Dataset Information

An interactive nomogram to predict healthcare-associated infections in ICU patients: A multicenter study in GuiZhou Province, China.

ABSTRACT:

Objective

To develop and validate an interactive nomogram to predict healthcare-associated infections (HCAIs) in the intensive care unit (ICU).

Methods

A multicenter retrospective study was conducted to review 2017 data from six hospitals in Guizhou Province, China. A total of 1,782 ICU inpatients were divided into either a training set (n = 1,189) or a validation set (n = 593). The patients' demographic characteristics, basic clinical features from the previous admission, and their need for bacterial culture during the current admission were extracted from electronic medical records of the hospitals to predict HCAI. Univariate and multivariable analyses were used to identify independent risk factors of HCAI in the training set. The multivariable model's performance was evaluated in both the training set and the validation set, and an interactive nomogram was constructed according to multivariable regression model. Moreover, the interactive nomogram was used to predict the possibility of a patient developing an HCAI based on their prior admission data. Finally, the clinical usefulness of the interactive nomogram was estimated by decision analysis using the entire dataset.

Results

The nomogram model included factor development (local economic development levels), length of stay (LOS; days of hospital stay), fever (days of persistent fever), diabetes (history of diabetes), cancer (history of cancer) and culture (the need for bacterial culture). The model showed good calibration and discrimination in the training set [area under the curve (AUC), 0.871; 95% confidence interval (CI), 0.848-0.894] and in the validation set (AUC, 0.862; 95% CI, 0.829-0.895). The decision curve demonstrated the clinical usefulness of our interactive nomogram.

Conclusions

The developed interactive nomogram is a simple and practical instrument for quantifying the individual risk of HCAI and promptly identifying high-risk patients.

SUBMITTER: Zhang M

PROVIDER: S-EPMC6629073 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

An interactive nomogram to predict healthcare-associated infections in ICU patients: A multicenter study in GuiZhou Province, China.

Zhang Man M Yang Huai H Mou Xia X Wang Lu L He Min M Zhang Qunling Q Wu Kaiming K Cheng Juan J Wu Wenjuan W Li Dan D Xu Yan Y Chao Jianqian J

PloS one 20190715 7

<h4>Objective</h4>To develop and validate an interactive nomogram to predict healthcare-associated infections (HCAIs) in the intensive care unit (ICU).<h4>Methods</h4>A multicenter retrospective study was conducted to review 2017 data from six hospitals in Guizhou Province, China. A total of 1,782 ICU inpatients were divided into either a training set (n = 1,189) or a validation set (n = 593). The patients' demographic characteristics, basic clinical features from the previous admission, and the ...[more]

PMID: 31306445

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Project description:BackgroundPatients diagnosed with small cell lung cancer (SCLC) typically experience a poor prognosis, and it is essential to predict overall survival (OS) and stratify patients based on distinct prognostic risks.MethodsTotally 2309 SCLC patients from the hospitals in 15 cities of Shandong from 2010 - 2014 were included in this multicenter, population-based retrospective study. The data of SCLC patients during 2010-2013 and in 2014 SCLC were used for model development and validation, respectively. OS served as the primary outcome. Univariate and multivariate Cox regression were applied to identify the independent prognostic factors of SCLC, and a prognostic model was developed based on these factors. The discrimination and calibration of this model were assessed by the time-dependent C-index, time-dependent receiver operator characteristic curves (ROC), and calibration curves. Additionally, Decision Curve Analysis (DCA) curves, Net Reclassification Improvement (NRI), and Integrated Discriminant Improvement (IDI) were used to assess the enhanced clinical utility and predictive accuracy of the model compared to TNM staging systems.ResultsMultivariate analysis showed that region (Southern/Eastern, hazard ratio [HR] = 1.305 [1.046 - 1.629]; Western/Eastern, HR = 0.727 [0.617 - 0.856]; Northern/Eastern, HR = 0.927 [0.800 - 1.074]), sex (female/male, HR = 0.838 [0.737 - 0.952]), age (46-60/≤45, HR = 1.401 [1.104 - 1.778]; 61-75/≤45, HR = 1.500 [1.182 - 1.902]; >75/≤45, HR = 1.869 [1.382 - 2.523]), TNM stage (II/I, HR = 1.119[0.800 - 1.565]; III/I, HR = 1.478 [1.100 - 1.985]; IV/I, HR = 1.986 [1.477 - 2.670], surgery (yes/no, HR = 0.677 [0.521 - 0.881]), chemotherapy (yes/no, HR = 0.708 [0.616 - 0.813]), and radiotherapy (yes/no, HR = 0.802 [0.702 - 0.917]) were independent prognostic factors of SCLC patients and were included in the nomogram. The time-dependent AUCs of this model in the training set were 0.699, 0.683, and 0.683 for predicting 1-, 3-, and 5-year OS, and 0.698, 0.698, and 0.639 in the validation set, respectively. The predicted calibration curves aligned with the ideal curves, and the DCA curves, the IDI, and the NRI collectively demonstrated that the prognostic model had a superior net benefit than the TNM staging system.ConclusionThe nomogram using SCLC patients in Shandong surpassed the TNM staging system in survival prediction accuracy and enabled the stratification of patients with distinct prognostic risks based on nomogram scores.

Dataset Information

An interactive nomogram to predict healthcare-associated infections in ICU patients: A multicenter study in GuiZhou Province, China.

Objective

Methods

Results

Conclusions

Publications

An interactive nomogram to predict healthcare-associated infections in ICU patients: A multicenter study in GuiZhou Province, China.

Similar Datasets

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