scholarly journals Machine learning in knee arthroplasty: specific data are key—a systematic review

2022 ◽  
Author(s):  
Florian Hinterwimmer ◽  
Igor Lazic ◽  
Christian Suren ◽  
Michael T. Hirschmann ◽  
Florian Pohlig ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Knee Arthroplasty ◽  
Prediction Models ◽  
Biomechanical Properties ◽  
Cochrane Library ◽  
Level Of Evidence ◽  
Medical Specialties ◽  
Specific Data ◽  
Novel Approach

Abstract Purpose Artificial intelligence (AI) in healthcare is rapidly growing and offers novel options of data analysis. Machine learning (ML) represents a distinct application of AI, which is capable of generating predictions and has already been tested in different medical specialties with various approaches such as diagnostic applications, cost predictions or identification of risk factors. In orthopaedics, this technology has only recently been introduced and the literature on ML in knee arthroplasty is scarce. In this review, we aim to investigate which predictions are already feasible using ML models in knee arthroplasty to identify prerequisites for the effective use of this novel approach. For this reason, we conducted a systematic review of ML algorithms for outcome prediction in knee arthroplasty. Methods A comprehensive search of PubMed, Medline database and the Cochrane Library was conducted to find ML applications for knee arthroplasty. All relevant articles were systematically retrieved and evaluated by an orthopaedic surgeon and a data scientist on the basis of the PRISMA statement. The search strategy yielded 225 articles of which 19 were finally assessed as eligible. A modified Coleman Methodology Score (mCMS) was applied to account for a methodological evaluation. Results The studies presented in this review demonstrated fair to good results (AUC median 0.76/range 0.57–0.98), while heterogeneous prediction models were analysed: complications (6), costs (4), functional outcome (3), revision (2), postoperative satisfaction (2), surgical technique (1) and biomechanical properties (1) were investigated. The median mCMS was 65 (range 40–80) points. Conclusion The prediction of distinct outcomes with ML models applying specific data is already feasible; however, the prediction of more complex outcomes is still inaccurate. Registry data on knee arthroplasty have not been fully analysed yet so that specific parameters have not been sufficiently evaluated. The inclusion of specific input data as well as the collaboration of orthopaedic surgeons and data scientists are essential prerequisites to fully utilize the capacity of ML in knee arthroplasty. Future studies should investigate prospective data with specific and longitudinally recorded parameters. Level of evidence III.

scholarly journals Safety of intraarticular corticosteroid injection preceding hip and knee arthroplasty: a systematic review and meta-analysis amid resolving COVID-19 arthroplasty restrictions

2021 ◽  
Author(s):  
Tim Cheok ◽  
Matthew Jennings ◽  
Alessandro Aprato ◽  
Narlaka Jayasekera ◽  
Ruurd L Jaarsma
Keyword(s):  
Systematic Review ◽  
Knee Arthroplasty ◽  
Hip Arthroplasty ◽  
Subgroup Analysis ◽  
Corticosteroid Injection ◽  
Meta Analysis ◽  
Joint Infection ◽  
Cochrane Library ◽  
Level Of Evidence ◽  
Hip And Knee Arthroplasty

ABSTRACT Intraarticular corticosteroid injection (ICSI) is a widely practiced management for hip and knee osteoarthritis. Imposed delays to arthroplasty during coronavirus disease 2019 pandemic have led us to postulate that many patients have opted for recent ICSI. We compared the odds of prosthetic joint infection (PJI) in patients who were or were not administered ICSI within 12 months prior to hip or knee arthroplasty. A systematic search of PubMed, Embase, The Cochrane Library and Web of Science was performed in February 2021, with studies assessing the effect of ICS on PJI rates identified. All studies, which included patients that received ICSI in the 12 months prior to primary hip and knee arthroplasty, were included. In total 12 studies were included: four studies with 209 353 hips and eight studies with 438 440 knees. ICSI administered in the 12 months prior to hip arthroplasty increased the odds of PJI [odds ratio (OR) = 1.17, P = 0.04]. This was not the case for knees. Subgroup analysis showed significantly higher odds of PJI in both hip [OR = 1.45, P = 0.002] and knee arthroplasty [OR = 2.04; P = 0.04] when ICSI was within the preceding 3 months of surgery. A significantly higher odds of PJI were seen in patients receiving ICSI within the 12 months prior to hip arthroplasty. Subgroup analysis showed increased odds of PJI in both hip and knee arthroplasty, in patients receiving ICSI within 3 months prior to their arthroplasty. We recommend delaying knee arthroplasty for at least 3 months after ICSI and possibly longer for hip arthroplasty. Level of Evidence Level III - Systematic Review of Level II and III Studies.

