Impact of Digital Health Technology on Quality of Life in Patients With Heart Failure.

BACKGROUND: Digital health tools may improve quality of life (QoL) in patients with heart failure (HF) by promoting self-care, knowledge, and engagement. OBJECTIVES: This study evaluates the effect of 3 digital technologies on QoL in patients with HF. METHODS: A total of 182 patients were randomized to usual care or one of the technologies promoting self-care: Bodyport (cardiac scale), Conversa (conversational platform), or Noom (smartphone application). The primary outcome was 90-day change in QoL, as assessed by the Kansas City Cardiomyopathy Questionnaire (KCCQ) Overall Summary Score (OSS). RESULTS: A total of 151 participants (83%) completed their 90-day surveys. The median age of enrolled participants was 61 years (IQR: 53-69 years), and 37.9% were women. No group had any significant change in KCCQ OSS or improvement relative to usual care. However, symptoms and physical function at 90 days, as assessed by the Total Symptom Score (TSS) and Clinical Summary Score (CSS), were significantly improved in the Noom group relative to usual care: TSS median change of +4.2 points (IQR -1 to +16.7) vs -1 points (IQR: -13.5 to +7.8; P = 0.006); CSS median change of +2.8 points (IQR: -1 to +14.6) vs -3.1 points (IQR: -10.2 to +3; P = 0.002). CONCLUSIONS: Three digital interventions showed no independent effect on QoL as assessed by the KCCQ OSS. However, participants randomized to the Noom technology demonstrated improved KCCQ TSS and CSS relative to usual care. Although digital tools may be an important component of longitudinal care for patients with HF, larger studies are needed to better understand their effectiveness and optimal deployment. (Evaluating Efficacy of Digital Health Technology in the Treatment of Congestive Heart Failure; NCT04394754).

Leveraging transcriptomics to develop bronchopulmonary dysplasia endotypes: a concept paper.

IMPACT: Bronchopulmonary dysplasia has multiple definitions that are currently based on phenotypic characteristics. Using an unsupervised machine learning approach, we created BPD subclasses (e.g., endotypes) by clustering whole microarray data. T helper 17 cell differentiation was the most significant pathway differentiating the BPD endotypes. INTRODUCTION: Bronchopulmonary dysplasia (BPD) is the most common complication of extreme prematurity. Discovery of BPD endotypes in an unbiased format, derived from the peripheral blood transcriptome, may uncover patterns underpinning this complex lung disease. METHODS: An unsupervised agglomerative hierarchical clustering approach applied to genome-wide expression of profiling from 62 children at day of life five was used to identify BPD endotypes. To identify which genes were differentially expressed across the BPD endotypes, we formulated a linear model based on least-squares minimization with empirical Bayes statistics. RESULTS: Four BPD endotypes (A, B,C,D) were identified using 7,319 differentially expressed genes. Across BPD endotypes, 5,850 genes had a p value < 0.05 after multiple comparison testing. Endotype A consisted of neonates with a higher gestational age and birthweight. Endotypes B-D included neonates between 25 and 26 weeks and a birthweight range of 640 to 940 g. Endotype D appeared to have a protective role against BPD compared to Endotypes B and C (36% vs. 62% vs. 60%, respectively). The most significant pathway focused on T helper 17 cell differentiation. CONCLUSION: Bioinformatic analyses can help identify BPD endotypes that associate with clinical definitions of BPD.

A bioinformatics approach towards bronchopulmonary dysplasia.

Background and Objective: Bronchopulmonary dysplasia (BPD) is the most common morbidity associated with prematurity and remains a significant clinical challenge. Bioinformatic approaches, such as genomics, transcriptomics, and proteomics, have emerged as novel methods for studying the underlying mechanisms driving BPD pathogenesis. These methods can be used alongside clinical data to develop a better understanding of BPD and potentially identify the most at risk neonates within the first few weeks of neonatal life. The objective of this review is to provide an overview of the current state-of-the-art in bioinformatics for BPD research. Methods: We conducted a literature review of bioinformatics approaches for BPD using PubMed. The following keywords were used: "biomedical informatics", "bioinformatics", "bronchopulmonary dysplasia", and "omics". Key Content and Findings: This review highlighted the importance of omic-approaches to better understand BPD and potential avenues for future research. We described the use of machine learning (ML) and the need for systems biology methods for integrating large-scale data from multiple tissues. We summarized a handful of studies that utilized bioinformatics for BPD in order to better provide a view of where things currently stand, identify areas of ongoing research, and concluded with challenges that remain in the field. Conclusions: Bioinformatics has the potential to enable a more comprehensive understanding of BPD pathogenesis, facilitating a personalized and precise approach to neonatal care. As we continue to push the boundaries of biomedical research, biomedical informatics (BMI) will undoubtedly play a key role in unraveling new frontiers in disease understanding, prevention, and treatment.

