Construction and evaluation of a predictive model for compassion fatigue among emergency department nurses: A cross-sectional study

Xie, Wanqing; Liu, Manli; Okoli, Chizimuzo T.C.; Zeng, Li; Huang, Shuqi; Ye, Xin; Liu, Fan; Wang, Jialin

doi:10.1016/j.ijnurstu.2023.104613

Cited by 6 publications

(2 citation statements)

References 56 publications

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“…In the training set, the least absolute shrinkage and selection operator (LASSO) regression was used to select potential predictor variables, and 10-fold cross-validation was used to confirm the appropriate tuning parameters (λ) of the LASSO regression analysis to screen the best subset of predictor variables [35]. The collinearity of the potential predictor variables was diagnosed using variance inflation factor (VIF) test, where VIF < 5 and Tolerance > 0.1 indicated the absence of significant collinearity [36].…”

Section: Methodsmentioning

confidence: 99%

Development and validation of a risk prediction model for frailty in Chinese middle-aged and elderly people with arthritis

Li,

Zhuang

et al. 2024

Preprint

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Background Frailty is common in people with arthritis and may result in a range of adverse consequences. This study aimed to investigate risk factors for frailty in people with arthritis and to develop and validate a nomogram prediction model. Methods The study used data from the 2015 China Health and Retirement Longitudinal Study (CHARLS). This study analyzed 36 indicators including socio-demographic, behavioral, and health status factors. Participants were randomly included in training and validation sets in a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression was used on the training set to screen the best predictor variables of the model through 10-fold cross-validation. Binary logistic regression was used to explore the related factors of frailty in people with arthritis. Construct nomograms to develop prediction models. Use receiver operating characteristic (ROC) curves to evaluate the discrimination ability of the model, Calibration curves to evaluate calibration, and decision curve analysis (DCA) to evaluate clinical validity. Results A total of 6209 people with arthritis were included in this study, of whom 952 (15.3%) suffered from frailty. The nomogram model includes 9 predictive factors: age, gender, activities of daily living (ADL), waistline, cognitive function, depressive symptoms, hearing status, self-perceived health status, and inpatient needs. The model shows good consistency and accuracy. The AUC values for the model in the training set and validation set are 0.866 (95% CI = 0.852-0.880) and 0.854 (95% CI = 0.832-0.876) respectively. The calibration curves showed good accuracy between the nomogram model and actual observations. ROC and DCA showed that the nomogram had good predictive performance. Conclusions The frailty risk prediction model constructed in this study has good discrimination, calibration, and clinical validity in people with arthritis. It is a promising and convenient tool that can be used as an objective guide for the clinical screening of high-risk populations.

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Section: Methodsmentioning

confidence: 99%

Development and validation of a risk prediction model for frailty in Chinese middle-aged and elderly people with arthritis

Li,

Zhuang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Based on the regression results, multiple line segments are drawn in specific proportions, and through plotting, the disease risk or survival probability of an individual can be conveniently calculated [ 26 ]. Many studies have used a nomogram to predict the probability of fatigue occurrence in different populations, and have validated the accuracy of the nomogram [ 27 – 30 ].…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a nomogram for predicting sever cancer-related fatigue in patients with cervical cancer

Gu,

Yang,

Zhang

et al. 2024

BMC Cancer

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Objective Cancer-related fatigue (CRF) has been considered the biggest influencing factor for cancer patients after surgery. This study aimed to develop and validate a nomogram for severe cancer-related fatigue (CRF) patients with cervical cancer (CC). Methods A cross-sectional study was conducted to develop and validate a nomogram (building set = 196; validation set = 88) in the Department of Obstetrics and Gynecology of a Class III hospital in Shenyang, Liaoning Province. We adopted the questionnaire method, including the Cancer Fatigue Scale (CFS), Medical Uncertainty in Illness Scale (MUIS), Medical Coping Modes Questionnaire (MCMQ), Multidimensional Scale of Perceived Social Support (MSPSS), and Sense of Coherence-13 (SOC-13). Binary logistic regression was used to test the risk factors of CRF. The R4.1.2 software was used to develop and validate the nomogram, including Bootstrap resampling method, the ability of Area Under Curve (AUC), Concordance Index (C-Index), Hosmer Lemeshow goodness of fit test, Receiver Operating Characteristic (ROC) curve, Calibration calibration curve, and Decision Curve Analysis curve (DCA). Results The regression equation was Logit(P) = 1.276–0.947 Monthly income + 0.989 Long-term passive smoking − 0.952 Physical exercise + 1.512 Diagnosis type + 1.040 Coping style − 0.726 Perceived Social Support − 2.350 Sense of Coherence. The C-Index of the nomogram was 0.921 (95% CI: 0.877$$ \sim $$0.958). The ROC curve showed the sensitivity of the nomogram was 0.821, the specificity was 0.900, and the accuracy was 0.857. AUC was 0.916 (95% CI: 0.876$$ \sim $$0.957). The calibration showed that the predicted probability of the nomogram fitted well with the actual probability. The DCA curve showed when the prediction probability was greater than about 10%, the benefit of the nomogram was positive. The results in the validation group were similar. Conclusion This nomogram had good identifiability, accuracy and clinical practicality, and could be used as a prediction and evaluation tool for severe cases of clinical patients with CC.

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Comment on Xie et al. (2023) ‘Construction and evaluation of a predictive model for compassion fatigue among emergency department nurses: A cross-sectional study’

Song,

Wang,

Chi

et al. 2024

International Journal of Nursing Studies

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Construction and evaluation of a predictive model for compassion fatigue among emergency department nurses: A cross-sectional study

Cited by 6 publications

References 56 publications

Development and validation of a risk prediction model for frailty in Chinese middle-aged and elderly people with arthritis

Development and validation of a risk prediction model for frailty in Chinese middle-aged and elderly people with arthritis

Development and validation of a nomogram for predicting sever cancer-related fatigue in patients with cervical cancer

Comment on Xie et al. (2023) ‘Construction and evaluation of a predictive model for compassion fatigue among emergency department nurses: A cross-sectional study’

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