gram negative sepsis patients requiring early intensive care or high dependency unit admission.BMC infectious diseases · 2026Observational
- Machine Learning-Based Early Prediction of Lower Extremity Deep Vein Thrombosis in the ICU: A Multicenter Study.Risk management and healthcare policy · 2026Article
- Enhancing nurses' competence in mechanical ventilation: a quasi-experimental study and its association with patient severity using APACHE II.Frontiers in medicine · 2026Article
- Bronchoalveolar Lavage Fluid Microbial and Metabolic Alterations Associated with Acute Respiratory Distress Syndrome in Hospitalized Patients with Community-Acquired Pneumonia.Journal of inflammation research · 2026Article
- Risk factors and prediction model for sleep disorders in respiratory critically ill patients after intensive care unit transfer.American journal of translational research · 2026Article
10 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aims to evaluate the accuracy of the Acute Physiology and Chronic Health Evaluation (APACHE) II score on different days in predicting the mortality of critically ill patients to identify the best time point for the APACHE II score.
methodsThe demographic and clinical data are retrieved from the Medical Information Mart for Intensive Care (MIMIC)-IV dataset. APACHE II scores on days 1, 2, 3, 5, 7, 14, and 28 of hospitalization are calculated, and their performance is evaluated using the area under the receiver operating characteristic (AUROC) analysis. The cut-off for defining the high risk of mortality is determined using Youden's index. The APACHE II score on day 3 is the best time point to predict hospital mortality of ICU patients. The Hosmer-Lemeshow goodness-of-fit test is then applied to evaluate the calibration of the day 3 APACHE II score.
resultsWe recruited 6,374 eligible subjects from the MIMIC-IV database. Day 3 is the optimal time point for obtaining the APACHE II score to predict the hospital mortality of patients. The best cut-off for day 3 APACHE II score is 17. When APACHE II score ≥17, the sensitivity for the non-survivors and survivors is 92.8 and 82.2%, respectively, and the positive predictive value (PPV) is 23.1%. When APACHE II socre <17, the specificity for non-survivors and survivors is 90.1 and 80.2%, respectively, and the negative predictive value (NPV) is 87.8%. When day-3 APACHE II is used to predict the hospital mortality, the AUROC is 0.743 (
conclusionDay-3 APACHE II score is an optimal biomarker to predict the outcomes of ICU patients; 17 is the best cut-off for defining patients at high risk of mortality.
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.