Evidence map›Paper›PMID 40529351›Full record

ArticleInternational journal of general medicine2025

A Mortality Risk Prediction Model for Septic Shock in Patients Aged ≥50: Role of Norepinephrine Index and Procalcitonin.

Xue-Lin Li, Te Mi, Cancan Liu, Mingchen Feng

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Article in International journal of general medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Xue-Lin Li *Department of Intensive Care Unit, Jining No. 1 People's Hospital, Shandong First Medical University, Jining, People's Republic of China.ORCID 0000-0002-6724-6932
Te Mi *Department of Intensive Care Unit, Jining No. 1 People's Hospital, Shandong First Medical University, Jining, People's Republic of China.
Cancan LiuDepartment of Intensive Care Unit, Jining No. 1 People's Hospital, Shandong First Medical University, Jining, People's Republic of China.ORCID 0009-0006-7420-5780
Mingchen FengDepartment of Intensive Care Unit, Jining No. 1 People's Hospital, Shandong First Medical University, Jining, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Septic shock is a high-mortality syndrome, particularly in patients aged 50 and older. Predicting mortality in this population is challenging due to clinical heterogeneity and limitations of traditional scoring systems like SOFA and APACHE II. This study aimed to develop and validate a predictive model using norepinephrine index (NEI)-a novel biomarker defined as the norepinephrine dose administered within the first 24 hours of ICU admission divided by BMI and 24 hours-and procalcitonin (PCT) to improve risk stratification and clinical decision-making. Methods: A retrospective cohort of 94 patients aged ≥50 years with septic shock was analyzed. Key clinical variables within the first 24 hours were collected, and univariate and stepwise logistic regression identified predictors of 28-day mortality. The model's performance was evaluated with ROC curves, AUC, and confusion matrices, alongside internal validation through stratified analysis, bootstrap resampling, and training-test splits. External validation was conducted in an independent cohort of 57 patients. Results: The final model incorporating NEI and PCT achieved an AUC of 0.91, outperforming individual biomarkers (NEI: AUC = 0.86; PCT: AUC = 0.69). Nonlinear analysis identified NEI > 4mg· m² / (kg· 24h) and PCT < 50 ng/mL as critical thresholds for high mortality risk. Conclusion: The NEI and PCT-based prognostic model provides a reliable tool for predicting 28-day mortality in septic shock patients aged 50 and above. However, as a single-center study with a relatively small sample size, the generalizability of these findings may be limited. Future multicenter studies with larger sample sizes are necessary to validate this model's applicability across populations. This model holds potential to optimize clinical management, enabling timely interventions such as more intensive hemodynamic support and infection control.

Indexed as

machine learningmortality predictionnorepinephrine indexprocalcitoninrisk stratificationseptic shock

Identifiers

PMID40529351
PMCPMC12170855

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.