Constructing Inpatient Pressure Injury Prediction Models Using Machine Learning Techniques

Machine learning-based prediction models for pressure injury: A systematic review and meta-analysis

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.

Development of a Predictive Model for Hospital-Acquired Pressure Injuries

Hospital-acquired pressure injuries are a challenge for healthcare systems, and the nurse's role is essential in their prevention. The first step is risk assessment. The development of advanced data-driven methods based on machine learning techniques can improve risk assessment through the use of routinely collected data. We studied 24 227 records from 15 937 distinct patients admitted to medical and surgical units between April 1, 2019, and March 31, 2020. Two predictive models were developed: random forest and long short-term memory neural network. Model performance was then evaluated and compared with the Braden score. The areas under the receiver operating characteristic curve, the specificity, and the accuracy of the long short-term memory neural network model (0.87, 0.82, and 0.82, respectively) were higher than those of the random forest model (0.80, 0.72, and 0.72, respectively) and the Braden score (0.72, 0.61, and 0.61, respectively). The sensitivity of the Braden score (0.88) was higher than that of long short-term memory neural network model (0.74) and the random forest model (0.73). The long short-term memory neural network model has the potential to support nurses in clinical decision-making. Implementation of this model in the electronic health record could improve assessment and allow nurses to focus on higher-priority interventions.

The prediction model for intraoperatively acquired pressure injuries in orthopedics based on the new risk factors: a real-world prospective observational, cross-sectional study

Introduction: Orthopedic patients are at high risk for intraoperatively acquired pressure injuries (IAPI), which cause a serious issue and lead to high-expense burden in patient care. However, there are currently no clinically available scales or models to assess IAPI associated with orthopedic surgery. Methods: In this real-world, prospective observational, cross-sectional study, we identified pressure injuries (PI)-related risk factors using a systematic review approach and clinical practice experience. We then prepared a real-world cohort to identify and confirm risk factors using multiple modalities. We successfully identified new risk factors while constructing a predictive model for PI in orthopedic surgery. Results: We included 28 orthopedic intraoperative PI risk factors from previous studies and clinical practice. A total of 422 real-world cases were also included, and three independent risk factors-preoperative limb activity, intraoperative wetting of the compressed tissue, and duration of surgery-were successfully identified using chi-squared tests and logistic regression. Finally, the three independent risk factors were successfully used to construct a nomogram clinical prediction model with good predictive validity (area under the ROC curve = 0.77), which is expected to benefit clinical patients. Conclusion: In conclusion, we successfully identified new independent risk factors for IAPI-related injury in orthopedic patients and developed a clinical prediction model to serve as an important complement to existing scales and provide additional benefits to patients. Our study also suggests that a single measure is not sufficient for the prevention of IAPI in orthopedic surgery patients and that a combination of measures may be required for the effective prevention of IAPI.

An Integrated System of Braden Scale and Random Forest Using Real-Time Diagnoses to Predict When Hospital-Acquired Pressure Injuries (Bedsores) Occur

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.

Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review

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).

The role of artificial intelligence in enhancing clinical nursing care: A scoping review

AIM

To present an overview of how artificial intelligence has been used to improve clinical nursing care.

BACKGROUND

Artificial intelligence has been reshaping the healthcare industry but little is known about its applicability in enhancing nursing care.

EVALUATION

A scoping review was conducted. Seven electronic databases (CINAHL, Cochrane Library, EMBASE, IEEE Xplore, PubMed, Scopus, and Web of Science) were searched from 1 January 2010 till 20 December 2020. Grey literature and reference lists of included articles were also searched.

KEY ISSUES

Thirty-seven studies encapsulating the use of artificial intelligence in improving clinical nursing care were included in this review. Six use cases were identified - documentation, formulating nursing diagnoses, formulating nursing care plans, patient monitoring, patient care prediction such as falls prediction (most common) and wound management. Various techniques of machine learning and classification were used for predictive analyses and to improve nurses' preparedness and management of patients' conditions CONCLUSION: This review highlighted the potential of artificial intelligence in improving the quality of nursing care. However, more randomized controlled trials in real-life healthcare settings should be conducted to enhance the rigor of evidence.

IMPLICATIONS FOR NURSING MANAGEMENT

Education in the application of artificial intelligence should be promoted to empower nurses to lead technological transformations and not passively trail behind others.

A systematic review of predictive models for hospital-acquired pressure injury using machine learning

AIMS AND OBJECTIVES

To summarize the use of machine learning (ML) for hospital-acquired pressure injury (HAPI) prediction and to systematically assess the performance and construction process of ML models to provide references for establishing high-quality ML predictive models.

BACKGROUND

As an adverse event, HAPI seriously affects patient prognosis and quality of life, and causes unnecessary medical investment. At present, the performance of various scales used to predict HAPIs is still unsatisfactory. As a new statistical tool, ML has been applied to predict HAPIs. However, its performance has varied in different studies; moreover, some deficiencies in the model construction process were observed in each study.

