Data-Driven Comorbidity Phenotypes and In-Hospital Outcomes Among Patients With Nonvariceal Upper Gastrointestinal Bleeding: Formative Retrospective Cohort Study.

@article{wang2026datadriven,
  title = {{Data-Driven Comorbidity Phenotypes and In-Hospital Outcomes Among Patients With Nonvariceal Upper Gastrointestinal Bleeding: Formative Retrospective Cohort Study.}},
  author = {Fangning Wang and Yuhang Zhou and Qin Zhang and Yuan Liu and Yanli Zhang and Shiyu Du},
  journal = {JMIR formative research},
  year = {2026},
  month = oct,
  doi = {10.2196/108793},
  url = {https://europepmc.org/article/MED/42896118},
  note = {CC BY},
}

TY  - JOUR
TI  - Data-Driven Comorbidity Phenotypes and In-Hospital Outcomes Among Patients With Nonvariceal Upper Gastrointestinal Bleeding: Formative Retrospective Cohort Study.
AU  - Fangning Wang
AU  - Yuhang Zhou
AU  - Qin Zhang
AU  - Yuan Liu
AU  - Yanli Zhang
AU  - Shiyu Du
PY  - 2026
DA  - 2026/10/19
JO  - JMIR formative research
DO  - 10.2196/108793
UR  - https://europepmc.org/article/MED/42896118
AB  - Background  Existing risk scores for nonvariceal upper gastrointestinal bleeding (NVUGIB) may not adequately represent the heterogeneity of coexisting chronic conditions. Routinely collected hospital discharge records may enable data-driven characterization of clinically meaningful comorbidity profiles.Objective  This formative study aimed to identify reproducible comorbidity profiles among patients hospitalized with NVUGIB and examine their associations with in-hospital outcomes and health care use.Methods  We conducted a retrospective cohort study of 3193 adults hospitalized with NVUGIB at a tertiary hospital in China between 2015 and 2025. Of 55 prespecified chronic conditions, 33 with a prevalence of at least 1% were included as binary clustering variables. Patients were randomly divided into an 80% training subset and a 20% held-out validation subset. Comorbidity profiles were identified in the training subset using a self-organizing map and Ward hierarchical clustering and then assigned to the validation subset using the fixed clustering solution. Profile reproducibility was evaluated by comparing condition prevalence vectors between subsets. Multivariable regression models assessed associations with severe in-hospital outcomes, intensive care unit admission, in-hospital death, length of stay, and hospital costs.Results  At least 1 selected comorbidity was recorded in 89.7% (2864/3193) of patients. Six profiles were identified: low-burden heterogeneous (cluster 1), non-upper gastrointestinal neoplastic-metastatic (cluster 2), circulatory-renal (cluster 3), cardiometabolic-renal (cluster 4), atherosclerotic-vascular (cluster 5), and rheumatologic-neurological (cluster 6). Profile composition was broadly reproducible between the training and validation subsets (weighted mean Spearman correlation=0.891, SD 0.04; P=.001). The circulatory-renal profile had the strongest associations with severe in-hospital outcomes (adjusted odds ratio [aOR] 8.57, 95% CI 5.39-14.01), intensive care unit admission (aOR 10.38, 95% CI 4.87-25.76), and in-hospital death (aOR 4.66, 95% CI 1.97-13.75). The rheumatologic-neurological and cardiometabolic-renal profiles were also associated with higher odds of these outcomes. All 6 profiles were associated with longer hospital stays and higher hospital costs.Conclusions  Using routinely collected hospital discharge records, we identified clinically interpretable comorbidity profiles among patients with NVUGIB. The profiles were broadly reproducible within the study population and showed different associations with clinical outcomes and resource use. Profile-based information may complement established risk assessment and could be examined further as an electronic health record-based approach to clinical management and resource planning.
SN  - 2561-326X
ER  -