Post-Discharge Mortality and Expenditure Analysis for ECMO and MV Patients

S1 Table presents Cox regression results for 180-day post-discharge mortality, achieving a concordance of 0.718. The dataset contains statistical model outputs from analyses using age groups, comorbidity indices, and COVID-19 variant periods as predictors for mortality and medical expenditures. It was authored by Jun Kawabata and shared under a CC BY 4.0 license.

Use Cases

• Validate the reported concordance of 0.718 from the Cox regression analysis of 180-day post-discharge mortality.
• Compare mortality risk predictions between models using Charlson Comorbidity Index scores and Elixhauser Comorbidity Index scores.
• Analyze the relationship between COVID-19 variant periods and 180-day post-discharge total medical expenditures from the generalized linear model.
• Assess differences in 180-day outcomes between MV-only and ECMO patients as detailed in S9 Table.

Strengths

• Includes results from multiple validated statistical models (Cox regression, generalized linear models).
• Reports specific model performance metrics, such as a concordance of 0.718 with a standard error of 0.011.
• Analyses cover distinct predictors: age groups, two comorbidity indices, and COVID-19 variant periods.

Limitations

• The dataset is 558.0 KB, indicating it contains summary statistical tables, not the underlying patient-level data.
• Unknown row and column counts for the original analytical dataset limit understanding of sample size.
• Contains model outputs only; raw clinical variables like patient age or LOS are not directly accessible.

Provenance

Source

figshare, authored by Jun Kawabata.

Collection Method

Statistical analysis (Cox regression, generalized linear models) of clinical outcomes.

Time Range

Covers a 180-day post-discharge observation period; specific study dates unknown.

Freshness

Last updated on 2026-03-26.

Geography

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