Pragmatic randomized trial assessing the impact of digital health technology on quality of life in patients with heart failure: Design, rationale and implementation.

BACKGROUND: Self-care and patient engagement are important elements of heart failure (HF) care, endorsed in the guidelines. Digital health tools may improve quality of life (QOL) in HF patients by promoting care, knowledge, and engagement. This manuscript describes the rationale and challenges of the design and implementation of a pragmatic randomized controlled trial to evaluate the efficacy of three digital health technologies in improving QOL for patients with HF. HYPOTHESIS: We hypothesize that digital health interventions will improve QOL of HF patients through the early detection of warning signs of disease exacerbation, the opportunity of self-tracking symptoms, and the education provided, which enhances patient empowerment. METHODS: Using a fully electronic enrollment and consent platform, the trial will randomize 200 patients across HF clinics in the Yale New Haven Health system to receive either usual care or one of three digital technologies designed to promote self-management and provide critical data to clinicians. The primary outcome is the change in QOL as assessed by the Kansas City Cardiomyopathy Questionnaire at 3 months. RESULTS: First enrollment occurred in September 2021. Recruitment was anticipated to last 6-8 months and participants were followed for 6 months after randomization. Our recruitment efforts have highlighted the large digital divide in our population of interest. CONCLUSION: Assessing clinical outcomes, patient usability, and ease of clinical integration of digital technologies will be beneficial in determining the feasibility of the integration of such technologies into the healthcare system.

Peripheral Artery Disease and COVID-19 Outcomes: Insights from the Yale DOM-CovX Registry.

Both COVID-19 infection and peripheral arterial disease (PAD) cause hypercoagulability in patients, and it remains unknown whether PAD predisposes patients to experience worse outcomes when infected with SARS-CoV-2. The Yale DOM-CovX Registry consecutively enrolled inpatients for SARS-CoV-2 between March 1, 2020, and November 10, 2020. Adjusted logistic regression models examined associations between PAD and mortality, stroke, myocardial infarction (MI), and major adverse cardiovascular events (MACE, all endpoints combined). Of the 3830 patients were admitted with SARS-CoV-2, 50.5% were female, mean age was 63.1 ± 18.4 years, 50.7% were minority race, and 18.3% (n = 693) had PAD. PAD was independently associated with increased mortality (OR = 1.45, 95% CI 1.11-1.88) and MACE (OR = 1.48, 95% CI 1.16-1.87). PAD was not independently associated with stroke (P = 0.06) and MI (P = 0.22). Patients with PAD have a >40% odds of mortality and MACE when admitted with a SARS-CoV-2, independent of known risk factors.

A neutrophil activation signature predicts critical illness and mortality in COVID-19.

Pathologic immune hyperactivation is emerging as a key feature of critical illness in COVID-19, but the mechanisms involved remain poorly understood. We carried out proteomic profiling of plasma from cross-sectional and longitudinal cohorts of hospitalized patients with COVID-19 and analyzed clinical data from our health system database of more than 3300 patients. Using a machine learning algorithm, we identified a prominent signature of neutrophil activation, including resistin, lipocalin-2, hepatocyte growth factor, interleukin-8, and granulocyte colony-stimulating factor, which were the strongest predictors of critical illness. Evidence of neutrophil activation was present on the first day of hospitalization in patients who would only later require transfer to the intensive care unit, thus preceding the onset of critical illness and predicting increased mortality. In the health system database, early elevations in developing and mature neutrophil counts also predicted higher mortality rates. Altogether, these data suggest a central role for neutrophil activation in the pathogenesis of severe COVID-19 and identify molecular markers that distinguish patients at risk of future clinical decompensation.

Electronic health record alerts for acute kidney injury: multicenter, randomized clinical trial.

