[CORRIGENDUM: Development of a Diabetic Foot Ulceration Prediction Model and Nomogram].

This corrects the article on p. 280 in vol. 51, PMID: 34215707.

[Development of a Diabetic Foot Ulceration Prediction Model and Nomogram].

PURPOSE: This study aimed to identify the risk factors for diabetic foot ulceration (DFU) to develop and evaluate the performance of a DFU prediction model and nomogram among people with diabetes mellitus (DM). METHODS: This unmatched case-control study was conducted with 379 adult patients (118 patients with DM and 261 controls) from four general hospitals in South Korea. Data were collected through a structured questionnaire, foot examination, and review of patients' electronic health records. Multiple logistic regression analysis was performed to build the DFU prediction model and nomogram. Further, their performance was analyzed using the Lemeshow-Hosmer test, concordance statistic (C-statistic), and sensitivity/specificity analyses in training and test samples. RESULTS: The prediction model was based on risk factors including previous foot ulcer or amputation, peripheral vascular disease, peripheral neuropathy, current smoking, and chronic kidney disease. The calibration of the DFU nomogram was appropriate (χ² = 5.85, p = .321). The C-statistic of the DFU nomogram was .95 (95% confidence interval .93~.97) for both the training and test samples. For clinical usefulness, the sensitivity and specificity obtained were 88.5% and 85.7%, respectively at 110 points in the training sample. The performance of the nomogram was better in male patients or those having DM for more than 10 years. CONCLUSION: The nomogram of the DFU prediction model shows good performance, and is thereby recommended for monitoring the risk of DFU and preventing the occurrence of DFU in people with DM.

Exploration of critical care data by using unsupervised machine learning.

BACKGROUND AND OBJECTIVE: Identification of subgroups may be useful to understand the clinical characteristics of ICU patients. The purposes of this study were to apply an unsupervised machine learning method to ICU patient data to discover subgroups among them; and to examine their clinical characteristics, therapeutic procedures conducted during the ICU stay, and discharge dispositions. METHODS: K-means clustering method was used with 1503 observations and 9 types of laboratory test results as features. RESULTS: Three clusters were identified from this specific population. Blood urea nitrogen, creatinine, potassium, hemoglobin, and red blood cell were distinctive between the clusters. Cluster Three presented the highest blood products transfusion rate (19.8%), followed by Cluster One (15.5%) and cluster Two (9.3%), which was significantly different. Hemodialysis was more frequently provided to Cluster Three while bronchoscopy was done to Cluster One and Two. Cluster Three showed the highest mortality (30.4%), which was more than two-fold compared to Cluster One (14.1%) and Two (12.2%). CONCLUSION: Three subgroups were identified and their clinical characteristics were compared. These findings may be useful to anticipate treatment strategies and probable outcomes of ICU patients. Unsupervised machine learning may enable ICU multi-dimensional data to be organized and to make sense of the data.

Prediction Model for Hospital-Acquired Pressure Ulcer Development: Retrospective Cohort Study.

