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Reese TJ, Domenico HJ, Hernandez A, Byrne DW, Moore RP, Williams JB, Douthit BJ, Russo E, McCoy AB, Ivory CH, Steitz BD, Wright A. Implementable Prediction of Pressure Injuries in Hospitalized Adults: Model Development and Validation. JMIR Med Inform 2024; 12:e51842. [PMID: 38722209 PMCID: PMC11094428 DOI: 10.2196/51842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 03/08/2024] [Accepted: 03/10/2024] [Indexed: 05/18/2024] Open
Abstract
Background Numerous pressure injury prediction models have been developed using electronic health record data, yet hospital-acquired pressure injuries (HAPIs) are increasing, which demonstrates the critical challenge of implementing these models in routine care. Objective To help bridge the gap between development and implementation, we sought to create a model that was feasible, broadly applicable, dynamic, actionable, and rigorously validated and then compare its performance to usual care (ie, the Braden scale). Methods We extracted electronic health record data from 197,991 adult hospital admissions with 51 candidate features. For risk prediction and feature selection, we used logistic regression with a least absolute shrinkage and selection operator (LASSO) approach. To compare the model with usual care, we used the area under the receiver operating curve (AUC), Brier score, slope, intercept, and integrated calibration index. The model was validated using a temporally staggered cohort. Results A total of 5458 HAPIs were identified between January 2018 and July 2022. We determined 22 features were necessary to achieve a parsimonious and highly accurate model. The top 5 features included tracheostomy, edema, central line, first albumin measure, and age. Our model achieved higher discrimination than the Braden scale (AUC 0.897, 95% CI 0.893-0.901 vs AUC 0.798, 95% CI 0.791-0.803). Conclusions We developed and validated an accurate prediction model for HAPIs that surpassed the standard-of-care risk assessment and fulfilled necessary elements for implementation. Future work includes a pragmatic randomized trial to assess whether our model improves patient outcomes.
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Affiliation(s)
- Thomas J Reese
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Henry J Domenico
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Antonio Hernandez
- Department of Anesthesiology, Division of Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Daniel W Byrne
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Ryan P Moore
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Jessica B Williams
- Department of Nursing, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Brian J Douthit
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Elise Russo
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Allison B McCoy
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Catherine H Ivory
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Bryan D Steitz
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Adam Wright
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
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Wang Y, Liu J, Chen S, Zheng C, Zou X, Zhou Y. Exploring risk factors and their differences on suicidal ideation and suicide attempts among depressed adolescents based on decision tree model. J Affect Disord 2024; 352:87-100. [PMID: 38360368 DOI: 10.1016/j.jad.2024.02.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 02/04/2024] [Accepted: 02/11/2024] [Indexed: 02/17/2024]
Abstract
BACKGROUND Suicide has been recognized as a major global public health issue. Depressed adolescents are more prone to experiencing it. We explore risk factors and their differences on suicidal ideation and suicide attempts to further enhance our understanding of suicidal behavior. METHODS 2343 depressed adolescents aged 12-18 from 9 provinces/cities in China participated in this cross-sectional study. We utilized decision tree model, incorporating 32 factors encompassing participants' suicidal behavior. The feature importance of each factor was measured using Gini coefficients. RESULTS The decision tree model demonstrated a good fit with high accuracy (SI = 0.86, SA = 0.85 and F-Score (SI = 0.85, SA = 0.83). The predictive importance of each factor varied between groups with suicidal ideation and with suicide attempts. The most significant risk factor in both groups was depression (SI = 16.7 %, SA = 19.8 %). However, factors such as academic stress (SI = 7.2 %, SA = 1.6 %), hopelessness (SI = 9.1 %, SA = 5.0 %), and age (SI = 7.1 %, SA = 3.2 %) were more closely associated with suicidal ideation than suicide attempts. Factors related to the schooling status (SI = 3.5 %, SA = 10.1 %), total years of education (SI = 2.6 %, SA = 8.6 %), and loneliness (SI = 2.3 %, SA = 7.4 %) were relatively more important in the suicide attempt stage compared to suicidal ideation. LIMITATIONS The cross-sectional design limited the ability to capture changes in suicidal behavior among depressed adolescents over time. Possible bias may exist in the measurement of suicidal ideation. CONCLUSION The relative importance of each risk factor for suicidal ideation and attempted suicide varies. These findings provide further empirical evidence for understanding suicide behavior. Targeted treatment measures should be taken for different stages of suicide in clinical interventions.
