Citations

Aim: AI research, development, and implementation are expanding at an exponential pace across healthcare. This paradigm shift in healthcare research has led to increased demands for clinical outcomes, all at the expense of a significant gap in AI literacy within the healthcare field. This has further translated to a lack of tools in creating a framework for literature in the AI in medicine domain. We propose HUMANE (Harmonious Understanding of Machine Learning Analytics Network), a checklist for establishing an international consensus for authors and reviewers involved in research focused on artificial intelligence (AI) or machine learning (ML) in medicine. Methods: This study was conducted using the Delphi method by devising a survey using the Google Forms platform. The survey was developed as a checklist containing 8 sections and 56 questions with a 5-point Likert scale. Results: A total of 33 survey respondents were part of the initial Delphi process with the majority (45%) in the 36–45 years age group. The respondents were located across the USA (61%), UK (24%), and Australia (9%) as the top 3 countries, with a pre-dominant healthcare background (42%) as early-career professionals (3–10 years’ experience) (42%). Feedback showed an overall agreeable consensus (mean ranges 4.1–4.8, out of 5) as cumulative scores throughout all sections. The majority of the consensus was agreeable with the Discussion (Other) section of the checklist (median 4.8 (interquartile range (IQR) 4.8-4.8)), whereas the least agreed section was the Ground Truth (Expert(s) review) section (median 4.1 (IQR 3.9–4.2)) and the Methods (Outcomes) section (median 4.1 (IQR 4.1–4.1)) of the checklist. The final checklist after consensus and revision included a total of 8 sections and 50 questions. Conclusions: The HUMANE international consensus has reflected on further research on the potential of this checklist as an established consensus in improving the reliability and quality of research in this field.

OBJECTIVES: There is a paucity of literature regarding administrative approvals required for clinical studies during a pandemic. We aimed to evaluate variation in duration of administrative approvals within the Viral Infection and Respiratory illness Universal Study (VIRUS): A Global COVID-19 Registry. DESIGN, SETTING, AND SUBJECTS: Survey analysis of 188 investigators who participated in the VIRUS: COVID-19 registry, a prospective, observational global registry database of 287 sites. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: For each study site approved through December 8, 2020, we assessed the duration in days: 1) from institutional review board (IRB) submission to IRB approval, 2) from IRB approval to Research Electronic Data Capture (REDCap) access, 3) from REDCap access to first patient data entry in REDCap, and 4) total duration from IRB submission to first patient data entry in REDCap. Analysis of variance and Wilcoxon rank-sum test were used to compare time durations. Of 287 sites, 188 sites (United States = 155, non-United States = 33) provided complete administrative data. There was considerable variability in duration from IRB submission to first patient data entry with median (interquartile range) of 28 days (16–50 d), with differences not significantly different by country (United States: 30 [17–50] vs non-United States: 23 d [8–46 d]; p = 0.08) or previous “multisite trial experience” (experienced: 27 [15–51] vs not experienced: 29 d [13–47 d]; p = 0.67). The U.S. sites had a higher proportion of female principal investigators (n = 77; 50%), compared with non-U.S. sites (n = 7; 21%; p = 0.002). Non-U.S. sites had a significantly shorter time to first patient data entry after REDCap access: 7 (1–28) versus 3 days (1–6 d) (p = 0.02). CONCLUSIONS: In this Society of Critical Care Medicine global VIRUS: COVID-19 Registry, we identified considerable variability in time from IRB submission to first patient data entry with no significant differences by country or prior multicenter trial experience. However, there was a significant difference between US and non-U.S. sites in the time from REDCap access to first data entry.

