Patient-reported symptomatic events do not adequately reflect atrial arrhythmia.

BACKGROUND: Management of atrial fibrillation is frequently geared toward improving symptoms. Yet, the magnitude of symptom-rhythm discordance is not well known in the setting of monitoring by ambulatory electrocardiography (AECG). OBJECTIVE: We aimed to quantify the symptom-rhythm correlation (SRC) for atrial arrhythmia (atrial tachycardia/atrial fibrillation [AT/AF]) events. METHODS: This was a retrospective cohort analysis of AECG data at a tertiary care center. All AECGs of ≥7 days with at least 1 AT/AF were included. Patient-triggered symptoms included shortness of breath, tiredness, palpitations, dizziness, or passing out with or without concurrent AT/AF. SRC was calculated for each patient. In addition, AT/AF-symptom association was evaluated at the event level by multivariable mixed effects logistic regression. RESULTS: We identified 742 patients with qualifying AECG data; mean age was 64 years, 50% were female, and 22% had heart failure. The mean CHA2DS2-VASc score was 2.5. There were 6289 symptomatic events and 6900 AT/AF episodes. Of symptomatic events, 1013 (16%) had shortness of breath, 839 (13%) tiredness, 2640 (42%) palpitations, 783 (12%) dizziness, and 93 (1%) passing out. Overall SRC was 0.39 (range, 0-1.0), but presence of AT/AF increased odds of symptoms by ∼8.3 times in adjusted analyses (P < .01). In multivariable analysis, prior AF rhythm control treatment and lower heart rate were associated with worse SRC (P < .01). CONCLUSION: Whereas AT/AF events increase the chances of symptoms, there is poor overall correlation between symptomatic events and documented AT/AF. Patient factors and prior treatments influence SRC. An improved understanding of this relationship correlation is needed to optimize clinical outcomes and to improve the rigor of AF research.

Characteristics of a transgender and gender-diverse patient population in Utah: Use of electronic health records to advance clinical and health equity research.

Transgender and gender-diverse (TGD) people, individuals whose gender identity differs from their sex assigned at birth, face unique challenges in accessing gender-affirming care and often experience disparities in a variety of health outcomes. Clinical research on TGD health is limited by a lack of standardization on how to best identify these individuals. The objective of this retrospective cohort analysis was to accurately identify and describe TGD adults and their use of gender-affirming care from 2003-2023 in a healthcare system in Utah, United States. International Classification of Disease (ICD)-9 and 10 codes and surgical procedure codes, along with sexual orientation and gender identity data were used to develop a dataset of 4,587 TGD adults. During this time frame, 2,985 adults received gender-affirming hormone therapy (GAHT) and/or gender-affirming surgery (GAS) within one healthcare system. There was no significant difference in race or ethnicity between TGD adults who received GAHT and/or GAS compared to TGD adults who did not receive such care. TGD adults who received GAHT and/or GAS were more likely to have commercial insurance coverage, and adults from rural communities were underrepresented. Patients seeking estradiol-based GAHT tended to be older than those seeking testosterone-based GAHT. The first GAS occurred in 2013, and uptake of GAS have doubled since 2018. This study provides a methodology to identify and examine TGD patients in other health systems and offers insights into emerging trends and access to gender-affirming care.

Performance of Off-the-Shelf Machine Learning Architectures and Biases in Detection of Low Left Ventricular Ejection Fraction.

Artificial intelligence - machine learning (AI-ML) is a computational technique that has been demonstrated to be able to extract meaningful clinical information from diagnostic data that are not available using either human interpretation or more simple analysis methods. Recent developments have shown that AI-ML approaches applied to ECGs can accurately predict different patient characteristics and pathologies not detectable by expert physician readers. There is an extensive body of literature surrounding the use of AI-ML in other fields, which has given rise to an array of predefined open-source AI-ML architectures which can be translated to new problems in an "off-the-shelf" manner. Applying "off-the-shelf" AI-ML architectures to ECG-based datasets opens the door for rapid development and identification of previously unknown disease biomarkers. Despite the excellent opportunity, the ideal open-source AI-ML architecture for ECG related problems is not known. Furthermore, there has been limited investigation on how and when these AI-ML approaches fail and possible bias or disparities associated with particular network architectures. In this study, we aimed to: (1) determine if open-source, "off-the-shelf" AI-ML architectures could be trained to classify low LVEF from ECGs, (2) assess the accuracy of different AI-ML architectures compared to each other, and (3) to identify which, if any, patient characteristics are associated with poor AI-ML performance.

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.

Quantifying the Impact of Atrial Fibrillation on Heart Failure-Related Patient-Reported Outcomes in the Utah mEVAL Program.

