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September 28, 2026BMC MedicineOpen Access

Multimorbidity network patterns improve the explanation of inpatient outcomes beyond conventional comorbidity assessment: a retrospective cohort study across 32 tertiary hospitals

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Key result

Multimorbidity networks improve outcome prediction over ACCI alone, identifying clusters with ~59% higher readmission risk.

  • n=2,042,063

Why the study?

Do multimorbidity network patterns improve the prediction of inpatient outcomes (LOS, cost, 30-day readmission) beyond the age-adjusted Charlson Comorbidity Index in hospitalized patients with multimorbidity?

Population

2,042,063 hospital admissions with multimorbidity from 32 tertiary hospitals in Shanxi Province, China.

Comparison

Multimorbidity network patterns vs Age-adjusted Charlson Comorbidity Index alone

Design

Cohort

Follow-up

30-day (for readmission)

Authors

NQNa QinJXJun XuXWXin Wang

Discussion

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Overview

Multimorbidity network patterns provide additional explanatory value over the age-adjusted Charlson Comorbidity Index for forecasting hospitalization outcomes like length of stay, costs, and readmission.

Key Points

  • To evaluate the incremental predictive value of multimorbidity network patterns beyond the age-adjusted Charlson Comorbidity Index for inpatient length of stay, hospital costs, and 30-day readmissions.
  • Retrospective multicenter cohort study analyzing electronic health records from 2,042,063 admissions with physical multimorbidity (≥2 chronic conditions) across 32 tertiary hospitals in Shanxi Province, China (2018–2022).
  • Constructed a disease network (304 nodes, 2,513 edges) using the Louvain modularity algorithm to identify disease communities.
  • Fitted nested regression models for length of stay, total hospitalization costs, and 30-day readmission risk, evaluating incremental fit via AIC and BIC and calculating population-level attribution via counterfactual predictions.
  • Identified nine disease communities with hypertension, type 2 diabetes, and stroke serving as central network hubs, where incorporating network patterns significantly improved model fit over the index alone via reductions in AIC and BIC.
  • Community 6 (endocrine-immune-renal diseases) showed the highest readmission risk (OR = 1.591) and lower hospitalization costs (mean ratio = 0.696), whereas Community 8 (cardiovascular-arrhythmic diseases) had shorter length of stay (rate ratio = 0.691) and lower readmission risk (OR = 0.478).
  • The age-adjusted Charlson Comorbidity Index contributed 25.21% to 30-day readmission risk, while individual multimorbidity network patterns demonstrated substantial variability in risk contributions across outcomes.

Study Design

Type

Cohort (n=2,042,063)

Multicenter

Yes

Structured PICO

Do multimorbidity network patterns improve the prediction of inpatient outcomes (LOS, cost, 30-day readmission) beyond the age-adjusted Charlson Comorbidity Index in hospitalized patients with multimorbidity?

P
Population
2,042,063 hospital admissions with multimorbidity (≥2 physical chronic conditions) across 32 tertiary hospitals in China between 2018 and 2022.
E
Exposure
Multimorbidity network patterns (disease communities detected using Louvain modularity algorithm)
C
Comparator
Age-adjusted Charlson Comorbidity Index (ACCI) alone
O
Outcome
Length of stay (LOS), total hospitalization cost, and 30-day readmission risk

Multimorbidity network patterns provide additional explanatory value over the age-adjusted Charlson Comorbidity Index for forecasting hospitalization outcomes like length of stay, costs, and readmission.

Cite This Study

Qin et al. (2026) conducted a cohort in Multimorbidity (n=2,042,063). Multimorbidity network patterns vs. Age-adjusted Charlson Comorbidity Index (ACCI) was evaluated on Length of stay, total hospitalization cost, and 30-day readmission risk. Multimorbidity network patterns improved outcome prediction over ACCI alone, with Community 6 showing the highest 30-day readmission risk (OR 1.591) and Community 8 showing lower risk (OR 0.478).

synapsesocial.com/papers/6ab9b4bc7822ec8fc3d8ec31https://doi.org/10.1186/s12916-026-05264-2
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