scholarly journals Risk Scores and Machine Learning to Identify Patients With Acute Periprosthetic Joints Infections That Will Likely Fail Classical Irrigation and Debridement

2021 ◽  
Vol 8 ◽  
Author(s):  
Marjan Wouthuyzen-Bakker ◽  
Noam Shohat ◽  
Javad Parvizi ◽  
Alex Soriano
Keyword(s):  
Machine Learning ◽  
Joint Infection ◽  
Treatment Strategies ◽  
Risk Scores ◽  
Related Factors ◽  
Duration Of Symptoms ◽  
Treatment Regimens ◽  
Irrigation And Debridement ◽  
Individualize Treatment ◽  
Independent Risk Factors

The most preferred treatment for acute periprosthetic joint infection (PJI) is surgical debridement, antibiotics and retention of the implant (DAIR). The reported success of DAIR varies greatly and depends on a complex interplay of several host-related factors, duration of symptoms, the microorganism(s) causing the infection, its susceptibility to antibiotics and many others. Thus, there is a great clinical need to predict failure of the “classical” DAIR procedure so that this surgical option is offered to those most likely to succeed, but also to identify those patients who may benefit from more intensified antibiotic treatment regimens or new and innovative treatment strategies. In this review article, the current recommendations for DAIR will be discussed, a summary of independent risk factors for DAIR failure will be provided and the advantages and limitations of the clinical use of preoperative risk scores in early acute (post-surgical) and late acute (hematogenous) PJIs will be presented. In addition, the potential of implementing machine learning (artificial intelligence) in identifying patients who are at highest risk for failure of DAIR will be addressed. The ultimate goal is to maximally tailor and individualize treatment strategies and to avoid treatment generalization.

scholarly journals The incidence, risk factors and predictive nomograms for early death of lung cancer with synchronous brain metastasis: A retrospective study in the SEER database

2021 ◽  
Author(s):  
Heng Shen ◽  
Gang Deng ◽  
Qianxue Chen ◽  
Jin Qian
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Brain Metastases ◽  
Treatment Strategies ◽  
Early Death ◽  
Gleason Grade ◽  
Related Factors ◽  
Prediction Ability ◽  
Short Period ◽  
Independent Risk Factors

Abstract Background Patients of lung cancer with synchronous brain metastases (LCBM) have a poor prognosis and die within a short period of time. However, little is known about the early mortality and related factors of LCBM patients. Methods Patients with LCBM diagnosed between 2010 and 2016 were enrolled from the surveillance, epidemiology, and end result (SEER). Significant independent prognostic factors were identified by univariate and multivariate logistic regression analyses. Nomograms of overall and cancer-specific early death were constructed using independent risk factors. The prediction ability and clinical application value of the model was verified by receiver operating characteristic (ROC) and decision curve analyses (DCAs). Results A total of 29902 cases of LCBM patients were enrolled in this study. 44.4% had early deaths, of which 38.2% died of lung cancer. Age, race, gender, Gleason grade, histological type, T stage, N stage, bone metastasis, liver metastasis, surgery, radiotherapy, chemotherapy and marital status were significant independent risk factors of overall and cancer-specific early death and was used to construct the nomogram. The areas under the curve (AUC) of the training group were 0.828 (95%CI: 0.822–0.833) and 0.800 (95%CI: 0.794–0.806) in the model of overall and cancer-specific early death, respectively. The DCA analysis showed that the model had good clinical benefits and utility Conclusions We established a comprehensive nomogram to distinguish early death in lung cancer patients with synchronous brain metastases which may help oncologists develop better treatment strategies, such as clinical trials and hospice care.

scholarly journals 2020 Frank Stinchfield Award: Identifying who will fail following irrigation and debridement for prosthetic joint infection

2020 ◽  
Vol 102-B (7_Supple_B) ◽  
pp. 11-19 ◽  
Author(s):  
Noam Shohat ◽  
Karan Goswami ◽  
Timothy L. Tan ◽  
Michael Yayac ◽  
Alex Soriano ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Prosthetic Joint Infection ◽  
Cross Validation ◽  
Joint Infection ◽  
Specific Patient ◽  
Musculoskeletal Infection ◽  
Prosthetic Joint ◽  
Irrigation And Debridement ◽  
Orthopaedic Literature