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Affiliation(s)
- Yang Wang
- College of Management, Shenzhen University, Shenzhen, China
| | - Jiayao Liu
- College of Management, Shenzhen University, Shenzhen, China
| | - Siyu Chen
- College of Management, Shenzhen University, Shenzhen, China
| | - Chengyi Zheng
- College of Management, Shenzhen University, Shenzhen, China
| | - Xinwen Zou
- School of Business Informatics and Mathematics, University of Mannheim, Mannheim, Germany
| | - Yongjie Zhou
- Department of Psychiatric Rehabilitation, Shenzhen Kangning Hospital, Shenzhen, China.
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Wang I, Walker RM, Gillespie BM, Scott I, Sugathapala RDUP, Chaboyer W. Risk factors predicting hospital-acquired pressure injury in adult patients: An overview of reviews. Int J Nurs Stud 2024; 150:104642. [PMID: 38041937 DOI: 10.1016/j.ijnurstu.2023.104642] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 10/30/2023] [Accepted: 11/02/2023] [Indexed: 12/04/2023]
Abstract
BACKGROUND Hospital-acquired pressure injuries remain a significant patient safety threat. Current well-known pressure injury risk assessment tools have many limitations and therefore do not accurately predict the risk of pressure injury development over diverse populations. A contemporary understanding of the risk factors predicting pressure injury in adult hospitalised patients will inform pressure injury prevention and future researchers considering risk assessment tool development may benefit from our summary and synthesis of risk factors. OBJECTIVE To summarise and synthesise systematic reviews that identify risk factors for hospital-acquired pressure injury development in adult patients. DESIGN An overview of systematic reviews. METHODS Cochrane and the Joanna Briggs Institute methodologies guided this overview. The Cochrane library, CINAHL, MEDLINE, and Embase databases were searched for relevant articles published in English from January 2008 to September 2022. Two researchers independently screened articles against the predefined inclusion and exclusion criteria, extracted data and assessed the quality of the included reviews using "a measurement tool to assess systematic reviews" (AMSTAR version 2). Data were categorised using an inductive approach and synthesised according to the recent pressure injury conceptual frameworks. RESULTS From 11 eligible reviews, 37 risk factors were categorised inductively into 14 groups of risk factors. From these, six groups were classified into two domains: four to mechanical boundary conditions and two to susceptibility and tolerance of the individual. The remaining eight groups were evident across both domains. Four main risk factors, including diabetes, length of surgery or intensive care unit stay, vasopressor use, and low haemoglobin level were synthesised. The overall quality of the included reviews was low in five studies (45 %) and critically low in six studies (55 %). CONCLUSIONS Our findings highlighted the limitations in the methodological quality of the included reviews that may have influenced our results regarding risk factors. Current risk assessment tools and conceptual frameworks do not fully explain the complex and changing interactions amongst risk factors. This may warrant the need for more high-quality research, such as cohort studies, focussing on predicting hospital-acquired pressure injury in adult patients, to reconsider these risk factors we synthesised. REGISTRATION This overview was registered with the PROSPERO (CRD42022362218) on 27 September 2022.
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Affiliation(s)
- Isabel Wang
- Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia; School of Nursing and Midwifery, Griffith University, Gold Coast, Australia.