DESIGN

Systematic review.

METHODS

Relevant articles published between 2010-2021 were identified in the PubMed, Web of Science, Scopus, Embase and CINHAL databases. Study selection was performed in accordance with the preferred reporting items for systematic reviews and meta-analysis guidelines. The quality of the included articles was assessed using the prediction model risk of bias assessment tool.

RESULTS

Twenty-three studies out of 1793 articles were considered in this systematic review. The sample size of each study ranged from 149-75353; the prevalence of pressure injuries ranged from 0.5%-49.8%. ML showed good performance for HAPI prediction. However, some deficiencies were observed in terms of data management, data pre-processing and model validation.

CONCLUSIONS

ML, as a powerful decision-making assistance tool, is helpful for the prediction of HAPIs. However, existing studies have been insufficient in terms of data management, data pre-processing and model validation. Future studies should address these issues to establish ML models for HAPI prediction that can be widely used in clinical practice.

RELEVANCE TO CLINICAL PRACTICE

This review highlights that ML is helpful in predicting HAPI; however, in the process of data management, data pre-processing and model validation, some deficiencies still need to be addressed. The ultimate goal of integrating ML into HAPI prediction is to develop a practical clinical decision-making tool. A complete and rigorous model construction process should be followed in future studies to develop high-quality ML models that can be applied in clinical practice.

Pressure Injury Risk Assessment and Prevention in Patients With COVID-19 in the Intensive Care Unit

BACKGROUND

Patients critically ill with COVID-19 are at risk for hospital-acquired pressure injury, including device-related pressure injury.

METHODS

Braden Scale predictive validity was compared between patients with and without COVID-19, and a logistic regression model was developed to identify risk factors for device-related pressure injury.

RESULTS

A total of 1920 patients were included in the study sample, including 407 with COVID-19. Among the latter group, at least 1 hospital-acquired pressure injury developed in each of 120 patients (29%); of those, device-related pressure injury developed in 55 patients (46%). The Braden Scale score area under the receiver operating characteristic curve was 0.72 in patients without COVID-19 and 0.71 in patients with COVID-19, indicating fair to poor discrimination.

CONCLUSIONS

Fragile skin and prone positioning during mechanical ventilatory support were risk factors for device-related pressure injury. Clinicians may consider incorporating factors not included in the Braden Scale (eg, oxygenation and perfusion) in routine risk assessment and should maintain vigilance in their efforts to protect patients with COVID-19 from device-related pressure injury.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

BACKGROUND

The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

OBJECTIVES

This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

METHODS

We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

RESULTS

Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

CONCLUSION

This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

Development and validation of a machine learning algorithm-based risk prediction model of pressure injury in the intensive care unit

The study aimed to establish a machine learning-based scoring nomogram for early recognition of likely pressure injuries in an intensive care unit (ICU) using large-scale clinical data. A retrospective cohort study design was employed to develop and validate a top-performing clinical feature panel accessibly in the electronic medical records (EMRs), which was in the mode of a quantifiable nomogram. Clinical factors regarding demographics, admission cause, clinical laboratory index, medical history and nursing scales were extracted as risk candidates. The performance improvement was based on the application of the machine learning technique, comprising logistic regression, decision tree and random forest algorithm with five-fold cross-validation (CV) technique. The comprehensive assessment of sensitivity, specificity and the area under the receiver operating characteristic curve (AUROC) was considered in the evaluation of predictive performance. The receiver operating characteristic curves revealed the top performance for the logistic regression model in respect to machine learning improvement, achieving the highest sensitivity and AUC among three types of classifiers. Compared against the 23-point Braden scale routinely recorded online, an incorporated nomogram of logistic regression model and Braden scale achieved the best performance with an AUC of 0.87 ± 0.07 and 0.84 ± 0.05 in training and test cohort, respectively. Our findings suggest that the machine learning technique potentiated the limited predictive validity of routinely recorded clinical data on pressure injury development during ICU hospitalisation. Easily accessible electronic records held the potentials to substitute the traditional Braden score in the prediction of pressure injury in intensive care unit. Preoperative prediction of pressure injury facilitates the exemption from the severe consequences.

Modeling and prediction of pressure injury in hospitalized patients using artificial intelligence

BACKGROUND

Hospital-acquired pressure injuries (PIs) induce significant patient suffering, inflate healthcare costs, and increase clinical co-morbidities. PIs are mostly due to bed-immobility, sensory impairment, bed positioning, and length of hospital stay. In this study, we use electronic health records and administrative data to examine the contributing factors to PI development using artificial intelligence (AI).

METHODS

We used advanced data science techniques to first preprocess the data and then train machine learning classifiers to predict the probability of developing PIs. The AI training was based on large, incongruent, incomplete, heterogeneous, and time-varying data of hospitalized patients. Both model-based statistical methods and model-free AI strategies were used to forecast PI outcomes and determine the salient features that are highly predictive of the outcomes.

