Epidemiology and risk factors for the development of cutaneous toxicities in patients treated with immune-checkpoint inhibitors: A United States population-level analysis.

BACKGROUND: A variety of dermatoses have been reported in the growing number of patients treated with immune-checkpoint inhibitors (ICIs), but the current understanding of cutaneous immune-related adverse events (irAEs) is limited. OBJECTIVE: To determine the cumulative incidence, distribution, and risk factors of cutaneous irAEs after ICI initiation. METHODS: This was a retrospective cohort study of patients in a national insurance claims database including cancer patients treated with ICIs and matched controls. RESULTS: The study included 8637 ICI patients and 8637 matched controls. The overall incidence of cutaneous irAEs was 25.1%, with a median onset time of 113 days. The ICI group had a significantly higher incidence of pruritus, mucositis, erythroderma, maculopapular eruption, vitiligo, lichen planus, bullous pemphigoid, Grover disease, rash, other nonspecific eruptions, and drug eruption or other nonspecific drug reaction. Patients with melanoma and renal cell carcinoma and those receiving combination therapy were at a higher risk of cutaneous irAEs. LIMITATIONS: Retrospective design without access to patient chart data. CONCLUSIONS: This study identifies cutaneous irAEs in a real-world clinical setting and highlights patient groups that are particularly at risk. The results can aid dermatologists at the bedside in the diagnosis of cutaneous irAEs and in formulating management recommendations to referring oncologists regarding the continuation of ICI therapy.

Real-world comorbidities of atopic dermatitis in the US adult ambulatory population.

BACKGROUND: Atopic dermatitis (AD) is a pruritic, inflammatory skin disease associated with various comorbidities. However, comprehensive analyses of real-world comorbidities in adult patients with AD are limited. OBJECTIVE: To characterize the real-world comorbidities associated with adult AD in an ambulatory population. METHODS: We queried the MarketScan Commercial Claims and Encounters database from January 1, 2017 to December 31, 2017. Multivariable logistic regressions were performed to compare comorbidities in adult patients with AD versus age- and sex-matched controls. RESULTS: A total of 39,779 patients with AD and 353,743 controls were identified. Increased odds of psychiatric conditions, including anxiety (odds ratio [OR] 1.44) and mood disorders (OR 1.31), were observed in patients with AD. Patients with AD had higher likelihoods of autoimmune diseases, including vitiligo (OR 4.44) and alopecia areata (OR 6.01). Adult AD was also associated with infections, including impetigo (OR 9.72), methicillin-resistant Staphylococcus aureus (OR 3.92), and cellulitis (OR 2.52). Patients with AD were more likely to have systemic conditions, including lymphoid/hematopoietic malignancy (OR 1.91), atherosclerosis (OR 1.69), and metabolic syndrome (OR 1.47). For all the above, P < .001. LIMITATIONS: Retrospective analysis of health care claims data. CONCLUSION: Adult AD is associated with various psychiatric and systemic comorbidities, emphasizing the systemic nature of AD and the need for the collaborative management of these patients.

Development of Severe COVID-19 Adaptive Risk Predictor (SCARP), a Calculator to Predict Severe Disease or Death in Hospitalized Patients With COVID-19.

