Distance-based novelty detection model for identifying individuals at risk of developing Alzheimer's disease.

Introduction: Novelty detection (ND, also known as one-class classification) is a machine learning technique used to identify patterns that are typical of the majority class and can discriminate deviations as novelties. In the context of Alzheimer's disease (AD), ND could be employed to detect abnormal or atypical behavior that may indicate early signs of cognitive decline or the presence of the disease. To date, few research studies have used ND to discriminate the risk of developing AD and mild cognitive impairment (MCI) from healthy controls (HC). Methods: In this work, two distinct cohorts with highly heterogeneous data, derived from the Australian Imaging Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing project and the Fujian Medical University Union Hospital (FMUUH) China, were employed. An innovative framework with built-in easily interpretable ND models constructed solely on HC data was introduced along with proposing a strategy of distance to boundary (DtB) to detect MCI and AD. Subsequently, a web-based graphical user interface (GUI) that incorporates the proposed framework was developed for non-technical stakeholders. Results: Our experimental results indicate that the best overall performance of detecting AD individuals in AIBL and FMUUH datasets was obtained by using the Mixture of Gaussian-based ND algorithm applied to single modality, with an AUC of 0.8757 and 0.9443, a sensitivity of 96.79% and 89.09%, and a specificity of 89.63% and 90.92%, respectively. Discussion: The GUI offers an interactive platform to aid stakeholders in making diagnoses of MCI and AD, enabling streamlined decision-making processes. More importantly, the proposed DtB strategy could visually and quantitatively identify individuals at risk of developing AD.

Analysis of SARS-CoV-2 antibody seroprevalence in Northern Ireland during 2020-2021.

Background: With the spread of SARS-CoV-2 impacting upon public health directly and socioeconomically, further information was required to inform policy decisions designed to limit virus spread during the pandemic. This study sought to contribute to serosurveillance work within Northern Ireland to track SARS-CoV-2 progression and guide health strategy. Methods: Sera/plasma samples from clinical biochemistry laboratories were analysed for anti-SARS-CoV-2 antibodies. Samples were assessed using an Elecsys anti-SARS-CoV-2 or anti-SARS-CoV-2 S ECLIA (Roche) on an automated cobas e 801 analyser. Samples were also assessed via an anti-SARS-CoV-2 ELISA (Euroimmun). A subset of samples assessed via the Elecsys anti-SARS-CoV-2 ECLIA were subsequently analysed in an ACE2 pseudoneutralisation assay using a V-PLEX SARS-CoV-2 Panel 7 for IgG and ACE2 (Meso Scale Diagnostics). Results: Across three testing rounds (June-July 2020, November-December 2020 and June-July 2021 (rounds 1-3 respectively)), 4844 residual sera/plasma specimens were assayed for anti-SARS-CoV-2 antibodies. Seropositivity rates increased across the study, peaking at 11.6 % (95 % CI 10.4 %-13.0 %) during round 3. Varying trends in SARS-CoV-2 seropositivity were noted based on demographic factors. For instance, highest rates of seropositivity shifted from older to younger demographics across the study period. In round 3, Alpha (B.1.1.7) variant neutralising antibodies were most frequently detected across age groups, with median concentration of anti-spike protein antibodies elevated in 50-69 year olds and anti-S1 RBD antibodies elevated in 70+ year olds, relative to other age groups. Conclusions: With seropositivity rates of <15 % across the assessment period, it can be concluded that the significant proportion of the Northern Ireland population had not yet naturally contracted the virus by mid-2021.

Genomic, Proteomic, and Phenotypic Biomarkers of COVID-19 Severity: Protocol for a Retrospective Observational Study.

