Three Bedside Factors Predict Death in E. coli Bloodstream Infections

Escherichia coli is the most common cause of bloodstream infection worldwide, and while most patients survive, in-hospital death rates range from 5 to 30 percent depending on the population and setting. Clinicians have long lacked a simple, validated tool to tell at the bedside which patient with E. coli in the blood is likely to deteriorate and which will recover with standard care. A new retrospective cohort study from The University of Hong Kong-Shenzhen Hospital, published in BMC Infectious Diseases, proposes a strikingly minimal answer: a nomogram built from just three variables that together predict in-hospital mortality with an area under the receiver operating characteristic curve approaching 0.97.

The study, led by Bingqiang Yu, Lei Rong, Wei Xu and Yulan Yan of the hospital’s Department of Respiratory Medicine, analyzed 202 consecutive adult patients with E. coli bacteraemia admitted between 2019 and 2023. Across the entire cohort, 19 patients died in hospital, an overall mortality rate of 9.4 percent. That figure sits squarely within the international range but represents a statistical challenge: with only 19 events spread over five years, traditional logistic regression risks producing unstable, overfitted estimates that would not survive contact with new patients.

The researchers confronted this problem directly with a statistical approach that remains underused in clinical prediction work. Because the number of deaths was low relative to the number of candidate predictors, they used Firth penalized logistic regression, a bias-reduced variant of the standard method, as their primary modeling technique. Firth regression shrinks coefficient estimates away from extreme values, taming the inflated odds ratios that ordinary maximum-likelihood methods produce when events are scarce. As sensitivity analyses, the team also fitted ridge regression, which applies an L2 penalty, and LASSO regression, which uses an L1 penalty that can drive coefficients to exactly zero, effectively performing variable selection. The consistency of findings across these three penalization strategies strengthens confidence that the identified predictors are not statistical artifacts.

Three variables emerged as independent predictors of in-hospital death. Respiratory failure carried a Firth odds ratio of 100.13, with a 95 percent confidence interval of 13.58 to 738.13. Malignancy was nearly as dominant, with an odds ratio of 93.36 and a confidence interval of 8.16 to 1068.64. The third predictor was blood urea nitrogen, or BUN, which carried an odds ratio of 1.25 for each 1 mmol/L increase, with a confidence interval of 1.11 to 1.41. The clinical logic is coherent: respiratory failure signals that the infection has compromised vital organ function, malignancy reflects a compromised immune landscape and often a fragile physiological reserve, and elevated BUN marks renal hypoperfusion and the metabolic stress of sepsis.

The confidence intervals deserve attention. They are wide, in some cases stretching across more than two orders of magnitude, which is the unavoidable price of modeling rare events in a single-center cohort of 202 patients. Wide intervals do not mean the associations are wrong, but they do mean the point estimates, particularly the near-hundredfold odds ratios for respiratory failure and malignancy, should be read as indicators of direction and rough magnitude rather than precise multipliers. The authors themselves frame the work as developmental, and the extreme coefficients are precisely the kind of numbers that penalized regression is designed to keep from becoming even more extreme.

Discrimination, the model’s ability to separate patients who die from those who survive, was strong. The apparent area under the receiver operating characteristic curve was 0.974 in the development cohort. Because apparent performance always flatters a model fitted to its own data, the researchers applied bootstrap optimism correction with 1,000 resampled replicates, and the corrected AUC remained 0.969, a drop of only half a percentage point. Calibration, which asks whether predicted probabilities match observed frequencies, was assessed with an apparent calibration slope near 1.13, close to the ideal value of 1. Decision curve analysis, a technique that evaluates the net clinical benefit of acting on a model’s predictions across a range of threshold probabilities, showed positive net benefit at threshold probabilities between 5 and 50 percent, a range that plausibly covers the risk levels at which clinicians would consider escalation of monitoring or empiric intensification.

Internal validation was deliberately layered. Beyond bootstrap correction, the team performed 5-fold cross-validation, repeatedly partitioning the data so that each patient served as a test case for a model that had not seen them. They also attempted a temporal split, developing the model on patients from 2019 to 2021, comprising 118 patients and 17 deaths, and testing it on patients from 2022 to 2023, comprising 84 patients. The validation window contained only 2 deaths, far too few for meaningful performance estimation, so the authors used it strictly as an exploratory sensitivity analysis rather than as evidence of temporal generalizability. This restraint is methodologically honest: with two events, any AUC or calibration estimate would be essentially noise, and presenting it as validation would have been misleading.

The choice of a nomogram as the delivery format reflects a resurgence of interest in transparent, pencil-and-paper risk tools. A nomogram converts a regression equation into visual axes, where each predictor’s value maps to a points scale, and the summed points map to a predicted probability. Unlike a black-box machine learning model, every contribution is visible and auditable, which matters in infection medicine where decisions about escalation, intensive care referral and goals-of-care conversations depend on clinicians understanding why a risk score says what it says. A three-variable tool can be computed from routine admission labs and bedside assessment without waiting for culture sensitivities, which typically take 24 to 48 hours after blood cultures are drawn.

The authors are explicit that the model is not ready for clinical deployment. The single-center design means the case mix, antibiotic practices and patient demographics of one Hong Kong-Shenzhen hospital may not transfer to other Chinese populations or to settings elsewhere. The event count of 19 is below the conventional guidance of at least 10 events per predictor variable for each candidate considered, which is exactly why penalization was necessary and why external validation in independent, ideally multicentre, cohorts is the stated next step. The authors also note that a recently reported multicentre mortality score called PROBAC exists for bloodstream infection generally, but that validated bedside tools specific to E. coli bacteraemia in Chinese populations remain scarce, which is the gap this nomogram is intended to begin filling.

The study was funded by the Shenzhen Medical Research Fund under grant C2401017, with no funder role in design, analysis or writing, and the authors declare no competing interests. The work was approved by the hospital’s ethics committee under approval number HKUSZH-2024-276, with written informed consent waived owing to the retrospective, anonymized design, and conducted in accordance with the Declaration of Helsinki. Published open access on 24 September 2026 under a Creative Commons Attribution 4.0 license, the study offers a template for how rare-event prediction modeling can be done rigorously: penalized regression to stabilize estimates, multiple internal validation strategies, and a candid distinction between what the data show and what remains to be proven. If external cohorts confirm the three-variable model, clinicians could gain a same-hour mortality estimate for one of the world’s most common serious infections, using nothing more than a respiratory exam, a cancer history and a routine blood test.

Subject of Research: Development of a nomogram for predicting in-hospital mortality in Escherichia coli bloodstream infection using Firth penalized regression

Article Title: Development of a nomogram for predicting in-hospital mortality in Escherichia coli bloodstream infection: a retrospective cohort study

Article References: Yu, B., Rong, L., Xu, W., & Yan, Y. (2026). Development of a nomogram for predicting in-hospital mortality in Escherichia coli bloodstream infection: a retrospective cohort study. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14480-3

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14480-3

Keywords: E. coli bacteraemia, bloodstream infection, nomogram, in-hospital mortality, prediction model, Firth penalized regression, LASSO regression, bootstrap validation, BUN, sepsis risk stratification, retrospective cohort study, decision curve analysis