Background Patients in intensive care units are at higher risk for development of pressure ulcers than other patients. In order to prevent pressure ulcers from developing in intensive care patients, risk for development of pressure ulcers must be assessed accurately. Objectives To evaluate the predictive validity of the Braden scale for assessing risk for development of pressure ulcers in intensive care patients by using 4 years of data from electronic health records. Methods Data from the electronic health records of patients admitted to intensive care units between January 1, 2007, and December 31, 2010, were extracted from the data warehouse of an academic medical center. Predictive validity was measured by using sensitivity, specificity, positive predictive value, and negative predictive value. The receiver operating characteristic curve was generated, and the area under the curve was reported. Results A total of 7790 intensive care patients were included in the analysis. A cutoff score of 16 on the Braden scale had a sensitivity of 0.954, specificity of 0.207, positive predictive value of 0.114, and negative predictive value of 0.977. The area under the curve was 0.672 (95% CI, 0.663–0.683). The optimal cutoff for intensive care patients, determined from the receiver operating characteristic curve, was 13. Conclusions The Braden scale shows insufficient predictive validity and poor accuracy in discriminating intensive care patients at risk of pressure ulcers developing. The Braden scale may not sufficiently reflect characteristics of intensive care patients. Further research is needed to determine which possibly predictive factors are specific to intensive care units in order to increase the usefulness of the Braden scale for predicting pressure ulcers in intensive care patients.
Background Obesity contributes to immobility and subsequent pressure on skin surfaces. Knowledge of the relationship between obesity and development of pressure ulcers in intensive care patients will provide better understanding of which patients are at high risk for pressure ulcers and allow more efficient prevention. Objectives To examine the incidence of pressure ulcers in patients who differ in body mass index and to determine whether inclusion of body mass index enhanced use of the Braden scale in the prediction of pressure ulcers. Methods In this retrospective cohort study, data were collected from the medical records of 4 groups of patients with different body mass index values: underweight, normal weight, obese, and extremely obese. Data included patients’ demographics, body weight, score on the Braden scale, and occurrence of pressure ulcers. Results The incidence of pressure ulcers in the underweight, normal weight, obese, and extremely obese groups was 8.6%, 5.5%, 2.8%, and 9.9%, respectively. When both the score on the Braden scale and the body mass index were predictive of pressure ulcers, extremely obese patients were about 2 times more likely to experience an ulcer than were normal weight patients. In the final model, the area under the curve was 0.71. The baseline area under the curve for the Braden scale was 0.68. Conclusions Body mass index and incidence of pressure ulcers were related in intensive care patients. Addition of body mass index did not appreciably improve the accuracy of the Braden scale for predicting pressure ulcers.
BackgroundWe develop predictive models enabling clinicians to better understand and explore patient clinical data along with risk factors for pressure ulcers in intensive care unit patients from electronic health record data. Identifying accurate risk factors of pressure ulcers is essential to determining appropriate prevention strategies; in this work we examine medication, diagnosis, and traditional Braden pressure ulcer assessment scale measurements as patient features. In order to predict pressure ulcer incidence and better understand the structure of related risk factors, we construct Bayesian networks from patient features. Bayesian network nodes (features) and edges (conditional dependencies) are simplified with statistical network techniques. Upon reviewing a network visualization of our model, our clinician collaborators were able to identify strong relationships between risk factors widely recognized as associated with pressure ulcers.MethodsWe present a three-stage framework for predictive analysis of patient clinical data: 1) Developing electronic health record feature extraction functions with assistance of clinicians, 2) simplifying features, and 3) building Bayesian network predictive models. We evaluate all combinations of Bayesian network models from different search algorithms, scoring functions, prior structure initializations, and sets of features.ResultsFrom the EHRs of 7,717 ICU patients, we construct Bayesian network predictive models from 86 medication, diagnosis, and Braden scale features. Our model not only identifies known and suspected high PU risk factors, but also substantially increases sensitivity of the prediction - nearly three times higher comparing to logistical regression models - without sacrificing the overall accuracy. We visualize a representative model with which our clinician collaborators identify strong relationships between risk factors widely recognized as associated with pressure ulcers.ConclusionsGiven the strong adverse effect of pressure ulcers on patients and the high cost for treating pressure ulcers, our Bayesian network based model provides a novel framework for significantly improving the sensitivity of the prediction model. Thus, when the model is deployed in a clinical setting, the caregivers can suitably respond to conditions likely associated with pressure ulcer incidence.
Dysphagia is a common problem in patients with neurologic disease and is often associated with significant morbidity and mortality. To evaluate primary brain tumor patients who complained of dysphagia, we adapted grading scales for severity of complaint and level of alertness (scale of 1 to 4) and bedside swallowing assessment and videofluoroscopic examination (scales of 1 to 5). Over 13 months, we prospectively screened 117 patients for dysphagia. Seventeen of these (14.5%) complained of dysphagia (mean age, 50.2 years; range, 20 to 75); an additional six control patients were studied from a group with no dysphagic complaints. Scoring for severity of complaint (mean, 2.3) and level of alertness (mean, 2.2) was mild-moderate in the majority of patients. Eleven of 17 patients scored > or = grade 3 (mean 3.2, moderate impairment, requiring supervision) on bedside testing, and six of seven scored > or = grade 3 (mean 3.8, moderate-moderately severe abnormality, trace or frequent aspiration) during videofluoroscopic evaluation. Bedside testing scores of the study group differed significantly (p < 0.001) from those of the control group. Level of alertness correlated strongly with bedside (r = 0.794) and videofluoroscopic (r = 0.780) scoring. Primary brain tumor patients with dysphagia are likely to have impairment of swallowing out of proportion to their complaints and therefore are at risk for aspiration and nutritional compromise. We recommend that these patients undergo formal swallowing assessment followed by rehabilitation or implementation of alternative feeding methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.