Machine learning-based preoperative analytics for the prediction of anastomotic leakage in colorectal surgery: a swiss pilot study.

BACKGROUND: Anastomotic leakage (AL), a severe complication following colorectal surgery, arises from defects at the anastomosis site. This study evaluates the feasibility of predicting AL using machine learning (ML) algorithms based on preoperative data. METHODS: We retrospectively analyzed data including 21 predictors from patients undergoing colorectal surgery with bowel anastomosis at four Swiss hospitals. Several ML algorithms were applied for binary classification into AL or non-AL groups, utilizing a five-fold cross-validation strategy with a 90% training and 10% validation split. Additionally, a holdout test set from an external hospital was employed to assess the models' robustness in external validation. RESULTS: Among 1244 patients, 112 (9.0%) suffered from AL. The Random Forest model showed an AUC-ROC of 0.78 (SD: ± 0.01) on the internal test set, which significantly decreased to 0.60 (SD: ± 0.05) on the external holdout test set comprising 198 patients, including 7 (3.5%) with AL. Conversely, the Logistic Regression model demonstrated more consistent AUC-ROC values of 0.69 (SD: ± 0.01) on the internal set and 0.61 (SD: ± 0.05) on the external set. Accuracy measures for Random Forest were 0.82 (SD: ± 0.04) internally and 0.87 (SD: ± 0.08) externally, while Logistic Regression achieved accuracies of 0.81 (SD: ± 0.10) and 0.88 (SD: ± 0.15). F1 Scores for Random Forest moved from 0.58 (SD: ± 0.03) internally to 0.51 (SD: ± 0.03) externally, with Logistic Regression maintaining more stable scores of 0.53 (SD: ± 0.04) and 0.51 (SD: ± 0.02). CONCLUSION: In this pilot study, we evaluated ML-based prediction models for AL post-colorectal surgery and identified ten patient-related risk factors associated with AL. Highlighting the need for multicenter data, external validation, and larger sample sizes, our findings emphasize the potential of ML in enhancing surgical outcomes and inform future development of a web-based application for broader clinical use.

Development and validation of a predictive model of the hospital cost associated with bariatric surgery.

Hospitals are facing difficulties in predicting, evaluating, and managing cost-affecting parameters in patient treatments. Inaccurate cost prediction leads to a deficit in operational revenue. This study aims to determine the ability of Machine Learning (ML) algorithms to predict the cost of care in bariatric and metabolic surgery and develop a predictive tool for improved cost analysis. 602 patients who underwent bariatric and metabolic surgery at Wetzikon hospital from 2013 to 2019 were included in the study. Multiple variables including patient factors, surgical factors, and post-operative complications were tested using a number of predictive modeling strategies. The study was registered under Req 2022-00659 and approved by an institutional review board. The cost was defined as the sum of all costs incurred during the hospital stay, expressed in CHF (Swiss Francs). The data was preprocessed and split into a training set (80%) and a test set (20%) to build and validate models. The final model was selected based on the mean absolute percentage error (MAPE). The Random Forest model was found to be the most accurate in predicting the overall cost of bariatric surgery with a mean absolute percentage error of 12.7. The study provides evidence that the Random Forest model could be used by hospitals to help with financial calculations and cost-efficient operation. However, further research is needed to improve its accuracy. This study serves as a proof of principle for an efficient ML-based prediction tool to be tested on multi-center data in future phases of the study.

A coiled-coil-based design strategy for the thermostabilization of G-protein-coupled receptors.