scholarly journals Machine learning in perioperative medicine: a systematic review

2022 ◽  
Vol 2 (1) ◽  
Author(s):  
Valentina Bellini ◽  
Marina Valente ◽  
Giorgia Bertorelli ◽  
Barbara Pifferi ◽  
Michelangelo Craca ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Health Care ◽  
Random Forest ◽  
Risk Stratification ◽  
Prediction Models ◽  
Cochrane Library ◽  
Gradient Boosting ◽  
Support Vector ◽  
Systemic Complications

Abstract Background Risk stratification plays a central role in anesthetic evaluation. The use of Big Data and machine learning (ML) offers considerable advantages for collection and evaluation of large amounts of complex health-care data. We conducted a systematic review to understand the role of ML in the development of predictive post-surgical outcome models and risk stratification. Methods Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, we selected the period of the research for studies from 1 January 2015 up to 30 March 2021. A systematic search in Scopus, CINAHL, the Cochrane Library, PubMed, and MeSH databases was performed; the strings of research included different combinations of keywords: “risk prediction,” “surgery,” “machine learning,” “intensive care unit (ICU),” and “anesthesia” “perioperative.” We identified 36 eligible studies. This study evaluates the quality of reporting of prediction models using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist. Results The most considered outcomes were mortality risk, systemic complications (pulmonary, cardiovascular, acute kidney injury (AKI), etc.), ICU admission, anesthesiologic risk and prolonged length of hospital stay. Not all the study completely followed the TRIPOD checklist, but the quality was overall acceptable with 75% of studies (Rev #2, comm #minor issue) showing an adherence rate to TRIPOD more than 60%. The most frequently used algorithms were gradient boosting (n = 13), random forest (n = 10), logistic regression (LR; n = 7), artificial neural networks (ANNs; n = 6), and support vector machines (SVM; n = 6). Models with best performance were random forest and gradient boosting, with AUC > 0.90. Conclusions The application of ML in medicine appears to have a great potential. From our analysis, depending on the input features considered and on the specific prediction task, ML algorithms seem effective in outcomes prediction more accurately than validated prognostic scores and traditional statistics. Thus, our review encourages the healthcare domain and artificial intelligence (AI) developers to adopt an interdisciplinary and systemic approach to evaluate the overall impact of AI on perioperative risk assessment and on further health care settings as well.

scholarly journals Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review

2021 ◽  
Vol 18 (9) ◽  
pp. 4499
Author(s):  
Anil Babu Payedimarri ◽  
Diego Concina ◽  
Luigi Portinale ◽  
Massimo Canonico ◽  
Deborah Seys ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Public Transportation ◽  
Prediction Models ◽  
Positive Impact ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Commercial Activities

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.

Infrared Thermography for the Evaluation of Inflammatory and Degenerative Joint Diseases: A Systematic Review

Cartilage ◽  
2021 ◽  
Vol 13 (2_suppl) ◽  
pp. 1790S-1801S
Author(s):  
Guglielmo Schiavon ◽  
Gianluigi Capone ◽  
Monique Frize ◽  
Stefano Zaffagnini ◽  
Christian Candrian ◽  
...  
Keyword(s):  
Systematic Review ◽  
Disease Progression ◽  
Infrared Thermography ◽  
Rheumatic Diseases ◽  
English Language ◽  
Response To Treatment ◽  
Cochrane Library ◽  
Joint Diseases ◽  
Level Of Evidence ◽  
Post Evaluation