BACKGROUND: Health organizations and countries around the world have found it difficult to control the spread of COVID-19. To minimize the future impact on the UK National Health Service and improve patient care, there is a pressing need to identify individuals who are at a higher risk of being hospitalized because of severe COVID-19. Early targeted work was successful in identifying angiotensin-converting enzyme-2 receptors and type II transmembrane serine protease dependency as drivers of severe infection. Although a targeted approach highlights key pathways, a multiomics approach will provide a clearer and more comprehensive picture of severe COVID-19 etiology and progression. OBJECTIVE: The COVID-19 Response Study aims to carry out an integrated multiomics analysis to identify biomarkers in blood and saliva that could contribute to host susceptibility to SARS-CoV-2 and the development of severe COVID-19. METHODS: The COVID-19 Response Study aims to recruit 1000 people who recovered from SARS-CoV-2 infection in both community and hospital settings on the island of Ireland. This protocol describes the retrospective observational study component carried out in Northern Ireland (NI; Cohort A); the Republic of Ireland cohort will be described separately. For all NI participants (n=519), SARS-CoV-2 infection has been confirmed by reverse transcription-quantitative polymerase chain reaction. A prospective Cohort B of 40 patients is also being followed up at 1, 3, 6, and 12 months postinfection to assess longitudinal symptom frequency and immune response. Data will be sourced from whole blood, saliva samples, and clinical data from the electronic care records, the general health questionnaire, and a 12-item general health questionnaire mental health survey. Saliva and blood samples were processed to extract DNA and RNA before whole-genome sequencing, RNA sequencing, DNA methylation analysis, microbiome analysis, 16S ribosomal RNA gene sequencing, and proteomic analysis were performed on the plasma. Multiomics data will be combined with clinical data to produce sensitive and specific prognostic models for severity risk. RESULTS: An initial demographic and clinical profile of the NI Cohort A has been completed. A total of 249 hospitalized patients and 270 nonhospitalized patients were recruited, of whom 184 (64.3%) were female, and the mean age was 45.4 (SD 13) years. High levels of comorbidity were evident in the hospitalized cohort, with cardiovascular disease and metabolic and respiratory disorders being the most significant (P<.001), grouped according to the International Classification of Diseases 10 codes. CONCLUSIONS: This study will provide a comprehensive opportunity to study the mechanisms of COVID-19 severity in recontactable participants. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50733.

Primary Care Antibiotic Prescribing and Infection-Related Hospitalisation.

Inappropriate prescribing of antibiotics has been widely recognised as a leading cause of antimicrobial resistance, which in turn has become one of the most significant threats to global health. Given that most antibiotic prescriptions are issued in primary care settings, investigating the associations between primary care prescribing of antibiotics and subsequent infection-related hospitalisations affords a valuable opportunity to understand the long-term health implications of primary care antibiotic intervention. A narrative review of the scientific literature studying associations between primary care antibiotic prescribing and subsequent infection-related hospitalisation was conducted. The Web of Science database was used to retrieve 252 potentially relevant studies, with 23 of these studies included in this review (stratified by patient age and infection type). The majority of studies (n = 18) were published in the United Kingdom, while the remainder were conducted in Germany, Spain, Denmark, New Zealand, and the United States. While some of the reviewed studies demonstrated that appropriate and timely antibiotic prescribing in primary care could help reduce the need for hospitalisation, excessive antibiotic prescribing can lead to antimicrobial resistance, subsequently increasing the risk of infection-related hospitalisation. Few studies reported no association between primary care antibiotic prescriptions and subsequent infection-related hospitalisation. Overall, the disparate results in the extant literature attest to the conflicting factors influencing the decision-making regarding antibiotic prescribing and highlight the necessity of adopting a more patient-focussed perspective in stewardship programmes and the need for increased use of rapid diagnostic testing in primary care.

Artificial intelligence for dementia prevention.

INTRODUCTION: A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding. METHODS: ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field. RESULTS: Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics. DISCUSSION: ML is not yet widely used but has considerable potential to enhance precision in dementia prevention. HIGHLIGHTS: Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk-profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk-management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials.

Artificial intelligence for neurodegenerative experimental models.

INTRODUCTION: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials. METHODS: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research. RESULTS: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability. DISCUSSION: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data. HIGHLIGHTS: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery.

Artificial intelligence for dementia research methods optimization.

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.

