Artificial intelligence-powered clinical decision making within gastrointestinal surgery: A systematic review.

BACKGROUND: Clinical decision-making in gastrointestinal surgery is complex due to the unpredictability of tumoral behavior and postoperative complications. Artificial intelligence (AI) could aid in clinical decision-making by predicting these surgical outcomes. The current status of AI-based clinical decision-making within gastrointestinal surgery is unknown in recent literature. This review aims to provide an overview of AI models used for clinical decision-making within gastrointestinal surgery. METHODS: A systematic literature search was performed in databases PubMed, EMBASE, Cochrane, and Web of Science. To be eligible for inclusion, studies needed to use AI models for clinical decision-making involving patients undergoing gastrointestinal surgery. Studies reporting on reviews, children, and study abstracts were excluded. The Probast risk of bias tool was used to evaluate the methodological quality of AI methods. RESULTS: Out of 1073 studies, 10 articles were eligible for inclusion. AI models have been used to make clinical decisions between surgical procedures, selection of chemotherapy, selection of postoperative follow up programs, and implementation of a temporary ileostomy. Most studies have used a Random Forest or Gradient Boosting model with AUCs up to 0.97. All studies involved a retrospective study design, in which external validation was performed in one study. CONCLUSIONS: This review shows that AI models have the potentiality to select the most optimal treatments for patients undergoing gastrointestinal surgery. Clinical benefits could be gained if AI models were used for clinical decision-making. However, prospective studies and randomized controlled trials will reveal the definitive role of AI models in clinical decision-making.

Elements of organisation of integrated maternity care and their associations with outcomes: a scoping review protocol.

INTRODUCTION: Integrated care is seen as an enabling strategy in organising healthcare to improve quality, finances, personnel and sustainability. Developments in the organisation of maternity care follow this trend. The way care is organised should support the general aims and outcomes of healthcare systems. Organisation itself consists of a variety of smaller 'elements of organisation'. Various elements of organisation are implemented in different organisations and networks. We will examine which elements of integrated maternity care are associated with maternal and neonatal health outcomes, experiences of women and professionals, healthcare spending and care processes. METHODS AND ANALYSIS: We will conduct this review using the JBI methodology for scoping reviews and the reporting guideline PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews). We will undertake a systematic search in the databases PubMed, Scopus, Cochrane and PsycINFO. A machine learning tool, ASReview, will be used to select relevant papers. These papers will be analysed and classified thematically using the framework of the Rainbow Model of Integrated Care (RMIC). The Population Concept Context framework for scoping reviews will be used in which 'Population' is defined as elements of the organisation of integrated maternity care, 'Context' as high-income countries and 'Concepts' as outcomes stated in the objective of this review. We will include papers from 2012 onwards, in Dutch or English language, which describe both 'how the care is organised' (elements) and 'outcomes'. ETHICS AND DISSEMINATION: Since this is a scoping review of previously published summary data, ethical approval for this study is not needed. Findings will be published in a peer-reviewed international journal, discussed in a webinar and presented at (inter)national conferences and meetings of professional associations.The findings of this scoping review will give insight into the nature and effectiveness of elements of integrated care and will generate hypotheses for further research.

Virtual Reality Breathing Interventions for Mental Health: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Breathing exercises have been shown to reduce mental health problems among clinical and non-clinical populations. Although virtual reality (VR) breathing interventions are assumed to have potential benefits, it remains unclear whether VR breathing interventions are more effective at improving mental health than non-VR breathing interventions. We conducted a systematic literature search in six electronic databases (Web of Science, PsycINFO, Embase, Cochrane Central Register of Controlled Trials, Scopus, and PubMed) from inception to 30th September, 2022. We included randomized controlled trials in adults evaluating effects of VR compared to non-VR breathing interventions on primary outcomes of mental health (stress, anxiety and mood), and secondary outcomes of physiological stress measures (e.g., heart rate (HR), heart rate variability (HRV)). Within these selected studies, we explored differences in likeability and future use between VR and non-VR breathing interventions. 2.848 records were identified of which 65 full-text articles were assessed. Six RCTs were included, of which five were suitable for meta-analyses. Comparing VR to non-VR breathing interventions, there were no significant differences in overall mental health, stress, anxiety or mood, nor in HR or HRV. There was no evidence that participants liked VR breathing interventions more than non-VR, nor would use them more in the future. These results suggest that there is no evidence that VR breathing interventions are more effective than non-VR in improving mental health outcomes, HR, HRV. Further research is required to determine whether there may be advantages to longer-term VR-implementation and practice, and explore possible mechanisms.

