Dataset Information

Modeling Patient Risk for Hospital-Acquired Pressure Injuries During COVID-19: A Retrospective Study.

ABSTRACT:

Background

COVID-19 negatively impacts many organ systems including the skin. One of the most significant skin-associated adverse events related to hospitalization are pressure injuries.

Purpose

The aim of this study was to determine 8 risk factors that would place hospitalized patients at a higher risk for hospital-acquired pressure injuries (HAPIs) during the COVID-19 pandemic.

Methods

A retrospective, descriptive analysis was conducted in an urban academic health science center located in the southeastern United States.

Results

There were 247 of 23 093 patients who had pressure injuries and 1053 patients who had a positive COVID-19 diagnosis. Based on the generalized estimating equation model, diagnosis of COVID-19, age, male gender, risk of mortality, severity of illness, and length of stay are statistically significant factors associated with the development of HAPIs.

Conclusions

Further study should explore pathology of COVID-19 skin changes and what interventions are effective against HAPIs in the COVID-19 population taking into consideration current treatments.

SUBMITTER: Montgomery AP

PROVIDER: S-EPMC8860131 | biostudies-literature | 2022 Apr-Jun 01

REPOSITORIES: biostudies-literature

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Publications

Modeling Patient Risk for Hospital-Acquired Pressure Injuries During COVID-19: A Retrospective Study.

Montgomery Aoyjai P AP Patrician Patricia A PA Hall Allyson A Miltner Rebecca S RS Enogela Ene M EM Polancich Shea S

Journal of nursing care quality 20220401 2

<h4>Background</h4>COVID-19 negatively impacts many organ systems including the skin. One of the most significant skin-associated adverse events related to hospitalization are pressure injuries.<h4>Purpose</h4>The aim of this study was to determine 8 risk factors that would place hospitalized patients at a higher risk for hospital-acquired pressure injuries (HAPIs) during the COVID-19 pandemic.<h4>Methods</h4>A retrospective, descriptive analysis was conducted in an urban academic health science ...[more]

PMID: 34611108

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Project description:ObjectivesTo separately examine and comprehensively compare the risk factors for hospital-acquired (HAPIs) and community-acquired pressure injuries (CAPIs).DesignA mixed case-control study.SettingFour medical centres in China.ParticipantsInclusion criteria included patients who were (1) aged ≥18 years on admission; (2) admitted between January 2014 and December 2018, and (3) diagnosed with HAPIs (cases) or with no HAPIs (controls) during hospitalisation in the HAPIs study, and confirmed with CAPIs (cases) or with no PIs (controls) on admission in the CAPIs study. The exclusion criteria were as follows: (1) admitted for childbirth, psychiatric reasons or rehabilitation; (2) admitted for observation; (3) transferred from another hospital and (4) confirmed to have suffered PIs from previous hospitalisations in the CAPIs study. In total, 320 cases and 1657 controls were included in the HAPIs study, and 1763 cases and 1786 controls were included in the CAPIs study.Primary and secondary outcome measuresThe outcome variable was the occurrence of PIs.ResultsThe existence of PIs or scars from previous PIs on admission, presence of forced posture, use of medical devices and surgery during hospitalisation were found to be independent risk factors for HAPIs, as evidenced by the corresponding OR and 95% CI values of 51.931 (34.241 to 78.763), 2.006 (1.405 to 2.864), 3.226 (1.709 to 6.089) and 2.161 (1.452 to 3.215), respectively. Age, sex, Braden rating and diabetes were found to be independent risk factors for CAPIs, as evidenced by the corresponding OR and 95% CI values of 1.031 (1.026 to 1.036), 0.810 (0.698 to 0.941), 1.235 (1.167 to 1.307) and 2.059 (1.332 to 3.184), respectively.ConclusionsThe existence of PIs or scars from previous PIs on admission, presence of forced posture, use of medical devices and surgery during hospitalisation are suggested to be included as independent items for the risk assessment of PIs, together with the Braden scale. The Braden rating plays different roles in the development of CAPIs and HAPIs.

Project description: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.

