{
  "abstract": "Background: Longitudinal severity assessment may provide more informative risk stratification than reliance on a single admission score in intensive care. This study developed and internally validated an explainable machine learning framework for predicting a subsequent APACHE-II-defined high-severity state using repeated APACHE-II assessments in intensive care unit (ICU) patients receiving total parenteral nutrition (TPN). The endpoint was defined as an APACHE-II score of ≥ 20 at the final assessment (T10), while predictor information was restricted to measurements obtained at T0-T9. Methods: A retrospective institutional cohort of 844 adult ICU patients was analyzed, of whom 214 (25.4%) met the predefined high-severity endpoint. Four principal feature representations were evaluated: admission APACHE-II, longitudinal APACHE-II information combining the raw T0-T9 sequence with derived trajectory descriptors, longitudinal laboratory information, and multimodal combinations including baseline characteristics. Six machine learning algorithms were compared using stratified five-fold cross-validation and independent hold-out testing. Additional analyses included simpler APACHE-II comparators, sequential observation truncation, nested cross-validation, calibration assessment, threshold sensitivity analysis, and SHAP-based model interpretation. Because equivalent longitudinal APACHE-II measurements and an equivalent endpoint were unavailable in eICU, a separate harmonized XGBoost model was used only for exploratory cross-cohort portability assessment. Results: The longitudinal APACHE-II CatBoost model achieved the highest performance, with a cross-validated ROC-AUC of 0.924 and an independent hold-out ROC-AUC of 0.921 (95% CI: 0.878-0.960). Hold-out calibration was good (Brier score = 0.095, calibration intercept = - 0.03, slope = 0.98), and nested cross-validation yielded a pooled out-of-fold ROC-AUC of 0.908 and a mean outer-fold ROC-AUC of 0.920 ± 0.038. The latest APACHE-II assessment alone (T9; ROC-AUC = 0.553), change from baseline (0.576), ordinal slope (0.539), and logistic regression using engineered APACHE-II descriptors (0.633) performed substantially below the full longitudinal CatBoost model. Sequential truncation showed that discrimination was retained after removing later observations, although performance varied non-monotonically across truncated sequences. SHAP analysis showed that multiple APACHE-II observations contributed prominently to model predictions, with trajectory-derived descriptors providing complementary contributions. Adding laboratory trajectories or baseline characteristics did not improve discrimination over the longitudinal APACHE-II representation. Conclusions: Nonlinear modeling of repeated APACHE-II assessments provided strong internal discrimination of a subsequent APACHE-II-defined high-severity state in this TPN-treated ICU cohort and substantially outperformed single-score and simpler longitudinal comparators. However, the predictors and endpoint share the APACHE-II construct, observation indices were sequential rather than standardized clock-time intervals, and the primary model could not undergo conventional external validation in eICU. These findings therefore support the internal predictive value of longitudinal APACHE-II modeling for this specific severity-state task and warrant prospective multicenter evaluation using standardized timing and independent clinical outcomes.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Nutrition and Dietetics, İstanbul Okan University, Istanbul 34959, Türkiye.",
    "Department of Nutrition and Dietetics, Faculty of Health Sciences, Biruni University, Istanbul 34015, Türkiye."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "İrem Akpolat"
    },
    {
      "affs": [
        1
      ],
      "name": "Fatma Çelik"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bwa01mpj9n8zs.bib",
  "cited_by": 0,
  "doi": "10.3390/diagnostics16193118",
  "fulltext_url": "https://europepmc.org/articles/PMC13664044",
  "id": "bwa01mpj9n8zs",
  "issns": [
    "2075-4418"
  ],
  "landing_url": "https://europepmc.org/article/MED/42882273",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Machine Learning in Healthcare"
    ]
  ],
  "openalex": "W7214282921",
  "pdf_url": "https://europepmc.org/articles/PMC13664044?pdf=render",
  "pmcid": "PMC13664044",
  "pmid": "42882273",
  "published": "2026-09-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bwa01mpj9n8zs.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:16Z",
      "native_id": "MED:42882273",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42882273"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "journal-openalex:F27",
      "subfield": "general"
    }
  ],
  "title": "Explainable Machine Learning for Prediction of Future High-Severity States Using Longitudinal APACHE-II Trajectories: Internal Validation and Cross-Cohort Portability Assessment.",
  "title_text": "Explainable Machine Learning for Prediction of Future High-Severity States Using Longitudinal APACHE-II Trajectories: Internal Validation and Cross-Cohort Portability Assessment.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:16Z",
  "url": "https://research.draconitic.com/paper/bwa01mpj9n8zs",
  "venue": "Diagnostics (Basel, Switzerland)",
  "versions": []
}