A systematic review of (semi-)automatic quality control of T1-weighted MRI scans.

PURPOSE: Artifacts in magnetic resonance imaging (MRI) scans degrade image quality and thus negatively affect the outcome measures of clinical and research scanning. Considering the time-consuming and subjective nature of visual quality control (QC), multiple (semi-)automatic QC algorithms have been developed. This systematic review presents an overview of the available (semi-)automatic QC algorithms and software packages designed for raw, structural T1-weighted (T1w) MRI datasets. The objective of this review was to identify the differences among these algorithms in terms of their features of interest, performance, and benchmarks. METHODS: We queried PubMed, EMBASE (Ovid), and Web of Science databases on the fifth of January 2023, and cross-checked reference lists of retrieved papers. Bias assessment was performed using PROBAST (Prediction model Risk Of Bias ASsessment Tool). RESULTS: A total of 18 distinct algorithms were identified, demonstrating significant variations in methods, features, datasets, and benchmarks. The algorithms were categorized into rule-based, classical machine learning-based, and deep learning-based approaches. Numerous unique features were defined, which can be roughly divided into features capturing entropy, contrast, and normative measures. CONCLUSION: Due to dataset-specific optimization, it is challenging to draw broad conclusions about comparative performance. Additionally, large variations exist in the used datasets and benchmarks, further hindering direct algorithm comparison. The findings emphasize the need for standardization and comparative studies for advancing QC in MR imaging. Efforts should focus on identifying a dataset-independent measure as well as algorithm-independent methods for assessing the relative performance of different approaches.

Less is more? One-stage versus two-stage implant-based breast reconstruction: A systematic review and meta-analysis of comparative studies.

BACKGROUND: Most breast reconstructions are implant-based and can be performed either in a one-stage, direct-to-implant or in a two-stage, expander-implant-based reconstruction. The objective of this systematic review is to compare the safety and patient satisfaction of the two reconstruction approaches. METHODS: A literature search was conducted on 27 September 2022 using various databases. Studies comparing one-stage and two-stage implant reconstructions and reporting the following outcomes were included: patient satisfaction, aesthetics, complications, and/or costs. Reviews, case reports, or series with less than 20 patients and letters or comments were excluded. Comparisons were made between the one-stage reconstruction with and without acellular dermal matrix (ADM) and two-stage implant-based breast reconstruction groups. The data extracted from all articles were analysed using random-effects meta-analyses. RESULTS: Of the 1381 records identified, a total of 33 articles were included, representing 21529 patients. There were no significant differences between the one-stage and two-stage groups, except for the costs. The one-stage operation without ADM had lower costs than the two-stage operation without ADM, although the use of an ADM substantially increased the price of the operation to more than a two-stage reconstruction. DISCUSSION: Equal patient satisfaction, aesthetic outcomes, and complication rates with lower costs justify one-stage breast reconstruction in carefully selected patients. This review shows that there is no evidence-based superior surgical approach. Future research should focus on the costs of the ADM versus an additional stage and patient-reported outcomes.

Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review.

Rational: Deep learning (DL) has demonstrated a remarkable performance in diagnostic imaging for various diseases and modalities and therefore has a high potential to be used as a clinical tool. However, current practice shows low deployment of these algorithms in clinical practice, because DL algorithms lack transparency and trust due to their underlying black-box mechanism. For successful employment, explainable artificial intelligence (XAI) could be introduced to close the gap between the medical professionals and the DL algorithms. In this literature review, XAI methods available for magnetic resonance (MR), computed tomography (CT), and positron emission tomography (PET) imaging are discussed and future suggestions are made. Methods: PubMed, Embase.com and Clarivate Analytics/Web of Science Core Collection were screened. Articles were considered eligible for inclusion if XAI was used (and well described) to describe the behavior of a DL model used in MR, CT and PET imaging. Results: A total of 75 articles were included of which 54 and 17 articles described post and ad hoc XAI methods, respectively, and 4 articles described both XAI methods. Major variations in performance is seen between the methods. Overall, post hoc XAI lacks the ability to provide class-discriminative and target-specific explanation. Ad hoc XAI seems to tackle this because of its intrinsic ability to explain. However, quality control of the XAI methods is rarely applied and therefore systematic comparison between the methods is difficult. Conclusion: There is currently no clear consensus on how XAI should be deployed in order to close the gap between medical professionals and DL algorithms for clinical implementation. We advocate for systematic technical and clinical quality assessment of XAI methods. Also, to ensure end-to-end unbiased and safe integration of XAI in clinical workflow, (anatomical) data minimization and quality control methods should be included.