BACKGROUND: Predicting the clinical trajectory of individual patients hospitalized with coronavirus disease 2019 (COVID-19) is challenging but necessary to inform clinical care. The majority of COVID-19 prognostic tools use only data present upon admission and do not incorporate changes occurring after admission. OBJECTIVE: To develop the Severe COVID-19 Adaptive Risk Predictor (SCARP) (https://rsconnect.biostat.jhsph.edu/covid_trajectory/), a novel tool that can provide dynamic risk predictions for progression from moderate disease to severe illness or death in patients with COVID-19 at any time within the first 14 days of their hospitalization. DESIGN: Retrospective observational cohort study. SETTINGS: Five hospitals in Maryland and Washington, D.C. PATIENTS: Patients who were hospitalized between 5 March and 4 December 2020 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) confirmed by nucleic acid test and symptomatic disease. MEASUREMENTS: A clinical registry for patients hospitalized with COVID-19 was the primary data source; data included demographic characteristics, admission source, comorbid conditions, time-varying vital signs, laboratory measurements, and clinical severity. Random forest for survival, longitudinal, and multivariate (RF-SLAM) data analysis was applied to predict the 1-day and 7-day risks for progression to severe disease or death for any given day during the first 14 days of hospitalization. RESULTS: Among 3163 patients admitted with moderate COVID-19, 228 (7%) became severely ill or died in the next 24 hours; an additional 355 (11%) became severely ill or died in the next 7 days. The area under the receiver-operating characteristic curve (AUC) for 1-day risk predictions for progression to severe disease or death was 0.89 (95% CI, 0.88 to 0.90) and 0.89 (CI, 0.87 to 0.91) during the first and second weeks of hospitalization, respectively. The AUC for 7-day risk predictions for progression to severe disease or death was 0.83 (CI, 0.83 to 0.84) and 0.87 (CI, 0.86 to 0.89) during the first and second weeks of hospitalization, respectively. LIMITATION: The SCARP tool was developed by using data from a single health system. CONCLUSION: Using the predictive power of RF-SLAM and longitudinal data from more than 3000 patients hospitalized with COVID-19, an interactive tool was developed that rapidly and accurately provides the probability of an individual patient's progression to severe illness or death on the basis of readily available clinical information. PRIMARY FUNDING SOURCE: Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.

Cost-effectiveness of a Digital Health Intervention for Acute Myocardial Infarction Recovery.

BACKGROUND: Acute myocardial infarction (AMI) is a common cause of hospital admissions, readmissions, and mortality worldwide. Digital health interventions (DHIs) that promote self-management, adherence to guideline-directed therapy, and cardiovascular risk reduction may improve health outcomes in this population. The "Corrie" DHI consists of a smartphone application, smartwatch, and wireless blood pressure monitor to support medication tracking, education, vital signs monitoring, and care coordination. We aimed to assess the cost-effectiveness of this DHI plus standard of care in reducing 30-day readmissions among AMI patients in comparison to standard of care alone. METHODS: A Markov model was used to explore cost-effectiveness from the hospital perspective. The time horizon of the analysis was 1 year, with 30-day cycles, using inflation-adjusted cost data with no discount rate. Currencies were quantified in US dollars, and effectiveness was measured in quality-adjusted life-years (QALYs). The results were interpreted as an incremental cost-effectiveness ratio at a threshold of $100,000 per QALY. Univariate sensitivity and multivariate probabilistic sensitivity analyses tested model uncertainty. RESULTS: The DHI reduced costs and increased QALYs on average, dominating standard of care in 99.7% of simulations in the probabilistic analysis. Based on the assumption that the DHI costs $2750 per patient, use of the DHI leads to a cost-savings of $7274 per patient compared with standard of care alone. CONCLUSIONS: Our results demonstrate that this DHI is cost-saving through the reduction of risk for all-cause readmission following AMI. DHIs that promote improved adherence with guideline-based health care can reduce hospital readmissions and associated costs.

Digital Health Interventions for Cardiac Rehabilitation: Systematic Literature Review.