Artificial Intelligence for Dementia Research Methods Optimization.

INTRODUCTION: Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater. METHODS: We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research. RESULTS: We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future. DISCUSSION: ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia.

Prophylactic onlay mesh placement techniques for optimal abdominal wall closure: randomized controlled trial in an ex vivo biomechanical model.

BACKGROUND: Incisional hernias occur after up to 40 per cent of laparotomies. Recent RCTs have demonstrated the role of prophylactic mesh placement in reducing the risk of developing an incisional hernia. An onlay approach is relatively straightforward; however, a variety of techniques have been described for mesh fixation. The biomechanical properties have not been interrogated extensively to date. METHODS: This ex vivo randomized controlled trial using porcine abdominal wall investigated the biomechanical properties of three techniques for prophylactic onlay mesh placement at laparotomy closure. A classical onlay, anchoring onlay, and novel bifid onlay approach were compared with small-bite primary closure. A biomechanical abdominal wall model and ball burst test were used to assess transverse stretch, bursting force, and loading characteristics. RESULTS: Mesh placement took an additional 7-15 min compared with standard primary closure. All techniques performed similarly, with no clearly superior approach. The minimum burst force was 493 N, and the maximum 1053 N. The classical approach had the highest mean burst force (mean(s.d.) 853(152) N). Failure patterns fell into either suture-line or tissue failures. Classical and anchoring techniques provided a second line of defence in the event of primary suture failure, whereas the bifid method demonstrated a more compliant loading curve. All mesh approaches held up at extreme quasistatic loads. CONCLUSION: Subtle differences in biomechanical properties highlight the strengths of each closure type and suggest possible uses. The failure mechanisms seen here support the known hypotheses for early fascial dehiscence. The influence of dynamic loading needs to be investigated further in future studies.

Harnessing the potential of machine learning and artificial intelligence for dementia research.

Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal data sets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention. Machine learning methods can enhance results of genetic studies, help determine biological effects and facilitate the identification of drug targets based on genetic and transcriptomic information. The use of unsupervised learning for understanding disease mechanisms for drug discovery is promising, while analysis of multimodal data sets to characterise and quantify disease severity and subtype are also beginning to contribute to optimisation of clinical trial recruitment. Data-driven experimental medicine is needed to analyse data across modalities and develop novel algorithms to translate insights from animal models to human disease biology. AI methods in neuroimaging outperform traditional approaches for diagnostic classification, and although challenges around validation and translation remain, there is optimism for their meaningful integration to clinical practice in the near future. AI-based models can also clarify our understanding of the causality and commonality of dementia risk factors, informing and improving risk prediction models along with the development of preventative interventions. The complexity and heterogeneity of dementia requires an alternative approach beyond traditional design and analytical approaches. Although not yet widely used in dementia research, machine learning and AI have the potential to unlock current challenges and advance precision dementia medicine.

Prophylactic cholecystectomy offers best outcomes following ERCP clearance of common bile duct stones: a meta-analysis.

BACKGROUND: Symptomatic calculus biliary disease is common with associated morbidity and occasional mortality, further confounded when there is concomitant common bile duct (CBD) stones. Choledocholithiasis and clearance of the duct reduces recurrent cholangitis, but the question is whether after clearance of the CBD if there is a need to perform a cholecystectomy. This meta-analysis evaluated outcomes in patients undergoing ERCP with or without sphincterotomy to determine if cholecystectomy post-ERCP clearance offers optimal outcomes over a wait-and-see approach. METHODS: A Prospero registered meta-analysis of the literature using PRISMA guidelines incorporating articles related to ERCP, choledocholithiasis, cholangitis and cholecystectomy was undertaken for papers published between 1st January 1991 and 31st May 2021. Existing research that demonstrates outcomes of ERCP with no cholecystectomy versus ERCP and cholecystectomy was reviewed to determine the related key events, complications and mortality of leaving the gallbladder in situ and removing it. Odds ratios (OR) were calculated using Review Manager Version 5.4 and meta-analyses performed using OR using fixed-effect (or random-effect) models, depending on the heterogeneity of studies. RESULTS: 13 studies (n = 2598), published between 2002 and 2019, were included in this meta-analysis, 6 retrospective, 2 propensity score-matched retrospective studies, 3 prospective studies and 2 randomised control trials from a total of 11 countries. There were 1433 in the no cholecystectomy cohort (55.2%) and 1165 in the prophylactic cholecystectomy (44.8%) cohort. Cholecystectomy resulted in a decreased risk of cholecystitis (OR = 0.15; CI 0.07-0.36; p < 0.0001), cholangitis (OR = 0.51; CI 0.26-1.00; p = 0.05) and mortality (OR = 0.38; CI 0.16-0.9; p = 0.03). In addition, prophylactic cholecystectomy resulted in a significant reduction in biliary events, biliary pain and pancreatitis. CONCLUSIONS: In patients undergoing CBD clearance, consideration should be given to performing prophylactic cholecystectomy to optimise outcomes.