Urinary volatile organic compounds for colorectal cancer screening: A systematic review and meta-analysis.

BACKGROUND: The faecal immunochemical test (FIT) suffers from suboptimal performance and participation in colorectal cancer (CRC) screening. Urinary volatile organic compounds (VOCs) may be a useful alternative. We aimed to determine the diagnostic potential of urinary VOCs for CRC/adenomas. By relating VOCs to known pathways, we aimed to gain insight into the pathophysiology of colorectal neoplasia. METHODS: A systematic search was performed in PubMed, EMBASE and Web of Science. Original studies on urinary VOCs for CRC/adenoma detection with a control group were included. QUADAS-2 tool was used for quality assessment. Meta-analysis was performed by adopting a bivariate model for sensitivity/specificity. Fagan's nomogram estimated the performance of combined FIT-VOC. Neoplasm-associated VOCs were linked to pathways using the KEGG database. RESULTS: Sixteen studies-involving 837 CRC patients and 1618 controls-were included; 11 performed chemical identification and 7 chemical fingerprinting. In all studies, urinary VOCs discriminated CRC from controls. Pooled sensitivity and specificity for CRC based on chemical fingerprinting were 84% (95% CI 73-91%) and 70% (95% CI 63-77%), respectively. The most distinctive individual VOC was butanal (AUC 0.98). The estimated probability of having CRC following negative FIT was 0.38%, whereas 0.09% following negative FIT-VOC. Combined FIT-VOC would detect 33% more CRCs. In total 100 CRC-associated urinary VOCs were identified; particularly hydrocarbons, carboxylic acids, aldehydes/ketones and amino acids, and predominantly involved in TCA-cycle or alanine/aspartate/glutamine/glutamate/phenylalanine/tyrosine/tryptophan metabolism, which is supported by previous research on (colorectal)cancer biology. The potential of urinary VOCs to detect precancerous adenomas or gain insight into their pathophysiology appeared understudied. CONCLUSION: Urinary VOCs hold potential for non-invasive CRC screening. Multicentre validation studies are needed, especially focusing on adenoma detection. Urinary VOCs elucidate underlying pathophysiologic processes.

The quality of geriatric rehabilitation from the patients' perspective: a scoping review.

BACKGROUND: the efficacy and outcomes of geriatric rehabilitation (GR) have previously been investigated. However, a systematic synthesis of the aspects that are important to patients regarding the quality of GR does not exist. OBJECTIVE: the aim of this scoping review was to systematically synthesise the patients' perspective on the quality of GR. METHODS: we followed the Scoping Review framework and gathered literature including a qualitative study design from multiple databases. The inclusion criteria were: a qualitative study design; a geriatric population; that patients had participated in a geriatric rehabilitation programme and that geriatric rehabilitation was assessed by the patient. The results sections of the included studies were analysed using a thematic analysis approach. RESULTS: twenty articles were included in this review. The main themes identified were: (i) the need for information about the rehabilitation process, (ii) the need for telling one's story, (iii) the need for support (physical, psychological, social and how to cope with limitations), (iv) the need for shared decision-making and autonomy, (v) the need for a stimulating rehabilitation environment and (vi) the need for rehabilitation at home. CONCLUSION: in this study, we identified the aspects that determine the quality of rehabilitation from the patient's perspective, which may lead to a more holistic perspective on the quality of GR.

Effectiveness of outpatient geriatric rehabilitation after inpatient geriatric rehabilitation or hospitalisation: a systematic review and meta-analysis.

