Learning a Continuous Sepsis Severity Score Without Hour-by-Hour Supervision: A Two-Site Retrospective Study

• URL: http://arxiv.org/abs/2608.27421v1
• Date: Thu, 27 Aug 2026 17:46:21 GMT
• Title: Learning a Continuous Sepsis Severity Score Without Hour-by-Hour Supervision: A Two-Site Retrospective Study
• Abstract summary: Currently used sepsis severity indices rely on fixed variables and weights established decades ago.<n>We developed a mortality index using 43 routinely charted variables over a 72-hour treatment window.<n>Within-patient change in the index correlated with change in lactate.
• Score: 6.58570893992725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Currently used sepsis severity indices rely on fixed variables and weights established decades ago, which are coarsely discretized and calibrated to a cohort that no longer reflects contemporary critical care. No alternative learned directly from patient trajectories is in routine use. We conducted a retrospective two-cohort study on a total of 29,116 and 7,691 adult patients meeting Sepsis-3 criteria from two hospital systems in Massachusetts and Georgie, respectively. We developed a sepsis index using 43 routinely charted variables over a 72-hour treatment window. Unlike previous studies, we use mortality as a treatment-level ranking signal rather than a per-state target, allowing credit to be redistributed non-uniformly across timesteps. Evaluation was done on a permanent 20% test holdout, using clinical vignettes and Spearman correlation. Uncertainty intervals were obtained by bootstrap resampling of whole patients. Under this ranking scheme, non-survivors scored 1.19-1.64 points higher than survivors on a 0-10 scale within all strata of baseline SOFA-2, with similar results stratifying within lactate, mean arterial pressure (MAP), and creatinine. Within-patient change in the index correlated with change in lactate (Spearman rho = 0.39; n = 1,854). Similar, weaker correlations were found for MAP and creatinine. On a cohort level, cross-institutional agreement measured by Spearman correlation between models trained on different sites, were 70-77% of same-site correlation. External within-patient correlations were 0.54 and 0.59 against ceilings of 0.92 and 0.90. Our index also correlated with established indices, while null controls stayed near zero. Our index demonstrated hourly prognostic information that meaningfully separates patient outcomes and is consistent with clinical expectation, indicating potential as a decision support tool complementing clinical judgement.

Related papers

• A Multi-Cohort Validation of Censoring-Aware Conformal Lower Predictive Bounds for Pathology Survival Models [0.0]
  Whole-slide survival models often provide risk rankings without calibrated statements about individual event times.<n>We evaluate fixed-cutoff drcosarc, a post-hoc conformal wrapper for discrete-time multiple-instance learning survival heads.
  arXiv  Detail & Related papers  (2026-07-23T12:58:04Z)
• Simultaneous hyperkinetic movement disorders phenotyping: a cross-cohort pediatric transfer study using routine videos, markerless pose estimation and a tabular foundation model [0.0]
  The framework combines markerless pose estimation, kinematic descriptors, and a pretrained fondation model.<n>Results: After local calibration of the decision layer, performance improved consistently on the held-out pediatric patients.
  arXiv  Detail & Related papers  (2026-06-04T18:13:13Z)
• Explainable Admission-Level Predictive Modeling for Prolonged Hospital Stay in Elderly Populations: Challenges in Low- and Middle-Income Countries [65.4286079244589]
  Prolonged length of stay (pLoS) is a significant factor associated with the risk of adverse in-hospital events.<n>We develop and explain a predictive model for pLos using admission-level patient and hospital administrative data.
  arXiv  Detail & Related papers  (2026-01-07T23:35:24Z)
• Early Prediction of Multi-Label Care Escalation Triggers in the Intensive Care Unit Using Electronic Health Records [1.116791976498254]
  This study proposes a multi-label classification framework to predict Care Escalation Triggers (CETs)<n>CETs are defined using rule-based criteria applied to data from hours 24 to 72.<n>We train and evaluate multiple classification models on a cohort of 85,242 ICU stays.
  arXiv  Detail & Related papers  (2025-09-15T20:40:45Z)
• Early Mortality Prediction in ICU Patients with Hypertensive Kidney Disease Using Interpretable Machine Learning [3.4335475695580127]
  Hypertensive kidney disease (HKD) patients in intensive care units (ICUs) face high short-term mortality.<n>We developed a machine learning framework to predict 30-day in-hospital mortality among ICU patients with HKD.
  arXiv  Detail & Related papers  (2025-07-25T00:48:23Z)
• Predicting Length of Stay in Neurological ICU Patients Using Classical Machine Learning and Neural Network Models: A Benchmark Study on MIMIC-IV [49.1574468325115]
  This study explores multiple ML approaches for predicting LOS in ICU specifically for the patients with neurological diseases based on the MIMIC-IV dataset.<n>The evaluated models include classic ML algorithms (K-Nearest Neighbors, Random Forest, XGBoost and CatBoost) and Neural Networks (LSTM, BERT and Temporal Fusion Transformer)
  arXiv  Detail & Related papers  (2025-05-23T14:06:42Z)
• Quantifying Circadian Desynchrony in ICU Patients and Its Association with Delirium [1.5188407765710805]
  The circadian desynchrony index was significantly higher among critically ill ICU patients compared with healthy subjects.<n>Most ICU patients had a circadian desynchrony index greater than 9 hours.
  arXiv  Detail & Related papers  (2025-03-11T03:56:10Z)
• A Federated Learning Framework for Stenosis Detection [70.27581181445329]
  This study explores the use of Federated Learning (FL) for stenosis detection in coronary angiography images (CA) Two heterogeneous datasets from two institutions were considered: dataset 1 includes 1219 images from 200 patients, which we acquired at the Ospedale Riuniti of Ancona (Italy) dataset 2 includes 7492 sequential images from 90 patients from a previous study available in the literature.
  arXiv  Detail & Related papers  (2023-10-30T11:13:40Z)
• Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies [62.840338941861134]
  We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset. We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis. This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
  arXiv  Detail & Related papers  (2023-02-16T17:06:23Z)
• Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan [49.209225484926634]
  We propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification. Our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the converted time.
  arXiv  Detail & Related papers  (2020-05-07T12:16:37Z)
• Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT [48.785596536318884]
  The proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States.
  arXiv  Detail & Related papers  (2020-04-02T21:49:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.