Aims Failure of irrigation and debridement (I&D) for prosthetic joint infection (PJI) is influenced by numerous host, surgical, and pathogen-related factors. We aimed to develop and validate a practical, easy-to-use tool based on machine learning that may accurately predict outcome following I&D surgery taking into account the influence of numerous factors. Methods This was an international, multicentre retrospective study of 1,174 revision total hip (THA) and knee arthroplasties (TKA) undergoing I&D for PJI between January 2005 and December 2017. PJI was defined using the Musculoskeletal Infection Society (MSIS) criteria. A total of 52 variables including demographics, comorbidities, and clinical and laboratory findings were evaluated using random forest machine learning analysis. The algorithm was then verified through cross-validation. Results Of the 1,174 patients that were included in the study, 405 patients (34.5%) failed treatment. Using random forest analysis, an algorithm that provides the probability for failure for each specific patient was created. By order of importance, the ten most important variables associated with failure of I&D were serum CRP levels, positive blood cultures, indication for index arthroplasty other than osteoarthritis, not exchanging the modular components, use of immunosuppressive medication, late acute (haematogenous) infections, methicillin-resistant Staphylococcus aureus infection, overlying skin infection, polymicrobial infection, and older age. The algorithm had good discriminatory capability (area under the curve = 0.74). Cross-validation showed similar probabilities comparing predicted and observed failures indicating high accuracy of the model. Conclusion This is the first study in the orthopaedic literature to use machine learning as a tool for predicting outcomes following I&D surgery. The developed algorithm provides the medical profession with a tool that can be employed in clinical decision-making and improve patient care. Future studies should aid in further validating this tool on additional cohorts. Cite this article: Bone Joint J 2020;102-B(7 Supple B):11–19.

scholarly journals An age-dependent outcome analysis of microvascular decompression and percutaneous thermocoagulation in trigeminal neuralgia

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Naureen Keric ◽  
Darius Kalasauskas ◽  
Sophia L. Kreth ◽  
Martin B. Glaser ◽  
Harald Krenzlin ◽  
...  
Keyword(s):  
Trigeminal Neuralgia ◽  
Microvascular Decompression ◽  
Complication Rate ◽  
Age Groups ◽  
Treatment Strategies ◽  
The Elderly ◽  
Outcome Analysis ◽  
Neurovascular Compression ◽  
Duration Of Symptoms ◽  
Ganglion Gasseri

Abstract Background Trigeminal neuralgia (TN) is a severe pain condition and the most common facial neuralgia. While microvascular decompression (MVD) presents an excellent treatment in neurovascular compression cases, percutaneous thermocoagulation (PT) of the ganglion Gasseri is an alternative option. This study aimed to evaluate post-operative complication rate and outcome of both treatment strategies related to the patient’s age. Methods The medical records of all patients with the diagnosis of trigeminal neuralgia undergoing an MVD or PT of the ganglion Gasseri (between January 2007 and September 2017) were reviewed to determine the efficacy and the complication rate of both methods in regard to the patient’s age. Results Seventy-nine patients underwent MVD surgery and 39 a PT. The mean age of patients in the MVD group was 61 years and 73 years in the PT group. There were 59 (50%) female patients. Nerve-vessel conflict could be identified in 78 (98.7%) MVD and 17 (43.6%) PT patients on preoperative MRI. Charlson comorbidity index was significantly higher in PT group (2.4 (1.8) versus 3.8 (1.8) p < 0.001). The Barrow pain score (BPS) at the last follow-up demonstrated higher scores after PT (p = 0.007). The complication rate was markedly higher in PT group, mostly due to the facial hypesthesia (84.6% versus 27.8%; p < 0.001). Mean symptom-free survival was significantly shorter in the PT group (9 vs. 26 months, p < 0.001). It remained statistically significant when stratified into age groups: (65 years and older: 9 vs. 18 months, p = 0.001). Duration of symptoms (OR 1.005, 95% CI 1.000–1.010), primary procedure (OR 6.198, 95% CI 2.650–14.496), patient age (OR 1.033, 95% CI 1.002–1.066), and postoperative complication rate (OR 2.777, 95% CI 1.309–5.890) were associated with treatment failure. Conclusion In this patient series, the MVD is confirmed to be an excellent treatment option independent of patient’s age. However, while PT is an effective procedure, time to pain recurrence is shorter, and the favorable outcome (BPS 1 and 2) rate is lower compared to MVD. Hence MVD should be the preferred treatment and PT should remain an alternative in very selected cases when latter is not possible but not in the elderly patient per se.