BACKGROUND: Due to the increasing number of older people with multi-morbidity, the demand for outpatient geriatric rehabilitation (OGR) will also increase. OBJECTIVE: To assess the effects of OGR on the primary outcome functional performance (FP) and secondary outcomes: length of in-patient stay, re-admission rate, patients' and caregivers' quality of life, mortality and cost-effectiveness. We also aim to describe the organisation and content of OGR. METHODS: Systematic review and meta-analysis. Five databases were queried from inception to July 2022. We selected randomised controlled trials written in English, focusing on multidisciplinary interventions related to OGR, included participants aged ≥65 and reported one of the main outcomes. A meta-analysis was performed on FP, patients' quality of life, length of stay and re-admissions. The structural, procedural and environmental aspects of OGR were systematically mapped. RESULTS: We selected 24 studies involving 3,405 participants. The meta-analysis showed no significant effect on the primary outcome FP (activity). It demonstrated a significant effect of OGR on shortening length of in-patient stay (P = 0.03, MD = -2.41 days, 95%CI: [-4.61-0.22]). Frequently used elements of OGR are: inpatient start of OGR with an interdisciplinary rehabilitation team, close cooperation with primary care, an OGR coordinator, individual goal setting and education for both patient and caregiver. CONCLUSION: This review showed that OGR is as effective as usual care on FP activity. It shows low certainty of evidence for OGR being effective in reducing the length of inpatient stay. Further research is needed on the various frequently used elements of OGR.

Outcomes after Surgical Treatment for Rectal Atresia in Children: Is There a Preferred Approach? A Systematic Review.

Rectal atresia (RA) affects only 1 to 2% of all children with anorectal malformations. No consensus on optimal treatment strategy is yet achieved. Therefore, the aim of this systematic review is to summarize all surgical interventions for RA and outcomes described in the current literature. A literature search was conducted in PubMed, Embase, Web of Science, and Cochrane Library on January 24, 2022. All studies describing treatment for RA in children (< 18 years) were included. Operation technique and postoperative complications were listed. Only descriptive analysis was anticipated. Quality of the studies was assessed using Johanna Briggs Institute critical appraisal checklist for case reports and series. The search yielded 6,716 studies of which, after duplicate removal, 4,028 were excluded based on title and abstract screening. After full-text assessment, 22 of 90 studies were included, yielding 70 patients. Posterior sagittal anorectoplasty (PSARP) and pull-through were most performed (43/70 and 18/70 patients, respectively). Four patients experienced postoperative complications: anal stenosis (n = 1), anastomotic stenosis (n = 2), and death due to a pulmonary complication (n = 1). In the low-quality literature available, most patients with RA are treated with PSARP or pull-through technique. A low complication rate of both has been described but follow-up was often not mentioned. Larger well-designed studies should be performed to determine optimal treatment strategy for children with RA. This study reflects level of evidence V.

Machine learning applications in upper gastrointestinal cancer surgery: a systematic review.

BACKGROUND: Machine learning (ML) has seen an increase in application, and is an important element of a digital evolution. The role of ML within upper gastrointestinal surgery for malignancies has not been evaluated properly in the literature. Therefore, this systematic review aims to provide a comprehensive overview of ML applications within upper gastrointestinal surgery for malignancies. METHODS: A systematic search was performed in PubMed, EMBASE, Cochrane, and Web of Science. Studies were only included when they described machine learning in upper gastrointestinal surgery for malignancies. The Cochrane risk-of-bias tool was used to determine the methodological quality of studies. The accuracy and area under the curve were evaluated, representing the predictive performances of ML models. RESULTS: From a total of 1821 articles, 27 studies met the inclusion criteria. Most studies received a moderate risk-of-bias score. The majority of these studies focused on neural networks (n = 9), multiple machine learning (n = 8), and random forests (n = 3). Remaining studies involved radiomics (n = 3), support vector machines (n = 3), and decision trees (n = 1). Purposes of ML included predominantly prediction of metastasis, detection of risk factors, prediction of survival, and prediction of postoperative complications. Other purposes were predictions of TNM staging, chemotherapy response, tumor resectability, and optimal therapy. CONCLUSIONS: Machine Learning algorithms seem to contribute to the prediction of postoperative complications and the course of disease after upper gastrointestinal surgery for malignancies. However, due to the retrospective character of ML studies, these results require trials or prospective studies to validate this application of ML.

