Significance of the Modified NUTRIC Score for Predicting Clinical Outcomes in Patients with Severe Community-Acquired Pneumonia

Nutritional status could affect clinical outcomes in critical patients. We aimed to determine the prognostic accuracy of the modified Nutrition Risk in Critically Ill (mNUTRIC) score for hospital mortality and treatment outcomes in patients with severe community-acquired pneumonia (SCAP) compared to other clinical prediction rules. We enrolled SCAP patients in a multi-center setting retrospectively. The mNUTRIC score and clinical prediction rules for pneumonia, as well as clinical factors, were calculated and recorded. Clinical outcomes, including mortality status and treatment outcome, were assessed after the patient was discharged. We used the receiver operating characteristic (ROC) curve method and multivariate logistic regression analysis to determine the prognostic accuracy of the mNUTRIC score for predicting clinical outcomes compared to clinical prediction rules, while 815 SCAP patients were enrolled. ROC curve analysis showed that the mNUTRIC score was the most effective at predicting each clinical outcome and had the highest area under the ROC curve value. The cut-off value for predicting clinical outcomes was 5.5. By multivariate logistic regression analysis, the mNUTRIC score was also an independent predictor of both clinical outcomes in SCAP patients. We concluded that the mNUTRIC score is a better prognostic factor for predicting clinical outcomes in SCAP patients compared to other clinical prediction rules.

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Prognostic Accuracy of Clinical Prediction Rules for Early Post-Pulmonary Embolism All-Cause Mortality

CHEST Journal ◽

10.1378/chest.14-1888 ◽

2015 ◽

Vol 147 (4) ◽

pp. 1043-1062 ◽

Cited By ~ 46

Author(s):

Christine G. Kohn ◽

Elizabeth S. Mearns ◽

Matthew W. Parker ◽

Adrian V. Hernandez ◽

Craig I. Coleman

Keyword(s):

Pulmonary Embolism ◽

Clinical Prediction ◽

Clinical Prediction Rules ◽

Prediction Rules ◽

Prognostic Accuracy ◽

All Cause Mortality

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A Comparison of the Prognostic Accuracy of Three Clinical Prediction Rules for Post-Pulmonary Embolism In-Hospital Mortality

CHEST Journal ◽

10.1378/chest.2255232 ◽

2015 ◽

Vol 148 (4) ◽

pp. 1009A

Author(s):

Whitney Saulsberry ◽

Christine Kohn ◽

Craig Coleman ◽

Diana Sobieraj

Keyword(s):

Pulmonary Embolism ◽

Hospital Mortality ◽

Clinical Prediction ◽

Clinical Prediction Rules ◽

Prediction Rules ◽

Prognostic Accuracy

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Clinical prediction rules in community-acquired pneumonia: lies, damn lies and statistics

QJM ◽

10.1093/qjmed/hcu096 ◽

2014 ◽

Vol 107 (7) ◽

pp. 595-596 ◽

Cited By ~ 11

Author(s):

M. S. Abers ◽

D. M. Musher

Keyword(s):

Community Acquired Pneumonia ◽

Clinical Prediction ◽

Clinical Prediction Rules ◽

Prediction Rules

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Clinical prediction rules and PTSD

PsycEXTRA Dataset ◽

10.1037/e518362011-003 ◽

2008 ◽

Author(s):

Jeffrey Sonis

Keyword(s):

Clinical Prediction ◽

Clinical Prediction Rules ◽

Prediction Rules

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Deregulation of lncRNA HIST1H2AG-6 and AIM1-3 in peripheral blood mononuclear cells is associated with newly diagnosed type 2 diabetes

BMC Medical Genomics ◽

10.1186/s12920-021-00994-z ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Hui Jiang ◽

Peian Lou ◽

Xiaoluo Chen ◽

Chenguang Wu ◽

Shihe Shao

Keyword(s):

Roc Curve ◽

Multivariate Logistic Regression Analysis ◽

Rank Correlation ◽

Differentially Expressed ◽

Healthy Controls ◽

Biological Functions ◽

Multivariate Logistic Regression ◽

Spearman’S Rank Correlation ◽

Spearman’S Rank Correlation Coefficient

Abstract Background Type 2 diabetes mellitus (T2DM) is mainly affected by genetic and environmental factors; however, the correlation of long noncoding RNAs (lncRNAs) with T2DM remains largely unknown. Methods Microarray analysis was performed to identify the differentially expressed lncRNAs and messenger RNAs (mRNAs) in patients with T2DM and healthy controls, and the expression of two candidate lncRNAs (lnc-HIST1H2AG-6 and lnc-AIM1-3) were further validated using quantitative real-time polymerase chain reaction (qRT-PCR). Spearman’s rank correlation coefficient was used to measure the degree of association between the two candidate lncRNAs and differentially expressed mRNAs. Furthermore, the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and GO (Gene Ontology) enrichment analysis were used to reveal the biological functions of the two candidate lncRNAs. Additionally, multivariate logistic regression analysis and receiver operating characteristic (ROC) curve analysis were performed. Results The microarray analysis revealed that there were 55 lncRNAs and 36 mRNAs differentially expressed in patients with T2DM compared with healthy controls. Notably, lnc-HIST1H2AG-6 was significantly upregulated and lnc-AIM1-3 was significantly downregulated in patients with T2DM, which was validated in a large-scale qRT-PCR examination (90 controls and 100 patients with T2DM). Spearman’s rank correlation coefficient revealed that both lncRNAs were correlated with 36 differentially expressed mRNAs. Furthermore, functional enrichment (KEGG and GO) analysis demonstrated that the two lncRNA-related mRNAs might be involved in multiple biological functions, including cell programmed death, negative regulation of insulin receptor signal, and starch and sucrose metabolism. Multivariate logistic regression analysis revealed that lnc-HIST1H2AG-6 and lnc-AIM1-3 were significantly correlated with T2DM (OR = 5.791 and 0.071, respectively, both P = 0.000). Furthermore, the ROC curve showed that the expression of lnc-HIST1H2AG-6 and lnc-AIM1-3 might be used to differentiate patients with T2DM from healthy controls (area under the ROC curve = 0.664 and 0.769, respectively). Conclusion The profiles of lncRNA and mRNA were significantly changed in patients with T2DM. The expression levels of lnc-HIST1H2AG-6 and lnc-AIM1-3 genes were significantly correlated with some features of T2DM, which may be used to distinguish patients with T2DM from healthy controls and may serve as potential novel biomarkers for diagnosis in the future.

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