scholarly journals Incidence and risk factors for deep venous thrombosis of lower extremity after surgical treatment of isolated patella fractures

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Zhanchao Tan ◽  
Hongzhi Hu ◽  
Xiangtian Deng ◽  
Jian Zhu ◽  
Yanbin Zhu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lower Extremity ◽  
Venous Thrombosis ◽  
Deep Venous Thrombosis ◽  
Medical Data ◽  
Limited Information ◽  
Related Factors ◽  
Patella Fractures ◽  
Independent Risk Factors ◽  
Associated Risk Factors

Abstract Background Limited information exists on the incidence of postoperative deep venous thromboembolism (DVT) in patients with isolated patella fractures. The objective of this study was to investigate the postoperative incidence and locations of deep venous thrombosis (DVT) of the lower extremity in patients who underwent isolated patella fractures and identify the associated risk factors. Methods Medical data of 716 hospitalized patients was collected. The patients had acute isolated patella fractures and were admitted at the 3rd Hospital of Hebei Medical University between January 1, 2016, and February 31, 2019. All patients met the inclusion criteria. Medical data was collected using the inpatient record system, which included the patient demographics, patient’s bad hobbies, comorbidities, past medical history, fracture and surgery-related factors, hematological biomarkers, total hospital stay, and preoperative stay. Doppler examination was conducted for the diagnosis of DVT. Univariate analyses and multivariate logistic regression analyses were used to identify the independent risk factors. Results Among the 716 patients, DVT was confirmed in 29 cases, indicating an incidence of 4.1%. DVT involved bilateral limbs (injured and uninjured) in one patient (3.4%). DVT involved superficial femoral common vein in 1 case (3.4%), popliteal vein in 6 cases (20.7%), posterior tibial vein in 11 cases (37.9%), and peroneal vein in 11 cases (37.9%). The median of the interval between surgery and diagnosis of DVT was 4.0 days (range, 1.0-8.0 days). Six variables were identified to be independent risk factors for DVT which included age category (> 65 years old), OR, 4.44 (1.34-14.71); arrhythmia, OR, 4.41 (1.20-16.15); intra-operative blood loss, OR, 1.01 (1.00-1.02); preoperative stay (delay of each day), OR, 1.43 (1.15-1.78); surgical duration, OR, 1.04 (1.03-1.06); LDL-C (> 3.37 mmol/L), OR, 2.98 (1.14-7.76). Conclusion Incidence of postoperative DVT in patients with isolated patella fractures is substantial. More attentions should be paid on postoperative DVT prophylaxis in patients with isolated patella fractures. Identification of associated risk factors can help clinicians recognize the risk population, assess the risk of DVT, and develop personalized prophylaxis strategies.

scholarly journals Bacterial Membrane Vesicles in Pneumonia: From Mediators of Virulence to Innovative Vaccine Candidates

2021 ◽  
Vol 22 (8) ◽  
pp. 3858
Author(s):  
Felix Behrens ◽  
Teresa C. Funk-Hilsdorf ◽  
Wolfgang M. Kuebler ◽  
Szandor Simmons
Keyword(s):  
Extracellular Vesicles ◽  
Inflammatory Responses ◽  
Significant Proportion ◽  
Treatment Strategies ◽  
Membrane Vesicles ◽  
Multidrug Resistant ◽  
Biochemical Properties ◽  
Outer Membrane Vesicles ◽  
Vaccine Candidates ◽  
Treatment Regimens

Pneumonia due to respiratory infection with most prominently bacteria, but also viruses, fungi, or parasites is the leading cause of death worldwide among all infectious disease in both adults and infants. The introduction of modern antibiotic treatment regimens and vaccine strategies has helped to lower the burden of bacterial pneumonia, yet due to the unavailability or refusal of vaccines and antimicrobials in parts of the global population, the rise of multidrug resistant pathogens, and high fatality rates even in patients treated with appropriate antibiotics pneumonia remains a global threat. As such, a better understanding of pathogen virulence on the one, and the development of innovative vaccine strategies on the other hand are once again in dire need in the perennial fight of men against microbes. Recent data show that the secretome of bacteria consists not only of soluble mediators of virulence but also to a significant proportion of extracellular vesicles—lipid bilayer-delimited particles that form integral mediators of intercellular communication. Extracellular vesicles are released from cells of all kinds of organisms, including both Gram-negative and Gram-positive bacteria in which case they are commonly termed outer membrane vesicles (OMVs) and membrane vesicles (MVs), respectively. (O)MVs can trigger inflammatory responses to specific pathogens including S. pneumonia, P. aeruginosa, and L. pneumophila and as such, mediate bacterial virulence in pneumonia by challenging the host respiratory epithelium and cellular and humoral immunity. In parallel, however, (O)MVs have recently emerged as auspicious vaccine candidates due to their natural antigenicity and favorable biochemical properties. First studies highlight the efficacy of such vaccines in animal models exposed to (O)MVs from B. pertussis, S. pneumoniae, A. baumannii, and K. pneumoniae. An advanced and balanced recognition of both the detrimental effects of (O)MVs and their immunogenic potential could pave the way to novel treatment strategies in pneumonia and effective preventive approaches.