| | - Rachel M Walker
- Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia; The Princess Alexandra Hospital, Brisbane, Australia. https://twitter.com/rachelmwalker
| | - Brigid M Gillespie
- Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia; Gold Coast University Hospital, Gold Coast, Australia. https://twitter.com/bgillespie6
| | - Ian Scott
- The Princess Alexandra Hospital, Brisbane, Australia; School of Clinical Medicine, University of Queensland, Brisbane, Queensland, Australia
| | | | - Wendy Chaboyer
- Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia. https://twitter.com/WendyChaboyer
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Pei J, Guo X, Tao H, Wei Y, Zhang H, Ma Y, Han L. Machine learning-based prediction models for pressure injury: A systematic review and meta-analysis. Int Wound J 2023; 20:4328-4339. [PMID: 37340520 PMCID: PMC10681397 DOI: 10.1111/iwj.14280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 06/01/2023] [Indexed: 06/22/2023] Open
Abstract
Despite the fact that machine learning (ML) algorithms to construct predictive models for pressure injury development are widely reported, the performance of the model remains unknown. The goal of the review was to systematically appraise the performance of ML models in predicting pressure injury. PubMed, Embase, Cochrane Library, Web of Science, CINAHL, Grey literature and other databases were systematically searched. Original journal papers were included which met the inclusion criteria. The methodological quality was assessed independently by two reviewers using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed with Metadisc software, with the area under the receiver operating characteristic curve, sensitivity and specificity as effect measures. Chi-squared and I2 tests were used to assess the heterogeneity. A total of 18 studies were included for the narrative review, and 14 of them were eligible for meta-analysis. The models achieved excellent pooled AUC of 0.94, sensitivity of 0.79 (95% CI [0.78-0.80]) and specificity of 0.87 (95% CI [0.88-0.87]). Meta-regressions did not provide evidence that model performance varied by data or model types. The present findings indicate that ML models show an outstanding performance in predicting pressure injury. However, good-quality studies should be conducted to verify our results and confirm the clinical value of ML in pressure injury development.
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Affiliation(s)
- Juhong Pei
- The First Clinical Medical College, School of NursingLanzhou UniversityLanzhouChina
| | | | - Hongxia Tao
- The First Clinical Medical College, School of NursingLanzhou UniversityLanzhouChina
| | - Yuting Wei
- School of NursingLanzhou UniversityLanzhouChina
| | - Hongyan Zhang
- Department of NursingGansu Provincial HospitalLanzhouChina
| | - Yuxia Ma
- School of NursingLanzhou UniversityLanzhouChina
| | - Lin Han
- The First Clinical Medical College, School of NursingLanzhou UniversityLanzhouChina
- Department of NursingGansu Provincial HospitalLanzhouChina
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Barghouthi ED, Owda AY, Asia M, Owda M. Systematic Review for Risks of Pressure Injury and Prediction Models Using Machine Learning Algorithms. Diagnostics (Basel) 2023; 13:2739. [PMID: 37685277 PMCID: PMC10486671 DOI: 10.3390/diagnostics13172739] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/11/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
Pressure injuries are increasing worldwide, and there has been no significant improvement in preventing them. This study is aimed at reviewing and evaluating the studies related to the prediction model to identify the risks of pressure injuries in adult hospitalized patients using machine learning algorithms. In addition, it provides evidence that the prediction models identified the risks of pressure injuries earlier. The systematic review has been utilized to review the articles that discussed constructing a prediction model of pressure injuries using machine learning in hospitalized adult patients. The search was conducted in the databases Cumulative Index to Nursing and Allied Health Literature (CINAHIL), PubMed, Science Direct, the Institute of Electrical and Electronics Engineers (IEEE), Cochrane, and Google Scholar. The inclusion criteria included studies constructing a prediction model for adult hospitalized patients. Twenty-seven articles were included in the study. The defects in the current method of identifying risks of pressure injury led health scientists and nursing leaders to look for a new methodology that helps identify all risk factors and predict pressure injury earlier, before the skin changes or harms the patients. The paper critically analyzes the current prediction models and guides future directions and motivations.
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Affiliation(s)
- Eba’a Dasan Barghouthi
- Health Sciences Department, Arab American University, Ramallah P600, Palestine; (E.D.B.); (M.A.)
| | - Amani Yousef Owda
- Department of Natural Engineering and Technology Sciences, Arab American University, Ramallah P600, Palestine
| | - Mohammad Asia
- Health Sciences Department, Arab American University, Ramallah P600, Palestine; (E.D.B.); (M.A.)