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death worldwide. Despite strong evidence supporting the benefits of cardiac rehabilitation (CR), over 80% of eligible patients do not participate in CR. Digital health technologies (ie, the delivery of care using the internet, wearable devices, and mobile apps) have the potential to address the challenges associated with traditional facility-based CR programs, but little is known about the comprehensiveness of these interventions to serve as digital approaches to CR. Overall, there is a lack of a systematic evaluation of the current literature on digital interventions for CR. OBJECTIVE: The objective of this systematic literature review is to provide an in-depth analysis of the potential of digital health technologies to address the challenges associated with traditional CR. Through this review, we aim to summarize the current literature on digital interventions for CR, identify the key components of CR that have been successfully addressed through digital interventions, and describe the gaps in research that need to be addressed for sustainable and scalable digital CR interventions. METHODS: Our strategy for identifying the primary literature pertaining to CR with digital solutions (defined as technology employed to deliver remote care beyond the use of the telephone) included a consultation with an expert in the field of digital CR and searches of the PubMed (MEDLINE), Embase, CINAHL, and Cochrane databases for original studies published from January 1990 to October 2018. RESULTS: Our search returned 31 eligible studies, of which 22 were randomized controlled trials. The reviewed CR interventions primarily targeted physical activity counseling (31/31, 100%), baseline assessment (30/31, 97%), and exercise training (27/31, 87%). The most commonly used modalities were smartphones or mobile devices (20/31, 65%), web-based portals (18/31, 58%), and email-SMS (11/31, 35%). Approximately one-third of the studies addressed the CR core components of nutrition counseling, psychological management, and weight management. In contrast, less than a third of the studies addressed other CR core components, including the management of lipids, diabetes, smoking cessation, and blood pressure. CONCLUSIONS: Digital technologies have the potential to increase access and participation in CR by mitigating the challenges associated with traditional, facility-based CR. However, previously evaluated interventions primarily focused on physical activity counseling and exercise training. Thus, further research is required with more comprehensive CR interventions and long-term follow-up to understand the clinical impact of digital interventions.

Efficacy of Mobile Health for Self-management of Cardiometabolic Risk Factors: A Theory-Guided Systematic Review.

BACKGROUND: Although mobile health (mHealth) technologies are burgeoning in the research arena, there is a lack of mHealth interventions focused on improving self-management of individuals with cardiometabolic risk factors (CMRFs). OBJECTIVE: The purpose of this article was to critically and systematically review the efficacy of mHealth interventions for self-management of CMRF while evaluating quality, limitations, and issues with disparities using the technology acceptance model as a guiding framework. METHODS: PubMed, CINAHL, EMBASE, and Lilacs were searched to identify research articles published between January 2008 and November 2018. Articles were included if they were published in English, included adults, were conducted in the United States, and used mHealth to promote self-care or self-management of CMRFs. A total of 28 articles were included in this review. RESULTS: Studies incorporating mHealth have been linked to positive outcomes in self-management of diabetes, physical activity, diet, and weight loss. Most mHealth interventions included modalities such as text messaging, mobile applications, and wearable technologies. There was a lack of studies that are (1) in resource-poor settings, (2) theoretically driven, (3) community-engaged research, (4) measuring digital/health literacy, (5) measuring and evaluating engagement, (6) measuring outcomes related to disease self-management, and (7) focused on vulnerable populations, especially immigrants. CONCLUSION: There is still a lack of mHealth interventions created specifically for immigrant populations, especially within the Latino community-the largest growing minority group in the United States. In an effort to meet this challenge, more culturally tailored mHealth interventions are needed.

Clinical risk prediction with random forests for survival, longitudinal, and multivariate (RF-SLAM) data analysis.

BACKGROUND: Clinical research and medical practice can be advanced through the prediction of an individual's health state, trajectory, and responses to treatments. However, the majority of current clinical risk prediction models are based on regression approaches or machine learning algorithms that are static, rather than dynamic. To benefit from the increasing emergence of large, heterogeneous data sets, such as electronic health records (EHRs), novel tools to support improved clinical decision making through methods for individual-level risk prediction that can handle multiple variables, their interactions, and time-varying values are necessary. METHODS: We introduce a novel dynamic approach to clinical risk prediction for survival, longitudinal, and multivariate (SLAM) outcomes, called random forest for SLAM data analysis (RF-SLAM). RF-SLAM is a continuous-time, random forest method for survival analysis that combines the strengths of existing statistical and machine learning methods to produce individualized Bayes estimates of piecewise-constant hazard rates. We also present a method-agnostic approach for time-varying evaluation of model performance. RESULTS: We derive and illustrate the method by predicting sudden cardiac arrest (SCA) in the Left Ventricular Structural (LV) Predictors of Sudden Cardiac Death (SCD) Registry. We demonstrate superior performance relative to standard random forest methods for survival data. We illustrate the importance of the number of preceding heart failure hospitalizations as a time-dependent predictor in SCA risk assessment. CONCLUSIONS: RF-SLAM is a novel statistical and machine learning method that improves risk prediction by incorporating time-varying information and accommodating a large number of predictors, their interactions, and missing values. RF-SLAM is designed to easily extend to simultaneous predictions of multiple, possibly competing, events and/or repeated measurements of discrete or continuous variables over time. TRIAL REGISTRATION: LV Structural Predictors of SCD Registry (clinicaltrials.gov, NCT01076660), retrospectively registered 25 February 2010.