BACKGROUND: A pressure ulcer is injury to the skin or underlying tissue, caused by pressure, friction, and moisture. Hospital-acquired pressure ulcers (HAPUs) may not only result in additional length of hospital stay and associated care costs but also lead to undesirable patient outcomes. Intensive care unit (ICU) patients show higher risk for HAPU development than general patients. We hypothesize that the care team's decisions relative to HAPU risk assessment and prevention may be better supported by a data-driven, ICU-specific prediction model. OBJECTIVE: The aim of this study was to determine whether multiple logistic regression with ICU-specific predictor variables was suitable for ICU HAPU prediction and to compare the performance of the model with the Braden scale on this specific population. METHODS: We conducted a retrospective cohort study by using the data retrieved from the enterprise data warehouse of an academic medical center. Bivariate analyses were performed to compare the HAPU and non-HAPU groups. Multiple logistic regression was used to develop a prediction model with significant predictor variables from the bivariate analyses. Sensitivity, specificity, positive predictive values, negative predictive values, area under the receiver operating characteristic curve (AUC), and Youden index were used to compare with the Braden scale. RESULTS: The total number of patient encounters studied was 12,654. The number of patients who developed an HAPU during their ICU stay was 735 (5.81% of the incidence rate). Age, gender, weight, diabetes, vasopressor, isolation, endotracheal tube, ventilator episode, Braden score, and ventilator days were significantly associated with HAPU. The overall accuracy of the model was 91.7%, and the AUC was .737. The sensitivity, specificity, positive predictive value, negative predictive value, and Youden index were .650, .693, .211, 956, and .342, respectively. Male patients were 1.5 times more, patients with diabetes were 1.5 times more, and patients under isolation were 3.1 times more likely to have an HAPU than female patients, patients without diabetes, and patients not under isolation, respectively. CONCLUSIONS: Using an extremely large, electronic health record-derived dataset enabled us to compare characteristics of patients who develop an HAPU during their ICU stay with those who did not, and it also enabled us to develop a prediction model from the empirical data. The model showed acceptable performance compared with the Braden scale. The model may assist with clinicians' decision on risk assessment, in addition to the Braden scale, as it is not difficult to interpret and apply to clinical practice. This approach may support avoidable reductions in HAPU incidence in intensive care.

Predictive models for pressure ulcers from intensive care unit electronic health records using Bayesian networks.

BACKGROUND: We 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. METHODS: We 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. RESULTS: From 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. CONCLUSIONS: Given 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.

Intravenous Infiltration Risk by Catheter Dwell Time Among Hospitalized Children.

PURPOSE: This study was aimed to examine the cumulative risk for infiltration over IV catheter dwell time by general or catheterization-specific characteristics of pediatric patients with IV therapy. DESIGN AND METHODS: This secondary data analysis was done with the data of 1596 children who received peripheral IV therapy at least once during their hospital stay between August 1st and October 30th, 2011 and in June, 2013 in an academic medical center, Yangsan, Republic of Korea. The survival functions of infiltration were determined by using the Kaplan-Meier analysis. RESULT: The cumulative risk for infiltration had rapidly increased from 1.5% after 24 hours of catheter dwell time to 17.3% after 96 hours. The survival functions were significantly different in the medical than in the surgical department (p=.005), lower extremities than upper ones (p=.001), and use of 10% dextrose (p=.001), ampicillin/sulbactam (p<.001), vancomycin (p=.024), high-concentration electrolytes (p=.001), and phenytoin (p<.001). CONCLUSION: When catheter dwell times are similar, the cumulative risk for infiltration was higher in cases wherein the patient had a risk factor. The cumulative risk for infiltration has rapidly increased after 24 hours in patients who have 10% dextrose, high-concentration electrolytes, and phenytoin. PRACTICE IMPLICATIONS: The results suggest that nurses are required to assess the IV site every hour after 24 hours of catheter dwell time for the infusion of irritants for a safer practice of IV therapy. However, this monitoring time may be modified by the age of child, previous IV complications, and/or hemodynamic issues which may impact IV integrity.

Predictive Modeling for Pressure Ulcers from Intensive Care Unit Electronic Health Records.

Our goal in this study is to find risk factors associated with Pressure Ulcers (PUs) and to develop predictive models of PU incidence. We focus on Intensive Care Unit (ICU) patients since patients admitted to ICU have shown higher incidence of PUs. The most common PU incidence assessment tool is the Braden scale, which sums up six subscale features. In an ICU setting it's known drawbacks include omission of important risk factors, use of subscale features not significantly associated with PU incidence, and yielding too many false positives. To improve on this, we extract medication and diagnosis features from patient EHRs. Studying Braden, medication, and diagnosis features and combinations thereof, we evaluate six types of predictive models and find that diagnosis features significantly improve the models' predictive power. The best models combine Braden and diagnosis. Finally, we report the top diagnosis features which compared to Braden improve AUC by 10%.