Pharmacological Optimization of PSMA-Based Radioligand Therapy.

Prostate cancer (PCa) is the most common malignancy in men of middle and older age. The standard treatment strategy for PCa ranges from active surveillance in low-grade, localized PCa to radical prostatectomy, external beam radiation therapy, hormonal treatment and chemotherapy. Recently, the use of prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT) for metastatic castration-resistant PCa has been approved. PSMA is predominantly, but not exclusively, expressed on PCa cells. Because of its high expression in PCa, PSMA is a promising target for diagnostics and therapy. To understand the currently used RLT, knowledge about pharmacokinetics (PK) and pharmacodynamics (PD) of the PSMA ligand and the PSMA protein itself is crucial. PK and PD properties of the ligand and its target determine the duration and extent of the effect. Knowledge on the concentration-time profile, the target affinity and target abundance may help to predict the effect of RLT. Increased specific binding of radioligands to PSMA on PCa cells may be associated with better treatment response, where nonspecific binding may increase the risk of toxicity in healthy organs. Optimization of the radioligand, as well as synergistic effects of concomitant agents and an improved dosing strategy, may lead to more individualized treatment and better overall survival.

Sleep in hospitalized pediatric and adult patients - A systematic review and meta-analysis.

Background: Sleep is essential for recovery from illness. As a result, researchers have shown a growing interest in the sleep of hospitalized patients. Although many studies have been conducted over the past years, an up to date systematic review of the results is missing. Objective: The objective of this systematic review was to assess sleep quality and quantity of hospitalized patients and sleep disturbing factors. Methods: A systematic literature search was conducted within four scientific databases. The search focused on synonyms of 'sleep' and 'hospitalization'. Papers written in English or Dutch from inception to April 25th,2022 were included for hospitalized patients >1 year of age. Papers exclusively reporting about patients receiving palliative, obstetric or psychiatric care were excluded, as well as patients in rehabilitation and intensive care settings, and long-term hospitalized geriatric patients. This review was performed in accordance with the PRISMA guidelines. Results: Out of 542 full text studies assessed for eligibility, 203 were included, describing sleep quality and/or quantity of 17,964 patients. The median sample size of the studies was 51 patients (IQR 67, range 6-1472). An exploratory meta-analysis of the Total Sleep Time showed an average of 7.2 h (95%-CI 4.3, 10.2) in hospitalized children, 5.7 h (95%-CI 4.8, 6.7) in adults and 5.8 h (95%-CI 5.3, 6.4) in older patients (>60y). In addition, a meta-analysis of the Wake After Sleep Onset (WASO) showed a combined high average of 1.8 h (95%-CI 0.7, 2.9). Overall sleep quality was poor, also due to nocturnal awakenings. The most frequently cited external factors for poor sleep were noise and number of patients in the room. Among the variety of internal/disease-related factors, pain and anxiety were most frequently mentioned to be associated with poor sleep. Conclusion: Of all studies, 76% reported poor sleep quality and insufficient sleep duration in hospitalized patients. Children sleep on average 0.7-3.8 h less in the hospital than recommended. Hospitalized adults sleep 1.3-3.2 h less than recommended for healthy people. This underscores the need for interventions to improve sleep during hospitalization to support recovery.

Systemic Therapy for Patients with HER2-Positive Breast Cancer and Brain Metastases: A Systematic Review and Meta-Analysis.

