Context-Dependent Clinical Narrative Signals of Renal Function Deterioration in Cross-Language Settings: Retrospective Multicohort Study

Abstract Background Kidney Disease: Improving Global Outcomes (KDIGO) classifies chronic kidney disease (CKD) using categories based on both glomerular filtration rate and albuminuria. Structured measures remain essential but may not capture contextual clinical concerns documented in narrative records. Objective This study examined whether text-derived clinical-status language features in clinician notes were associated with deterioration in renal function in independent Greek- and English-language clinical cohorts. Methods We retrospectively analyzed a Greek general-nephrology cohort (314 patients; 10,306 estimated glomerular filtration rate [eGFR] measurements) and an independent English-language Medical Information Mart for Intensive Care IV (MIMIC-IV) replication cohort. The Greek primary analysis used a linear mixed effects model over a follow-up period of up to 1797 days. The MIMIC-IV primary analysis used a 90-day, patient-level fixed-horizon competing-outcome model (N=87,533). Separate analyses of Greek and MIMIC-IV data evaluated discrimination, calibration, and sensitivity to alternative feature specifications. Results In the Greek cohort, a 1-SD higher negativity was associated with lower eGFR ( β =−3.273 mL/min/1.73 m², 95% CI −5.049 to −1.498; P <.001) and a more negative time trajectory (time-by-score β =−1.324, 95% CI −2.553 to −0.096; P =.035). In MIMIC-IV, a 1-SD higher negative-vs-reassuring polarity contrast was associated with renal deterioration (relative risk ratio [RRR]=1.090, 95% CI 1.070‐1.110; P <.001). Within eGFR G categories, polarity-score areas under the curve (AUCs) were 0.506 to 0.552 using discharge eGFR and 0.518 to 0.534 using the worst observed eGFR. Discrimination for renal deterioration remained modest (one-vs-rest AUC=0.6134, 95% CI 0.6092‐0.6175), and adding both natural language processing (NLP) features to the clinical model increased AUC by only 0.0018 (95% CI 0.0010‐0.0025). Conclusions Independently constructed clinical-language measures were associated with renal function deterioration across two languages and health care settings. The findings support cross-language replication of an association signal, not formal external validation, measurement equivalence, or clinically useful stand-alone prediction.

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Journal
JMIR Medical Informatics
Published
2026-11-02
DOI
https://doi.org/10.2196/94249
Primary Topic
Machine Learning in Healthcare
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article
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article

Context-Dependent Clinical Narrative Signals of Renal Function Deterioration in Cross-Language Settings: Retrospective Multicohort Study

Christos Pleros, Yannis Pantazis, Georgios Aletras, Eleni Drosataki et al.
JMIR Medical Informatics
Machine Learning in Healthcare
article

Context-Dependent Clinical Narrative Signals of Renal Function Deterioration in Cross-Language Settings: Retrospective Multicohort Study

Christos Pleros, Yannis Pantazis, Georgios Aletras, Eleni Drosataki, Hariklia Gakiopoulou, Ioannis Petrakis, Ariadni Androvitsanea, Kostas Stylianou, Myrto Konidaki, Dimitra Lygerou, Nikolaos G. Papadakis, Eleftheria‐Kleio Dermitzaki, Konstantina Kydonaki, Alexandros Georgios Kavvadias, Andreas Antonakis
article en

Abstract

Abstract Background Kidney Disease: Improving Global Outcomes (KDIGO) classifies chronic kidney disease (CKD) using categories based on both glomerular filtration rate and albuminuria. Structured measures remain essential but may not capture contextual clinical concerns documented in narrative records. Objective This study examined whether text-derived clinical-status language features in clinician notes were associated with deterioration in renal function in independent Greek- and English-language clinical cohorts. Methods We retrospectively analyzed a Greek general-nephrology cohort (314 patients; 10,306 estimated glomerular filtration rate [eGFR] measurements) and an independent English-language Medical Information Mart for Intensive Care IV (MIMIC-IV) replication cohort. The Greek primary analysis used a linear mixed effects model over a follow-up period of up to 1797 days. The MIMIC-IV primary analysis used a 90-day, patient-level fixed-horizon competing-outcome model (N=87,533). Separate analyses of Greek and MIMIC-IV data evaluated discrimination, calibration, and sensitivity to alternative feature specifications. Results In the Greek cohort, a 1-SD higher negativity was associated with lower eGFR ( β =−3.273 mL/min/1.73 m², 95% CI −5.049 to −1.498; P <.001) and a more negative time trajectory (time-by-score β =−1.324, 95% CI −2.553 to −0.096; P =.035). In MIMIC-IV, a 1-SD higher negative-vs-reassuring polarity contrast was associated with renal deterioration (relative risk ratio [RRR]=1.090, 95% CI 1.070‐1.110; P <.001). Within eGFR G categories, polarity-score areas under the curve (AUCs) were 0.506 to 0.552 using discharge eGFR and 0.518 to 0.534 using the worst observed eGFR. Discrimination for renal deterioration remained modest (one-vs-rest AUC=0.6134, 95% CI 0.6092‐0.6175), and adding both natural language processing (NLP) features to the clinical model increased AUC by only 0.0018 (95% CI 0.0010‐0.0025). Conclusions Independently constructed clinical-language measures were associated with renal function deterioration across two languages and health care settings. The findings support cross-language replication of an association signal, not formal external validation, measurement equivalence, or clinically useful stand-alone prediction.

JMIR Medical InformaticsVol. 14
University of Crete (GR), National and Kapodistrian University of Athens (GR), FORTH Institute of Applied and Computational Mathematics (GR), The General Hospital of Heraklion "Venizeleio-Pananio" (GR), University Hospital of Heraklion (GR)
Good health and well-being
Openalex Percentile: Top 17%
Machine Learning in Healthcare
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