Citations

The World Health Organization’s Operational Framework for Climate Resilient and Low Carbon Health Systems outlines 10 broad components for the development and maintenance of health systems that respond effectively to climate-related challenges. This work analyses the WHO framework from a participatory health informatics perspective and identifies specific initiatives involving non-professionals that align with the framework. Citizen and patient participation in the development of climate-resilient health systems is not only possible but also necessary to maximize climate resiliency at the community and global levels.

Patients with low bone mineral density (BMD) face an increased risk of fractures, yet are frequently undiagnosed. Consequently, it is imperative to have opportunistically screen for low BMD in patients undergoing other medical evaluations. This retrospective study encompassed 422 patients aged ≥ 50 who underwent both dual-energy X-ray absorptiometry (DXA) and hand radiographs (modality of digital X-ray) from three different vendors within a 12-month period. The dataset was randomly divided into training/validation (n=338) and test (n=84) datasets. we sought to predict osteoporosis/osteopenia and establish correlations between bone textural analysis and DXA measurements. Our results demonstrate that the deep learning model achieved an accuracy of 77.38%, sensitivity of 77.38%, specificity of 73.63%, and an area under the curve (AUC) of 83% in detecting osteoporosis/osteopenia. These findings suggest that hand radiographs can serve as a viable screening tool for identifying individuals warranting formal DXA assessment for osteoporosis/osteopenia.

Childhood mental health problems are a leading cause of disability and frequently go untreated. Barriers to children receiving the most effective care available include shortfalls in three areas: identification, referral to specialists, and delivery of evidence-based treatment (EBT). The current paper details an effort to develop a digital health intervention, the Mental Health Advisor (MHA), to increase the number of children with mental health problems who receive optimal care through identification, specialty referral, and fidelity to EBT. We present this pilot as a case example to help guide other efforts to improve mental health care through technology.

Abstract Objective To develop machine learning models using patient and migraine features that can predict treatment responses to commonly used migraine preventive medications. Background Currently, there is no accurate way to predict response to migraine preventive medications, and the standard trial‐and‐error approach is inefficient. Methods In this cohort study, we analyzed data from the Mayo Clinic Headache database prospectively collected from 2001 to December 2023. Adult patients with migraine completed questionnaires during their initial headache consultation to record detailed clinical features and then at each follow‐up to track preventive medication changes and monthly headache days. We included patients treated with at least one of the following migraine preventive medications: topiramate, beta‐blockers (propranolol, metoprolol, atenolol, nadolol, timolol), tricyclic antidepressants (amitriptyline, nortriptyline), verapamil, gabapentin, onabotulinumtoxinA, and calcitonin gene‐related peptide (CGRP) monoclonal antibodies (mAbs) (erenumab, fremanezumab, galcanezumab, eptinezumab). We pre‐trained a deep neural network, “TabNet,” using 145 variables, then employed TabNet‐embedded data to construct prediction models for each medication to predict binary outcomes (responder vs. non‐responder). A treatment responder was defined as having at least a 30% reduction in monthly headache days from baseline. All model performances were evaluated, and metrics were reported in the held‐out test set (train 85%, test 15%). SHapley Additive exPlanations (SHAP) were conducted to determine variable importance. Results Our final analysis included 4260 patients. The responder rate for each medication ranged from 28.7% to 34.9%, and the mean time to treatment outcome for each medication ranged from 151.3 to 209.5 days. The CGRP mAb prediction model achieved a high area under the receiver operating characteristics curve (AUC) of 0.825 (95% confidence interval [CI] 0.726, 0.920) and an accuracy of 0.80 (95% CI 0.70, 0.88). The AUCs of prediction models for beta‐blockers, tricyclic antidepressants, topiramate, verapamil, gabapentin, and onabotulinumtoxinA were: 0.664 (95% CI 0.579, 0.745), 0.611 (95% CI 0.562, 0.682), 0.605 (95% CI 0.520, 0.688), 0.673 (95% CI 0.569, 0.724), 0.628 (0.533, 0.661), and 0.581 (95% CI 0.550, 0.632), respectively. Baseline monthly headache days, age, body mass index (BMI), duration of migraine attacks, responses to previous medication trials, cranial autonomic symptoms, family history of headache, and migraine attack triggers were among the most important variables across all models. A variable could have different contributions; for example, lower BMI predicts responsiveness to CGRP mAbs and beta‐blockers, while higher BMI predicts responsiveness to onabotulinumtoxinA, topiramate, and gabapentin. Conclusion We developed an accurate prediction model for CGRP mAbs treatment response, leveraging detailed migraine features gathered from a headache questionnaire before starting treatment. Employing the same methods, the model performances for other medications were less impressive, though similar to the machine learning models reported in the literature for other diseases. This may be due to CGRP mAbs being migraine‐specific. Incorporating medical comorbidities, genomic, and imaging factors might enhance the model performance. We demonstrated that migraine characteristics are important in predicting treatment responses and identified the most crucial predictors for each of the seven types of preventive medications. Our results suggest that precision migraine treatment is feasible.