AIM: Patients with HER2-positive (HER2+) metastatic breast cancer (mBC) develop brain metastases (BM) in up to 30% of cases. Treatment of patients with BM can consist of local treatment (surgery and/or radiotherapy) and/or systemic treatment. We undertook a systematic review and meta-analysis to determine the effect of different systemic therapies in patients with HER2+ mBC and BM. METHODS: A systematic search was performed in the databases PubMed, Embase.com, Clarivate Analytics/Web of Science Core Collection and the Wiley/Cochrane Library. Eligible articles included prospective or retrospective studies reporting on the effect of systemic therapy on objective response rate (ORR) and/or median progression free survival (mPFS) in patients with HER2+ mBC and BM. The timeframe within the databases was from inception to 19 January 2022. Fixed-effects meta-analyses were used. Quality appraisal was performed using the ROBINS-I tool. RESULTS: Fifty-one studies were included, involving 3118 patients. Most studies, which contained the largest patient numbers, but also often carried a moderate-serious risk of bias, investigated lapatinib and capecitabine (LC), trastuzumab-emtansine (T-DM1) or pyrotinib. The best quality data and/or highest ORR were described with tucatinib (combined with trastuzumab and capecitabine, TTC) and trastuzumab-deruxtecan (T-DXd). TTC demonstrated an ORR of 47.3% in patients with asymptomatic and/or active BM. T-DXd achieved a pooled ORR of 64% (95% CI 43-85%, I2 0%) in a heavily pretreated population with asymptomatic BM (3 studies, n = 96). CONCLUSIONS: Though our meta-analysis should be interpreted with caution due to the heterogeneity of included studies and a related serious risk of bias, this review provides a comprehensive overview of all currently available systemic treatment options. T-Dxd and TTC that appear to constitute the most effective systemic therapy in patients with HER2+ mBC and BM, while pyrotinib might be an option in Asian patients.

Experiences of living with leprosy: A systematic review and qualitative evidence synthesis.

OBJECTIVE: The objective of the review was to identify, appraise, and synthesise qualitative studies on the lived experience of individuals diagnosed with leprosy, the impact of the disease, and how they coped with the disease burden. INTRODUCTION: Leprosy is a chronic disease with long-term biopsychosocial impact and is a leading cause of preventable disabilities. It traps the individuals with leprosy in a vicious circle of disease, stigma, and poverty. The efforts to reduce stigma and discrimination and improve their quality of life have not kept pace with the success of the multidrug treatment. INCLUSION CRITERIA: This review considered published literature on the lived experience of individuals diagnosed with leprosy. There were no limitations on gender, background, or country. All qualitative or mixed-methods studies were accepted. METHODS: The review followed the JBI meta-aggregation approach for qualitative systematic reviews. A structured literature search was undertaken using multiple electronic databases: PubMed, Embase, Web of Science, and CINAHL. RESULTS: The search identified 723 publications, and there were 446 articles after deduplication. Forty-nine studies met the inclusion criteria. The final 173 findings were synthesised into ten categories and aggregated into four synthesised findings: biophysical impact, social impact, economic impact, and mental and emotional impact. These synthesised findings were consistent across the included studies from a patient's perspective. The way people coped with leprosy depended on their interpretation of the disease and its treatment. It affected their help-seeking behaviour and their adherence to treatment and self-care. The review has identified a multi-domain effect on the affected individuals, which goes beyond the biological and physical effects, looking at the social issues, specific difficulties, emotions, and economic hardships. CONCLUSIONS: The researchers, health professionals, and policymakers could use the synthesised findings to address the concerns and needs of the leprosy-affected individuals and offer appropriate support to manage their lives. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO Registration number: CRD42021243223.

Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review.

BACKGROUND: Machine learning (ML) has been introduced in various fields of healthcare. In colorectal surgery, the role of ML has yet to be reported. In this systematic review, an overview of machine learning models predicting surgical outcomes after colorectal surgery is provided. METHODS: Databases PubMed, EMBASE, Cochrane, and Web of Science were searched for studies using machine learning models for patients undergoing colorectal surgery. To be eligible for inclusion, studies needed to apply machine learning models for patients undergoing colorectal surgery. Absence of machine learning or colorectal surgery or studies reporting on reviews, children, study abstracts were excluded. The Probast risk of bias tool was used to evaluate the methodological quality of machine learning models. RESULTS: A total of 1821 studies were analysed, resulting in the inclusion of 31 articles. A vast proportion of ML algorithms have been used to predict the course of disease and response to neoadjuvant chemoradiotherapy. Radiomics have been applied most frequently, along with predictive accuracies up to 91%. However, most studies included a retrospective study design without external validation or calibration. CONCLUSIONS: Machine learning models have shown promising potential in predicting surgical outcomes after colorectal surgery. However, large-scale data is warranted to bridge the gap between calibration and external validation. Clinical implementation is needed to demonstrate the contribution of ML within daily practice.

Can Quality Improvement Methodologies Derived from Manufacturing Industry Improve Care in Cardiac Surgery? A Systematic Review.