OBJECTIVE: To determine whether electronic health record alerts for acute kidney injury would improve patient outcomes of mortality, dialysis, and progression of acute kidney injury. DESIGN: Double blinded, multicenter, parallel, randomized controlled trial. SETTING: Six hospitals (four teaching and two non-teaching) in the Yale New Haven Health System in Connecticut and Rhode Island, US, ranging from small community hospitals to large tertiary care centers. PARTICIPANTS: 6030 adult inpatients with acute kidney injury, as defined by the Kidney Disease: Improving Global Outcomes (KDIGO) creatinine criteria. INTERVENTIONS: An electronic health record based "pop-up" alert for acute kidney injury with an associated acute kidney injury order set upon provider opening of the patient's medical record. MAIN OUTCOME MEASURES: A composite of progression of acute kidney injury, receipt of dialysis, or death within 14 days of randomization. Prespecified secondary outcomes included outcomes at each hospital and frequency of various care practices for acute kidney injury. RESULTS: 6030 patients were randomized over 22 months. The primary outcome occurred in 653 (21.3%) of 3059 patients with an alert and in 622 (20.9%) of 2971 patients receiving usual care (relative risk 1.02, 95% confidence interval 0.93 to 1.13, P=0.67). Analysis by each hospital showed worse outcomes in the two non-teaching hospitals (n=765, 13%), where alerts were associated with a higher risk of the primary outcome (relative risk 1.49, 95% confidence interval 1.12 to 1.98, P=0.006). More deaths occurred at these centers (15.6% in the alert group v 8.6% in the usual care group, P=0.003). Certain acute kidney injury care practices were increased in the alert group but did not appear to mediate these outcomes. CONCLUSIONS: Alerts did not reduce the risk of our primary outcome among patients in hospital with acute kidney injury. The heterogeneity of effect across clinical centers should lead to a re-evaluation of existing alerting systems for acute kidney injury. TRIAL REGISTRATION: ClinicalTrials.gov NCT02753751.

The Association of COVID-19 With Acute Kidney Injury Independent of Severity of Illness: A Multicenter Cohort Study.

RATIONALE & OBJECTIVE: Although coronavirus disease 2019 (COVID-19) has been associated with acute kidney injury (AKI), it is unclear whether this association is independent of traditional risk factors such as hypotension, nephrotoxin exposure, and inflammation. We tested the independent association of COVID-19 with AKI. STUDY DESIGN: Multicenter, observational, cohort study. SETTING & PARTICIPANTS: Patients admitted to 1 of 6 hospitals within the Yale New Haven Health System between March 10, 2020, and August 31, 2020, with results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing via polymerase chain reaction of a nasopharyngeal sample. EXPOSURE: Positive test for SARS-CoV-2. OUTCOME: AKI by KDIGO (Kidney Disease: Improving Global Outcomes) criteria. ANALYTICAL APPROACH: Evaluated the association of COVID-19 with AKI after controlling for time-invariant factors at admission (eg, demographic characteristics, comorbidities) and time-varying factors updated continuously during hospitalization (eg, vital signs, medications, laboratory results, respiratory failure) using time-updated Cox proportional hazard models. RESULTS: Of the 22,122 patients hospitalized, 2,600 tested positive and 19,522 tested negative for SARS-CoV-2. Compared with patients who tested negative, patients with COVID-19 had more AKI (30.6% vs 18.2%; absolute risk difference, 12.5% [95% CI, 10.6%-14.3%]) and dialysis-requiring AKI (8.5% vs 3.6%) and lower rates of recovery from AKI (58% vs 69.8%). Compared with patients without COVID-19, patients with COVID-19 had higher inflammatory marker levels (C-reactive protein, ferritin) and greater use of vasopressors and diuretic agents. Compared with patients without COVID-19, patients with COVID-19 had a higher rate of AKI in univariable analysis (hazard ratio, 1.84 [95% CI, 1.73-1.95]). In a fully adjusted model controlling for demographic variables, comorbidities, vital signs, medications, and laboratory results, COVID-19 remained associated with a high rate of AKI (adjusted hazard ratio, 1.40 [95% CI, 1.29-1.53]). LIMITATIONS: Possibility of residual confounding. CONCLUSIONS: COVID-19 is associated with high rates of AKI not fully explained by adjustment for known risk factors. This suggests the presence of mechanisms of AKI not accounted for in this analysis, which may include a direct effect of COVID-19 on the kidney or other unmeasured mediators. Future studies should evaluate the possible unique pathways by which COVID-19 may cause AKI.