Structure elucidation of inactive-state GPCRs still mostly relies on X-ray crystallography. The major goal of our work was to create a new GPCR tool that would provide receptor stability and additional soluble surface for crystallization. Towards this aim, we selected the two-stranded antiparallel coiled coil as a domain fold that satisfies both criteria. A selection of antiparallel coiled coils was used for structure-guided substitution of intracellular loop 3 of the ß3 adrenergic receptor. Unexpectedly, only the two GPCR variants containing thermostable coiled coils were expressed. We showed that one GPCR chimera is stable upon purification in detergent, retains ligand-binding properties, and can be crystallized. However, the quality of the crystals was not suitable for structure determination. By using two other examples, 5HTR2C and α2BAR, we demonstrate that our approach is generally suitable for the stabilization of GPCRs. To provide additional surface for promoting crystal contacts, we replaced in a structure-based approach the loop connecting the antiparallel coiled coil by T4L. We found that the engineered GPCR is even more stable than the coiled-coil variant. Negative-staining TEM revealed a homogeneous distribution of particles, indicating that coiled-coil-T4L receptor variants might also be promising candidate proteins for structure elucidation by cryo-EM. Our approach should be of interest for applications that benefit from stable GPCRs.

Gastrointestinal Stromal Tumor (GIST): A Remarkable Case Report and Literature Review.

Gastrointestinal stromal tumor (GIST) makes up less than 1% of all gastrointestinal tumors, but it is the most common mesenchymal tumor of the digestive system. It is commonly found in the stomach and the small intestine and rarely seen in the colon and the esophagus. Additionally, sigmoid GIST is quite rare since colorectal GIST often occurs in the rectum. A total of 21 patients (including the study case) were looked at for this study, of which 14 (66.6%) were males and seven (33.3%) were females. We focused on GIST and conducted an online search and systematic analysis of all case presentations.

A Scoping Review of Footwear Worn by People With Diabetes in Low- and Middle-Income Countries: Implications for Ulcer Prevention Programs.

International guidelines advise people with diabetes to wear close-toed, thick-soled footwear to protect against foot ulceration. However, this type of footwear is incompatible with some of the cultures, climates, and socioeconomic conditions in many low- and middle-income countries (LMICs). This scoping review aims to summarize what is known about footwear used by people with diabetes in LMICs and consider whether international diabetic foot guidelines are practical and actionable in these contexts, given current practices. PubMed, CINAHL, Scopus, Embase, Web of Science, Latin American and Caribbean Health Sciences Literature, and African Journals Online were searched for articles that documented the footwear used by people with diabetes in LMICs. Twenty-five studies from 13 countries were eligible for inclusion and indicated that a large proportion of people with diabetes wear footwear that is considered inappropriate by current guidance, with sandals and flip-flops being popular choices in a majority of the studies. Reasons given for these choices include poverty, lack of awareness of and provider communication about the importance of footwear selection, comfort, and cultural norms. We recommend that LMIC health care systems relying on international guidelines critically consider whether their recommendations are sensible in their settings. Diabetic foot experts and LMIC-based health care stakeholders should collaborate to design alternative guidelines, strategies, and interventions specifically for LMIC contexts to increase preventative practice feasibility and uptake.

Corrigendum: The development of machine learning in bariatric surgery.

[This corrects the article DOI: 10.3389/fsurg.2023.1102711.].

The development of machine learning in bariatric surgery.

Background: Machine learning (ML), is an approach to data analysis that makes the process of analytical model building automatic. The significance of ML stems from its potential to evaluate big data and achieve quicker and more accurate outcomes. ML has recently witnessed increased adoption in the medical domain. Bariatric surgery, otherwise referred to as weight loss surgery, reflects the series of procedures performed on people demonstrating obesity. This systematic scoping review aims to explore the development of ML in bariatric surgery. Methods: The study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR). A comprehensive literature search was performed of several databases including PubMed, Cochrane, and IEEE, and search engines namely Google Scholar. Eligible studies included journals published from 2016 to the current date. The PRESS checklist was used to evaluate the consistency demonstrated during the process. Results: A total of seventeen articles qualified for inclusion in the study. Out of the included studies, sixteen concentrated on the role of ML algorithms in prediction, while one addressed ML's diagnostic capacity. Most articles (n = 15) were journal publications, whereas the rest (n = 2) were papers from conference proceedings. Most included reports were from the United States (n = 6). Most studies addressed neural networks, with convolutional neural networks as the most prevalent. Also, the data type used in most articles (n = 13) was derived from hospital databases, with very few articles (n = 4) collecting original data via observation. Conclusions: This study indicates that ML has numerous benefits in bariatric surgery, however its current application is limited. The evidence suggests that bariatric surgeons can benefit from ML algorithms since they will facilitate the prediction and evaluation of patient outcomes. Also, ML approaches to enhance work processes by making data categorization and analysis easier. However, further large multicenter studies are required to validate results internally and externally as well as explore and address limitations of ML application in bariatric surgery.

