ArticleCureus2025
Predicting 72-Hour Fatality in Severe Hyperphosphatemia: A Comparative Analysis of Multivariate Logistic Regression and Machine Learning Models in a Single-Center Study.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- An explainable machine learning model for predicting high phosphorus risk in patients on maintenance hemodialysis: a multicenter retrospective study.BMC medical informatics and decision making · 2026Article
- Routine Laboratory Tests Predict 72-h Fatality in Patients With D-Dimer Levels ≥ 2 μg/mL: A Retrospective Cohort Study Comparing Statistical and Machine Learning Models.Journal of clinical laboratory analysis · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHyperphosphatemia is associated with several serious diseases, including chronic kidney disease, tumor lysis syndrome (TLS), rhabdomyolysis, sepsis, and acute respiratory distress syndrome. This study investigates the critical issue of predicting 72-hour fatality in patients with severe hyperphosphatemia (≥ 10 mg/dL).
methodsWe analyzed data from 530 patients treated at the Saitama Medical Center, Japan, from 2004 to 2019, including 153 72-hour fatalities. Multivariate logistic regression analysis (MLRA), Prediction One™ (Sony Network Communications Inc., Tokyo, Japan, https://predictionone.sony.biz/), and Light Gradient Boosting Machine (LightGBM) were used to predict fatalities. These methods were evaluated on a validation set of 331 patients from 2020 to 2023, including 104 fatalities. Calibration plots for training and validation data were used for comparison.
resultsThe fatality rate was 28.9% in the training data and 31.4% in the validation data. MLRA identified five fatality factors: age, low albumin, high aspartate aminotransferase, and elevated potassium and magnesium levels, with an area under the curve (AUC) of 0.848 (95% CI: 0.801, 0.890), sensitivity of 0.862, and specificity of 0.704. Prediction One™ achieved an AUC of 0.770 (95% CI: 0.722, 0.818), sensitivity of 0.654, and specificity of 0.769. LightGBM achieved an AUC of 0.948 (95% CI: 0.923, 0.973), sensitivity of 0.863, and specificity of 0.889. The validation calibration plot showed that MLRA had the closest regression coefficient to 1.0 at 0.903.
conclusionAlthough MLRA was the most accurate in predicting 72-hour fatalities, machine learning methods provided valuable insights into the importance of variables. Considering the high mortality rates associated with severe hyperphosphatemia, timely and accurate prognostication is essential in guiding immediate interventions and improving outcomes in emergency settings.
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.