A Comparison Study of Coronavirus Disease 2019 Outcomes in Hospitalized Kidney Transplant Recipients.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can infect any human host, but kidney transplant recipients (KTR) are considered more susceptible on the basis of previous experience with other viral infections. We evaluated rates of hospital complications between SARS-CoV-2-positive KTR and comparator groups. Methods: We extracted data from the electronic health record on patients who were hospitalized with SARS-CoV-2, testing at six hospitals from March 4 through September 9, 2020. We compared outcomes between SARS-CoV-2-positive KTR and controls: SARS-CoV-2-positive non-KTR, SARS-CoV-2-negative KTR, and SARS-CoV-2-negative non-KTR. Results: Of 31,540 inpatients, 3213 tested positive for SARS-CoV-2. There were 32 SARS-CoV-2-positive and 224 SARS-CoV-2-negative KTR. SARS-CoV-2-positive KTR had higher ferritin levels (1412; interquartile range, 748-1749 versus 553; interquartile range, 256-1035; P<0.01) compared with SARS-CoV-2-positive non-KTR. SARS-CoV-2-positive KTR had higher rates of ventilation (34% versus 14%, P<0.01; versus 9%, P<0.01; versus 5%, P<0.01), vasopressor use (41% versus 16%, P<0.01; versus 17%, P<0.01; versus 12%, P<0.01), and AKI (47% versus 15%, P<0.01; versus 23%, P<0.01; versus 10%, P<0.01) compared with SARS-CoV-2-positive non-KTR, SARS-CoV-2-negative KTR, and SARS-CoV-2-negative non-KTR, respectively. SARS-CoV-2-positive KTR continued to have increased odds of ventilation, vasopressor use, and AKI compared with SARS-CoV-2-positive non-KTR independent of Elixhauser score, Black race, and baseline eGFR. Mortality was not significantly different between SARS-CoV-2-positive KTR and non-KTR, but there was a notable trend toward higher mortality in SARS-CoV-2-positive KTR (25% versus 16%, P=0.15, respectively). Conclusions: Hospitalized SARS-CoV-2-positive KTR had a high rate of mortality and hospital complications, such as requiring ventilation, vasopressor use, and AKI. Additionally, they had higher odds of hospital complications compared with SARS-CoV-2-positive non-KTR after adjusting for Elixhauser score, Black race, and baseline eGFR. Future studies with larger sample size of KTR are needed to validate our findings. Podcast: This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/K360/2021_03_25_KID0005652020.mp3.

Integrating Patient Priorities with Science by Community Engagement in the Kidney Precision Medicine Project.

The Kidney Precision Medicine Project (KPMP) is a multisite study designed to improve understanding of CKD attributed to diabetes or hypertension and AKI by performing protocol-driven kidney biopsies. Study participants and their kidney tissue samples undergo state-of-the-art deep phenotyping using advanced molecular, imaging, and data analytical methods. Few patients participate in research design or concepts for discovery science. A major goal of the KPMP is to include patients as equal partners to inform the research for clinically relevant benefit. The purpose of this report is to describe patient and community engagement and the value they bring to the KPMP. Patients with CKD and AKI and clinicians from the study sites are members of the Community Engagement Committee, with representation on other KPMP committees. They participate in KPMP deliberations to address scientific, clinical, logistic, analytic, ethical, and community engagement issues. The Community Engagement Committee guides KPMP research priorities from perspectives of patients and clinicians. Patients led development of essential study components, including the informed consent process, no-fault harm insurance coverage, the ethics statement, return of results plan, a "Patient Primer" for scientists and the public, and Community Advisory Boards. As members across other KPMP committees, the Community Engagement Committee assures that the science is developed and conducted in a manner relevant to study participants and the clinical community. Patients have guided the KPMP to produce research aligned with their priorities. The Community Engagement Committee partnership has set new benchmarks for patient leadership in precision medicine research.

Prediction of outcomes after acute kidney injury in hospitalised patients: protocol for a systematic review.

INTRODUCTION: Acute kidney injury (AKI) is common and is associated with negative long-term outcomes. Given the heterogeneity of the syndrome, the ability to predict outcomes of AKI may be beneficial towards effectively using resources and personalising AKI care. This systematic review will identify, describe and assess current models in the literature for the prediction of outcomes in hospitalised patients with AKI. METHODS AND ANALYSIS: Relevant literature from a comprehensive search across six databases will be imported into Covidence. Abstract screening and full-text review will be conducted independently by two team members, and any conflicts will be resolved by a third member. Studies to be included are cohort studies and randomised controlled trials with at least 100 subjects, adult hospitalised patients, with AKI. Only those studies evaluating multivariable predictive models reporting a statistical measure of accuracy (area under the receiver operating curve or C-statistic) and predicting resolution of AKI, progression of AKI, subsequent dialysis and mortality will be included. Data extraction will be performed independently by two team members, with a third reviewer available to resolve conflicts. Results will be reported using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Risk of bias will be assessed using Prediction model Risk Of Bias ASsessment Tool. ETHICS AND DISSEMINATION: We are committed to open dissemination of our results through the registration of our systematic review on PROSPERO and future publication. We hope that our review provides a platform for future work in realm of using artificial intelligence to predict outcomes of common diseases. PROSPERO REGISTRATION NUMBER: CRD42019137274.

A neutrophil activation signature predicts critical illness and mortality in COVID-19.