Objective Inflammation plays a central role in the pathophysiology of rheumatic diseases as well as in osteoarthritis. Temperature, which can be quantified using infrared thermography, provides information about the inflammatory component of joint diseases. This systematic review aims at assessing infrared thermography potential and limitations in these pathologies. Design A systematic review was performed on 3 major databases: PubMed, Cochrane library, and Web of Science, on clinical reports of any level of evidence in English language, published from 1990 to May 2021, with infrared thermography used for diagnosis of osteoarthritis and rheumatic diseases, monitoring disease progression, or response to treatment. Relevant data were extracted, collected in a database, and analyzed for the purpose of this systematic review. Results Of 718 screened articles 32 were found to be eligible for inclusion, for a total of 2094 patients. Nine studies reported the application to osteoarthritis, 21 to rheumatic diseases, 2 on both. The publication trend showed an increasing interest in the last decade. Seven studies investigated the correlation of temperature changes with osteoarthritis, 16 with rheumatic diseases, and 2 with both, whereas 2 focused on the pre-post evaluation to investigate treatment results in patients with osteoarthritis and 5 in patients with rheumatic diseases. A correlation was shown between thermal findings and disease presence and stage, as well as the clinical assessment of disease activity and response to treatment, supporting infrared thermography role in the study and management of rheumatic diseases and osteoarthritis. Conclusions The systematic literature review showed an increasing interest in this technology, with several applications in different joints affected by inflammatory and degenerative pathologies. Infrared thermography proved to be a simple, accurate, noninvasive, and radiation-free method, which could be used in addition to the currently available tools for screening, diagnosis, monitoring of disease progression, and response to medical treatment.

Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review

2021 ◽  
Author(s):  
Nghia H Nguyen ◽  
Dominic Picetti ◽  
Parambir S Dulai ◽  
Vipul Jairath ◽  
William J Sandborn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Risk Prediction ◽  
Statistical Models ◽  
Prediction Models ◽  
Risk Of Bias ◽  
Learning Models ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
Machine Learning Models

Abstract Background and Aims There is increasing interest in machine learning-based prediction models in inflammatory bowel diseases (IBD). We synthesized and critically appraised studies comparing machine learning vs. traditional statistical models, using routinely available clinical data for risk prediction in IBD. Methods Through a systematic review till January 1, 2021, we identified cohort studies that derived and/or validated machine learning models, based on routinely collected clinical data in patients with IBD, to predict the risk of harboring or developing adverse clinical outcomes, and reported its predictive performance against a traditional statistical model for the same outcome. We appraised the risk of bias in these studies using the Prediction model Risk of Bias ASsessment (PROBAST) tool. Results We included 13 studies on machine learning-based prediction models in IBD encompassing themes of predicting treatment response to biologics and thiopurines, predicting longitudinal disease activity and complications and outcomes in patients with acute severe ulcerative colitis. The most common machine learnings models used were tree-based algorithms, which are classification approaches achieved through supervised learning. Machine learning models outperformed traditional statistical models in risk prediction. However, most models were at high risk of bias, and only one was externally validated. Conclusions Machine learning-based prediction models based on routinely collected data generally perform better than traditional statistical models in risk prediction in IBD, though frequently have high risk of bias. Future studies examining these approaches are warranted, with special focus on external validation and clinical applicability.

scholarly journals The effect of hemodilution on free flap survival: A systematic review of clinical and experimental studies

2020 ◽  
Vol 75 (4) ◽  
pp. 457-466
Author(s):  
Matteo Amoroso ◽  
Peter Apelgren ◽  
Anna Elander ◽  
Karin Säljö ◽  
Lars Kölby
Keyword(s):  
Systematic Review ◽  
Literature Search ◽  
Experimental Studies ◽  
Free Flaps ◽  
Risk Of Bias ◽  
Cochrane Library ◽  
Level Of Evidence ◽  
Grade Scale ◽  
Reconstructive Microsurgery ◽  
The Cochrane Library

BACKGROUND: Acute normovolemic hemodilution (ANH) has been proposed as a microsurgical technique to improve blood flow in free flaps. OBJECTIVE: Here, we present the first systematic review of clinical and experimental studies on the effect of ANH. METHODS: We performed a systematic literature search of PubMed, Medline, the Cochrane Library, Google Scholar, and ClinicalTrials.gov using search strategies and a review process in agreement with the PRISMA statement and the Cochrane Handbook for systematic reviews of interventions. PICO criteria were defined before bibliometric processing of the retrieved articles, which were analyzed with the SYRCLE RoB tool for risk of bias and the GRADE scale for level of evidence. RESULTS: We retrieved 74 articles from the literature search, and after processing according to PICO criteria, only four articles remained, all of which were experimental. The rating for risk of bias was uncertain according to SYRCLE RoB results, and the level of evidence was low according to GRADE evaluation. CONCLUSIONS: There is no clinical evidence for the effect of ANH on microcirculation in free flaps, and experimental studies provide weak evidence supporting the use of hemodilution in reconstructive microsurgery.