scholarly journals Nanotechnology as an Anti-Infection Strategy in Periprosthetic Joint Infections (PJI)

2021 ◽  
Vol 6 (2) ◽  
pp. 91
Author(s):  
Pier Francesco Indelli ◽  
Stefano Ghirardelli ◽  
Ferdinando Iannotti ◽  
Alessia Maria Indelli ◽  
Gennaro Pipino
Keyword(s):  
Joint Infection ◽  
Treatment Strategies ◽  
High Morbidity ◽  
Treatment Protocols ◽  
Joint Infections ◽  
Prevention And Treatment ◽  
Efficient Control ◽  
Cement Joint ◽  
Clay Nanotubes

Background: Periprosthetic joint infection (PJI) represents a devastating consequence of total joint arthroplasty (TJA) because of its high morbidity and its high impact on patient quality of life. The lack of standardized preventive and treatment strategies is a major challenge for arthroplasty surgeons. The purpose of this article was to explore the potential and future uses of nanotechnology as a tool for the prevention and treatment of PJI. Methods: Multiple review articles from the PubMed, Scopus and Google Scholar databases were reviewed in order to establish the current efficacy of nanotechnology in PJI preventive or therapeutic scenarios. Results: As a prevention tool, anti-biofilm implants equipped with nanoparticles (silver, silk fibroin, poly nanofibers, nanophase selenium) have shown promising antibacterial functionality. As a therapeutic tool, drug-loaded nanomolecules have been created and a wide variety of carrier materials (chitosan, titanium, calcium phosphate) have shown precise drug targeting and efficient control of drug release. Other nanotechnology-based antibiotic carriers (lipid nanoparticles, silica, clay nanotubes), when added to common bone cements, enhanced prolonged drug delivery, making this technology promising for the creation of antibiotic-added cement joint spacers. Conclusion: Although still in its infancy, nanotechnology has the potential to revolutionize prevention and treatment protocols of PJI. Nevertheless, extensive basic science and clinical research will be needed to investigate the potential toxicities of nanoparticles.

Artificial Intelligence Assisted Well Portfolio Optimization - An Automated Reservoir Management Advisory System to Maximize the Asset Value - Case Study from ADNOC Onshore

2021 ◽  
Author(s):  
Subba Ramarao Rachapudi Venkata ◽  
Nagaraju Reddicharla ◽  
Shamma Saeed Alshehhi ◽  
Indra Utama ◽  
Saber Mubarak Al Nuimi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Approach ◽  
Simulation Models ◽  
Production Optimization ◽  
Added Value ◽  
Risk Scores ◽  
Well Performance ◽  
Field Development ◽  
Advisory System ◽  
Gas Lift