Developing and validating a multivariable prediction model for predicting the cost of colon surgery.

Hospitals are burdened with predicting, calculating, and managing various cost-affecting parameters regarding patients and their treatments. Accuracy in cost prediction is further affected when a patient suffers from other health issues that hinder the traditional prognosis. This can lead to an unavoidable deficit in the final revenue of medical centers. This study aims to determine whether machine learning (ML) algorithms can predict cost factors based on patients undergoing colon surgery. For the forecasting, multiple predictors will be taken into the model to provide a tool that can be helpful for hospitals to manage their costs, ultimately leading to operating more cost-efficiently. This proof of principle will lay the groundwork for an efficient ML-based prediction tool based on multicenter data from a range of international centers in the subsequent phases of the study. With a mean absolute percentage error result of 18%-25.6%, our model's prediction showed decent results in forecasting the costs regarding various diagnosed factors and surgical approaches. There is an urgent need for further studies on predicting cost factors, especially for cases with anastomotic leakage, to minimize unnecessary hospital costs.

Cost and Quality Comparison of Hernia Surgery in Stationary, Day-Patient and Outpatient Care.

BACKGROUND: Medical progress is increasingly enabling more and more stationary treatment to be provided in the outpatient sector. This development should be welcomed, as healthcare costs have been rising for years. The design of efficient processes and a needs-based infrastructure enable further savings. According to international recommendations (EHS/IEHS), outpatient treatment of unilateral inguinal hernias is recommended. METHOD: Data from patients in GZO Hospital Wetzikon/Zurich between 2019 and 2021 for unilateral inguinal hernia repair was included in this study (n = 234). Any over- or under-coverage correlated with one of the three treatment groups: stationary, partially stationary and patients treated in outpatients clinic. Complications and 30-day readmissions were also monitored. RESULTS: Final revenue for all patients is -95.36 CHF. For stationary treatments, the mean shifts down to -575.01 CHF, for partially stationary treatments the mean shifts up to -24.73 CHF, and for patients in outpatient clinic final revenue is 793.12 CHF. This result is also consistent with the operation times, which are lowest in the outpatient clinic with a mean of 36 min, significantly longer in the partially stationary setting with 58 min, and longest in the stationary setting with 76 min. The same applies to the anesthesia times and the relevant care times by the nurses as the most important cost factors in addition to the supply and allocation costs. CONCLUSIONS: We show that cost-effective elective unilateral inguinal hernia care in the outpatient clinic with profit (mean 793.12 CHF) is possible. Stationary unilateral hernia care (mean -575.01 CHF) is loss-making. Crucial factors for cost efficiency are optimized processes in the operating room (anesthesia, surgical technique and quality, operating time), as well as optimized care processes with minimal preoperative services and care times for the patient. However, at the same time, these optimizations pose a challenge to surgical and anesthesiology training and structures with high levels of preoperative and Postoperative services and pay-as-you-go costs. The complication rate is 0.91% lower than in a comparable study. The readmission within 30 days post-operation results with a positive deviation of -3.53% (stationary) and with a negative deviation of +2.29% (outpatient clinic) compared to a comparative study.

Targeting virulence regulation to disarm Acinetobacter baumannii pathogenesis.