Occult Metastasis in Laryngeal Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis

2020 ◽  
Vol 130 (1) ◽  
pp. 67-77
Author(s):  
Daniel D. Sharbel ◽  
Mary Abkemeier ◽  
Michael W. Groves ◽  
William G. Albergotti ◽  
J. Kenneth Byrd ◽  
...  
Keyword(s):  
Systematic Review ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Distant Metastasis ◽  
Laryngeal Squamous Cell Carcinoma ◽  
Cochrane Library ◽  
Elective Neck Dissection ◽  
Occult Metastasis ◽  
Level Of Evidence

Objective: The incidence of occult metastasis (OM) in laryngeal squamous cell carcinoma (SCC) is still widely debated. In this systematic review, we aim to determine the rate of OM in laryngeal SCC, its impact on recurrence, and the role of elective neck dissection (END) in the management of the clinically negative neck. Methods: A systematic review of the English-language literature in Web of Science, PubMed, MEDLINE, and Cochrane Library databases on occult metastasis in laryngeal SCC from 1977 to 2018 was conducted. Studies evaluating occult metastasis (OM) in patients with laryngeal SCC with clinically negative necks undergoing surgery were included. Studies evaluating other head and neck subsites, clinically node positive, and salvage patients were excluded. Results: Twenty-one articles with a total of 5630 patients were included. The overall rate of OM was 20.5% and was 23% and 12.2% in supraglottic and glottic tumors, respectively. The OM rate in T1-T2 tumors was 13% and 25% in T3-T4 tumors. T3-T4 tumors had significantly greater odds of developing OM compared to T1-T2 tumors (Odds Ratio [OR] = 2.61, 95% Confidence Interval [CI] = 1.92-3.55, P < .00001). Patients with OM were more likely to develop distant metastasis (OR = 5.65, 95% CI = 3.36-9.51, P < .00001). Conclusions: Patients with advanced T-stage laryngeal SCC should undergo elective neck treatment. More aggressive treatment for patients with history of OM should be considered due to the risk of subsequent regional and distant metastasis. Level of Evidence: II

scholarly journals Safety and Efficacy of Therapeutic Taping in Primary Dysmenorrhea: a Systematic Review and Meta-analysis

2021 ◽  
Author(s):  
Bandara EMIA ◽  
Kularathne WNI ◽  
K Brain ◽  
Weerasekara I
Keyword(s):  
Systematic Review ◽  
Meta Analysis ◽  
Cochrane Library ◽  
Adult Women ◽  
Primary Dysmenorrhea ◽  
Level Of Evidence ◽  
Effective Measure ◽  
Safety And Efficacy ◽  
Pain Anxiety

Abstract Primary dysmenorrhea (PD) is a common gynecological complaint among adolescents and adult women. Various pharmacological and alternative therapies such as therapeutic taping have been used as a treatment of PD. Although several studies have been conducted to evaluate the safety and efficacy of therapeutic taping in PD, these studies have not provided adequate level of evidence related to the safety and efficacy of therapeutic taping in PD. Hence, a systematic review and meta-analysis was performed to evaluate the safety and efficacy of therapeutic taping in PD. The following databases; Medline, Cochrane Library, Embase, PEDro, CINAHL and any other gray literature sources were searched for randomized controlled trials (RCTs) that used therapeutic taping to treat PD from inception to June 2021 with the language restricted to English. Independently screened articles by two reviewers were extracted according to the study objectives. A total of nine studies were included in the systematic review, involving 577 participants. Three studies were eligible for meta-analysis to find the pooled effect of taping on pain intensity. The review indicates that therapeutic taping is an effective measure in improving pain, anxiety and quality of life of women with PD. Meta-analysis conducted to compare the effect of elastic therapeutic taping (ETT) to sham taping showed that the ETT is an effective measure in improving pain among women with PD (MD = -3.12 (95% CI -5.64, -0.60); p=0.02; I2=95 %). The quality of the studies was assessed using the PEDro scale and the included RCTs indicated a fair to good level of quality. Our systematic review and meta-analysis demonstrated that therapeutic taping is an effective intervention for PD. However, RCTs with higher quality and larger sample sizes are necessary to verify the current results of the review.

