Measuring clinical outcomes of highly multiplex molecular diagnostics for respiratory infections: A systematic review and conceptual framework.

Objectives: To review methodologies and outcomes reporting among these studies and to develop a conceptual framework of outcomes to assist in guiding studies and production of clinical metrics. Data sources: PubMed and Embase from January 1, 2012, thru December 1, 2021. Study eligibility criteria: Studies evaluating highly multiplex molecular respiratory diagnostics and their impact on either clinical or economic outcomes. Methods: A systematic literature review (SLR) of methodologies and outcomes reporting was performed. A qualitative synthesis of identified SLRs and associated primary studies was conducted to develop conceptual framework for outcomes. Results: Ultimately, 4 systemic literature reviews and their 12 associated primary studies were selected for review. Most primary studies included patient outcomes focusing on antimicrobial exposure changes such as antibiotic (80%) and antiviral use (50%) or occupancy changes such as hospital length of stay (60%). Economic outcomes were infrequently reported, and societal outcomes, such as antibiotic resistance impact, were absent from the reviewed literature. Qualitative evidence synthesis of reported outcomes yielded a conceptual framework of outcomes to include operational, patient, economic, and societal domains. Conclusions: Our review highlights the significant heterogeneity in outcomes reporting among clinical impact studies for highly multiplex molecular respiratory diagnostics. Furthermore, we developed a conceptual framework of outcomes domains that may act as a guide to improve considerations in outcomes selection and reporting when evaluating clinical impact of these tests. These improvements may be important in synthesizing the evidence for informing clinical decision making, guidelines, and financial reimbursement.

Assessing EHR Data for Use in Clinical Improvement and Research.

ABSTRACT: Data from electronic health records (EHRs) are becoming accessible for use in clinical improvement projects and nursing research. But the data quality may not meet clinicians' and researchers' needs. EHR data, which are primarily collected to document clinical care, invariably contain errors and omissions. This article introduces nurses to the secondary analysis of EHR data, first outlining the steps in data acquisition and then describing a theory-based process for evaluating data quality and cleaning the data. This process involves methodically examining the data using six data quality dimensions-completeness, correctness, concordance, plausibility, currency, and relevance-and helps the clinician or researcher to determine whether data for each variable are fit for use. Two case studies offer examples of problems that can arise and their solutions.

Hospital acquired pressure injury prediction in surgical critical care patients.

BACKGROUND: Hospital-acquired pressure injuries (HAPrIs) are areas of damage to the skin occurring among 5-10% of surgical intensive care unit (ICU) patients. HAPrIs are mostly preventable; however, prevention may require measures not feasible for every patient because of the cost or intensity of nursing care. Therefore, recommended standards of practice include HAPrI risk assessment at routine intervals. However, no HAPrI risk-prediction tools demonstrate adequate predictive validity in the ICU population. The purpose of the current study was to develop and compare models predicting HAPrIs among surgical ICU patients using electronic health record (EHR) data. METHODS: In this retrospective cohort study, we obtained data for patients admitted to the surgical ICU or cardiovascular surgical ICU between 2014 and 2018 via query of our institution's EHR. We developed predictive models utilizing three sets of variables: (1) variables obtained during routine care + the Braden Scale (a pressure-injury risk-assessment scale); (2) routine care only; and (3) a parsimonious set of five routine-care variables chosen based on availability from an EHR and data warehouse perspective. Aiming to select the best model for predicting HAPrIs, we split each data set into standard 80:20 train:test sets and applied five classification algorithms. We performed this process on each of the three data sets, evaluating model performance based on continuous performance on the receiver operating characteristic curve and the F1 score. RESULTS: Among 5,101 patients included in analysis, 333 (6.5%) developed a HAPrI. F1 scores of the five classification algorithms proved to be a valuable evaluation metric for model performance considering the class imbalance. Models developed with the parsimonious data set had comparable F1 scores to those developed with the larger set of predictor variables. CONCLUSIONS: Results from this study show the feasibility of using EHR data for accurately predicting HAPrIs and that good performance can be found with a small group of easily accessible predictor variables. Future study is needed to test the models in an external sample.

Risk Factors for Hospital-Acquired Pressure Injury in Surgical Critical Care Patients.

BACKGROUND: Hospital-acquired pressure injuries disproportionately affect critical care patients. Although risk factors such as moisture, illness severity, and inadequate perfusion have been recognized, nursing skin assessment data remain unexamined in relation to the risk for hospital-acquired pressure injuries. OBJECTIVE: To identify factors associated with hospital-acquired pressure injuries among surgical critical care patients. The specific aim was to analyze data obtained from routine nursing skin assessments alongside other potential risk factors identified in the literature. METHODS: This retrospective cohort study included 5101 surgical critical care patients at a level I trauma center and academic medical center. Multivariate logistic regression using the least absolute shrinkage and selection operator method identified important predictors with parsimonious representation. Use of specialty pressure redistribution beds was included in the model as a known predictive factor because specialty beds are a common preventive intervention. RESULTS: Independent risk factors identified by logistic regression were skin irritation (rash or diffuse, nonlocalized redness) (odds ratio, 1.788; 95% CI, 1.404-2.274; P < .001), minimum Braden Scale score (odds ratio, 0.858; 95% CI, 0.818-0.899; P < .001), and duration of intensive care unit stay before the hospital-acquired pressure injury developed (odds ratio, 1.003; 95% CI, 1.003-1.004; P < .001). CONCLUSIONS: The strongest predictor was irritated skin, a potentially modifiable risk factor. Irritated skin should be treated and closely monitored, and the cause should be eliminated to allow the skin to heal.