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Background <p>Complications significantly impact the prognosis and healthcare burden of hospitalized patients, making early identification of high-risk individuals crucial. While nutritional and metabolic status are influencing factors, existing tools struggle to provide an integrated assessment. The Triglyceride-Cholesterol-Body weight Index (TCBI) is a novel indicator that concurrently reflects both nutritional and metabolic status, yet its value in predicting in-hospital complications remains unclear.</p> Methods <p>This observational study leveraged large-scale, multicenter real-world data, enrolling 8,288 eligible hospitalized patients. Demographic information, anthropometric measurements, laboratory results, and clinical outcomes were collected. Due to its skewed distribution, TCBI was analyzed using its natural logarithm-transformed value (TCBI-LN) and categorized into quartiles (Q1-Q4). The primary outcome was the occurrence of complications during hospitalization. Univariate analysis was used to compare inter-group differences. Multivariate logistic regression models were employed to analyze the independent association between TCBI-LN and complication risk. Restricted cubic splines were applied to explore the dose-response relationship. The robustness and generalizability of the association were assessed through subgroup analyses and interaction tests. We further compared five nested logistic regression models incorporating TCBI, its individual components, and existing indices (PNI and TyG) using AUC, NRI, IDI, AIC, and BIC, and performed causal mediation analysis to examine whether complications mediated the associations of TCBI with length of stay (LOS) and hospital cost.</p> Results <p>Complications occurred in 403 patients (4.9%). Patients with complications had significantly lower TCBI-LN levels compared to those without (6.83 ± 0.71 vs. 7.10 ± 0.83, <i>P</i> &lt; 0.001). Multivariate logistic regression analysis revealed that a higher TCBI-LN remained independently associated with a lower risk of complications even after adjusting for multiple potential confounders, including age, sex, body mass index, disease type, comorbidities, and related prognostic factors (adjusted OR = 0.707, 95% CI: 0.553–0.930, <i>P</i> = 0.012). Restricted cubic spline analysis suggested a linear inverse correlation between TCBI-LN and complication risk. Subgroup analyses indicated that the protective association of TCBI-LN was statistically significant in males, patients aged &lt; 65 years, those with a body mass index &lt; 18.5 or ≥ 24&#xa0;kg/m², and malnourished patients. No significant interactions were observed across all subgroups (P for interaction &gt; 0.05). A risk stratification cutoff was determined based on the Youden index. The complication rate was significantly higher in the high-risk group (6.3%) compared to the intermediate- (5.1%) and low-risk groups (2.9%). In model comparison, adding TCBI-LN to a clinical model significantly improved AUC, NRI and IDI, and the model combining TCBI-LN with PNI and TyG provided the best overall performance. Mediation analysis indicated that TCBI-LN shortened LOS predominantly through reducing in-hospital complications and partially attenuated its direct cost-increasing effect.</p> Conclusion <p>In a large-scale cohort study of hospitalized patients, lower TCBI-LN levels were independently associated with a higher risk of in-hospital complications, and this association was generalizable across different patient subgroups. As a composite index easily derived from routine laboratory tests, TCBI may serve as a practical tool for early identification of patients at high risk for in-hospital complications and ultimately improve clinical outcomes.</p>

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Lower triglyceride-cholesterol-body weight index is independently associated with increased in-hospital complication risk: a large multicenter real-world study

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  • 发表日期：

    2026年4月9日

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  • Yonghao Li,
  • Yilin Li,
  • Xiaoyi Luan,
  • Liru Chen,
  • Peng Liu,
  • Hongyuan Cui,
  • Mingwei Zhu

摘要

Background

Complications significantly impact the prognosis and healthcare burden of hospitalized patients, making early identification of high-risk individuals crucial. While nutritional and metabolic status are influencing factors, existing tools struggle to provide an integrated assessment. The Triglyceride-Cholesterol-Body weight Index (TCBI) is a novel indicator that concurrently reflects both nutritional and metabolic status, yet its value in predicting in-hospital complications remains unclear.

Methods

This observational study leveraged large-scale, multicenter real-world data, enrolling 8,288 eligible hospitalized patients. Demographic information, anthropometric measurements, laboratory results, and clinical outcomes were collected. Due to its skewed distribution, TCBI was analyzed using its natural logarithm-transformed value (TCBI-LN) and categorized into quartiles (Q1-Q4). The primary outcome was the occurrence of complications during hospitalization. Univariate analysis was used to compare inter-group differences. Multivariate logistic regression models were employed to analyze the independent association between TCBI-LN and complication risk. Restricted cubic splines were applied to explore the dose-response relationship. The robustness and generalizability of the association were assessed through subgroup analyses and interaction tests. We further compared five nested logistic regression models incorporating TCBI, its individual components, and existing indices (PNI and TyG) using AUC, NRI, IDI, AIC, and BIC, and performed causal mediation analysis to examine whether complications mediated the associations of TCBI with length of stay (LOS) and hospital cost.

Results

Complications occurred in 403 patients (4.9%). Patients with complications had significantly lower TCBI-LN levels compared to those without (6.83 ± 0.71 vs. 7.10 ± 0.83, P < 0.001). Multivariate logistic regression analysis revealed that a higher TCBI-LN remained independently associated with a lower risk of complications even after adjusting for multiple potential confounders, including age, sex, body mass index, disease type, comorbidities, and related prognostic factors (adjusted OR = 0.707, 95% CI: 0.553–0.930, P = 0.012). Restricted cubic spline analysis suggested a linear inverse correlation between TCBI-LN and complication risk. Subgroup analyses indicated that the protective association of TCBI-LN was statistically significant in males, patients aged < 65 years, those with a body mass index < 18.5 or ≥ 24 kg/m², and malnourished patients. No significant interactions were observed across all subgroups (P for interaction > 0.05). A risk stratification cutoff was determined based on the Youden index. The complication rate was significantly higher in the high-risk group (6.3%) compared to the intermediate- (5.1%) and low-risk groups (2.9%). In model comparison, adding TCBI-LN to a clinical model significantly improved AUC, NRI and IDI, and the model combining TCBI-LN with PNI and TyG provided the best overall performance. Mediation analysis indicated that TCBI-LN shortened LOS predominantly through reducing in-hospital complications and partially attenuated its direct cost-increasing effect.

Conclusion

In a large-scale cohort study of hospitalized patients, lower TCBI-LN levels were independently associated with a higher risk of in-hospital complications, and this association was generalizable across different patient subgroups. As a composite index easily derived from routine laboratory tests, TCBI may serve as a practical tool for early identification of patients at high risk for in-hospital complications and ultimately improve clinical outcomes.