BMJ Surgery Interventions Health Technologies最新文献

Objectives: To examine the current state of unique device identifier (UDI) implementation, including barriers and facilitators, among eight health systems participating in a research network committed to real-world evidence (RWE) generation for medical devices.

Design: Mixed methods, including a structured survey and semistructured interviews.

Setting: Eight health systems participating in the National Evaluation System for health Technology research network within the USA.

Participants: Individuals identified as being involved in or knowledgeable about UDI implementation or medical device identification from supply chain, information technology and high-volume procedural area(s) in their health system.

Main outcomes measures: Interview topics were related to UDI implementation, including barriers and facilitators; UDI use; benefits of UDI adoption; and vision for UDI implementation. Data were analysed using directed content analysis, drawing on prior conceptual models of UDI implementation and the Exploration, Preparation, Implementation, Sustainment framework. A brief survey of health system characteristics and scope of UDI implementation was also conducted.

Results: Thirty-five individuals completed interviews. Three of eight health systems reported having implemented UDI. Themes identified about barriers and facilitators to UDI implementation included knowledge of the UDI and its benefits among decision-makers; organisational systems, culture and networks that support technology and workflow changes; and external factors such as policy mandates and technology. A final theme focused on the availability of UDIs for RWE; lack of availability significantly hindered RWE studies on medical devices.

Conclusions: UDI adoption within health systems requires knowledge of and impetus to achieve operational and clinical benefits. These are necessary to support UDI availability for medical device safety and effectiveness studies and RWE generation.

Objectives: Perioperative nutrition aims to replenish nutritional stores before surgery and reduce postoperative complications. 'Immunonutrition' (including omega-3 fatty acids) may modulate the immune system and attenuate the postoperative inflammatory response. Hitherto, immunonutrition has overwhelmingly been administered in the postoperative period-however, this may be too late to provide benefit.

Design: A systematic literature search using MEDLINE and EMBASE for randomized controlled trials (RCTs).

Setting: Perioperative major gastrointestinal surgery.

Participants: Patients undergoing major gastrointestinal surgery.

Interventions: Omega-3 fatty acid supplementation commenced in the preoperative period, with or without continuation into postoperative period.

Main outcome measures: The effect of preoperative omega-3 fatty acids on inflammatory response and clinical outcomes.

Results: 833 studies were identified. After applying inclusion and exclusion criteria, 12 RCTs, involving 1456 randomized patients, were included. Ten articles exclusively enrolled patients with cancer. Seven studies used a combination of EPA (eicosapentaenoic acid) and DHA (docosahexaenoic acid) as the intervention and five studies used EPA alone. Eight out of 12 studies continued preoperative nutritional support into the postoperative period.Of the nine studies reporting mortality, no difference was seen. Duration of hospitalisation ranged from 4.5 to 18 days with intervention and 3.5 to 23.5 days with control. Omega-3 fatty acids had no effect on postoperative C-reactive protein and the effect on cytokines (including tumor necrosis factor-α, interleukin (IL)-6 and IL-10) was inconsistent. Ten of the 12 studies had low risk of bias, with one study having moderate bias from allocation and blinding.

Conclusions: There is insufficient evidence to support routine preoperative omega-3 fatty acid supplementation for major gastrointestinal surgery, even when this is continued after surgery.

Prospero registration number: CRD42018108333.

Objectives: Knee osteoarthritis is a major cause of physical disability and reduced quality of life, with end-stage disease often treated by total knee replacement (TKR). We set out to develop and externally validate a machine learning model capable of predicting the need for a TKR in 2 and 5 years time using routinely collected health data.

Design: A prospective study using datasets Osteoarthritis Initiative (OAI) and the Multicentre Osteoarthritis Study (MOST). OAI data were used to train the models while MOST data formed the external test set. The data were preprocessed using feature selection to curate 45 candidate features including demographics, medical history, imaging assessments, history of intervention and outcome.

Setting: The study was conducted using two multicentre USA-based datasets of participants with or at high risk of knee OA.

Participants: The study excluded participants with at least one existing TKR. OAI dataset included participants aged 45-79 years of which 3234 were used for training and 809 for internal testing, while MOST involved participants aged 50-79 and 2248 were used for external testing.

Main outcome measures: The primary outcome of this study was prediction of TKR onset at 2 and 5 years. Performance was evaluated using area under the curve (AUC) and F1-score and key predictors identified.

Results: For the best performing model (gradient boosting machine), the AUC at 2 years was 0.913 (95% CI 0.876 to 0.951), and at 5 years 0.873 (95% CI 0.839 to 0.907). Radiographic-derived features, questionnaire-based assessments alongside the patient's educational attainment were key predictors for these models.

Conclusions: Our approach suggests that routinely collected patient data are sufficient to drive a predictive model with a clinically acceptable level of accuracy (AUC>0.7) and is the first such tool to be externally validated. This level of accuracy is higher than previously published models utilising MRI data, which is not routinely collected.