The development of anti-virulence drug therapy against Acinetobacter baumannii infections would provide an alternative to traditional antibacterial therapy that are increasingly failing. Here, we demonstrate that the OmpR transcriptional regulator plays a pivotal role in the pathogenesis of diverse A. baumannii clinical strains in multiple murine and G. mellonella invertebrate infection models. We identified OmpR-regulated genes using RNA sequencing and further validated two genes whose expression can be used as robust biomarker to quantify OmpR inhibition in A. baumannii. Moreover, the determination of the structure of the OmpR DNA binding domain of A. baumannii and the development of in vitro protein-DNA binding assays enabled the identification of an OmpR small molecule inhibitor. We conclude that OmpR is a valid and unexplored target to fight A. baumannii infections and we believe that the described platform combining in silico methods, in vitro OmpR inhibitory assays and in vivo G. mellonella surrogate infection model will facilitate future drug discovery programs.

Electric fields control water-gated proton transfer in cytochrome c oxidase.

Aerobic life is powered by membrane-bound enzymes that catalyze the transfer of electrons to oxygen and protons across a biological membrane. Cytochrome c oxidase (CcO) functions as a terminal electron acceptor in mitochondrial and bacterial respiratory chains, driving cellular respiration and transducing the free energy from O2 reduction into proton pumping. Here we show that CcO creates orientated electric fields around a nonpolar cavity next to the active site, establishing a molecular switch that directs the protons along distinct pathways. By combining large-scale quantum chemical density functional theory (DFT) calculations with hybrid quantum mechanics/molecular mechanics (QM/MM) simulations and atomistic molecular dynamics (MD) explorations, we find that reduction of the electron donor, heme a, leads to dissociation of an arginine (Arg438)-heme a3 D-propionate ion-pair. This ion-pair dissociation creates a strong electric field of up to 1 V Å-1 along a water-mediated proton array leading to a transient proton loading site (PLS) near the active site. Protonation of the PLS triggers the reduction of the active site, which in turn aligns the electric field vectors along a second, "chemical," proton pathway. We find a linear energy relationship of the proton transfer barrier with the electric field strength that explains the effectivity of the gating process. Our mechanism shows distinct similarities to principles also found in other energy-converting enzymes, suggesting that orientated electric fields generally control enzyme catalysis.

The Effects of Anastomotic Leaks on the Net Revenue from Colon Surgery.

Background: Complications in colon surgery can have severe health consequences, while at the same time, they are associated with increased costs. An anastomotic leak (AL) is associated with significantly increased costs compared to cases without. The aim of our analysis was to evaluate, which individual processes and patient-unrelated factors influencing the treatment process of colon surgery are responsible for the financial burden in patients with AL. Methods: Data from 263 patients who underwent colon surgery in Wetzikon hospital between January 2018 and December 2020 and was analyzed. In these 263 cases, 12 anastomotic leaks occurred and were compared with 36 cases without AL using a Propensity Score Matching (PSM). The covariates for the PSM have been Age, Sex, and Type of Surgery (t value: −3.26, p-value: 0.001). Results: A total of 48 surgeries were broken down in terms of costs and profitability. This reflected a mean deficit of −37,527 CHF per case (range from −130.05 to +755 CHF) for patients with AL, whereas a mean profit of 1590 CHF per case (range from −24.37 to +12.65 CHF) for those without AL (p < 0.001). Thus, the difference in profit showed a factor of 24.6 with an overall significant negative outcome for the occurrence of AL. The main cost contributing factors were the length of hospital stay (~p < 0.05) and length of intensive care (p < 0.05), whereas neither surgical operation time and anesthesia time nor surgical access, insurance status, indication or type of operation had a significant influence on the net revenue. Conclusion: AL after colon surgery leads to a significant deficit regarding the net revenue. Regarding process optimization, our analysis identified several sectors of non-patient-related, yet cost-influencing variables that should be addressed in future evaluations and optimization of the colon surgery treatment processes.

The Development of Artificial Intelligence in Hernia Surgery: A Scoping Review.