Microvascular remodeling and capillary repopulation can occur after autologous hematopoietic stem cell transplant (HSCT) in patients with systemic sclerosis and systemic lupus erythematosus (SLE). We aim to report evidence for microvascular remodeling after autologous HSCT as observed by nailfold videocapillaroscopy (NVC). We describe a rare occurrence of features consistent with systemic sclerosis and SLE in a 33-year-old female with a complex clinical course refractory to conventional treatments, ultimately requiring autologous HSCT. We performed NVC before and after HSCT using optical video and light microscopy. At the microvascular level, morphologic changes in the capillary vascular bed were observed after HSCT. Pretransplant damage in capillary structure was noted as evidenced on NVC with architectural loss, ramifications, capillary drop, and decreased density. Posttransplant NVC revealed an increase in capillary density with evidence of microvascular remodeling. Further studies on the clinical use and impact of microvascular remodeling on disease progression are needed and looking into the application of NVC scoring to assess clinical response would be meaningful.

It is well known that individuals with liver cirrhosis are considered at high risk for cardiac surgery, with an increased risk for morbidity and mortality as the liver disease progresses. In the last decade, there have been considerable advances in transcatheter aortic valve implantation (TAVI) as an alternative to surgical aortic valve replacement (SAVR) in individuals deemed to be at high risk for surgery. However, research surrounding TAVI in the setting of liver cirrhosis has not been as widely studied. In this national population-based cohort study, we evaluated the trends of mortality, complications, and healthcare utilization in liver cirrhotic patients undergoing TAVI, as well as analyzed the basic demographics of these individuals. We found that from 2011 to 2020, the amount of TAVI procedures conducted in cirrhotic patients was increasing annually, while mortality, procedural complications, and healthcare utilization trends in these cirrhotic patients undergoing TAVI decreased. Overall, TAVI does seem to be reasonable management for aortic stenosis patients with liver cirrhosis who need aortic valve replacement.

Introduction Patient self-scheduling of medical appointments is becoming more common in many medical institutions. However, the complexity of scheduling multiple specialties, following scheduling guidelines, and managing appointment access requires a variety of processes for a diverse inventory of self-schedulable appointment types. Methods From 7 unique patient self-scheduling methods, we captured counts of successfully self-scheduled and completed appointments. A process map was created to show the paths of 5 different primary self-scheduling processes (new appointment self-scheduling) and 2 secondary self-scheduling processes (existing appointment self-rescheduling). Results There were 7 unique processes that led to 733,651 successfully self-scheduled completed visits from January 1 to December 31, 2023 at a multisite, multispecialty clinic. The self-scheduling processes consisted of the following: (1) Ticket offer (appointment “ticket” offers for specific visits generated by a provider order or system rules), the software “ticket” sent to the patient permits “admission” to self-schedule calendar templates (341,591 uses, 46.6%); (2) direct self-scheduled visit for prequalified visit types (203,593 uses, 27.6%); (3) self-reschedule option (patient option to reschedule existing appointment, 79,706 uses, 10.9%); (4) new patient self-scheduled visit via clinic website (does not require portal access, 54,367 uses, 7.4%). (5) automated waitlist self-rescheduled visit (38,649 uses, 5.3%); (6) automated waitlist self-scheduled visit of previously unscheduled visit (10,939 uses, 1.5%); and (7) self-triage self-scheduled visit (4806 uses, 0.7%). Conclusion The processes for self-scheduling are expanding. Our multispecialty clinic has implemented 7 different processes to help patients successfully self-schedule medical appointments. Some of the processes occur before initial scheduling (such as self-triage), and some are implemented after successful scheduling has already occurred (self-rescheduling option and self-rescheduling aided by an automated waitlist). Continued research is needed to look for measures of success beyond the ability to complete a self-scheduled visit, including the accuracy of the booking (right provider, location, and length of visit).

Spontaneous coronary artery dissection (SCAD) can be treated conservatively. However, some SCAD patients can develop cardiogenic shock (CS). We evaluated the outcomes of SCAD-related CS using data from a national population-based cohort study from January 1, 2016, to December 30, 2019. In our study of 32,640 patients with SCAD, about 10.6% of patients presented with CS. We found that SCAD patients with CS had higher mortality and greater complications including use of mechanical circulatory devices, arrhythmias, respiratory support, and acute heart failure compared to those without CS. When comparing CS due to SCAD with that due to coronary artery disease, we found that although mortality rates were similar, those with CS due to SCAD were associated with higher risk of use of mechanical circulatory support, major bleeding, blood transfusion, and respiratory failure.