Body mass index and pressure ulcers: improved predictability of 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.

Response to a mobile health decision-support system for screening and management of tobacco use.

PURPOSE/OBJECTIVES: To describe the predictors of nurse actions in response to a mobile health decision-support system (mHealth DSS) for guideline-based screening and management of tobacco use. DESIGN: Observational design focused on an experimental arm of a randomized, controlled trial. SETTING: Acute and ambulatory care settings in the New York City metropolitan area. SAMPLE: 14,115 patient encounters in which 185 RNs enrolled in advanced practice nurse (APN) training were prompted by an mHealth DSS to screen for tobacco use and select guideline-based treatment recommendations. METHODS: Data were entered and stored during nurse documentation in the mHealth DSS and subsequently stored in the study database where they were retrieved for analysis using descriptive statistics and logistic regressions. MAIN RESEARCH VARIABLES: Predictor variables included patient gender, patient race or ethnicity, patient payer source, APN specialty, and predominant payer source in clinical site. Dependent variables included the number of patient encounters in which the nurse screened for tobacco use, provided smoking cessation teaching and counseling, or referred patients for smoking cessation for patients who indicated a willingness to quit. FINDINGS: Screening was more likely to occur in encounters where patients were female, African American, and received care from a nurse in the adult nurse practitioner specialty or in a clinical site in which the predominant payer source was Medicare, Medicaid, or State Children's Health Insurance Program. In encounters where the patient payer source was other, nurses were less likely to provide tobacco cessation teaching and counseling. CONCLUSIONS: mHealth DSS has the potential to affect nurse provision of guideline-based care. However, patient, nurse, and setting factors influence nurse actions in response to an mHealth DSS for tobacco cessation. IMPLICATIONS FOR NURSING: The combination of a reminder to screen and integration of guideline-based recommendations into the mHealth DSS may reduce racial or ethnic disparities to screening, as well as clinician barriers related to time, training, and familiarity with resources.

Predictive validity of the Braden scale for patients in intensive care units.

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.

Mobile health-based approaches for smoking cessation resources.

PURPOSE/OBJECTIVES: To describe how the National Cancer Institute's Cancer Information Service (CIS) smoking-related resources on a mobile health (mHealth) platform were integrated into the workflow of RNs in advanced practice nurse (APN) training and to examine awareness and use of CIS resources and nurses' perceptions of the usefulness of those CIS resources. DESIGN: Descriptive analyses. SETTING: Acute and primary care sites affiliated with the School of Nursing at Columbia University. SAMPLE: 156 RNs enrolled in APN training. METHODS: The integration was comprised of (a) inclusion of CIS information into mHealth decision support system (DSS) plan of care, (b) addition of infobutton in the mHealth DSS, (c) Web-based information portal for smoking cessation accessible via desktop and the mHealth DSS, and (d) information prescriptions for patient referral. MAIN RESEARCH VARIABLES: Use and perceived usefulness of the CIS resources. FINDINGS: 86% of nurses used the mHealth DSS with integrated CIS resources. Of the 145 care plan items chosen, 122 were referrals to CIS resources; infobutton was used 1,571 times. Use of CIS resources by smokers and healthcare providers in the metropolitan area of New York City increased during the study period compared to the prestudy period. More than 60% of nurses perceived CIS resources as useful or somewhat useful. CONCLUSIONS: Integration of CIS resources into an mHealth DSS was seen as useful by most participants. IMPLICATIONS FOR NURSING: Implementation of evidence into workflow using an mHealth DSS can assist nurses in managing smoking cessation in patients and may expand their roles in referring smokers to reliable sources of information. KNOWLEDGE TRANSLATION: mHealth DSS and information prescriptions may support smoking cessation interventions in primary care settings. Smoking cessation interventions can be facilitated through informatics methods and mHealth platforms. Nurses' referrals of patients to smoking-related CIS resources may result in patients' use of the resources and subsequent smoking cessation.