Background: Artificial intelligence simulates human intelligence in machines that have undergone programming to make them think like human beings and imitate their activities. Artificial intelligence has dominated the medical sector to perform various patient diagnosis activities and improve communication between professionals and patients. The main goal of this study is to perform a scoping review to evaluate the development of artificial intelligence in all forms of hernia surgery except the diaphragm and upside-down hernia. Methods: The study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR) to guide the structuring of the manuscript and fulfill all the requirements of every subheading. The sources used to gather data are the PubMed, Cochrane, and EMBASE databases, IEEE and Google and Google Scholar search engines. AMSTAR tool is the most appropriate for assessing the methodological quality of the included studies. Results: The study exclusively included twenty articles, whereby seven focused on artificial intelligence in inguinal hernia surgery, six focused on abdominal hernia surgery, five on incisional hernia surgery, and two on AI in medical imaging and robotics in hernia surgery. Conclusion: The outcomes of this study reveal a significant literature gap on artificial intelligence in hernia surgery. The results also indicate that studies focus on inguinal hernia surgery more than any other types of hernia surgery since the articles addressing the topic are more. The study implies that more research is necessary for the field to develop and enjoy the benefits associated with AI. Thus, this situation will allow the integration of AI in activities like medical imaging and surgeon training.

Advancements of Artificial Intelligence in Liver-Associated Diseases and Surgery.

Background and Objectives: The advancement of artificial intelligence (AI) based technologies in medicine is progressing rapidly, but the majority of its real-world applications has not been implemented. The establishment of an accurate diagnosis with treatment has now transitioned into an artificial intelligence era, which has continued to provide an amplified understanding of liver cancer as a disease and helped to proceed better with the method of procurement. This article focuses on reviewing the AI in liver-associated diseases and surgical procedures, highlighting its development, use, and related counterparts. Materials and Methods: We searched for articles regarding AI in liver-related ailments and surgery, using the keywords (mentioned below) on PubMed, Google Scholar, Scopus, MEDLINE, and Cochrane Library. Choosing only the common studies suggested by these libraries, we segregated the matter based on disease. Finally, we compiled the essence of these articles under the various sub-headings. Results: After thorough review of articles, it was observed that there was a surge in the occurrence of liver-related surgeries, diagnoses, and treatments. Parallelly, advanced computer technologies governed by AI continue to prove their efficacy in the accurate screening, analysis, prediction, treatment, and recuperation of liver-related cases. Conclusions: The continual developments and high-order precision of AI is expanding its roots in all directions of applications. Despite being novel and lacking research, AI has shown its intrinsic worth for procedures in liver surgery while providing enhanced healing opportunities and personalized treatment for liver surgery patients.

The Development of Healthcare Jobs in the COVID-Pandemic-A New Economic Market.

Background: The COVID-19 pandemic commenced in China and has caused the death of numerous people globally. Despite the adverse effects, the outbreak has created room for job opportunities in healthcare, particularly the pharmaceutical domain. The main goal of this study is to examine how the current pandemic has triggered job creation in the healthcare domain and created a new economic market. Methods: The study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR) to structure the manuscript and the subheadings to use. The source used to gather data is the PubMed database. Results: The study exclusively included fourteen articles, five of which focused on the pharmaceutical sector, three focused on vaccine sales, three on vaccination centers, and three on testing centers. Conclusion: The COVID-19 pandemic has created job opportunities in the healthcare sector. Most jobs are in the pharmaceutical sector, vaccination, and testing centers. However, more comprehensive research on the topic is necessary to gather conclusive outcomes on whether these jobs will be relevant after the pandemic.

Thyroglossal Duct Cyst, a Case Report and Literature Review.

A thyroglossal duct cyst (TGDC) is one of the most commonly encountered congenital anomalies of the neck. However, it is difficult to diagnose as differentiating it from other cysts like brachial cysts, lymphangiomas, epidermoid cysts, dermoid cysts, and hydatid cysts, is challenging. In this paper, we systematically reviewed the literature of 47 patients-25 males (53.1%) and 21 females (44.7%)-about their TGDC to assess the clinical picture, therapy, and prognosis of the disease. Most of the patients were children under the age of ten (63.8%). All patients had a history of a painless swelling in the anterior midline of the neck that moved in response to deglutition and tongue protrusion, thus interfering with their daily activity. Post-resection recurrence was unusual, with only 3 of 47 patients (6.4%) experiencing recurrence.