| | - Majdi Owda
- Faculty of Data Science, Arab American University, Ramallah P600, Palestine;
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Dweekat OY, Lam SS, McGrath L. An Integrated System of Braden Scale and Random Forest Using Real-Time Diagnoses to Predict When Hospital-Acquired Pressure Injuries (Bedsores) Occur. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4911. [PMID: 36981818 PMCID: PMC10049700 DOI: 10.3390/ijerph20064911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 02/24/2023] [Accepted: 03/06/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVES Bedsores/Pressure Injuries (PIs) are the second most common diagnosis in healthcare system billing records in the United States and account for 60,000 deaths annually. Hospital-Acquired Pressure Injuries (HAPIs) are one classification of PIs and indicate injuries that occurred while the patient was cared for within the hospital. Until now, all studies have predicted who will develop HAPI using classic machine algorithms, which provides incomplete information for the clinical team. Knowing who will develop HAPI does not help differentiate at which point those predicted patients will develop HAPIs; no studies have investigated when HAPI develops for predicted at-risk patients. This research aims to develop a hybrid system of Random Forest (RF) and Braden Scale to predict HAPI time by considering the changes in patients' diagnoses from admission until HAPI occurrence. METHODS Real-time diagnoses and risk factors were collected daily for 485 patients from admission until HAPI occurrence, which resulted in 4619 records. Then for each record, HAPI time was calculated from the day of diagnosis until HAPI occurrence. Recursive Feature Elimination (RFE) selected the best factors among the 60 factors. The dataset was separated into 80% training (10-fold cross-validation) and 20% testing. Grid Search (GS) with RF (GS-RF) was adopted to predict HAPI time using collected risk factors, including Braden Scale. Then, the proposed model was compared with the seven most common algorithms used to predict HAPI; each was replicated for 50 different experiments. RESULTS GS-RF achieved the best Area Under the Curve (AUC) (91.20 ± 0.26) and Geometric Mean (G-mean) (91.17 ± 0.26) compared to the seven algorithms. RFE selected 43 factors. The most dominant interactable risk factors in predicting HAPI time were visiting ICU during hospitalization, Braden subscales, BMI, Stimuli Anesthesia, patient refusal to change position, and another lab diagnosis. CONCLUSION Identifying when the patient is likely to develop HAPI can target early intervention when it is needed most and reduces unnecessary burden on patients and care teams when patients are at lower risk, which further individualizes the plan of care.
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Affiliation(s)
- Odai Y. Dweekat
- Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA
| | - Sarah S. Lam
- Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA
| | - Lindsay McGrath
- Wound Ostomy Continence Nursing, ChristianaCare Health System, Newark, DE 19718, USA
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Dweekat OY, Lam SS, McGrath L. An Integrated System of Multifaceted Machine Learning Models to Predict If and When Hospital-Acquired Pressure Injuries (Bedsores) Occur. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20010828. [PMID: 36613150 PMCID: PMC9820011 DOI: 10.3390/ijerph20010828] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 12/21/2022] [Accepted: 12/27/2022] [Indexed: 06/12/2023]
Abstract
Hospital-Acquired Pressure Injury (HAPI), known as bedsore or decubitus ulcer, is one of the most common health conditions in the United States. Machine learning has been used to predict HAPI. This is insufficient information for the clinical team because knowing who would develop HAPI in the future does not help differentiate the severity of those predicted cases. This research develops an integrated system of multifaceted machine learning models to predict if and when HAPI occurs. Phase 1 integrates Genetic Algorithm with Cost-Sensitive Support Vector Machine (GA-CS-SVM) to handle the high imbalance HAPI dataset to predict if patients will develop HAPI. Phase 2 adopts Grid Search with SVM (GS-SVM) to predict when HAPI will occur for at-risk patients. This helps to prioritize who is at the highest risk and when that risk will be highest. The performance of the developed models is compared with state-of-the-art models in the literature. GA-CS-SVM achieved the best Area Under the Curve (AUC) (75.79 ± 0.58) and G-mean (75.73 ± 0.59), while GS-SVM achieved the best AUC (75.06) and G-mean (75.06). The research outcomes will help prioritize at-risk patients, allocate targeted resources and aid with better medical staff planning to provide intervention to those patients.