BACKGROUND: Atrial fibrillation (AF) frequently complicates heart failure (HF), and each is associated with lower overall health-related quality of life. We aimed to quantify the incremental burden of AF on the health-related quality of life of patients with HF in clinical practice. METHODS AND RESULTS: We used data from the Utah mEVAL program to analyze patient-reported outcomes (PROs) among patients with HF with and without AF. The primary outcome was the HF-specific Kansas City Cardiomyopathy Questionnaire, with generic PROs as secondary outcomes. Among 1707 patients with HF, 36% had AF (n = 616). Those with HF and AF were older (mean age 69 years vs 58 years, P < .001), more likely to have prior stroke (29% vs 17%, P < .001) and ischemic cardiomyopathy (28% vs 23%, P = .01), but had similar ejection fractions (mean 44% each, P = .6). After adjustment, and compared with HF alone, HF with AF was associated with worse Kansas City Cardiomyopathy Questionnaire scores (adjusted mean difference -3.45, 95% confidence interval [CI] -6.24 to -0.65), and worse Patient-Reported Outcomes Measurement Information System physical function scores (adjusted mean difference -1.63, 95% CI -2.59 to -0.67). The difference in visual analog scale general health was borderline (adjusted mean difference -2.01, 95% CI -4.51 to 0.49), and Patient-Reported Outcomes Measurement Information System depression scores were similar (adjusted mean difference 0.54, 95% CI -0.48 to 1.57). CONCLUSIONS: AF complicates nearly one-third of HF cases, and patients with HF and AF are substantially older and sicker. After adjustment, AF was independently associated with worse disease-specific and overall health-related quality of life than HF alone. Whether maintaining sinus rhythm can improve the HF-related health status of patients with HF in clinical practice should be explored further.

Removing hospital-based triage from suspected colorectal cancer pathways: the impact and learning from a primary care-led electronic straight-to-test pathway.

BACKGROUND: The 2-week wait referral pathway for suspected colorectal cancer was introduced in England to improve time from referral from a general practitioner (GP) to diagnosis and treatment. Patients are required to be seen by a hospital clinician within 2 weeks if their symptoms meet the criteria set by the National Institute for Health and Care Excellence (NICE) and to start cancer treatment within 62 days. To achieve this, many hospitals have introduced a straight-to-test (STT) strategy requiring hospital-based triage of referrals. We describe the impact and learning from a new pathway which has removed triage and moved the process of requesting tests from hospital to GPs in primary care. METHOD: An electronic STT pathway was introduced allowing GPs to book tests supported by a decision aid based on NICE guidance eliminating the need for a standard referral form or triage process. The hospital identified referrals as being on a cancer pathway and dealt with all ongoing management. Routinely collected cancer data were used to identify time to cancer diagnosis compared with national data RESULTS: 11357 patients were referred via the new pathway over 3 years. Time from referral to diagnosis reduced from 39 to 21 days and led to a dramatic improvement in patients starting treatment within 62 days. Challenges included adapting to a change in referral criteria and developing a robust hospital system to monitor the pathway. CONCLUSION: We have changed the way patients with suspected colorectal cancer are managed within the National Health Service by giving GPs the ability to order tests electronically within a monitored cancer pathway halving time from referral to diagnosis.

Patient-reported outcomes and subsequent management in atrial fibrillation clinical practice: Results from the Utah mEVAL AF program.

BACKGROUND: Atrial fibrillation (AF) significantly reduces health-related quality of life (HRQoL), previously measured in clinical trials using patient-reported outcomes (PROs). We examined AF PROs in clinical practice and their association with subsequent clinical management. METHODS: The Utah My Evaluation (mEVAL) program collects the Toronto AF Symptom Severity Scale (AFSS) in AF outpatients at the University of Utah. Baseline factors associated with worse AF symptom score (range 0-35, higher is worse) were identified in univariate and multivariable analyses. Secondary outcomes included AF burden and AF healthcare utilization. We also compared subsequent clinical management at 6 months between patients with better versus worse AF HRQoL. RESULTS: Overall, 1338 patients completed the AFSS symptom score, which varied by sex (mean 7.26 for males vs. 10.27 for females; p < .001), age (<65, 9.73; 65-74, 7.66; ≥75, 7.58; p < .001), heart failure (9.39 with HF vs. 7.67 without; p < .001), and prior ablation (7.28 with prior ablation vs. 8.84; p < .001). In multivariable analysis, younger age (mean difference 2.92 for <65 vs. ≥75; p < .001), female sex (mean difference 2.57; p < .001), pulmonary disease (mean difference 1.88; p < .001), and depression (mean difference 2.46; p < .001) were associated with higher scores. At 6-months, worse baseline symptom score was associated with the use of rhythm control (37.1% vs. 24.5%; p < .001). Similar cofactors and results were associated with increased AF burden and health care utilization scores. CONCLUSIONS: AF PROs in clinical practice identify highly-symptomatic patients, corroborating findings in more controlled, clinical trials. Increased AFSS score correlates with more aggressive clinical management, supporting the utility of disease-specific PROs guiding clinical practice.