Objectives: This study aimed to identify distinct clusters of very elderly kidney transplant recipients aged ≥80 and assess clinical outcomes among these unique clusters.

Design: Cohort study with machine learning (ML) consensus clustering approach.

Setting and participants: All very elderly (age ≥80 at time of transplant) kidney transplant recipients in the Organ Procurement and Transplantation Network/United Network for Organ Sharing database database from 2010 to 2019.

Main outcome measures: Distinct clusters of very elderly kidney transplant recipients and their post-transplant outcomes including death-censored graft failure, overall mortality and acute allograft rejection among the assigned clusters.

Results: Consensus cluster analysis was performed in 419 very elderly kidney transplant and identified three distinct clusters that best represented the clinical characteristics of very elderly kidney transplant recipients. Recipients in cluster 1 received standard Kidney Donor Profile Index (KDPI) non-extended criteria donor (ECD) kidneys from deceased donors. Recipients in cluster 2 received kidneys from older, hypertensive ECD deceased donors with a KDPI score ≥85%. Kidneys for cluster 2 patients had longer cold ischaemia time and the highest use of machine perfusion. Recipients in clusters 1 and 2 were more likely to be on dialysis at the time of transplant (88.3%, 89.4%). Recipients in cluster 3 were more likely to be preemptive (39%) or had a dialysis duration less than 1 year (24%). These recipients received living donor kidney transplants. Cluster 3 had the most favourable post-transplant outcomes. Compared with cluster 3, cluster 1 had comparable survival but higher death-censored graft failure, while cluster 2 had lower patient survival, higher death-censored graft failure and more acute rejection.

Conclusions: Our study used an unsupervised ML approach to cluster very elderly kidney transplant recipients into three clinically unique clusters with distinct post-transplant outcomes. These findings from an ML clustering approach provide additional understanding towards individualised medicine and opportunities to improve care for very elderly kidney transplant recipients.

Objectives: Brain tumours lead to significant morbidity including a neurocognitive, physical and psychological burden of disease. The extent to which they impact the multiple domains of health is difficult to capture leading to a significant degree of unmet needs. Mobile health tools such as Vinehealth have the potential to identify and address these needs through real-world data generation and delivery of personalised educational material and therapies. We aimed to establish the feasibility of Vinehealth integration into brain tumour care, its ability to collect real-world and (electronic) patient-recorded outcome (ePRO) data, and subjective improvement in care.

Design: A mixed-methodology IDEAL stage 1 study.

Setting: A single tertiary care centre.

Participants: Six patients consented and four downloaded and engaged with the mHealth application throughout the 12 weeks of the study.

Main outcome measures: Over a 12-week period, we collected real-world and ePRO data via Vinehealth. We assessed qualitative feedback from mixed-methodology surveys and semistructured interviews at recruitment and after 2 weeks.

Results: 565 data points were captured including, but not limited to: symptoms, activity, well-being and medication. EORTC QLQ-BN20 and EQ-5D-5L completion rates (54% and 46%) were impacted by technical issues; 100% completion rates were seen when ePROs were received. More brain cancer tumour-specific content was requested. All participants recommended the application and felt it improved care.

Conclusions: Our findings indicate value in an application to holistically support patients living with brain cancer tumours and established the feasibility and safety of further studies to more rigorously assess this.

Objectives: A multistakeholder expert group under the Women's Health Technology Coordinated Registry Network (WHT-CRN) was organized to develop the foundation for national infrastructure capturing the performance of long-acting and permanent contraceptives. The group, consisting of representatives from professional societies, the US Food and Drug Administration, academia, industry and the patient community, was assembled to discuss the role and feasibility of the CRN and to identify the core data elements needed to assess contraceptive medical product technologies.

Design: We applied a Delphi survey method approach to achieve consensus on a core minimum data set for the future CRN. A series of surveys were sent to the panel and answered by each expert anonymously and individually. Results from the surveys were collected, collated and analyzed by a study design team from Weill Cornell Medicine. After the first survey, questions for subsequent surveys were based on the analysis process and conference call discussions with group members. This process was repeated two times over a 6-month time period until consensus was achieved.

Results: Twenty-three experts participated in the Delphi process. Participation rates in the first and second round of the Delphi survey were 83% and 100%, respectively. The working group reached final consensus on 121 core data elements capturing reproductive/gynecological history, surgical history, general medical history, encounter information, long-acting/permanent contraceptive index procedures and follow-up, procedures performed in conjunction with the index procedure, product removal, medications, complications related to the long-acting and/or permanent contraceptive procedure, pregnancy and evaluation of safety and effectiveness outcomes.

Conclusions: The WHT-CRN expert group produced a consensus-based core set of data elements that allow the study of current and future contraceptives. These data elements influence patient and provider decisions about treatments and include important outcomes related to safety and effectiveness of these medical devices, which may benefit other women's health stakeholders.