Evidence map›Paper›PMID 36383295›Full record

ArticleEuropean journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology2023

28-day sepsis mortality prediction model from combined serial interleukin-6, lactate, and procalcitonin measurements: a retrospective cohort study.

Yinjing Xie, Dehua Zhuang, Huaisheng Chen, Shiqing Zou, Weibu Chen, Yue Chen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  8. Nomograms to predict outcome for patients undergoing venoarterial extracorporeal membrane oxygenation treatment for septic shock.Journal of artificial organs : the official journal of the Japanese Society for Artificial Organs · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yinjing XieDepartment of Medical Laboratory, Shenzhen People's Hospital, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of South University of Science and Technology, Shenzhen, 518020, Guangdong, China.
Dehua ZhuangDepartment of Medical Laboratory, Shenzhen People's Hospital, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of South University of Science and Technology, Shenzhen, 518020, Guangdong, China.
Huaisheng ChenDepartment of Critical Care Medicine, Shenzhen People's Hospital, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of South University of Science and Technology, Shenzhen, 518020, Guangdong, China.
Shiqing ZouDepartment of Medical Laboratory, Shenzhen People's Hospital, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of South University of Science and Technology, Shenzhen, 518020, Guangdong, China.
Weibu ChenDepartment of Medical Laboratory, Shenzhen People's Hospital, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of South University of Science and Technology, Shenzhen, 518020, Guangdong, China. chen.weibu@szhospital.com.ORCID http://orcid.org/0000-0002-1142-0671
Yue ChenDepartment of Medical Laboratory, Shenzhen People's Hospital, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of South University of Science and Technology, Shenzhen, 518020, Guangdong, China. chenyue_dr@163.com.

Funding

Innovative Research Group Project of the National Natural Science Foundation of China 82073284
6 · The paper itself

Abstract

Sepsis is a global medical issue owing to its unacceptably high mortality rate. Therefore, an effective approach to predicting patient outcomes is critically needed. We aimed to search for a novel 28-day sepsis mortality prediction model based on serial interleukin-6 (IL-6), lactate (LAC), and procalcitonin (PCT) measurements. We enrolled 367 septic patients based on Sepsis-3 (Third International Consensus Definitions for Sepsis and Septic Shock). Serum IL-6, LAC, and PCT levels were measured serially. Results collected within 24 and 48-72 h of admission were marked as D1 and D3 (e.g., IL-6D1/D3), respectively; the IL-6, LAC, and PCT clearance (IL-6c, LACc, PCTc) at D3 were calculated. Data were split into training and validation cohorts (7:3). Logistic regression analyses were used to select variables to develop models and choose the best one according to the Akaike information criterion (AIC). Receiver operating characteristic curves (ROC), calibration plots, and decision curve analysis (DCA) were used to test model performance. A nomogram was used to validate the model. There were 314 (85.56%) survivors and 53 (14.44%) non-survivors. Logistic regression analyses showed that IL-6D1, IL-6D3, PCTD1, PCTD3, and LACcD3 could be used to develop the best prediction model. The areas under the curves (AUC) of the training (0.849, 95% CI: 0.787-0.911) and validation cohorts (0.828, 95% CI: 0.727-0.929), calibration plot, and the DCA showed that the model performed well. Thus, the predictive value of the risk nomogram was verified. Combining IL-6D1, IL-6D3, PCTD1, PCTD3, and LACcD3 may create an accurate prediction model for 28-day sepsis mortality. Multiple-center research with a larger quantity of data is necessary to determine its clinical utility.

Indexed as

ProcalcitoninSepsisBiomarkersHumansInterleukin-6Lactic AcidPrognosisRetrospective StudiesROC CurveBiomarkersInterleukin-6Lactic AcidProcalcitoninInterleukin-6LactateMortalityPrediction modelProcalcitoninSepsis

Identifiers

PMID36383295
PMCPMC9816294

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Registered trials

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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.