Comparison of International Classification of Diseases and Related Health Problems, Tenth Revision Codes With Electronic Medical Records Among Patients With Symptoms of Coronavirus Disease 2019.

Importance: International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes are used to characterize coronavirus disease 2019 (COVID-19)-related symptoms. Their accuracy is unknown, which could affect downstream analyses. Objective: To compare the performance of fever-, cough-, and dyspnea-specific ICD-10 codes with medical record review among patients tested for COVID-19. Design, Setting, and Participants: This cohort study included patients who underwent quantitative reverse transcriptase-polymerase chain reaction testing for severe acute respiratory syndrome coronavirus 2 at University of Utah Health from March 10 to April 6, 2020. Data analysis was performed in April 2020. Main Outcomes and Measures: The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of ICD-10 codes for fever (R50*), cough (R05*), and dyspnea (R06.0*) were compared with manual medical record review. Performance was calculated overall and stratified by COVID-19 test result, sex, age group (<50, 50-64, and >64 years), and inpatient status. Bootstrapping was used to generate 95% CIs, and Pearson χ2 tests were used to compare different subgroups. Results: Among 2201 patients tested for COVD-19, the mean (SD) age was 42 (17) years; 1201 (55%) were female, 1569 (71%) were White, and 282 (13%) were Hispanic or Latino. The prevalence of fever was 66% (1444 patients), that of cough was 88% (1930 patients), and that of dyspnea was 64% (1399 patients). For fever, the sensitivity of ICD-10 codes was 0.26 (95% CI, 0.24-0.29), specificity was 0.98 (95% CI, 0.96-0.99), PPV was 0.96 (95% CI, 0.93-0.97), and NPV was 0.41 (95% CI, 0.39-0.43). For cough, the sensitivity of ICD-10 codes was 0.44 (95% CI, 0.42-0.46), specificity was 0.88 (95% CI, 0.84-0.92), PPV was 0.96 (95% CI, 0.95-0.97), and NPV was 0.18 (95% CI, 0.16-0.20). For dyspnea, the sensitivity of ICD-10 codes was 0.24 (95% CI, 0.22-0.26), specificity was 0.97 (95% CI, 0.96-0.98), PPV was 0.93 (95% CI, 0.90-0.96), and NPV was 0.42 (95% CI, 0.40-0.44). ICD-10 code performance was better for inpatients than for outpatients for fever (χ2 = 41.30; P < .001) and dyspnea (χ2 = 14.25; P = .003) but not for cough (χ2 = 5.13; P = .16). Conclusions and Relevance: These findings suggest that ICD-10 codes lack sensitivity and have poor NPV for symptoms associated with COVID-19. This inaccuracy has implications for any downstream data model, scientific discovery, or surveillance that relies on these codes.

Systematic collection of patient-reported outcomes in atrial fibrillation: feasibility and initial results of the Utah mEVAL AF programme.

AIMS: Incorporating patient-reported outcomes (PROs) into routine care of atrial fibrillation (AF) enables direct integration of symptoms, function, and health-related quality of life (HRQoL) into practice. We report our initial experience with a system-wide PRO initiative among AF patients. METHODS AND RESULTS: All patients with AF in our practice undergo PRO assessment with the Toronto AF Severity Scale (AFSS), and generic PROs, prior to electrophysiology clinic visits. We describe the implementation, feasibility, and results of clinic-based, electronic AF PRO collection, and compare AF-specific and generic HRQoL assessments. From October 2016 to February 2019, 1586 unique AF patients initiated 2379 PRO assessments, 2145 of which had all PRO measures completed (90%). The median completion time for all PRO measures per visit was 7.3 min (1st, 3rd quartiles: 6, 10). Overall, 38% of patients were female (n = 589), mean age was 68 (SD 12) years, and mean CHA2DS2-VASc score was 3.8 (SD 2.0). The mean AFSS symptom score was 8.6 (SD 6.6, 1st, 3rd quartiles: 3, 13), and the full range of values was observed (0, 35). Generic PROs of physical function, general health, and depression were impacted at the most severe quartiles of AF symptom score (P < 0.0001 for each vs. AFSS quartile). CONCLUSION: Routine clinic-based, PRO collection for AF is feasible in clinical practice and patient time investment was acceptable. Disease-specific AF PROs add value to generic HRQoL instruments. Further research into the relationship between PROs, heart rhythm, and AF burden, as well as PRO-guided management, is necessary to optimize PRO utilization.

