Length of Hospital Stay in Patients with Primary Liver Cancer Undergoing Surgery: Risk Factors and Predictive Model Development

Figures

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  Figure 1. STROBE flow diagram. From 966 surgical admissions (Jan 2020–Oct 2023) identified from the hospital data platform, 245 were excluded due to missing pathological subtype and 19 for non-primary liver cancer or rare diagnoses. The final cohort comprised 702 patients (HCC: 651; ICC: 51), all included in the primary (negative binomial) and secondary (logistic) regression analyses.

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  Figure 2. Distribution of LOS. Histogram (density-scaled) overlaid with a kernel density estimate. The dashed line indicates the median LOS; the dotted line indicates the 75th percentile used to define prolonged LOS in the secondary analysis.

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  Figure 3. Forest plot of factors associated with LOS. Incidence rate ratios (IRRs) and 95% confidence intervals from the negative binomial regression core model are shown. Continuous predictors are scaled as labeled. IRR > 1 indicates a longer expected LOS; IRR < 1 indicates a shorter expected LOS.

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  Figure 4. Calibration plot for continuous LOS prediction. Patients were grouped into deciles of predicted mean LOS. Points and the solid line represent the mean observed LOS versus mean predicted LOS in each decile; the dashed line is the line of identity (perfect calibration).

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  Figure 5. Nomogram to estimate expected LOS. The nomogram converts each predictor value into a point score and maps the total points to predicted mean LOS (in days) based on the core negative binomial model.

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  Figure 6. Decision curve analysis (DCA) for prolonged LOS (>17 days). The net benefit of using the model across a range of threshold probabilities is compared with treat-all and treat-none strategies.