Current State of Dermatology Mobile Applications With Artificial Intelligence Features.

Importance: With advancements in mobile technology and artificial intelligence (AI) methods, there has been a substantial surge in the availability of direct-to-consumer mobile applications (apps) claiming to aid in the assessment and management of diverse skin conditions. Despite widespread patient downloads, these apps exhibit limited evidence supporting their efficacy. Objective: To identify and characterize current English-language AI dermatology mobile apps available for download, focusing on aspects such as purpose, supporting evidence, regulatory status, clinician input, data privacy measures, and use of image data. Evidence Review: In this cross-sectional study, both Apple and Android mobile app stores were systematically searched for dermatology-related apps that use AI algorithms. Each app's purpose, target audience, evidence-based claims, algorithm details, data availability, clinician input during development, and data usage privacy policies were evaluated. Findings: A total of 909 apps were initially identified. Following the removal of 518 duplicates, 391 apps remained. Subsequent review excluded 350 apps due to nonmedical nature, non-English languages, absence of AI features, or unavailability, ultimately leaving 41 apps for detailed analysis. The findings revealed several concerning aspects of the current landscape of AI apps in dermatology. Notably, none of the apps were approved by the US Food and Drug Administration, and only 2 of the apps included disclaimers for the lack of regulatory approval. Overall, the study found that these apps lack supporting evidence, input from clinicians and/or dermatologists, and transparency in algorithm development, data usage, and user privacy. Conclusions and Relevance: This cross-sectional study determined that although AI dermatology mobile apps hold promise for improving access to care and patient outcomes, in their current state, they may pose harm due to potential risks, lack of consistent validation, and misleading user communication. Addressing challenges in efficacy, safety, and transparency through effective regulation, validation, and standardized evaluation criteria is essential to harness the benefits of these apps while minimizing risks.

Deep learning for Mpox: Advances, challenges, and opportunities.

Although deep-learning algorithms in dermatology have shown promise in diagnosing skin cancers, less is known about potential applications for the diagnosis of infectious diseases. In a recent publication in Nature Medicine, Thieme et al. develop a deep-learning algorithm to classify skin lesions from Mpox virus (MPXV) infections.1.

Recommendations for Better Adoption of Medical Photography as a Clinical Tool.

The use of photography in routine clinical practice has the potential to increase the efficiency of overall patient care as well as improve clinical documentation and provider-to-provider communication. This is particularly important in the setting of provider burnout in the electronic health record era and during the COVID-19 pandemic. Despite the potential of photographs to enhance workflows and patient care, challenges remain that hinder the successful incorporation of medical photography into clinical practice, often because of inconsistent structure and implementation. Our proposed consolidated framework for clinical photography consists of five key aspects: appropriate informed consent; proper preparation and positioning; image acquisition with consideration of the field of view, orientation, focus, resolution, scale, and color calibration; streamlined and secure image storage and documentation; and interoperable file exchange. Overall, this viewpoint is a forward-looking paper on leveraging medical photography as an electronic health record tool for clinical care, research, and education.

Expanding Personalized, Data-Driven Dermatology: Leveraging Digital Health Technology and Machine Learning to Improve Patient Outcomes.

