Retrospective Study Identifies Risk Factors That Prolong Hospital Stays After Massive Bleeding

A new retrospective study from China now points to five perioperative factors that appear to drive prolonged hospitalization in this vulnerable group, offering a statistical model that its authors hope will help flag high-risk patients early.

The research, conducted by a team at Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine in Hebei Province together with Cangzhou Central Hospital, analyzed the clinical records of 119 patients who received hemostatic resuscitation for intraoperative massive hemorrhage.

To separate relatively quick recoveries from extended ones, the researchers divided the cohort into two groups using the third quartile of hospital stay as the cutoff point: patients who remained hospitalized for more than 26 days were compared with those whose stays lasted 26 days or fewer. This data-driven threshold allowed the team to examine which variables measured around the time of surgery distinguished the two groups.

The statistical approach combined several methods. The Mann–Whitney U test and the independent samples t-test were used to assess how categorical variables influenced length of stay, the chi-square test handled count data, and Spearman correlation analyzed continuous variables. From these screening steps, the team built multivariate models to identify which factors retained independent predictive power.

The results were striking. Unplanned reoperation emerged as a strong independent risk factor, with an odds ratio of 6.743 (P = 0.008), meaning patients who required a second, unplanned operation were markedly more likely to experience a prolonged stay.

Impaired renal function proved to be the most powerful predictor of all, with an odds ratio of 11.554 (P < 0.001). Postoperative pulmonary inflammation also independently predicted longer hospitalization, with an odds ratio of 4.696 (P = 0.018).

Body mass index completed the list of independent risk factors, showing an odds ratio of 10.030 (P = 0.046). The authors suggest that identifying high-risk patients, including those with elevated BMI or abnormal kidney function, could allow more aggressive management aimed at minimizing complications.

When the team assembled these variables into a predictive model, the area under the receiver operating characteristic curve reached 0.908, a value indicating strong discriminatory ability within this dataset. The researchers caution, however, that the study is retrospective and based on a single cohort of 119 patients, and they emphasize that prospective, high-quality research is needed to validate the findings before the model can be recommended for routine clinical use.

Subject of Research: Perioperative risk factors for prolonged hospitalization after massive intraoperative hemorrhage

Article Title: Predictive models for prolonged hospitalization following major hemorrhage: a retrospective study

Article References: Bai, Y., Bai, J., Xu, Y.-K., Zheng, W.-C., Fu, Y., Sun, Y., & Geng, H.-Y. (2026). Predictive models for prolonged hospitalization following major hemorrhage: a retrospective study. Frontiers in Medicine, 13, Article 1968934. https://doi.org/10.3389/fmed.2026.1968934

Image Credits: AI Generated

DOI: 10.3389/fmed.2026.1968934

Keywords: massive hemorrhage, hemostatic resuscitation, prolonged hospital stay, risk factors, predictive model, renal function, body mass index, ICU admission, reoperation, pulmonary inflammation, retrospective study, Predictive