Xiaohua Zhou PhD , Peiya Tan Postgraduate , Miao Huo M.D. , Ying Wang M.M. , Qi Zhang M.D.
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The nomogram model was evaluated using the concordance index (C-index), decision curve analysis (DCA) curves, and receiver operating characteristic (ROC) curves.</p></div><div><h3>Results</h3><p>9 independent risk factors were identified and assembled into the nomogram. The nomogram exhibited reasonably accurate discrimination (area under the receiver operating characteristic curve (AUC-ROC): 0.931, <em>P</em> < 0.05) and calibration (C-index: 0.931, 95 % CI: 0.898–0.964) in the validation cohort. DCA and clinical impact curves demonstrated that the nomogram was clinically beneficial.</p></div><div><h3>Conclusions</h3><p>A visualization nomogram model was successfully established for predicting the outcome of the NHAP elderly patients. The model has extremely high reliability, extremely high predictive ability, and good clinical applicability.</p></div>","PeriodicalId":50740,"journal":{"name":"Applied Nursing Research","volume":"78 ","pages":"Article 151816"},"PeriodicalIF":2.7000,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and verification of a prediction model for outcomes of elderly patients with nursing home-acquired pneumonia\",\"authors\":\"Xiaohua Zhou PhD , Peiya Tan Postgraduate , Miao Huo M.D. , Ying Wang M.M. , Qi Zhang M.D.\",\"doi\":\"10.1016/j.apnr.2024.151816\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Among all infections in nursing homes, pneumonia has the highest mortality. Nurses have a 24-h relationship with patients and have a key role in identifying and preventing adverse outcomes. However, tools to engage nurses in pneumonia patient outcomes evaluation have not occurred.</p></div><div><h3>Purpose</h3><p>This study aimed to develop and validate a prediction model to predict the outcome of elderly patients with nursing home-acquired pneumonia (NHAP).</p></div><div><h3>Methodology</h3><p>A retrospective observational study was conducted with 219 elderly NHAP patients. Baseline characteristics, health history, and treatment/nursing status were collected. Variables for constructing nomograms were screened by univariate and multivariate analysis. The nomogram model was evaluated using the concordance index (C-index), decision curve analysis (DCA) curves, and receiver operating characteristic (ROC) curves.</p></div><div><h3>Results</h3><p>9 independent risk factors were identified and assembled into the nomogram. The nomogram exhibited reasonably accurate discrimination (area under the receiver operating characteristic curve (AUC-ROC): 0.931, <em>P</em> < 0.05) and calibration (C-index: 0.931, 95 % CI: 0.898–0.964) in the validation cohort. DCA and clinical impact curves demonstrated that the nomogram was clinically beneficial.</p></div><div><h3>Conclusions</h3><p>A visualization nomogram model was successfully established for predicting the outcome of the NHAP elderly patients. 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Development and verification of a prediction model for outcomes of elderly patients with nursing home-acquired pneumonia
Background
Among all infections in nursing homes, pneumonia has the highest mortality. Nurses have a 24-h relationship with patients and have a key role in identifying and preventing adverse outcomes. However, tools to engage nurses in pneumonia patient outcomes evaluation have not occurred.
Purpose
This study aimed to develop and validate a prediction model to predict the outcome of elderly patients with nursing home-acquired pneumonia (NHAP).
Methodology
A retrospective observational study was conducted with 219 elderly NHAP patients. Baseline characteristics, health history, and treatment/nursing status were collected. Variables for constructing nomograms were screened by univariate and multivariate analysis. The nomogram model was evaluated using the concordance index (C-index), decision curve analysis (DCA) curves, and receiver operating characteristic (ROC) curves.
Results
9 independent risk factors were identified and assembled into the nomogram. The nomogram exhibited reasonably accurate discrimination (area under the receiver operating characteristic curve (AUC-ROC): 0.931, P < 0.05) and calibration (C-index: 0.931, 95 % CI: 0.898–0.964) in the validation cohort. DCA and clinical impact curves demonstrated that the nomogram was clinically beneficial.
Conclusions
A visualization nomogram model was successfully established for predicting the outcome of the NHAP elderly patients. The model has extremely high reliability, extremely high predictive ability, and good clinical applicability.
期刊介绍：
Applied Nursing Research presents original, peer-reviewed research findings clearly and directly for clinical applications in all nursing specialties. Regular features include "Ask the Experts," research briefs, clinical methods, book reviews, news and announcements, and an editorial section. Applied Nursing Research covers such areas as pain management, patient education, discharge planning, nursing diagnosis, job stress in nursing, nursing influence on length of hospital stay, and nurse/physician collaboration.
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