Abstract Matured hydrocarbon fields are continuously deteriorating and selection of well interventions turn into critical task with an objective of achieving higher business value. Time consuming simulation models and classical decision-making approach making it difficult to rapidly identify the best underperforming, potential rig and rig-less candidates. Therefore, the objective of this paper is to demonstrate the automated solution with data driven machine learning (ML) & AI assisted workflows to prioritize the intervention opportunities that can deliver higher sustainable oil rate and profitability. The solution consists of establishing a customized database using inputs from various sources including production & completion data, flat files and simulation models. Automation of Data gathering along with technical and economical calculations were implemented to overcome the repetitive and less added value tasks. Second layer of solution includes configuration of tailor-made workflows to conduct the analysis of well performance, logs, output from simulation models (static reservoir model, well models) along with historical events. Further these workflows were combination of current best practices of an integrated assessment of subsurface opportunities through analytical computations along with machine learning driven techniques for ranking the well intervention opportunities with consideration of complexity in implementation. The automated process outcome is a comprehensive list of future well intervention candidates like well conversion to gas lift, water shutoff, stimulation and nitrogen kick-off opportunities. The opportunity ranking is completed with AI assisted supported scoring system that takes input from technical, financial and implementation risk scores. In addition, intuitive dashboards are built and tailored with the involvement of management and engineering departments to track the opportunity maturation process. The advisory system has been implemented and tested in a giant mature field with over 300 wells. The solution identified more techno-economical feasible opportunities within hours instead of weeks or months with reduced risk of failure resulting into an improved economic success rate. The first set of opportunities under implementation and expected a gain of 2.5MM$ with in first one year and expected to have reoccurring gains in subsequent years. The ranked opportunities are incorporated into the business plan, RMP plans and drilling & workover schedule in accordance to field development targets. This advisory system helps in maximizing the profitability and minimizing CAPEX and OPEX. This further maximizes utilization of production optimization models by 30%. Currently the system was implemented in one of ADNOC Onshore field and expected to be scaled to other fields based on consistent value creation. A hybrid approach of physics and machine learning based solution led to the development of automated workflows to identify and rank the inactive strings, well conversion to gas lift candidates & underperforming candidates resulting into successful cost optimization and production gain.

scholarly journals Dynamic Detection of Delayed Cerebral Ischemia Using Machine Learning

2020 ◽  
Author(s):  
Murad Megjhani ◽  
Kalijah Terilli ◽  
Ayham Alkhachroum ◽  
David J. Roh ◽  
Sachin Agarwal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cerebral Ischemia ◽  
Characteristic Curve ◽  
Delayed Cerebral Ischemia ◽  
Risk Scores ◽  
Support Vector ◽  
Model Parameters ◽  
Learning Approaches ◽  
Physiologic Data ◽  
Over Time

AbstractObjectiveTo develop a machine learning based tool, using routine vital signs, to assess delayed cerebral ischemia (DCI) risk over time.MethodsIn this retrospective analysis, physiologic data for 540 consecutive acute subarachnoid hemorrhage patients were collected and annotated as part of a prospective observational cohort study between May 2006 and December 2014. Patients were excluded if (i) no physiologic data was available, (ii) they expired prior to the DCI onset window (< post bleed day 3) or (iii) early angiographic vasospasm was detected on admitting angiogram. DCI was prospectively labeled by consensus of treating physicians. Occurrence of DCI was classified using various machine learning approaches including logistic regression, random forest, support vector machine (linear and kernel), and an ensemble classifier, trained on vitals and subject characteristic features. Hourly risk scores were generated as the posterior probability at time t. We performed five-fold nested cross validation to tune the model parameters and to report the accuracy. All classifiers were evaluated for good discrimination using the area under the receiver operating characteristic curve (AU-ROC) and confusion matrices.ResultsOf 310 patients included in our final analysis, 101 (32.6%) patients developed DCI. We achieved maximal classification of 0.81 [0.75-0.82] AU-ROC. We also predicted 74.7 % of all DCI events 12 hours before typical clinical detection with a ratio of 3 true alerts for every 2 false alerts.ConclusionA data-driven machine learning based detection tool offered hourly assessments of DCI risk and incorporated new physiologic information over time.

scholarly journals Acute Lymphoblastic Leukemia, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology

2021 ◽  
Vol 19 (9) ◽  
pp. 1079-1109
Author(s):  
Patrick A. Brown ◽  
Bijal Shah ◽  
Anjali Advani ◽  
Patricia Aoun ◽  
Michael W. Boyer ◽  
...  
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Supportive Care ◽  
Lymphoblastic Leukemia ◽  
Philadelphia Chromosome ◽  
Treatment Strategies ◽  
Adolescent And Young Adult ◽  
Cancer Center ◽  
Nccn Guidelines ◽  
Treatment Regimens

The NCCN Guidelines for Acute Lymphoblastic Leukemia (ALL) focus on the classification of ALL subtypes based on immunophenotype and cytogenetic/molecular markers; risk assessment and stratification for risk-adapted therapy; treatment strategies for Philadelphia chromosome (Ph)-positive and Ph-negative ALL for both adolescent and young adult and adult patients; and supportive care considerations. Given the complexity of ALL treatment regimens and the required supportive care measures, the NCCN ALL Panel recommends that patients be treated at a specialized cancer center with expertise in the management of ALL This portion of the Guidelines focuses on the management of Ph-positive and Ph-negative ALL in adolescents and young adults, and management in relapsed settings.

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