The Potential of a Stratified Approach to Drug Repurposing in Alzheimer's Disease.

Alzheimer's disease (AD) is a complex neurodegenerative condition that is characterized by the build-up of amyloid-beta plaques and neurofibrillary tangles. While multiple theories explaining the aetiology of the disease have been suggested, the underlying cause of the disease is still unknown. Despite this, several modifiable and non-modifiable factors that increase the risk of developing AD have been identified. To date, only eight AD drugs have ever gained regulatory approval, including six symptomatic and two disease-modifying drugs. However, not all are available in all countries and high costs associated with new disease-modifying biologics prevent large proportions of the patient population from accessing them. With the current patient population expected to triple by 2050, it is imperative that new, effective, and affordable drugs become available to patients. Traditional drug development strategies have a 99% failure rate in AD, which is far higher than in other disease areas. Even when a drug does reach the market, additional barriers such as high cost and lack of accessibility prevent patients from benefiting from them. In this review, we discuss how a stratified medicine drug repurposing approach may address some of the limitations and barriers that traditional strategies face in relation to drug development in AD. We believe that novel, stratified drug repurposing studies may expedite the discovery of alternative, effective, and more affordable treatment options for a rapidly expanding patient population in comparison with traditional drug development methods.

Identifying pre-existing conditions and multimorbidity patterns associated with in-hospital mortality in patients with COVID-19.

We investigated the association between a wide range of comorbidities and COVID-19 in-hospital mortality and assessed the influence of multi morbidity on the risk of COVID-19-related death using a large, regional cohort of 6036 hospitalized patients. This retrospective cohort study was conducted using Patient Administration System Admissions and Discharges data. The International Classification of Diseases 10th edition (ICD-10) diagnosis codes were used to identify common comorbidities and the outcome measure. Individuals with lymphoma (odds ratio [OR], 2.78;95% CI,1.64-4.74), metastatic cancer (OR, 2.17; 95% CI,1.25-3.77), solid tumour without metastasis (OR, 1.67; 95% CI,1.16-2.41), liver disease (OR: 2.50, 95% CI,1.53-4.07), congestive heart failure (OR, 1.69; 95% CI,1.32-2.15), chronic obstructive pulmonary disease (OR, 1.43; 95% CI,1.18-1.72), obesity (OR, 5.28; 95% CI,2.92-9.52), renal disease (OR, 1.81; 95% CI,1.51-2.19), and dementia (OR, 1.44; 95% CI,1.17-1.76) were at increased risk of COVID-19 mortality. Asthma was associated with a lower risk of death compared to non-asthma controls (OR, 0.60; 95% CI,0.42-0.86). Individuals with two (OR, 1.79; 95% CI, 1.47-2.20; P < 0.001), and three or more comorbidities (OR, 1.80; 95% CI, 1.43-2.27; P < 0.001) were at increasingly higher risk of death when compared to those with no underlying conditions. Furthermore, multi morbidity patterns were analysed by identifying clusters of conditions in hospitalised COVID-19 patients using k-mode clustering, an unsupervised machine learning technique. Six patient clusters were identified, with recognisable co-occurrences of COVID-19 with different combinations of diseases, namely, cardiovascular (100%) and renal (15.6%) diseases in patient Cluster 1; mental and neurological disorders (100%) with metabolic and endocrine diseases (19.3%) in patient Cluster 2; respiratory (100%) and cardiovascular (15.0%) diseases in patient Cluster 3, cancer (5.9%) with genitourinary (9.0%) as well as metabolic and endocrine diseases (9.6%) in patient Cluster 4; metabolic and endocrine diseases (100%) and cardiovascular diseases (69.1%) in patient Cluster 5; mental and neurological disorders (100%) with cardiovascular diseases (100%) in patient Cluster 6. The highest mortality of 29.4% was reported in Cluster 6.