The Increasing Investment of Real Estate in the Health System-A Comparison between the USA and Europe.

BACKGROUND: This study aimed to compare property development and increasing investment in real estate by the healthcare system organizations in the USA and Europe. Real estate investments have upsurged in healthcare due to the multiple benefits to patients and medical practitioners. METHODS: The approach of acquiring data was through secondary sources and online questionnaires. The researchers applied inclusion and exclusion criteria by exclusively including the articles published after 2014 to ensure the validity and reliability of the information. RESULTS: A total of 53.33% of the articles reviewed focused on the United States, while 46.67% concentrated on Europe. The development of real estate in healthcare is essential in both regions due to the challenges faced with the current infrastructure. Study Limitation: Currently, there are very few studies concentrating on the research topic. CONCLUSIONS: The USA and Europe should focus on increasing real estate investments in healthcare by focusing on hospitals and trusts, rehabilitation centers, and nursing homes.

The Development of Telemedicine and eHealth in Surgery during the SARS-CoV-2 Pandemic.

SARS-CoV-2 has hampered healthcare systems worldwide, but some countries have found new opportunities and methods to combat it. In this study, we focused on the rapid growth of telemedicine during the pandemic around the world. We conducted a systematic literature review of all the articles published up to the present year, 2021, by following the requirements of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The data extracted comprised eHealth and telemedicine in surgery globally, and independently in Europe, the United States, and Switzerland. This review explicitly included fifty-nine studies. Out of all the articles included, none of them found that telemedicine causes poor outcomes in patients. Telemedicine has created a new path in the world of healthcare, revolutionizing how healthcare is delivered to patients and developing alternative methods for clinicians.

Greenhead (Tabanus nigrovittatus) Wolbachia and Its Microbiome: A Preliminary Study.

Endosymbiotic Wolbachia bacteria are known to influence the host physiology, microbiota composition, and dissemination of pathogens. We surveyed a population of Tabanus nigrovittatus, commonly referred to as "greenheads," from Crane Beach (Ipswich, MA, USA) for the presence of the alphaproteobacterial symbiont Wolbachia. We studied the COI (mitochondrial cytochrome oxidase) marker gene to evaluate the phylogenetic diversity of the studied specimens. The DNA sequences show strong similarity (between 99.9 and 98%) among the collected specimens but lower similarity to closely related entries in the NCBI database (only between 96.3 and 94.7%), suggesting a more distant relatedness. Low levels of Wolbachia presence necessitated a nested PCR approach, and using 5 markers (ftsZ, fbpA, dnaA, coxA, and gatB), we determined that two recognized "supergroups" of Wolbachia species were represented in the studied specimens, members of clades A and B. Using next-generation sequencing, we also surveyed the insect gut microbiomes of a subset of flies, using Illumina and PacBio 16S rRNA gene sequencing with barcoded primers. The composition of Proteobacteria also varied from fly to fly, with components belonging to Gammaproteobacteria making up the largest percentage of organisms (30 to 70%) among the microbiome samples. Most of the samples showed the presence of Spiroplasma, a member of the phylum Mollicutes, although the frequency of its presence was variable, ranging from 2 to 57%. Another noteworthy bacterial phylum consistently identified was Firmicutes, though the read abundances were typically below 10%. Of interest is an association between Wolbachia presence and higher Alphaproteobacteria representation in the microbiomes, suggesting that the presence of Wolbachia affects the host microbiome. IMPORTANCE Tabanus nigrovittatus greenhead populations contain two supergroups of Wolbachia endosymbionts, members of supergroups A and B. Analysis of the greenhead microbiome using next-generation sequencing revealed that the majority of bacterial species detected belonged to Gammaproteobacteria, with most of the samples also showing the presence of Spiroplasma, a member of the Mollicutes phylum also known to infect insects. An association between Wolbachia presence and higher Alphaproteobacteria representation in the microbiomes suggests that Wolbachia presence affects the host microbiome composition.