Dynamic Risk Prediction for Hospital-Acquired Pressure Injury in Adult Critical Care Patients

Shui, Amy; Kim, Philip; Aribindi, Vamsi; Huang, Chiung‐Yu; Kim, Miok; Rangarajan, Sachin; Schorger, Kaelan; Aldrich, Janet; Lee, Hanmin

doi:10.1097/cce.0000000000000580

Cited by 8 publications

(7 citation statements)

References 45 publications

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“…Previous research has found that patients with PIs are more likely to suffer severe complications. [12][13][14][15] In the present study, patients in the PI group had a higher incidence of various postoperative adverse events and longer hospital and ICU lengths of stay, in comparison with the non-PI group. According to the literature, risk factors related to postoperative PI include age, high BMI, heart failure, diabetes, low preoperative Braden Scale score, and operation duration.…”

Section: Discussionsupporting

confidence: 43%

Prediction Model for Postoperative Pressure Injury in Patients with Acute Type A Aortic Dissection

Wang,

Feng,

et al. 2024

Adv Skin Wound Care

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OBJECTIVE To establish a risk assessment model to predict postoperative National Pressure Injury Advisory Panel stage 2 or higher pressure injury (PI) risk in patients undergoing acute type A aortic dissection surgery. METHODS This retrospective assessment included consecutive patients undergoing acute type A aortic dissection surgery in the authors’ hospital from September 2017 to June 2021. The authors used LASSO (logistic least absolute shrinkage and selection operator) regression analysis to identify the most relevant variables associated with PI by running cyclic coordinate descent with 10-times cross-validation. The variables selected by LASSO regression analysis were subjected to multivariate logistic analysis. A calibration plot, receiver operating characteristic curve, and decision curve analysis were used to validate the model. RESULTS There were 469 patients in the study, including 94 (27.5%) with postoperative PI. Ten variables were selected from LASSO regression: body mass index, diabetes, Marfan syndrome, stroke, preoperative skin moisture, hemoglobin, albumin, serum creatinine, platelet, and d-dimer. Four risk factors emerged after multivariate logistic regression: Marfan syndrome, preoperative skin moisture, albumin, and serum creatinine. The area under the receiver operating characteristic curve of the model was 0.765. The calibration plot and the decision curve analysis both suggested that the model was suitable for predicting postoperative PI. CONCLUSIONS This study built an efficient predictive model that could help identify high-risk patients.

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Section: Discussionsupporting

confidence: 43%

Prediction Model for Postoperative Pressure Injury in Patients with Acute Type A Aortic Dissection

Wang,

Feng,

et al. 2024

Adv Skin Wound Care

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“…Several intrinsic and extrinsic risk factors for IAPIs already have been identified in patients undergoing surgery 25 . Undoubtedly, previous studies have shown that the use of vasoconstrictors such as norepinephrine and vasopressin plays a crucial role in the occurrence of PI, as these drugs promote peripheral vascular contraction and may lead to peripheral tissue ischemia 26,27 . Norepinephrine and vasopressin were significantly associated with development of pressure ulcers; vasopressin was the only significant predictor in multivariate analysis.…”

Section: Discussionmentioning

confidence: 97%

“…25 Undoubtedly, previous studies have shown that the use of vasoconstrictors such as norepinephrine and vasopressin plays a crucial role in the occurrence of PI, as these drugs promote peripheral vascular contraction and may lead to peripheral tissue ischemia. 26,27 Norepinephrine and vasopressin were significantly associated with development of pressure ulcers; vasopressin was the only significant predictor in multivariate analysis. In addition, MAP <60 mmHg in patients receiving vasopressors was predictive of development of pressure ulcers.…”

Section: Blood Pressure Iapismentioning

confidence: 90%

Effect of “micromovement” in preventing intraoperative acquired pressure injuries among patients undergoing surgery in supine position