Prophylactic onlay mesh at emergency laparotomy: promising early outcomes with long-acting synthetic resorbable mesh.

BACKGROUND: Careful surgical strategy is paramount in balancing the prevention of fascial dehiscence, incisional hernia (IH) and fear of additional mesh-related wound complications post-laparotomy. This study aims to review early outcomes of patients undergoing an emergency laparotomy with prophylactic TIGR® mesh, used to reduce early fascial dehiscence and potential subsequent IH. METHOD: A retrospective, ethically approved review of 24 consecutive patients undergoing prophylactic TIGR® mesh placement during emergency laparotomies by a single surgeon between January 2017 and June 2021 at a University Hospital. A standardized approach included onlay positioning of the mesh, small-bite fascial closure, and a wound bundle. We recorded patient demographics, operative indications, findings, degree of peritonitis, postoperative complications, and mortality. RESULT: The study included 24 patients; 16/24 (66.6%) were female and median age was 72.5 (range 31-86); 14/24 patients were ASA grade III or greater; 4/24 patients (16.6%) developed six complications and 3/6 occurred in a single patient. Complications included subphrenic abscess, seroma, intrabdominal hematoma, enterocutaneous fistula leading to deep wound infection and small bowel perforation. Five (20.8%) patients died in hospital; central venous catheter sepsis (n = 1), fungal septicaemia (n = 1) and multiorgan failure (n = 3). Surgical site infection and seroma rates were low, occurring in 2/24 patients (4% each). CONCLUSION: This study has identified that prophylactic onlay mesh in patients undergoing an emergency laparotomy is not associated with significant wound infection or seroma when used with an active wound bundle. The wider use of TIGR® to prevent fascial dehiscence and potential long-term IH prevention should be considered.

Patient-reported outcome measures (PROMs) after laparoscopic cholecystectomy: systematic review.

BACKGROUND: Healthcare requires patient feedback to improve outcomes and experience. This study undertook a systematic review of the depth, variability, and digital suitability of current patient-reported outcome measures (PROMs) in patients undergoing laparoscopic cholecystectomy. METHODS: A PROSPERO-registered (registration number CRD42021261707) systematic review was undertaken for all relevant English language articles using PubMed version of MEDLINE, Scopus, and Web of Science electronic databases in June 2021. The search used Boolean operators and wildcards and included the keywords: laparoscopic cholecystectomy AND patient outcome OR patient-reported outcome OR patient-reported outcome measure OR PRO OR PROM. Medical Subjects Heading terms were used to search PubMed and Scopus. Articles published from 1 January 2011 to 2 June 2021 were included. RESULTS: A total of 4960 individual articles were reviewed in this study, of which 44 were found to evaluate PROMs in patients undergoing laparoscopic cholecystectomy and underwent methodological index for non-randomized studies (MINORS) grading. Twenty-one articles spanning 19 countries and four continents met all inclusion criteria and were included in the qualitative data synthesis. There was significant heterogeneity in PROMs identified with eight different comprehensive PROM tools used in the 21 studies. There was wide variation in the time points at which PROMs were recorded. Fourteen of 21 studies recorded PROMs before and after surgery, and 7 of 21 recorded PROMs only after surgery. Follow-up intervals ranged from 3 days to 2 years after surgery. CONCLUSIONS: This study identified that while post-laparoscopic cholecystectomy PROMs are infrequently measured currently, tools are widely available to achieve this in clinical practice. PROMs may not capture all the outcomes but should be incorporated into future cholecystectomy outcome research. The EQ-5D™ (EuroQoL Group, Rotterdam, the Netherlands) provides a simple platform for the modern digital era.