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Affiliation(s)
- Odai Y. Dweekat
- Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA
| | - Sarah S. Lam
- Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA
| | - Lindsay McGrath
- Wound Ostomy Continence Nursing, ChristianaCare Health System, Newark, DE 19718, USA
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Dweekat OY, Lam SS, McGrath L. Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:796. [PMID: 36613118 PMCID: PMC9819814 DOI: 10.3390/ijerph20010796] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/21/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
Pressure Injuries (PI) are one of the most common health conditions in the United States. Most acute or long-term care patients are at risk of developing PI. Machine Learning (ML) has been utilized to manage patients with PI, in which one systematic review describes how ML is used in PI management in 32 studies. This research, different from the previous systematic review, summarizes the previous contributions of ML in PI from January 2007 to July 2022, categorizes the studies according to medical specialties, analyzes gaps, and identifies opportunities for future research directions. PRISMA guidelines were adopted using the four most common databases (PubMed, Web of Science, Scopus, and Science Direct) and other resources, which result in 90 eligible studies. The reviewed articles are divided into three categories based on PI time of occurrence: before occurrence (48%); at time of occurrence (16%); and after occurrence (36%). Each category is further broken down into sub-fields based on medical specialties, which result in sixteen specialties. Each specialty is analyzed in terms of methods, inputs, and outputs. The most relevant and potentially useful applications and methods in PI management are outlined and discussed. This includes deep learning techniques and hybrid models, integration of existing risk assessment tools with ML that leads to a partnership between provider assessment and patients' Electronic Health Records (EHR).
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Affiliation(s)
- Odai Y. Dweekat
- Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA
| | - Sarah S. Lam
- Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA
| | - Lindsay McGrath
- Wound Ostomy Continence Nursing, ChristianaCare Health System, Newark, DE 19718, USA
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Dweekat OY, Lam SS, McGrath L. A Hybrid System of Braden Scale and Machine Learning to Predict Hospital-Acquired Pressure Injuries (Bedsores): A Retrospective Observational Cohort Study. Diagnostics (Basel) 2022; 13:diagnostics13010031. [PMID: 36611323 PMCID: PMC9818183 DOI: 10.3390/diagnostics13010031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/16/2022] [Accepted: 12/19/2022] [Indexed: 12/25/2022] Open
Abstract
Background: The Braden Scale is commonly used to determine Hospital-Acquired Pressure Injuries (HAPI). However, the volume of patients who are identified as being at risk stretches already limited resources, and caregivers are limited by the number of factors that can reasonably assess during patient care. In the last decade, machine learning techniques have been used to predict HAPI by utilizing related risk factors. Nevertheless, none of these studies consider the change in patient status from admission until discharge. Objectives: To develop an integrated system of Braden and machine learning to predict HAPI and assist with resource allocation for early interventions. The proposed approach captures the change in patients' risk by assessing factors three times across hospitalization. Design: Retrospective observational cohort study. Setting(s): This research was conducted at ChristianaCare hospital in Delaware, United States. Participants: Patients discharged between May 2020 and February 2022. Patients with HAPI were identified from Nursing documents (N = 15,889). Methods: Support Vector Machine (SVM) was adopted to predict patients' risk for developing HAPI using multiple risk factors in addition to Braden. Multiple performance metrics were used to compare the results of the integrated system versus Braden alone. Results: The HAPI rate is 3%. The integrated system achieved better sensitivity (74.29 ± 1.23) and detection prevalence (24.27 ± 0.16) than the Braden scale alone (sensitivity (66.90 ± 4.66) and detection prevalence (41.96 ± 1.35)). The most important risk factors to predict HAPI were Braden sub-factors, overall Braden, visiting ICU during hospitalization, and Glasgow coma score. Conclusions: The integrated system which combines SVM with Braden offers better performance than Braden and reduces the number of patients identified as at-risk. Furthermore, it allows for better allocation of resources to high-risk patients. It will result in cost savings and better utilization of resources. Relevance to clinical practice: The developed model provides an automated system to predict HAPI patients in real time and allows for ongoing intervention for patients identified as at-risk. Moreover, the integrated system is used to determine the number of nurses needed for early interventions. Reporting Method: EQUATOR guidelines (TRIPOD) were adopted in this research to develop the prediction model. Patient or Public Contribution: This research was based on a secondary analysis of patients' Electronic Health Records. The dataset was de-identified and patient identifiers were removed before processing and modeling.
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Affiliation(s)
- Odai Y. Dweekat
- Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA
- Correspondence:
| | - Sarah S. Lam
- Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA
| | - Lindsay McGrath
- Wound Ostomy Continence Nursing, ChristianaCare Health System, Newark, DE 19718, USA
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