RESULTS

Our findings reveal that PI prediction by model-free techniques outperform model-based forecasts. The performance of all AI methods is improved by rebalancing the training data and by including the Braden in the model learning phase. Compared to neural networks and linear modeling, with and without rebalancing or using Braden scores, Random forest consistently generated the optimal PI forecasts.

CONCLUSIONS

AI techniques show promise to automatically identify patients at risk for hospital acquired PIs in different surgical services. Our PI prediction model provide a first generation of AI guidance to prescreen patients at risk for developing PIs.

CLINICAL IMPACT

This study provides a foundation for designing, implementing, and assessing novel interventions addressing specific healthcare needs. Specifically, this approach allows examining the impact of various dynamic, personalized, and clinical-environment effects on PI prevention for hospital patients receiving care from various surgical services.

Supervised machine learning-based prediction for in-hospital pressure injury development using electronic health records: A retrospective observational cohort study in a university hospital in Japan

BACKGROUND

In hospitals, nurses are responsible for pressure injury risk assessment using several kinds of risk assessment scales. However, their predictive validity is insufficient to initiate targeted preventive strategy for each patient. The use of electronic health records with machine learning technique is a promising strategy to provide automated clinical decision-making aid.

OBJECTIVE

The purpose of this study was to construct a predictive model for pressure injury development which included feature variables that can be collected on the first day of hospitalization by nurses who routinely input the data to electronic health records.

DESIGN

Retrospective observational cohort study.

SETTING

This study was conducted at a university hospital in Japan.

PARTICIPANTS

This study used electronic health records, which include entry/discharge records, basic nursing records, and pressure injury management documents (N = 75,353).

METHODS

The outcome measure was the pressure injuries which developed outside of an operation theatre and frequently appeared on the specific body parts at high risk of pressure injury development. We utilized four major classifiers: logistic regression, random forest, linear support vector machine, and extreme gradient boosting (XGBoost) with 5-fold cross-validation technique. The area under the receiver operating characteristic curve (AUC) was used for evaluating predictive performance.

RESULTS

The proportion of hospital-acquired pressure injuries was 0.52%. The receiver operating characteristic curves revealed the best predictive performance for the XGBoost model, achieving the highest sensitivity of 0.78±0.03 and AUC of 0.80±0.02 amongst four types of classifiers. Variables related to difficulty in activities of daily living, anorexia, and respiratory or cardiac disorders were extracted as important features.

CONCLUSIONS

Our findings suggest that routinely collected health data by nurses on the first day of patient admission have the potential to help determine high-risk patients for pressure injury development. Tweetable abstract: Machine learning models on routinely collected electronic health records data successfully predict pressure injury development during hospitalization.

FUNDING

This work was supported by a JSPS KAKENHI Grant-in-Aid for Exploratory Research (16K15865).

The Random Forest Model Has the Best Accuracy Among the Four Pressure Ulcer Prediction Models Using Machine Learning Algorithms

Purpose

Build machine learning models for predicting pressure ulcer nursing adverse event, and find an optimal model that predicts the occurrence of pressure ulcer accurately.

Patients and Methods

Retrospectively enrolled 5814 patients, of which 1673 suffer from pressure ulcer events. Support vector machine (SVM), decision tree (DT), random forest (RF) and artificial neural network (ANN) models were used to construct the pressure ulcer prediction models, respectively. A total of 19 variables are included, and the importance of screening variables is evaluated. Meanwhile, the performance of the prediction models is evaluated and compared.

Results

The experimental results show that the four pressure ulcer prediction models all achieve good performance. Also, the AUC values of the four models are all greater than 0.95. Besides, the comparison of the four models indicates that RF model achieves a higher accuracy for the prediction of pressure ulcer.

Conclusion

This research verifies the feasibility of developing a management system for predicting nursing adverse event based on big data technology and machine learning technology. The random forest and decision tree model are more suitable for constructing a pressure ulcer prediction model. This study provides a reference for future pressure ulcer risk warning based on big data.

Evaluation of AI-Assisted Telemedicine Service Using a Mobile Pet Application

This study indirectly verifies the possibility of telemedicine for humans through a mobile application (app) targeting pets. It examined the perception of telemedicine services and the current status of the companion animal industry, the app platform, and its applied technology by industry domain, and four representative types of artificial intelligence (AI) technologies applicable in the medical field. A survey was conducted through an app implementing pet telemedicine, and hypotheses were established and statistically tested based on the adoption period of pets, health status, mobile service utilization (as an index measuring the ease of use of recent AI functions), and positive and negative perceptions of telemedicine services. As revealed by prospect theory, users with a negative perception of pet telemedicine tended to maintain negative perceptions about telemedicine for humans. This study proved that the severity of pet diseases and the ease of use of recent AI technologies act as a moderating effect on the perception of telemedicine services through the verification of reinforcement and additional hypotheses. It suggests a plan to overcome sanctions against telemedicine by utilizing AI technology. A positive effect on changing the medical paradigm to telemedicine and the improvement of the medical legal system were also observed.