The current revolution of digital health technology and machine learning offers enormous potential to improve patient care. Nevertheless, it is essential to recognize that dermatology requires an approach different from those of other specialties. For many dermatological conditions, there is a lack of standardized methodology for quantitatively tracking disease progression and treatment response (clinimetrics). Furthermore, dermatological diseases impact patients in complex ways, some of which can be measured only through patient reports (psychometrics). New tools using digital health technology (e.g., smartphone applications, wearable devices) can aid in capturing both clinimetric and psychometric variables over time. With these data, machine learning can inform efforts to improve health care by, for example, the identification of high-risk patient groups, optimization of treatment strategies, and prediction of disease outcomes. We use the term personalized, data-driven dermatology to refer to the use of comprehensive data to inform individual patient care and improve patient outcomes. In this paper, we provide a framework that includes data from multiple sources, leverages digital health technology, and uses machine learning. Although this framework is applicable broadly to dermatological conditions, we use the example of a serious inflammatory skin condition, chronic cutaneous graft-versus-host disease, to illustrate personalized, data-driven dermatology.

Correction: Technical Guidance for Clinicians Interested in Partnering With Engineers in Mobile Health Development and Evaluation.

[This corrects the article DOI: 10.2196/14124.].

Cluster Analysis of Circulating Plasma Biomarkers in Prurigo Nodularis Reveals a Distinct Systemic Inflammatory Signature in African Americans.

Patients with prurigo nodularis (PN) suffer from intractable itch and dramatic reduction in QOL. Although there is significant clinical heterogeneity in the presentation of PN, disease endotypes remain unknown. We assayed circulating plasma cytokine concentrations in patients with PN (n = 20) along with matched healthy controls and utilized an unsupervised machine learning algorithm to identify disease endotypes. We found two distinct clusters of patients with PN with noninflammatory (cluster 1) and inflammatory (cluster 2) plasma profiles. Cluster 2 had more African Americans (82%, n = 9 vs. 33%, n = 3; P = 0.028), higher Worst Itch Numeric Rating Scale scores (9.5 ± 0.9 vs. 8.3 ± 1.2; P = 0.036), and lower QOL as reflected by higher Dermatology Life Quality Index scores (21.9 ± 6.4 vs. 13.0 ± 4.1; P = 0.015). In addition, cluster 1 had a higher rate of myelopathy (67%, n = 6 vs. 18%, n = 2; P = 0.028). Compared with cluster 1, cluster 2 had higher levels of IL-1α, IL-4, IL-5, IL-6, IL-10, IL-17A, IL-22, IL-25, and IFN-α. With population-level analysis, African American patients with PN had higher erythrocyte sedimentation rate, C-reactive protein, ferritin, and eosinophils and lower transferrin than Caucasian patients with PN. These findings indicate discrete clusters of patients with PN with plasma biomarker profiles corresponding to distinct demographic and clinical characteristics, potentially allowing for precision medicine approaches to treat PN.

Correction: Strategies for the Successful Implementation of a Novel iPhone Loaner System (iShare) in mHealth Interventions: Prospective Study.

[This corrects the article DOI: 10.2196/16391.].

Prediction of severe immune-related adverse events requiring hospital admission in patients on immune checkpoint inhibitors: study of a population level insurance claims database from the USA.

BACKGROUND: Immune-related adverse events (irAEs) are a serious side effect of immune checkpoint inhibitor (ICI) therapy for patients with advanced cancer. Currently, predisposing risk factors are undefined but understanding which patients are at increased risk for irAEs severe enough to require hospitalization would be beneficial to tailor treatment selection and monitoring. METHODS: We performed a retrospective review of patients with cancer treated with ICIs using unidentifiable claims data from an Aetna nationwide US health insurance database from January 3, 2011 to December 31, 2019, including patients with an identified primary cancer and at least one administration of an ICI. Regression analyses were performed. Main outcomes were incidence of and factors associated with irAE requiring hospitalization in ICI therapy. RESULTS: There were 68.8 million patients identified in the national database, and 14 378 patients with cancer identified with at least 1 administration of ICI in the study period. Patients were followed over 19 117 patient years and 504 (3.5%) developed an irAE requiring hospitalization. The incidence of irAEs requiring hospitalization per patient ICI treatment year was 2.6%, rising from 0% (0/71) in 2011 to 3.7% (93/2486) in 2016. Combination immunotherapy (OR: 2.44, p<0.001) was associated with increased odds of developing irAEs requiring hospitalization, whereas older patients (OR 0.98 per additional year, p<0.001) and those with non-lung cancer were associated with decreased odds of irAEs requiring hospitalization (melanoma OR: 0.70, p=0.01, renal cell carcinoma OR: 0.71, p=0.03, other cancers OR: 0.50, p<0.001). Sex, region, zip-code-imputed income, and zip-code unemployment were not associated with incidence of irAE requiring hospitalization. Prednisone (72%) and methylprednisolone (25%) were the most common immunosuppressive treatments identified in irAE hospitalizations. CONCLUSIONS: We found that 3.5% of patients initiating ICI therapy experienced irAEs requiring hospitalization and immunosuppression. The odds of irAEs requiring hospitalization were higher with younger age, treatment with combination ICI therapy (cytotoxic T lymphocyte-associated 4 and programmed cell death protein 1 (PD-1) or programmed death-ligand 1 (PD-L1)), and lower for other cancers compared with patients on PD-1 or PD-L1 inhibitors with lung cancer. This evidence from the first nationwide study of irAEs requiring hospitalization in the USA identified the real-world epidemiology, risk factors, and treatment patterns of these irAEs which may guide treatment and management decisions.