The Familiality of Rapid Renal Decline in Diabetes.

Sustained and rapid loss of glomerular filtration rate (GFR) is the predominant clinical feature of diabetic kidney disease and a requisite for the development of end-stage renal disease. Although GFR trajectories have been studied in several cohorts with diabetes and without diabetes, whether rapid renal decline clusters in families with diabetes has not been examined. To determine this, we estimated GFR (eGFR) from serum creatinine measurements obtained from 15,612 patients with diabetes at the University of Utah Health Sciences Center and established their renal function trajectories. Patients with rapid renal decline (eGFR slope < -5 mL/min/1.73 m2/year) were then mapped to pedigrees using extensive genealogical records from the Utah Population Database to identify high-risk rapid renal decline pedigrees. We identified 2,127 (13.6%) rapid decliners with a median eGFR slope of -8.0 mL/min/1.73 m2/year and 51 high-risk pedigrees (ranging in size from 1,450 to 24,501 members) with excess clustering of rapid renal decline. Familial analysis showed that rapid renal decline aggregates in these families and is associated with its increased risk among first-degree relatives. Further study of these families is necessary to understand the magnitude of the influence of shared familial factors, including environmental and genetic factors, on rapid renal decline in diabetes.

Impact of computerized provider order entry (CPOE) on length of stay and mortality.

Objective: To examine changes in patient outcome variables, length of stay (LOS), and mortality after implementation of computerized provider order entry (CPOE). Materials and Methods: A 5-year retrospective pre-post study evaluated 66 186 patients and 104 153 admissions (49 683 pre-CPOE, 54 470 post-CPOE) at an academic medical center. Generalized linear mixed statistical tests controlled for 17 potential confounders with 2 models per outcome. Results: After controlling for covariates, CPOE remained a significant statistical predictor of decreased LOS and mortality. LOS decreased by 0.90 days, P < .0001. Mortality decrease varied by model: 1 death per 1000 admissions (pre = 0.006, post = 0.0005, P < .001) or 3 deaths (pre = 0.008, post = 0.005, P < .01). Mortality and LOS decreased in medical and surgical units but increased in intensive care units. Discussion: This study examined CPOE at multiple levels. Given the inability to randomize CPOE assignment, these results may only be applicable to the local setting. Temporal trends found in this study suggest that hospital-wide implementations may have impacted nursing staff and new residents. Differences in the results were noted at the patient care unit and room levels. These differences may partly explain the mixed results from previous studies. Conclusion: Controlling for confounders, CPOE implementation remained a statistically significant predictor of LOS and mortality at this site. Mortality appears to be a sensitive outcome indicator with regard to hospital-wide implementations and should be further studied.

Family presence during cardiopulmonary resuscitation: using evidence-based knowledge to guide the advanced practice nurse in developing formal policy and practice guidelines.

PURPOSE: To provide advanced practice nurses (APNs) with the best available evidence for implementation of policies and procedures to allow family presence during cardiopulmonary resuscitation (CPR) in the acute care environment. DATA SOURCES: A comprehensive review of research-based articles from Ebsco Host, CINAHL, Pre-CINAHL, and Medline Plus, as well as statement alerts from nursing credentialing bodies, and practice guidelines were reviewed. Kolcaba's Theory of Comfort and Lewin's Three Step Change Theory provide a framework for implementation of formal policies and procedures. CONCLUSIONS: Best available evidence showed more support in favor of allowing families at the bedside during CPR. Implementation of policies and procedures allowing family presence enables facilities to change and grow in a holistic and family-oriented atmosphere. IMPLICATIONS FOR PRACTICE: With this evidence-based knowledge the APN will be able to disseminate information to facilitate collaborative change in current practices surrounding staff education, decision making, and self-governance. The APN can then address controversial changes when developing formal policies and procedures, which will increase patient satisfaction and outcomes.

Clinical decision support in critical care nursing.

A clinical decision support system (CDSS) is a computerized application that helps clinicians detect and prevent untoward clinical events such as drug interactions, errors of omission, and trends in symptomatology. A CDSS in healthcare usually is built around an alerting system based on rules of logic. The alerting system of a CDSS can notify clinicians immediately on clinical data entry, or it can generate alerts over time after relating data from multiple sources. A CDSS for nurses and patients offers immediate benefits for nurses and patients by detecting potential drug-laboratory and drug-drug combinations and impending pharmacologic complications, monitoring microbiology results, and helping nurses relate symptoms to pharmacology and medication side effects. Other benefits include savings in time and money and reductions in morbidity and mortality. A CDSS presents an opportunity for nursing informatics and critical care nursing to collaborate for the benefit of the patient and the profession.