Objectives To describe incidence and risk factors of loss of previous independent living through nonhome discharge or discharge home with health assistance in survivors of intensive care unit (ICU) admission for coronavirus disease 2019 (COVID-19). Design Multicenter observational study including patients admitted to the ICU from January 2020 till June 30, 2021. Hypothesis We hypothesized that there is a high risk of nonhome discharge in patients surviving ICU admission due to COVID-19. Setting Data were included from 306 hospitals in 28 countries participating in the SCCM Discovery Viral Infection and Respiratory Illness Universal Study COVID-19 registry. Patients Previously independently living adult ICU survivors of COVID-19. Interventions None. Measurements and Main Results The primary outcome was nonhome discharge. Secondary outcome was the requirement of health assistance among patients who were discharged home. Out of 10 820 patients, 7101 (66%) were discharged alive; 3791 (53%) of these survivors lost their previous independent living status, out of those 2071 (29%) through nonhome discharge, and 1720 (24%) through discharge home requiring health assistance. In adjusted analyses, loss of independence on discharge among survivors was predicted by patient age ≥ 65 years (adjusted odds ratio [aOR] 2.78, 95% confidence interval [CI] 2.47-3.14, P < .0001), former and current smoking status (aOR 1.25, 95% CI 1.08-1.46, P = .003 and 1.60 (95% CI 1.18-2.16), P = .003, respectively), substance use disorder (aOR 1.52, 95% CI 1.12-2.06, P = .007), requirement for mechanical ventilation (aOR 4.17, 95% CI 3.69-4.71, P < .0001), prone positioning (aOR 1.19, 95% CI 1.03-1.38, P = .02), and requirement for extracorporeal membrane oxygenation (aOR 2.28, 95% CI 1.55-3.34, P < .0001). Conclusions More than half of ICU survivors hospitalized for COVID-19 are unable to return to independent living status, thereby imposing a significant secondary strain on health care systems worldwide.

Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision‐making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. Methods and Results The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension‐related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real‐time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Conclusions Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption.

AbstractDemodex folliculorum is a commensal mite that inhabits the orifices of cutaneous pilosebaceous follicles. Overgrowth of these organisms can lead to Demodex folliculitis, which typically presents as papules and pustules predominantly involving the temples, cheeks, and occasionally the chest. We present a 51‐year‐old woman with iatrogenic Demodex folliculitis secondary to immunosuppressive treatment for an autoimmune connective tissue disease. Histopathological exam of a skin biopsy, which revealed follicular Demodex mites, confirmed the diagnosis. The eruption was treated with oral ivermectin and topical metronidazole gel, and the patient's immunosuppressive regimen was decreased, resulting in marked improvement in the eruption within 6 weeks and no worsening of her underlying autoimmune disorder. This case emphasizes the importance of considering Demodex folliculitis in the differential diagnosis of a new onset rash in the context of immunosuppressive treatment.

Background: There is a paucity of multicenter data describing the impact of coronavirus disease 2019 (COVID-19) on hospitalized pediatric oncology patients. Using a large, multicenter, Society of Critical Care Medicine (SCCM) Discovery Viral Infection and Respiratory Illness University Study (VIRUS) database, we aimed at assessing outcomes of COVID-19 infection in this population. Method: This is a matched-cohort study involving children below 18 years of age hospitalized with COVID-19 between March 2020 and January 2021. Using the VIRUS; COVID-19 Registry database, children with oncologic diseases were compared with propensity score matched (age groups, sex, race, and ethnicity) cohort of children without oncologic diseases for the prevalence of Multisystem Inflammatory Syndrome in Children (MIS-C), intensive care unit (ICU) admission, interventions, hospital, and ICU length of stay. Results: The number of children in the case and control groups was 45 and 180, respectively. ICU admission rate was similar in both groups ([47.7 vs 51.7%], P=0.63). The proportion of children requiring noninvasive and invasive mechanical ventilation, and its duration were similar between groups, same as hospital mortality. Interestingly, MIS-C was significantly lower in the oncology group compared with the control (2.4 vs 24.6%; P=0.0002). Conclusions: In this study using a multicenter VIRUS database, ICU admission rate, interventions, and outcomes of COVID-19 were similar in children with the oncologic disease compared with control patients. The incidence of MIS-C is lower in oncologic patients.

