The prevalence and incidence of community-acquired pressure injury: A protocol for systematic review and meta-analysis

Development and validation of a risk prediction model for community-acquired pressure injury in a cancer population: A case-control study

BACKGROUND

Patients with cancer are susceptible to pressure injuries, which accelerate deterioration and death. In patients with post-acute cancer, the risk of pressure injury is ignored in home or community settings.

OBJECTIVE

To develop and validate a community-acquired pressure injury risk prediction model for cancer patients.

METHODS

All research data were extracted from the hospital's electronic medical record system. The identification of optimal predictors is based on least absolute shrinkage and selection operator regression analysis combined with clinical judgment. The performance of the model was evaluated by drawing a receiver operating characteristic curve and calculating the area under the curve (AUC), calibration analysis and decision curve analysis. The model was used for internal and external validation, and was presented as a nomogram.

RESULTS

In total, 6257 participants were recruited for this study. Age, malnutrition, chronic respiratory failure, body mass index, and activities of daily living scores were identified as the final predictors. The AUC of the model in the training and validation set was 0.87 (95 % confidence interval [CI], 0.85-0.89), 0.88 (95 % CI, 0.85-0.91), respectively. The model demonstrated acceptable calibration and clinical benefits.

CONCLUSIONS

Comorbidities in patients with cancer are closely related to the etiology of pressure injury, and can be used to predict the risk of pressure injury.

IMPLICATIONS FOR PRACTICE

This study provides a tool to predict the risk of pressure injury for cancer patients. This suggests that improving the respiratory function and nutritional status of cancer patients may reduce the risk of community-acquired pressure injury.

Trends in inpatient burden from pressure injuries in the United States: Cross-sectional study National Inpatient Sample 2009-2019

Pressure injuries are a significant comorbidity and lead to increased overall healthcare costs. Several European and global studies have assessed the burden of pressure injuries; however, no comprehensive analysis has been completed in the United States. In this study, we investigated the trends in the burden of pressure injuries among hospitalised adults in the United States from 2009 to 2019, stratified by sociodemographic subgroups. The length of admission, total cost of hospitalisation, and sociodemographic data was extracted from the National Inpatient Sample provided by the Healthcare Cost and Utilisation Project, Agency for Healthcare Research and Quality. Overall, the annual prevalence of pressure injuries and annual mean hospitalisation cost increased ($69,499.29 to $102,939.14), while annual mean length of stay decreased (11.14-9.90 days). Among all races, minority groups had higher average cost and length of hospitalisation. Our findings suggest that while the length of hospitalisation is decreasing, hospital costs and prevalence are rising. In addition, differing trends among racial groups exist with decreasing prevalence in White patients. Further studies and targeted interventions are needed to address these differences, as well as discrepancies in racial groups.

Outcomes of hospital-acquired pressure injuries and present-on-admission pressure injuries: A propensity score matching analysis

BACKGROUND

Pressure injuries (PIs) continue to present significant challenges. In recent years, the number of patients with present-on-admission pressure injury (POA-PI) has increased, but researchers have devoted little attention to it, and little is known about its clinical outcome.

AIMS

To compare the clinical outcomes of POA-PI and hospital-acquired pressure injury (HAPI) patients.

METHODS

In this study, hospitalized patients with pressure injuries were divided into two groups based on whether they acquired the injury in the hospital or already present at the time of their admission. The disease prognosis, duration of stay, and healthcare costs of patients with HAPI and POA-PI were evaluated using propensity score matching analysis (PSM), t-tests, and Mann-Whitney U tests.

RESULTS

The information on 1871 patients was retrieved from the electronic case system retroactively. A total of 305 pairs of patients were effectively matched between the two groups using propensity score matching (HAPI group = 305, POA-PI group = 305). There was no statistically significant difference at characteristics between the two groups (P > 0.05). The percentage of POA-PI group patients who were discharged from the hospital was greater than that of the HAPI group (P < 0.05). Conversely, the percentage of POA-PI group patients who died, ceased receiving treatment, or transferred to the hospital was lower than that of the HAPI group. Patients in the POA-PI group had shorter hospital stays than those in the HAPI group (P < 0.05). Patients in the POA-PI group had lower healthcare costs than those in the HAPI group (P < 0.05).

CONCLUSIONS

Patients with POA-PI have superior clinical outcomes than patients with HAPI, but make up the overwhelming majority of hospitalized patients. It is imperative that future research focuses on the reduction of POA-PI and HAPI incidence and the identification of therapies that will enhance patient prevention for these conditions.

Development and Validation of a Risk Nomogram Model for Predicting Community-Acquired Pressure Injury Among the Older Adults in China: A Case-Control Study

Purpose

A predictive model of community-acquired pressure injury (CAPI) was established and validated to allow the early identification of the risk of pressure injuries by family caregivers and community workers.