OBJECTIVES: Healthcare is required to be effectively organised to ensure that growing, aging and medically more complex populations have timely access to high-quality, affordable care. Cardiac surgery is no exception to this, especially due to the competition for and demand on hospital resources, such as operating rooms and intensive care capacity. This is challenged more since the COVID-19 pandemic led to postponed care and prolonged waiting lists. In other sectors, Quality Improvement Methodologies (QIM) derived from the manufacturing industry have proven effective in enabling more efficient utilisation of existing capacity and resources and in improving the quality of care. We performed a systematic review to evaluate the ability of such QIM to improve care in cardiac surgery. METHODS: A literature search was performed in PubMed, Embase, Clarivate Analytics/Web of Science Core Collection and Wiley/the Cochrane Library according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis methodology. RESULTS: Ten articles were identified. The following QIM were used: Lean, Toyota Production System, Six Sigma, Lean Six Sigma, Root Cause Analysis, Kaizen and Plan-Do-Study-Act. All reported one or more relevant improvements in patient-related (e.g., infection rates, ventilation time, mortality, adverse events, glycaemic control) and process-related outcomes (e.g., shorter waiting times, shorter transfer time and productivity). Elements to enhance the success included: multidisciplinary team engagement, a patient-oriented, data-driven approach, a sense of urgency and a focus on sustainability. CONCLUSIONS: In all ten papers describing the application of QIM initiatives to cardiac surgery, positive results, of varying magnitude, were reported. While the consistency of the available data is encouraging, the limited quantity and heterogenous quality of the evidence base highlights that more rigorous evaluation, including how best to employ manufacturing industry-derived QIM in cardiac surgery is warranted.

Artificial Intelligence in Bariatric Surgery: Current Status and Future Perspectives.

BACKGROUND: Machine learning (ML) has been successful in several fields of healthcare, however the use of ML within bariatric surgery seems to be limited. In this systematic review, an overview of ML applications within bariatric surgery is provided. METHODS: The databases PubMed, EMBASE, Cochrane, and Web of Science were searched for articles describing ML in bariatric surgery. The Cochrane risk of bias tool and the PROBAST tool were used to evaluate the methodological quality of included studies. RESULTS: The majority of applied ML algorithms predicted postoperative complications and weight loss with accuracies up to 98%. CONCLUSIONS: In conclusion, ML algorithms have shown promising capabilities in the prediction of surgical outcomes after bariatric surgery. Nevertheless, the clinical introduction of ML is dependent upon the external validation of ML.

Civil-military cooperation in the management of infectious disease outbreaks: a scoping review.

INTRODUCTION: Civil-military cooperation (CMC) in infectious disease outbreak responses has become more common, and has its own cooperation dynamics. These collaborations fit WHO's call for multisectoral cooperation in managing health emergencies according to the emergency management cycle (EMC). However, the literature on CMC on this topic is fragmented. The core aim of this review is to understand the breadth and dynamics of this cooperation by using the EMC as a framework and by identifying challenges and opportunities in the management of outbreaks. METHODS: A scoping review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guideline was conducted. A systematic search for peer-reviewed journals was performed in PubMed, Embase, Web of Science and Scopus. Eligible papers addressed substantive contributions to the understanding of CMC. Papers were categorised by EMC phase and relevant information on study characteristics and areas of cooperation were extracted from the data. Recurring themes on challenges and opportunities in cooperation were identified by means of qualitative interpretation analysis. RESULTS: The search resulted in 8360 papers; 54 were included for analysis. Most papers provided a review of activities or expert opinions. CMC was described in all EMC phases, with the fewest references in the recovery phase (n=1). In total, eight areas of CMC were explored. Regarding the better understanding of cooperative dynamics, the qualitative analysis of the papers yielded five recurring themes covering challenges and opportunities in CMC: managing relations, framework conditions, integrating collective activities, governance and civil-military differences. CONCLUSION: Guided by these five themes, successful CMC requires sustainable relations, binding agreements, transparency, a clear operational perspective and acknowledgement of organisational cultural differences. Early and continuous engagement proves crucial to avoid distrust and tension among stakeholders, frequently caused by differences in strategical goals. Original research on this topic is limited.