The impact of hearing impairment and hearing aid use on progression to mild cognitive impairment in cognitively healthy adults: An observational cohort study.

INTRODUCTION: We assessed the association of self-reported hearing impairment and hearing aid use with cognitive decline and progression to mild cognitive impairment (MCI). METHODS: We used a large referral-based cohort of 4358 participants obtained from the National Alzheimer's Coordinating Center. The standard covariate-adjusted Cox proportional hazards model, the marginal structural Cox model with inverse probability weighting, standardized Kaplan-Meier curves, and linear mixed-effects models were applied to test the hypotheses. RESULTS: Hearing impairment was associated with increased risk of MCI (standardized hazard ratio [HR] 2.58, 95% confidence interval [CI: 1.73 to 3.84], P = .004) and an accelerated rate of cognitive decline (P < .001). Hearing aid users were less likely to develop MCI than hearing-impaired individuals who did not use a hearing aid (HR 0.47, 95% CI [0.29 to 0.74], P = .001). No difference in risk of MCI was observed between individuals with normal hearing and hearing-impaired adults using hearing aids (HR 0.86, 95% CI [0.56 to 1.34], P = .51). DISCUSSION: Use of hearing aids may help mitigate cognitive decline associated with hearing loss.

It's time for a minimum synoptic operation template in patients undergoing laparoscopic cholecystectomy: a systematic review.

BACKGROUND: Despite the call to enhance accuracy and value of operation records few international recommended minimal standards for operative notes documentation have been described. This study undertook a systematic review of existing operative reporting systems for laparoscopic cholecystectomy (LC) to fashion a comprehensive, synoptic operative reporting template for the future. METHODS: A search for all relevant articles was conducted using PubMed version of Medline, Scopus and Web of Science databases in June 2021, for publications from January 1st 2011 to October 25th 2021, using the keywords: laparoscopic cholecystectomy AND operation notes OR operative notes OR proforma OR documentation OR report OR narrative OR audio-visual OR synoptic OR digital. Two reviewers (NOC, GMC) independently assessed each published study using a MINORS score of ≥ 16 for comparative and ≥ 10 for non-comparative for inclusion. This systematic review followed PRISMA guidelines and was registered with PROSPERO. Synoptic operative templates from published data were assimilated into one "ideal" laparoscopic operative report template following international input from the World Society of Emergency Surgery board. RESULTS: A total of 3567 articles were reviewed. Following MINORS grading 25 studies were selected spanning 14 countries and 4 continents. Twenty-two studies were prospective. A holistic overview of the operative procedure documentation was reported in 6/25 studies and a further 19 papers dealt with selective surgical aspects of LC. A unique synoptic LC operative reporting template was developed and translated into Chinese/Mandarin, French and Arabic. CONCLUSION: This systematic review identified a paucity of publications dealing with operative reporting of LC. The proposed new template may be integrated digitally with hospitals' medical systems and include additional narrative text and audio-visual data. The template may help define new OR (operating room) recording standards and impact on care for patients undergoing LC.

Clinical and operational insights from data-driven care pathway mapping: a systematic review.

