A Mobile App For Delirium Screening

Objective: The objective of this study is to describe the algorithm and technical implementation of a mobile app that uses adaptive testing to assess an efficient mobile app for the diagnosis of delirium. Materials and Methods: The app was used as part of a NIH-funded project to assess the feasibility, effectiveness, administration time, and costs of the 2-step delirium identification protocol when performed by physicians and nurses, and certified nursing assistants (CNA). The cohort included 535 hospitalized patients aged 79.7 (SD¼6.6) years enrolled at 2 different sites. Each patient was assessed on 2 consecutive days by the research associate who performed the reference delirium assessment. Thereafter, physicians, nurses, and CNAs performed adaptive delirium assessments using the app. Qualitative data to assess the experience of administering the 2-step protocol, and the app usability were also collected and analyzed from 50 physicians, 189 nurses, and 83 CNAs. We used extensible hypertext markup language (XHTML) and JavaScript to develop the app for the iOS–based iPad. The App was linked to Research Electronic Data Capture (REDCap), a relational database system, via a REDCap application programming interface (API) that sent and received data from/to the app. The data from REDCap were sent to the Statistical Analysis System for statistical analysis. Results: The app graphical interface was successfully implemented by XHTML and JavaScript. The API facilitated the instant updating and retrieval of delirium status data between REDCap and the app. Clinicians performed 881 delirium assessments using the app for 535 patients. The transmission of data between the app and the REDCap system showed no errors. Qualitative data indicated that the users were enthusiastic about using the app with no negative comments, 82% positive comments, and 18% suggestions of improvement. Delirium administration time for the 2-step protocol showed similar total time between nurses and physicians (103.9 vs 106.5 seconds). Weekly enrollment reports of the app data were generated for study tracking purposes, and the data are being used for statistical analyses for publications. Discussion: The app developed using iOS could be easily converted to other operating systems such as Android and could be linked to other relational databases beside REDCap, such as electronic health records to facilitate better data retrieval and updating of patient’s delirium status

Older adults’ goals of care in the emergency department setting: A qualitative study guided by the 4Ms framework

Abstract Background We sought to identify what matters to older adults (60 years and older) presenting to the emergency department (ED) and the challenges or concerns they identify related to medication, mobility, and mentation to inform how the 4Ms framework could improve care of older adults in the ED setting. Methods A qualitative study was conducted using the 4Ms to identify what matters to older adults (≥60 years old) presenting to the ED and what challenges or concerns they identify related to medication, mobility, and mentation. We conducted semi‐structured interviews with a convenience sample of patients in a single ED. Interview guide responses and interviewer field notes were entered into REDCap. Interviews were reviewed by the research team (2 coders per interview) who inductively assigned codes. A codebook was created through an iterative process and was used to group codes into themes and sub‐themes within the 4Ms framework. Results A total of 20 ED patients participated in the interviews lasting 30–60 minutes. Codes identified for “what matters” included problem‐oriented expectation, coordination and continuity, staying engaged, being with family, and getting back home. Codes related to the other 4Ms (medication, mobility, and mentation) described challenges. Medication challenges included: non‐adherence, side effects, polypharmacy, and knowledge. Mobility challenges included physical activity and independence. Last, mentation challenges included memory concerns, depressed mood, and stress and worry. Conclusions Our study used the 4Ms to identify “what matters” to older adults presenting to the ED and the challenges they face regarding medication, mobility, and mentation. Understanding what matters to patients and the specific challenges they face can help shape and individualize a patient‐centered approach to care to facilitate the goals of care discussion and handoff to the next care team

Usability of the 4Ms Worksheet in the Emergency Department for Older Patients: A Qualitative Study

Introduction: Older adults often have multiple comorbidities; therefore, they are at high risk for adverse events after discharge. The 4Ms framework—what matters, medications, mentation, mobility—has been used in acute and ambulatory care settings to identify risk factors for adverse events in older adults, although it has not been used in the emergency department (ED).Weaimed to determine whether 1) use of the 4Ms worksheet would help emergency clinicians understand older adult patients’ goals of care and 2) use of the worksheet was feasible in the ED. Methods: We conducted a qualitative, descriptive study among patients aged ≥60 years and emergency clinicians from January–June 2022. Patients were asked to fill out a 4Ms worksheet; following this, semi-structured interviews were conducted with patients and clinicians separately. We analysed data to create codes, which were divided into categories and sub-categories. Results: A total of 20 older patients and 19 emergency clinicians were interviewed. We identified two categories based on our aims: understanding patient goals of care (sub-categories: clinician/ patient concordance; understanding underlying goals of care; underlying goals of care discrepancy) and use of 4Ms Worksheet (sub-categories: worksheet to discussion discrepancy; challenges using worksheet; challenge completing worksheet before discharge). Rates of concordance between patient and clinician on main concern/goal of care and underlying goals of care were 82.4% and 15.4%, respectively. Conclusion: We found that most patients and emergency clinicians agreed on the main goal of care, although clinicians often failed to elicit patients’ underlying goal(s) of care. Additionally, many patients preferred to have the interviewer fill out the worksheet for them. There was often discrepancy between what was written and what was discussed with the interviewer. More research is needed to determine the best way to integrate the 4Ms framework within emergency care

Dynamic changes of injured myocardium very early after acute myocardial infarction quantified using T1 mapping cardiovascular magnetic resonance technique

Background It has recently been suggested that myocardial oedema follows a bimodal pattern early post ST-segment elevation myocardial infarction (STEMI). Yet, water content, quantified using tissue desiccation, did not return to normal values unlike oedema quantified by cardiovascular magnetic resonance (CMR) imaging. We studied the temporal changes in the extent and intensity of injured myocardium using T1-mapping technique within the first week after STEMI. Methods A first group (n = 31) underwent 3 acute 3 T CMR scans (time-point (TP) and#60; 3 h, 24 h and 6 days), including cine, native shortened modified look-locker inversion recovery T1 mapping, T2* mapping and late gadolinium enhancement (LGE). A second group (n = 17) had a single scan at 24 h with an additional T2-weighted sequence to assess the difference in the extent of area-at-risk (AAR) compared to T1-mapping. Results The mean T1 relaxation time value within the AAR of the first group was reduced after 24 h (P and#60; 0.001 for TP1 vs.TP2) and subsequently increased at 6 days (P = 0.041 for TP2 vs.TP3). However, the extent of AAR quantified using T1-mapping did not follow the same course, and no change was detected between TP1andTP2 (P = 1.0) but was between TP2 andTP3 (P = 0.019). In the second group, the extent of AAR was significantly larger on T1-mapping compared to T2-weighted (42 ± 15% vs. 39 ± 15%, P = 0.025). No change in LGE was detected while microvascular obstruction and intra-myocardial haemorrhage peaked at different time points within the first week of reperfusion. Conclusion The intensity of oedema post-STEMI followed a bimodal pattern; while the extent of AAR did not track the same course. This discrepancy has implications for use of CMR in this context and may explain the previously reported disagreement between oedema quantified by imaging and tissue desiccation