scholarly journals Risk Prediction Models for Patients With Acute Heart Failure: A Protocol for Systematic Review, Critical Appraisal, and Meta-Analysis

2021 ◽  
Author(s):  
Xuecheng Zhang ◽  
Kehua Zhou ◽  
Jingjing Zhang ◽  
Ying Chen ◽  
Hengheng Dai ◽  
...  
Keyword(s):  
Heart Failure ◽  
Systematic Review ◽  
Prediction Model ◽  
Acute Heart Failure ◽  
Risk Prediction ◽  
Methodological Quality ◽  
Critical Appraisal ◽  
Prediction Models ◽  
Cochrane Library ◽  
Risk Prediction Models

Abstract Background Nearly a third of patients with acute heart failure (AHF) die or are readmitted within three months after discharge, accounting for the majority of costs associated with heart failure-related care. A considerable number of risk prediction models, which predict outcomes for mortality and readmission rates, have been developed and validated for patients with AHF. These models could help clinicians stratify patients by risk level and improve decision making, and provide specialist care and resources directed to high-risk patients. However, clinicians sometimes reluctant to utilize these models, possibly due to their poor reliability, the variety of models, and/or the complexity of statistical methodologies. Here, we describe a protocol to systematically review extant risk prediction models. We will describe characteristics, compare performance, and critically appraise the reporting transparency and methodological quality of risk prediction models for AHF patients. Method Embase, Pubmed, Web of Science, and the Cochrane Library will be searched from their inception onwards. A back word will be searched on derivation studies to find relevant external validation studies. Multivariable prognostic models used for AHF and mortality and/or readmission rate will be eligible for review. Two reviewers will conduct title and abstract screening, full-text review, and data extraction independently. Included models will be summarized qualitatively and quantitatively. We will also provide an overview of critical appraisal of the methodological quality and reporting transparency of included studies using the Prediction model Risk of Bias Assessment Tool(PROBAST tool) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis(TRIPOD statement). Discussion The result of the systematic review could help clinicians better understand and use the prediction models for AHF patients, as well as make standardized decisions about more precise, risk-adjusted management. Systematic review registration : PROSPERO registration number CRD42021256416.

scholarly journals Efficacy and safety of microbiota transfer therapy for the management of autism spectrum disorder in children: a systematic review

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 48
Author(s):  
Pablo Daniel Estrella Porter ◽  
Luis Eduardo Guzmán Freire ◽  
Joseth Paulina Adatty Molina ◽  
María Verónica Burneo Raza ◽  
Henry Alejandro Carrión Celi ◽  
...  
Keyword(s):  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Intervention Group ◽  
Autism Spectrum ◽  
Nervous System Development ◽  
Spectrum Disorder ◽  
Current Evidence ◽  
Cochrane Library ◽  
Efficacy And Safety ◽  
Level Of Evidence

Background: Autism spectrum disorder (ASD) is a neurodevelopmental condition associated with an unclear etiologic mechanism. Following suggestions in the literature of a close relation between the gut microbiota and the central nervous system development, neuroimmune and neuroendocrine systems, new theories and strategies of the management of ASD in children focus on the brain-gut axis via microbiota transfer therapy. Despite the regular appearance in the news, the level of evidence supporting this intervention is unclear and to this date, no systematic review on this issue has been published. Methods: We conducted a systematic literature review of the efficacy and safety of microbiota transfer therapy for the management of ASD in children. MEDLINE via PubMed, LILACS IBECS via BVS, EMBASE via Ovid, Scopus and Cochrane Library were searched on 19th April 2018. Results: One single study published in 2017 was identified. The intervention group included 18 patients and showed significant clinical improvements in the gastrointestinal and ASD-related symptoms. The clinical procedure was reported as safe and well-tolerated with some transitory adverse effects. Conclusions: The causality and correlation of the intervention and the expected outcomes cannot be assumed with current evidence. In addition, recommendations about the effectiveness or safety of microbiota transfer therapy in children with ASD cannot be currently issued. Randomized controlled trials and clinical protocols for the intervention are needed.

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