BACKGROUND: Accumulated electronic data from a wide variety of clinical settings has been processed using a range of informatics methods to determine the sequence of care activities experienced by patients. The "as is" or "de facto" care pathways derived can be analysed together with other data to yield clinical and operational information. It seems likely that the needs of both health systems and patients will lead to increasing application of such analyses. A comprehensive review of the literature is presented, with a focus on the study context, types of analysis undertaken, and the utility of the information gained. METHODS: A systematic review was conducted of literature abstracting sequential patient care activities ("de facto" care pathways) from care records. Broad coverage was achieved by initial screening of a Scopus search term, followed by screening of citations (forward snowball) and references (backwards snowball). Previous reviews of related topics were also considered. Studies were initially classified according to the perspective captured in the derived pathways. Concept matrices were then derived, classifying studies according to additional data used and subsequent analysis undertaken, with regard for the clinical domain examined and the knowledge gleaned. RESULTS: 254 publications were identified. The majority (n = 217) of these studies derived care pathways from data of an administrative/clinical type. 80% (n = 173) applied further analytical techniques, while 60% (n = 131) combined care pathways with enhancing data to gain insight into care processes. DISCUSSION: Classification of the objectives, analyses and complementary data used in data-driven care pathway mapping illustrates areas of greater and lesser focus in the literature. The increasing tendency for these methods to find practical application in service redesign is explored across the variety of contexts and research questions identified. A limitation of our approach is that the topic is broad, limiting discussion of methodological issues. CONCLUSION: This review indicates that methods utilising data-driven determination of de facto patient care pathways can provide empirical information relevant to healthcare planning, management, and practice. It is clear that despite the number of publications found the topic reviewed is still in its infancy.

Practical Strategies for Extreme Missing Data Imputation in Dementia Diagnosis.

Accurate computational models for clinical decision support systems require clean and reliable data but, in clinical practice, data are often incomplete. Hence, missing data could arise not only from training datasets but also test datasets which could consist of a single undiagnosed case, an individual. This work addresses the problem of extreme missingness in both training and test data by evaluating multiple imputation and classification workflows based on both diagnostic classification accuracy and computational cost. Extreme missingness is defined as having ∼50% of the total data missing in more than half the data features. In particular, we focus on dementia diagnosis due to long time delays, high variability, high attrition rates and lack of practical data imputation strategies in its diagnostic pathway. We identified and replicated the extreme missingness structure of data from a real-world memory clinic on a larger open dataset, with the original complete data acting as ground truth. Overall, we found that computational cost, but not accuracy, varies widely for various imputation and classification approaches. Particularly, we found that iterative imputation on the training dataset combined with a reduced-feature classification model provides the best approach, in terms of speed and accuracy. Taken together, this work has elucidated important factors to be considered when developing a predictive model for a dementia diagnostic support system.

Umbilical hernia repair and recurrence: need for a clinical trial?

INTRODUCTION: Umbilical hernia repair, despite its perceived simplicity, is associated with recurrence between 2.7 and 27%, in mesh repair and non mesh repair respectively. Many factors are recognized contributors to recurrence however multiple defects in the linea alba, known to occur in up to 30% of patients, appear to have been overlooked by surgeons. AIMS: This systematic review assessed reporting of second or multiple linea alba defects in patients undergoing umbilical hernia repair to establish if these anatomical variations could contribute to recurrence along with other potential factors. METHODS: A systematic review of all published English language articles was undertaken using databases PubMed, Embase, Web of Science and Cochrane Library from January 2014 to 2019. The search terms 'Umbilical hernia' AND 'repair' AND 'recurrence' were used across all databases. Analysis was specified in advance to avoid selection bias, was registered with PROSPERO (154173) and adhered to PRISMA statement. RESULTS: Six hundred and forty-six initial papers were refined to 10 following article review and grading. The presence of multiple linea alba defects as a contributor to recurrence was not reported in the literature. One paper mentioned the exclusion of six participants from their study due multiple defects. In all 11 factors were significantly associated with umbilical hernia recurrence. These included: large defect, primary closure without mesh, high BMI in 5/10 publications; smoking, diabetes mellitus, surgical site Infection (SSI) and concurrent hernia in 3/10. In addition, the type of mesh, advanced age, liver disease and non-closure of the defect were identified in individual papers. CONCLUSION: This study identified many factors already known to contribute to umbilical hernia recurrence in adults, but the existence of multiple defects in the linea, despite it prevalence, has evaded investigators. Surgeons need to be consider documentation of this potential confounder which may contribute to recurrence.