Patients and Methods

The participants were hospitalized patients 65 years and older from two branches of a tertiary hospital in China, one for model training set and the other for validation set. This study was a case-control study based on hospital electronic medical records. According to the presence of pressure injury at admission, patients were divided into a case group and a control group. In the model training set, LASSO regression was used to select the best predictors, and then logistic regression was used to construct a nomogram. The performance of the model was evaluated by drawing the receiver operating characteristic curve (ROC) and calculating the area under the curve (AUC), calibration analysis, and decision curve analysis. The model used a 10-fold crossover for internal and external validation.

Results

The study included a total of 20,235 subjects, including 11,567 in the training set and 8668 in the validation set. The prevalence of CAPI in the training and validation sets was 2.5% and 1.8%, respectively. A nomogram was constructed including eight variables: age ≥ 80, malnutrition status, cerebrovascular accidents, hypoproteinemia, respiratory failure, malignant tumor, paraplegia/hemiplegia, and dementia. The AUC of the prediction model in the original model, internal validation, and external validation were 0.868 (95% CI: 0.847, 0.890), mean 0.867, and 0.840 (95% CI: 0.807,03.873), respectively. The nomogram showed acceptable calibration and clinical benefit.

Conclusion

We constructed a nomogram to predict CAPI from the perspective of comorbidity that is suitable for use by non-specialists. This nomogram will help family caregivers and community workers with the early identification of PI risks.

An artificial intelligence-enabled smartphone app for real-time pressure injury assessment

The management of chronic wounds in the elderly such as pressure injury (also known as bedsore or pressure ulcer) is increasingly important in an ageing population. Accurate classification of the stage of pressure injury is important for wound care planning. Nonetheless, the expertise required for staging is often not available in a residential care home setting. Artificial-intelligence (AI)-based computer vision techniques have opened up opportunities to harness the inbuilt camera in modern smartphones to support pressure injury staging by nursing home carers. In this paper, we summarise the recent development of smartphone or tablet-based applications for wound assessment. Furthermore, we present a new smartphone application (app) to perform real-time detection and staging classification of pressure injury wounds using a deep learning-based object detection system, YOLOv4. Based on our validation set of 144 photos, our app obtained an overall prediction accuracy of 63.2%. The per-class prediction specificity is generally high (85.1%–100%), but have variable sensitivity: 73.3% (stage 1 vs. others), 37% (stage 2 vs. others), 76.7 (stage 3 vs. others), 70% (stage 4 vs. others), and 55.6% (unstageable vs. others). Using another independent test set, 8 out of 10 images were predicted correctly by the YOLOv4 model. When deployed in a real-life setting with two different ambient brightness levels with three different Android phone models, the prediction accuracy of the 10 test images ranges from 80 to 90%, which highlight the importance of evaluation of mobile health (mHealth) application in a simulated real-life setting. This study details the development and evaluation process and demonstrates the feasibility of applying such a real-time staging app in wound care management.

Association Between Charlson Comorbidity Index and Community-Acquired Pressure Injury in Older Acute Inpatients in a Chinese Tertiary Hospital

PURPOSE

To explore the correlation between community-acquired pressure injury (CAPI) and comorbidities in elderly patients with emergency admission.

PATIENTS AND METHODS

Patients aged 65 years or above were enrolled from multiple departments, such as Internal Medicine, Surgery, Geriatrics, and Intensive Care Unit of Wuhan Third Hospital, which is affiliated to Wuhan University, from January to December 2020. Comorbidity data were extracted using the 10th edition of the International Classification of Diseases (ICD-10) from the hospital electronic medical record system, and the Charlson Comorbidity Index (CCI) was calculated using these data. Participants were divided into two groups according to whether pressure injury was present at admission. The baseline characteristics of the two groups were compared using Student's t-tests, Mann-Whitney U-tests, and chi-square tests. Univariate and multivariate logistic regression models were constructed to explore the relationship between CAPI and the CCI. Smooth curve fitting was used to show the relationship between the CCI and CAPI. By drawing the receiver operating characteristic curve, the CCI was used to predict CAPI.

RESULTS

A total of 5759 participants with an average age of 75.1 ± 7.6 were included in this population-based study. The prevalence of CAPI was 4.3%. In logistic regression analysis, there was a positive relationship between the CCI and CAPI after adjustment for sex, age, hypoproteinemia, and anemia (OR = 1.37, 95% CI = 1.29-1.45, p < 0.001, trend test p < 0.001). The area under the receiver operating characteristic curve was 0.75, and the maximum value of the Youden index was 0.35, with a critical value of 5.5.

CONCLUSION

The development of CAPI was positively correlated with the CCI. The risk of developing pressure injury increases with the number and severity of comorbidities. This study shows that the CCI has certain reference value in predicting CAPI.