ImportanceThere is limited evidence for therapeutic options for pediatric COVID-19 outside of multisystem inflammatory syndrome in children (MIS-C).ObjectiveTo determine whether the use of steroids within 2 days of admission for non–MIS-C COVID-19 in children is associated with hospital length of stay (LOS). The secondary objective was to determine their association with intensive care unit (ICU) LOS, inflammation, and fever defervescence.Design, Setting, and ParticipantsThis cohort study analyzed data retrospectively for children (&amp;lt;18 years) who required hospitalization for non–MIS-C COVID-19. Data from March 2020 through September 2021 were provided by 58 hospitals in 7 countries who participate in the Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study (VIRUS) COVID-19 registry.ExposureAdministration of steroids within 2 days of admission.Main Outcomes and MeasuresLength of stay in the hospital and ICU. Adjustment for confounders was done by mixed linear regression and propensity score matching.ResultsA total of 1163 patients met inclusion criteria and had a median (IQR) age of 7 years (0.9-14.3). Almost half of all patients (601/1163, 51.7%) were male, 33.8% (392/1163) were non-Hispanic White, and 27.9% (324/1163) were Hispanic. Of the study population, 184 patients (15.8%) received steroids within 2 days of admission, and 979 (84.2%) did not receive steroids within the first 2 days. Among 1163 patients, 658 (56.5%) required respiratory support during hospitalization. Overall, patients in the steroids group were older and had greater severity of illness, and a larger proportion required respiratory and vasoactive support. On multivariable linear regression, after controlling for treatment with remdesivir within 2 days, country, race and ethnicity, obesity and comorbidity, number of abnormal inflammatory mediators, age, bacterial or viral coinfection, and disease severity according to ICU admission within first 2 days or World Health Organization ordinal scale of 4 or higher on admission, with a random intercept for the site, early steroid treatment was not significantly associated with hospital LOS (exponentiated coefficient, 0.94; 95% CI, 0.81-1.09; P = .42). Separate analyses for patients with an LOS of 2 days or longer (n = 729), those receiving respiratory support at admission (n = 286), and propensity score–matched patients also showed no significant association between steroids and LOS. Early steroid treatment was not associated with ICU LOS, fever defervescence by day 3, or normalization of inflammatory mediators.Conclusions and RelevanceSteroid treatment within 2 days of hospital admission in a heterogeneous cohort of pediatric patients hospitalized for COVID-19 without MIS-C did not have a statistically significant association with hospital LOS.

AbstractAimsTo explore participants' experiences with and perspective of a telephone‐based, peer‐led diabetes self‐management intervention targeting adults with type 2 diabetes (T2D) from speciality care settings. We also sought to identify areas for improvement for future iterations of the intervention.MethodsThis study recruited 25 adults with T2D from the intervention arm of a randomized controlled trial of a peer support intervention for diabetes. Individuals took part in semi‐structured interviews that explored the following topics: perceived impact of the intervention, relationship with peer leader, desirable characteristics in a peer leader, and suggestions for improving the intervention. Focus groups were recorded, transcribed, quality checked, coded, and analysed to develop themes and subthemes.ResultsFour core themes emerged: (1) importance of the ‘participant‐peer leader’ match, (2) peer leader roles and responsibilities, (3) need for flexible support models, and (4) factors affecting intervention implementation and engagement. The quality of the participant‐peer leader relationship appeared to be linked to intervention satisfaction. Beyond demographic features such as age and sex, key characteristics for forming a strong match included stage of life, lifestyle, diabetes‐related factors, and communication style.ConclusionsParticipants have unique ideas about what support should look like and preferences for how support is best delivered. Future models of peer support need to be customizable to individuals' needs and responsive to changes in life circumstances. If participants are the decision makers in the matching process, they may experience greater satisfaction and derive maximal benefits.