Clinicians for CARE: A Systematic Review and Meta-Analysis of Interventions to Support Caregivers of Patients With Heart Disease.

Background Caregivers provide critical support for patients with chronic diseases, including heart disease, but often experience caregiver stress that negatively impacts their health, quality of life, and patient outcomes. We aimed to inform health care teams on an evidence-based approach to supporting the caregivers of patients with heart disease. Methods and Results We conducted a systematic review and meta-analysis of randomized controlled trials written in English that evaluated interventions to support caregivers of patients with heart disease. We identified 15,561 articles as of April 2, 2020 from 6 databases; of which 20 unique randomized controlled trials were evaluated, representing a total of 1570 patients and 1776 caregivers. Most interventions focused on improving quality of life, and reducing burden, depression, and anxiety; 85% (17 of 20) of the randomized controlled trials provided psychoeducation for caregivers. Interventions had mixed results, with moderate non-significant effects observed for depression (Hedges' g=-0.64; 95% CI, -1.34 to 0.06) and burden (Hedges' g=-0.51; 95% CI, -2.71 to 1.70) at 2 to 4 months postintervention and small non-significant effects observed for quality of life and anxiety. These results were limited by the heterogeneity of outcome measures and intervention delivery methods. A qualitative synthesis of major themes of the interventions resulted in clinical recommendations represented with the acronym "CARE" (Caregiver-Centered, Active engagement, Reinforcement, Education). Conclusions This systematic review highlights the need for greater understanding of the challenges faced by caregivers and the development of guidelines to help clinicians address those challenges. More research is necessary to develop clinical interventions that consistently improve caregiver outcomes.

Transcriptomic analysis of atopic dermatitis in African Americans is characterized by Th2/Th17-centered cutaneous immune activation.

Atopic dermatitis (AD) often presents more severely in African Americans (AAs) and with greater involvement of extensor areas. To investigate immune signatures of AD in AAs with moderate to severe pruritus, lesional and non-lesional punch biopsies were taken from AA patients along with age-, race-, and sex-matched controls. Histology of lesional skin showed psoriasiform dermatitis and spongiotic dermatitis, suggesting both Th2 and Th17 activity. Gene Set Variation Analysis showed upregulation of Th2 and Th17 pathways in both lesional versus non-lesional and lesional versus control (p < 0.01), while Th1 and Th22 upregulation were observed in lesional versus control (p < 0.05). Evidence for a broad immune signature also was supported by upregulated Th1 and Th22 pathways, and clinically may represent greater severity of AD in AA. Furthermore, population-level analysis of data from TriNetX, a global federated health research network, revealed that AA AD patients had higher values for CRP, ferritin, and blood eosinophils compared to age-, sex-, and race-matched controls as well as white AD patients, suggesting broad systemic inflammation. Therefore, AA AD patients may feature broader immune activation than previously thought and may derive benefit from systemic immunomodulating therapies that modulate key drivers of multiple immune pathways.