Clinical outcomes of subtalar arthroereisis for the treatment of stage 1 flexible progressive collapsing foot deformity.

BACKGROUND: The use of subtalar arthroereisis as an adjunct to the surgical treatment of stage 1 flexible progressive collapsing foot deformity (PCFD) is controversial. The aim was to investigate the clinical outcomes and report the implant removal rate of subtalar arthroereisis as an adjunct for stage 1 PCFD. METHODS: A retrospective study of 212 consecutive feet undergoing operative management of stage 1 PCFD with adjunctive subtalar arthroereisis between October 2010 and April 2018. The primary outcome was the Foot and Ankle Outcome Score (FAOS). Secondary outcomes included Foot and Ankle Disability Index (FADI), Euroqol-5D-5L Index and implant removal rate. RESULTS: Post-operative clinical FAOS outcomes were collected for 153 feet (72.2%). At mean 2.5-year follow-up, the mean ± standard deviation FAOS for each domain was as follows; Pain: 81.5 ± 18.5, Symptoms: 79.5 ± 12.9, Activities of Daily Living: 82.5 ± 15.4 and Quality of Life: 64.2 ± 23.7. EQ-5D-5L Index was 0.884 ± 0.152. Pre-operative scores were available for 20 of these feet demonstrating a statistically significant improvement in all FAOS, FADI and EQ-5D-5L domains (p < 0.05). The implant removal rate for persistent sinus tarsi pain was 48.1% (n = 102). CONCLUSION: Use of a subtalar arthroereisis implant as an adjunct to conventional procedures in stage 1 flexible PCFD can result in significant improvement in pain and function. Patients should be counselled as to the relatively frequent rate of subsequent implant removal. LEVEL OF EVIDENCE: IV.

Assessing the Utility, Impact, and Adoption Challenges of an Artificial Intelligence-Enabled Prescription Advisory Tool for Type 2 Diabetes Management: Qualitative Study.

BACKGROUND: The clinical management of type 2 diabetes mellitus (T2DM) presents a significant challenge due to the constantly evolving clinical practice guidelines and growing array of drug classes available. Evidence suggests that artificial intelligence (AI)-enabled clinical decision support systems (CDSSs) have proven to be effective in assisting clinicians with informed decision-making. Despite the merits of AI-driven CDSSs, a significant research gap exists concerning the early-stage implementation and adoption of AI-enabled CDSSs in T2DM management. OBJECTIVE: This study aimed to explore the perspectives of clinicians on the use and impact of the AI-enabled Prescription Advisory (APA) tool, developed using a multi-institution diabetes registry and implemented in specialist endocrinology clinics, and the challenges to its adoption and application. METHODS: We conducted focus group discussions using a semistructured interview guide with purposively selected endocrinologists from a tertiary hospital. The focus group discussions were audio-recorded and transcribed verbatim. Data were thematically analyzed. RESULTS: A total of 13 clinicians participated in 4 focus group discussions. Our findings suggest that the APA tool offered several useful features to assist clinicians in effectively managing T2DM. Specifically, clinicians viewed the AI-generated medication alterations as a good knowledge resource in supporting the clinician's decision-making on drug modifications at the point of care, particularly for patients with comorbidities. The complication risk prediction was seen as positively impacting patient care by facilitating early doctor-patient communication and initiating prompt clinical responses. However, the interpretability of the risk scores, concerns about overreliance and automation bias, and issues surrounding accountability and liability hindered the adoption of the APA tool in clinical practice. CONCLUSIONS: Although the APA tool holds great potential as a valuable resource for improving patient care, further efforts are required to address clinicians' concerns and improve the tool's acceptance and applicability in relevant contexts.

Retrospective matched cohort study of incidence rates and excess length of hospital stay owing to pressure injuries in an Asian setting.

Background: Little is known about stage 1 and 2 pressure injuries that are health care-acquired. We report incidence rates of health care-acquired stage 1 and stage 2 pressure injuries, and, estimate the excess length of stay using four competing analytic methods. We discuss the merits of the different approaches. Methods: We calculated monthly incidence rates for stage 1 and 2 health care-acquired pressure injuries occurring in a large Singapore acute care hospital. To estimate excess stay, we conducted unadjusted comparisons with a control cohort, performed linear regression and then generalized linear regression with a gamma distribution. Finally, we fitted a simple state-based model. The design for the cost attribution work was a retrospective matched cohort study. Results: Incidence rates in 2016 were 0.553% (95% confidence interval [CI] 0.55, 0.557) and 0.469% (95% CI 0.466, 0.472) in 2017. For data censored at 60 days' maximum stay, the unadjusted comparisons showed the highest excess stay at 17.68 (16.43-18.93) days and multi-state models showed the lowest at 1.22 (0.19, 2.23) days. Conclusions: Poor-quality methods for attribution of excess length of stay to pressure injury generate inflated estimates that could mislead decision makers. The findings from the multi-state model, which is an appropriate method, are plausible and illustrate the likely bed-days saved from lowering the risk of these events. Stage 1 and 2 pressure injuries are common and increase costs by prolonging the length of stay. There will be economic value investing in prevention. Using biased estimates of excess length of stay will overstate the potential value of prevention.