Pathologic immune hyperactivation is emerging as a key feature of critical illness in COVID-19, but the mechanisms involved remain poorly understood. We carried out proteomic profiling of plasma from cross-sectional and longitudinal cohorts of hospitalized patients with COVID-19 and analyzed clinical data from our health system database of over 3,300 patients. Using a machine learning algorithm, we identified a prominent signature of neutrophil activation, including resistin, lipocalin-2, HGF, IL-8, and G-CSF, as the strongest predictors of critical illness. Neutrophil activation was present on the first day of hospitalization in patients who would only later require transfer to the intensive care unit, thus preceding the onset of critical illness and predicting increased mortality. In the health system database, early elevations in developing and mature neutrophil counts also predicted higher mortality rates. Altogether, we define an essential role for neutrophil activation in the pathogenesis of severe COVID-19 and identify molecular neutrophil markers that distinguish patients at risk of future clinical decompensation.

Real-Time Prediction of Acute Kidney Injury in Hospitalized Adults: Implementation and Proof of Concept.

RATIONALE & OBJECTIVE: Acute kidney injury (AKI) is diagnosed based on changes in serum creatinine concentration, a late marker of this syndrome. Algorithms that predict elevated risk for AKI are of great interest, but no studies have incorporated such an algorithm into the electronic health record to assist with clinical care. We describe the experience of implementing such an algorithm. STUDY DESIGN: Prospective observational cohort study. SETTING & PARTICIPANTS: 2,856 hospitalized adults in a single urban tertiary-care hospital with an algorithm-predicted risk for AKI in the next 24 hours>15%. Alerts were also used to target a convenience sample of 100 patients for measurement of 16 urine and 6 blood biomarkers. EXPOSURE: Clinical characteristics at the time of pre-AKI alert. OUTCOME: AKI within 24 hours of pre-AKI alert (AKI24). ANALYTICAL APPROACH: Descriptive statistics and univariable associations. RESULTS: At enrollment, mean predicted probability of AKI24 was 19.1%; 18.9% of patients went on to develop AKI24. Outcomes were generally poor among this population, with 29% inpatient mortality among those who developed AKI24 and 14% among those who did not (P<0.001). Systolic blood pressure<100mm Hg (28% of patients with AKI24 vs 18% without), heart rate>100 beats/min (32% of patients with AKI24 vs 24% without), and oxygen saturation<92% (15% of patients with AKI24 vs 6% without) were all more common among those who developed AKI24. Of all biomarkers measured, only hyaline casts on urine microscopy (72% of patients with AKI24 vs 25% without) and fractional excretion of urea nitrogen (20% [IQR, 12%-36%] among patients with AKI24 vs 34% [IQR, 25%-44%] without) differed between those who did and did not develop AKI24. LIMITATIONS: Single-center study, reliance on serum creatinine level for AKI diagnosis, small number of patients undergoing biomarker evaluation. CONCLUSIONS: A real-time AKI risk model was successfully integrated into the EHR.

A Time-Updated, Parsimonious Model to Predict AKI in Hospitalized Children.

BACKGROUND: Timely prediction of AKI in children can allow for targeted interventions, but the wealth of data in the electronic health record poses unique modeling challenges. METHODS: We retrospectively reviewed the electronic medical records of all children younger than 18 years old who had at least two creatinine values measured during a hospital admission from January 2014 through January 2018. We divided the study population into derivation, and internal and external validation cohorts, and used five feature selection techniques to select 10 of 720 potentially predictive variables from the electronic health records. Model performance was assessed by the area under the receiver operating characteristic curve in the validation cohorts. The primary outcome was development of AKI (per the Kidney Disease Improving Global Outcomes creatinine definition) within a moving 48-hour window. Secondary outcomes included severe AKI (stage 2 or 3), inpatient mortality, and length of stay. RESULTS: Among 8473 encounters studied, AKI occurred in 516 (10.2%), 207 (9%), and 27 (2.5%) encounters in the derivation, and internal and external validation cohorts, respectively. The highest-performing model used a machine learning-based genetic algorithm, with an overall receiver operating characteristic curve in the internal validation cohort of 0.76 [95% confidence interval (CI), 0.72 to 0.79] for AKI, 0.79 (95% CI, 0.74 to 0.83) for severe AKI, and 0.81 (95% CI, 0.77 to 0.86) for neonatal AKI. To translate this prediction model into a clinical risk-stratification tool, we identified high- and low-risk threshold points. CONCLUSIONS: Using various machine learning algorithms, we identified and validated a time-updated prediction model of ten readily available electronic health record variables to accurately predict imminent AKI in hospitalized children.