Nomogram Predicts 30-Day Mortality in Acinetobacter baumannii Bloodstream Infection

Researchers in China have developed a prediction tool that estimates a patient’s risk of dying within 30 days of a confirmed Acinetobacter baumannii bloodstream infection, using only age and routine laboratory tests that are available before antimicrobial susceptibility results come back. The tool, a nomogram, was described in a study published in Frontiers in Cellular and Infection Microbiology.

Acinetobacter baumannii bloodstream infection carries high mortality, and clinicians often face a difficult window after blood cultures confirm the bacterium but before the laboratory reports which antibiotics will work. The study aimed to fill that window with early risk stratification, drawing on information that is already collected as part of standard care.

The research was a single-center retrospective study conducted at the First Affiliated Hospital with Nanjing Medical University in Nanjing, China. The investigators split their patient cohort into a training set of 202 patients and a validation set of 87 patients, developing the model on the first group and testing its performance on the second.

For model development, the team collected demographic data, specifically sex and age, along with laboratory parameters measured at the time of diagnosis. Clinical characteristics were recorded only for baseline description of the cohort and were not used in the model. To identify independent predictors, the researchers applied LASSO regression, the Boruta algorithm, and multivariate logistic regression across sex, age, and all laboratory indicators.

The overall 30-day mortality rate in the study population was 33.17 percent, underscoring the severity of confirmed A. baumannii bloodstream infection. Five independent risk factors emerged from the analysis: older age, elevated blood urea nitrogen, elevated sodium, prolonged activated partial thromboplastin time, and lower mean corpuscular hemoglobin concentration.

Each of these markers is routinely measured in hospitalized patients. Blood urea nitrogen reflects kidney function, serum sodium can shift with fluid and hormonal disturbances, activated partial thromboplastin time measures clotting function, and mean corpuscular hemoglobin concentration is a standard red blood cell index. Combining them with age in a single scoring chart allowed the researchers to translate laboratory values into an individualized mortality estimate.

When the nomogram was evaluated, it showed good discrimination between patients who died and those who survived within 30 days. The area under the ROC curve was 0.813 in the training cohort and 0.808 in the validation cohort, indicating that performance held up in the separate patient group. Calibration and decision curve analyses, according to the authors, confirmed the model’s accuracy and clinical utility.

The authors emphasize that the nomogram is intended to support early risk assessment rather than to guide empirical antibiotic selection. Because the study is retrospective and conducted at a single center, the conclusion notes that further prospective validation is required before the tool can be relied upon more broadly.

If validated in future prospective studies, the approach could offer clinicians a rapid, low-cost way to flag patients at highest risk of early death from A. baumannii bloodstream infection at a moment when treatment decisions are still being made, using data that already exist in the hospital record.

Subject of Research: A prediction model for 30-day mortality in Acinetobacter baumannii bloodstream infection

Article Title: A nomogram for early risk stratification of 30-day mortality in confirmed Acinetobacter baumannii bloodstream infection

Article References: Ma, L., Cai, Z., Liu, J., Dong, N., Zhou, J., & Xu, J. (2026). A nomogram for early risk stratification of 30-day mortality in confirmed Acinetobacter baumannii bloodstream infection. Frontiers in Cellular and Infection Microbiology, 16, Article 1979847. https://doi.org/10.3389/fcimb.2026.1979847

Image Credits: AI Generated

DOI: 10.3389/fcimb.2026.1979847

Keywords: Acinetobacter baumannii, bloodstream infection, nomogram, 30-day mortality, risk stratification, LASSO regression, ROC curve, retrospective study, laboratory parameters, clinical prediction model, early, risk