Ranking Nursing Diagnoses by Predictive Relevance for Intensive Care Unit Transfer Risk in Adult and Pediatric Patients: A Machine Learning Approach with Random Forest
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design and Setting
2.2. Study Population and Inclusion Criteria
2.3. Variables
2.3.1. Dependent Variable (Outcome)
2.3.2. Independent Variables
2.4. Data Collection
2.4.1. PAI and PAIped Systems
2.4.2. HDR
2.5. Statistical Analysis
2.6. Ethical Considerations
3. Results
3.1. Population Characteristics
3.2. Feature Importance
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
CCC | Clinical Care Classification System |
DRG | Diagnosis-related Group |
EHR | Electronic Health Record |
HDR | Hospital Discharge Register |
ICT | Information and Communications Technology Service |
ICU | Intensive Care Unit |
IQR | Interquartile Range |
MEWS | Modified Early Warning Score |
ND | Nursing Diagnosis |
OOB | Out-Of-Bag |
PAI | Professional Assessment Instrument |
PAIped | Neonatal and Pediatric Professional Assessment Instrument |
SD | Standard Deviation |
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Variable | Descriptive Statistics | ||||||
---|---|---|---|---|---|---|---|
Adult Patients | |||||||
General population (N = 40,649) | Patients transferred to ICU (N = 3727) | Patients not transferred to ICU (N = 36,922) | p-Value a | ||||
Age (mean; SD) | 59.46 | 18.09 | 66.23 | 14.51 | 58.77 | 18.27 | <0.001 |
Gender (N; %) | <0.001 | ||||||
Male | 15,992 | 39.3 | 2122 | 56.9 | 13,870 | 37.6 | |
Female | 24,657 | 60.7 | 1605 | 43.1 | 23,052 | 62.4 | |
DRG weight (median, IQR) | 1.21 | 1 | 3.17 | 2 | 1.12 | 1 | <0.001 |
Elixhauser Comorbidity Index (mean; SD) | 0.63 | 0.83 | 1.02 | 0.95 | 0.59 | 1.17 | <0.001 |
NDs (N = 157,394) (mean; SD) | 3.87 | 3.05 | 6.75 | 3.21 | 3.58 | 2.87 | <0.001 |
Pediatric Patients | |||||||
General population (N = 2086) | Patients transferred to ICU (N = 330) | Patients not transferred to ICU (N = 1756) | p-Value a | ||||
Age (mean; SD) | 8.08 | 5.89 | 5.59 | 5.84 | 8.55 | 5.78 | <0.001 |
Gender (N; %) | 0.189 | ||||||
Male | 1183 | 56.7 | 198 | 60.0 | 985 | 56.1 | |
Female | 903 | 43.3 | 132 | 40.0 | 771 | 43.9 | |
DRG weight (median, IQR) | 0.79 | 1 | 2.11 | 1 | 0.75 | 1 | <0.001 |
NDs (N = 8504) (mean; SD) | 4.08 | 2.71 | 6.19 | 2.91 | 3.68 | 2.48 | <0.001 |
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Cesare, M.; Nurchis, M.C.; Nursing and Public Health Group; Damiani, G.; Cocchieri, A. Ranking Nursing Diagnoses by Predictive Relevance for Intensive Care Unit Transfer Risk in Adult and Pediatric Patients: A Machine Learning Approach with Random Forest. Healthcare 2025, 13, 1339. https://doi.org/10.3390/healthcare13111339
Cesare M, Nurchis MC, Nursing and Public Health Group, Damiani G, Cocchieri A. Ranking Nursing Diagnoses by Predictive Relevance for Intensive Care Unit Transfer Risk in Adult and Pediatric Patients: A Machine Learning Approach with Random Forest. Healthcare. 2025; 13(11):1339. https://doi.org/10.3390/healthcare13111339
Chicago/Turabian StyleCesare, Manuele, Mario Cesare Nurchis, Nursing and Public Health Group, Gianfranco Damiani, and Antonello Cocchieri. 2025. "Ranking Nursing Diagnoses by Predictive Relevance for Intensive Care Unit Transfer Risk in Adult and Pediatric Patients: A Machine Learning Approach with Random Forest" Healthcare 13, no. 11: 1339. https://doi.org/10.3390/healthcare13111339
APA StyleCesare, M., Nurchis, M. C., Nursing and Public Health Group, Damiani, G., & Cocchieri, A. (2025). Ranking Nursing Diagnoses by Predictive Relevance for Intensive Care Unit Transfer Risk in Adult and Pediatric Patients: A Machine Learning Approach with Random Forest. Healthcare, 13(11), 1339. https://doi.org/10.3390/healthcare13111339