Using the nursing interventions classification as a potential measure of nurse workload.

Standardized terminologies, such as the Nursing Interventions Classification (NIC) taxonomy, may be used in multiple ways to represent nursing constructs. This study is the first known to explore the NIC as a framework for the development of a nursing workload measure. While the NIC may not represent the complexity of nurses' work, the classification system may represent uniformly the work of nurses in health information systems to yield reliable data for a nursing workload measure.

Exploring the ability of natural language processing to extract data from nursing narratives.

Natural Language Processing (NLP) offers an approach for capturing data from narratives and creating structured reports for further computer processing. We explored the ability of a NLP system, Medical Language Extraction and Encoding (MedLEE), on nursing narratives. MedLEE extracted 490 concepts from narrative text in a sample of 553 oncology nursing process notes. The most frequently monitored and recorded signs and symptoms were related to chemotherapy care, such as adverse reactions, shortness of breath, nausea, pain, and bleeding. In terms of nursing interventions, chemotherapy, blood culture, medication, and blood transfusion were commonly recorded in free text. NLP may provide a feasible approach to extract data related to patient safety/quality measures and nursing outcomes by capturing nursing concepts that are not recorded through structured data entry. For better NLP performance in the domain of nursing, additional nursing terms and abbreviations must be added to MedLEE's lexicon.

Development and evaluation of nursing user interface screens using multiple methods.

Building upon the foundation of the Structured Narrative Electronic Health Record (EHR) model, we applied theory-based (combined Technology Acceptance Model and Task-Technology Fit Model) and user-centered methods to explore nurses' perceptions of functional requirements for an electronic nursing documentation system, design user interface screens reflective of the nurses' perspectives, and assess nurses' perceptions of the usability of the prototype user interface screens. The methods resulted in user interface screens that were perceived to be easy to use, potentially useful, and well-matched to nursing documentation tasks associated with Nursing Admission Assessment, Blood Administration, and Nursing Discharge Summary. The methods applied in this research may serve as a guide for others wishing to implement user-centered processes to develop or extend EHR systems. In addition, some of the insights obtained in this study may be informative to the development of safe and efficient user interface screens for nursing document templates in EHRs.

Iterative evaluation of the Health Level 7--Logical Observation Identifiers Names and Codes Clinical Document Ontology for representing clinical document names: a case report.

The authors summarize their experience in iteratively testing the adequacy of three versions of the Health Level Seven (HL7) Logical Observation Identifiers Names and Codes (LOINC) Clinical Document Ontology (CDO) to represent document names at Columbia University Medical Center. The percentage of documents fully represented increased from 23.4% (Version 1) to 98.5% (Version 3). The proportion of unique representations increased from 7.9% (Analysis 1) to 39.4% (Analysis 4); the proportion reflects the level of specificity in the document names as well as the completeness and level of granularity of the CDO. The authors shared the findings of each analysis with the Clinical LOINC committee and participated in the decision-making regarding changes to the CDO on the basis of those analyses and those conducted by the Department of Veterans Affairs. The authors encourage other institutions to actively engage in testing healthcare standards and participating in standards development activities to increase the likelihood that the evolving standards will meet institutional needs.

Nurses' use and perceptions of usefulness of National Cancer Institute's tobacco-related Cancer Information Service (CIS) resources.

As a strategy to increase the use of the National Cancer Institute's Cancer Information Service (CIS) resources by nurses and patients, we integrated tobacco-related CIS resources into an existing mobile decision support system. We then evaluated nurses' use and perceptions of usefulness of context-specific access to CIS resources and of patient referrals to CIS resources-information prescriptions.

Expanding DISCERN to create a tool for assessing the quality of Web-based health information resources.

Based on criteria for assessing the quality of health information extracted from a review of the literature, we expanded the DISCERN instrument by adding 14 questions. New questions addressed language of the web-based health information resource, level of readability of the resource, and usability of the web page or portal for the resource.