Jin,

Liu,

Shentu

et al. 2023

International Wound Journal

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To explore the clinical effect of “micromovement” in preventing intraoperative acquired pressure injures (IAPIs) among patients experiencing surgery in supine position. A total of 200 patients accepting elective surgery in supine position from 10 May 2023 to 4 July 2023 at Shulan (Hangzhou) Hospital were selected and randomized into two groups (experimental group, n = 100; control group, n = 100). For control group patients, soft silicone foam dressing was applied to the sacrococcygeal region. On the basis of the treatment for control group patients, “micromovement” was implemented among experimental group patients. During this process, the operating table was tilted for 15° leftwards and rightwards alternately every 1 h, and the tilt angle was maintained for 5 min to prevent IAPIs. Finally, comparisons between the two groups were made in terms of the sacrococcygeal IAPI incidence, relative temperature differences (ΔT) on sacrococcygeal skin, and job satisfaction of nurses. Compared with control group patients, patients from the experimental group exhibited lower IAPI incidence (2% vs. 10%), reduced ΔT between the sacrococcygeal skin and surrounding normal skin [0 (−0.1, 0.1) vs. 0.2 (−0.2, 0.4)], and elevated job satisfaction of nurses (80% vs. 66%). All the differences were statistically significant (p < 0.05). “Micromovement” implemented intraoperatively among patients receiving surgery in supine position is able to lower the IAPI incidence by five times and elevate job satisfaction of nurses.

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“…Design Sample Size Method Balancing [22] Cohort Retrospective 269 Not Reported [23] Cohort Prospective 648 Not Reported [15] Systematic Reviews 168-188,512 Not Applicable [17] Systematic Reviews 237,397 Not Applicable [24] Cohort Retrospective 486 Not Reported [25] Cohort Prospective 12,654 Random Oversampling [26] Cohort Retrospective 50,851 Synthetic Minority Oversampling and Random Oversampling [14] Cohort Retrospective 6376 Random Oversampling [4] Experimental Design 11,838 Under Sampling and Random Oversampling [16] Systematic Reviews and Meta 125,213 Not Applicable [27] Cohort Retrospective 100,355 Not Reported [28] Cohort Retrospective 4652 Random Oversampling [29] Cohort Retrospective 618 Random Oversampling [30] Cohort Retrospective 75,353 Random Oversampling [31] Case-Control 2341 Under Sampling [32] Cohort Prospective 13,254 Not Reported [33] Cohort Prospective 194 Not Reported [34] Cohort Retrospective 18,019 Not Reported [35] Cohort Prospective 149 Not Reported [36] Cohort Retrospective 15,310 Random Oversampling [37] Cohort Retrospective 9644 Not Reported [38] Cohort Retrospective 5101 Synthetic Minority Oversampling [39] Cohort Retrospective Different studies discussed the use of machine learning in constructing a prediction model for pressure injury; 27 studies were reviewed in the literature in terms of using machine learning to predict pressure injury [4,9,11,13,14,17,[20][21][22][23]26,27,[29]…”

Section: Referencementioning

confidence: 99%

“…[33] Oral mucosal, endotracheal tube, vasopressor, albumin, hematocrit, and steroids. [34] Age, body mass index, lactate serum, Braden score, vasopressor use, and antifungal medications.…”

Section: Referencesmentioning

confidence: 99%

Systematic Review for Risks of Pressure Injury and Prediction Models Using Machine Learning Algorithms

Barghouthi,

Owda,

Asia

et al. 2023

Diagnostics

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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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Dynamic Risk Prediction for Hospital-Acquired Pressure Injury in Adult Critical Care Patients

Abstract: Supplemental Digital Content is available in the text.

Cited by 8 publications

References 45 publications

Prediction Model for Postoperative Pressure Injury in Patients with Acute Type A Aortic Dissection

Prediction Model for Postoperative Pressure Injury in Patients with Acute Type A Aortic Dissection

Effect of “micromovement” in preventing intraoperative acquired pressure injuries among patients undergoing surgery in supine position

Systematic Review for Risks of Pressure Injury and Prediction Models Using Machine Learning Algorithms

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