Project description:IntroductionHospital-acquired infections of communicable viral diseases (CVDs) have been posing a tremendous challenge to healthcare workers globally. Healthcare personnel (HCP) is facing a consistent risk of viral infections, and subsequently higher rates of morbidity and mortality.Materials and methodsWe proposed a domain-knowledge-driven infection risk model to quantify the individual HCP and the population-level risks. For individual-level risk estimation, a time-variant infection risk model is proposed to capture the transmission dynamics of CVDs. At the population-level, the infection risk is estimated using a Bayesian network model constructed from three feature sets, including individual-level factors, engineering control factors, and administrative control factors. For model validation, we investigated the case study of the Coronavirus disease, in which the individual-level and population-level infection risk models were applied. The data were collected from various sources such as COVID-19 transmission databases, health surveys/questionaries from medical centers, U.S. Department of Labor databases, and cross-sectional studies.ResultsRegarding the individual-level risk model, the variance-based sensitivity analysis indicated that the uncertainty in the estimated risk was attributed to two variables: the number of close contacts and the viral transmission probability. Next, the disease transmission probability was computed using a multivariate logistic regression applied for a cross-sectional HCP data in the UK, with the 10-fold cross-validation accuracy of 78.23%. Combined with the previous result, we further validated the individual infection risk model by considering six occupations in the U.S. Department of Labor O*Net database. The occupation-specific risk evaluation suggested that the registered nurses, medical assistants, and respiratory therapists were the highest-risk occupations. For the population-level risk model validation, the infection risk in Texas and California was estimated, in which the infection risk in Texas was lower than that in California. This can be explained by California's higher patient load for each HCP per day and lower personal protective equipment (PPE) sufficiency level.ConclusionThe accurate estimation of infection risk at both individual level and population levels using our domain-knowledge-driven infection risk model will significantly enhance the PPE allocation, safety plans for HCP, and hospital staffing strategies.

Project description:PurposeThe purpose of this study was to examine clinical characteristics and risk factors for critically ill patients who develop pressure injuries and identify the proportion of validated unavoidable pressure injuries associated with the proposed risk factors for acute skin failure (ASF).DesignRetrospective case-control comparative study.Subjects and settingThe sample comprised adult critically ill participants hospitalized in critical care units such as surgical, trauma, cardiovascular surgical, cardiac, neuro, and medical intensive care and corresponding progressive care units in 5 acute care hospitals within a large Midwestern academic/teaching healthcare system. Participants who developed hospital-acquired pressure injuries (HAPIs) and patients without HAPIs (controls) were included.MethodsA secondary analysis of data from a previous study with HAPIs and matching data for the control sample without HAPIs were obtained from the electronic health record. Descriptive and multivariate logistic regression analyses were conducted.ResultsThe sample comprised 475 participants; 165 experienced a HAPI and acted as cases, whereas the remaining 310 acted as controls. Acute Physiology and Chronic Health Evaluation (APACHE II) mean score (23.8, 8.7%; P < .001), mortality (n = 45, 27.3%; P = .002), history of liver disease (n = 28, 17%; P < .001), and unintentional loss of 10 lb or more in 1 month (n = 20, 12%; P = .002) were higher in the HAPI group. Multivariate logistic regression analysis identified participants with respiratory failure (odds ratio [OR] = 3.00; 95% confidence interval [CI], 1.27-7.08; P = .012), renal failure (OR = 7.48; 95% CI, 3.49-16.01; P < .001), cardiac failure (OR = 4.50; 95% CI, 1.76-11.51; P = .002), severe anemia (OR = 10.89; 95% CI, 3.59-33.00; P < .001), any type of sepsis (OR = 3.15; 95% CI, 1.44-6.90; P = .004), and moisture documentation (OR = 11.89; 95% CI, 5.27-26.81; P <.001) were more likely to develop a HAPI. No differences between unavoidable HAPI, avoidable HAPI, or the control group were identified based on the proposed ASF risk factors.ConclusionThis study provides important information regarding avoidable and unavoidable HAPIs and ASF. Key clinical characteristics and risk factors, such as patient acuity, organ failure, tissue perfusion, sepsis, and history of prior pressure injury, are associated with avoidable and unavoidable HAPI development. In addition, we were unable to support a relationship between unavoidable HAPIs and the proposed risk factors for ASF. Unavoidability of HAPIs rests with the documentation of appropriate interventions and not necessarily with the identification of clinical risk factors.

Dataset Information

Modeling Patient Risk for Hospital-Acquired Pressure Injuries During COVID-19: A Retrospective Study.

Background

Purpose

Methods

Results

Conclusions

Publications

Modeling Patient Risk for Hospital-Acquired Pressure Injuries During COVID-19: A Retrospective Study.

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