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1.
Bioelectrochemistry ; 142: 107933, 2021 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-34560601

RESUMO

Crevice corrosion of X80 carbon steel in simulated seawater with the presence of SRB was studied by surface analysis and electrochemical measurements. The electrode inside crevice was seriously corroded. Large amount of corrosion products accumulated along the crevice mouth. Galvanic current densities measurements confirmed that there was a galvanic effect between the carbon steel at the crevice interior and exterior during the crevice corrosion. The difference in the sessile SRB cells quantities and SRB biofilms developments inside and outside crevice caused the galvanic effect between the carbon steel inside and outside the crevice, which further induced crevice corrosion. Increased crevice width reduced the galvanic effect, resulting in less crevice corrosion in wider crevice.

3.
Neurology ; 97(13): e1313-e1321, 2021 Sep 28.
Artigo em Inglês | MEDLINE | ID: mdl-34376505

RESUMO

BACKGROUND AND OBJECTIVES: Silent cerebrovascular disease (SCD), comprising silent brain infarction (SBI) and white matter disease (WMD), is commonly found incidentally on neuroimaging scans obtained in routine clinical care. Their prognostic significance is not known. We aimed to estimate the incidence of and risk increase in future stroke in patients with incidentally discovered SCD. METHODS: Patients in the Kaiser Permanente Southern California (KPSC) health system aged ≥50 years, without prior ischemic stroke, transient ischemic attack (TIA), or dementia/Alzheimer disease receiving a head CT or MRI between 2009 and 2019 were included. SBI and WMD were identified by natural language processing (NLP) from the neuroimage report. RESULTS: Among 262,875 individuals receiving neuroimaging, NLP identified 13,154 (5.0%) with SBI and 78,330 (29.8%) with WMD. The incidence of future stroke was 32.5 (95% confidence interval [CI] 31.1, 33.9) per 1,000 patient-years for patients with SBI: 19.3 (95% CI 18.9, 19.8) for patients with WMD and 6.8 (95% CI 6.7, 7.0) for patients without SCD. The crude hazard ratio (HR) associated with SBI was 3.40 (95% CI 3.25 to 3.56) and for WMD 2.63 (95% CI 2.54 to 2.71). With MRI-discovered SBI, the adjusted HR was 2.95 (95% CI 2.53 to 3.44) for those <65 years of age and 2.15 (95% CI 1.91 to 2.41) for those ≥65. With CT scan, the adjusted HR was 2.48 (95% CI 2.19 to 2.81) for those <65 and 1.81 (95% CI 1.71 to 1.91) for those ≥65. The adjusted HR associated with a finding of WMD was 1.76 (95% CI 1.69 to 1.82) and was not modified by age or imaging modality. DISCUSSION: Incidentally discovered SBI and WMD are common and associated with increased risk of subsequent symptomatic stroke, representing an important opportunity for stroke prevention.

4.
Chem Rec ; 2021 Aug 20.
Artigo em Inglês | MEDLINE | ID: mdl-34415677

RESUMO

Sulfate reducing bacteria (SRB) are blamed as main culprits in triggering huge corrosion damages by microbiologically influenced corrosion. They obtained their energy through enzymatic conversion of sulfates to sulfides which are highly corrosive. However, conventional SRB detection methods are complex, time-consuming and are not enough sensitive for reliable detection. The advanced biosensing technologies capable of overcoming the aforementioned drawbacks are in demand. So, nanomaterials being economical, environmental friendly and showing good electrocatalytic properties are promising candidates for electrochemical detection of SRB as compared with antibody based assays. Here, we summarize the recent advances in the detection of SRB using different techniques such as PCR, UV visible method, fluorometric method, immunosensors, electrochemical sensors and photoelectrochemical sensors. We also discuss the SRB detection based on determination of sulfide, typical metabolic product of SRB.

5.
AMIA Annu Symp Proc ; 2021: 152-160, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34457129

RESUMO

Models predicting health complications are increasingly attempting to reflect the temporally changing nature of patient status. However, both the practice of medicine and electronic health records (EHR) have yet to provide a true longitudinal representation of a patient's medical history as relevant data is often asynchronous and highly missing. To match the stringent requirements of many static time models, time-series data has to be truncated, and missing values in samples have to be filled heuristically. However, these data preprocessing procedures may unconsciously misinterpret real-world data, and eventually lead into failure in practice. In this work, we proposed an augmented gated recurrent unit (GRU), which formulate both missingness and timeline signals into GRU cells. Real patient data of post-operative bleeding (POB) after Colon and Rectal Surgery (CRS) was collected from Mayo Clinic EHR system to evaluate the effectiveness of proposed model. Conventional models were also trained with imputed dataset, in which event missingness or asynchronicity were approximated. The performance of proposed model surpassed current state-of-the-art methods in this POB detection task, indicating our model could be more eligible to handle EHR datasets.


Assuntos
Registros Eletrônicos de Saúde , Diagnóstico Precoce , Humanos
6.
AMIA Annu Symp Proc ; 2021: 410-419, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34457156

RESUMO

HL7 Fast Healthcare Interoperability Resources (FHIR) is one of the current data standards for enabling electronic healthcare information exchange. Previous studies have shown that FHIR is capable of modeling both structured and unstructured data from electronic health records (EHRs). However, the capability of FHIR in enabling clinical data analytics has not been well investigated. The objective of the study is to demonstrate how FHIR-based representation of unstructured EHR data can be ported to deep learning models for text classification in clinical phenotyping. We leverage and extend the NLP2FHIR clinical data normalization pipeline and conduct a case study with two obesity datasets. We tested several deep learning-based text classifiers such as convolutional neural networks, gated recurrent unit, and text graph convolutional networks on both raw text and NLP2FHIR inputs. We found that the combination of NLP2FHIR input and text graph convolutional networks has the highest F1 score. Therefore, FHIR-based deep learning methods has the potential to be leveraged in supporting EHR phenotyping, making the phenotyping algorithms more portable across EHR systems and institutions.


Assuntos
Aprendizado Profundo , Algoritmos , Registros Eletrônicos de Saúde , Humanos , Obesidade , Projetos Piloto
7.
AMIA Annu Symp Proc ; 2021: 515-524, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34457167

RESUMO

Natural language is continually changing. Given the prevalence of unstructured, free-text clinical notes in the healthcare domain, understanding the aspects of this change is of critical importance to clinical Natural Language Processing (NLP) systems. In this study, we examine two previously described semantic change laws based on word frequency and polysemy, and analyze how they apply to the clinical domain. We also explore a new facet of change: whether domain-specific clinical terms exhibit different change patterns compared to general-purpose English. Using a corpus spanning eighteen years of clinical notes, we find that the previously described laws of semantic change hold for our data set. We also find that domain-specific biomedical terms change faster compared to general English words.


Assuntos
Processamento de Linguagem Natural , Semântica , Humanos , Idioma , Unified Medical Language System
8.
AMIA Annu Symp Proc ; 2021: 624-633, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34457178

RESUMO

Lack of standardized representation of natural language processing (NLP) components in phenotyping algorithms hinders portability of the phenotyping algorithms and their execution in a high-throughput and reproducible manner. The objective of the study is to develop and evaluate a standard-driven approach - CQL4NLP - that integrates a collection of NLP extensions represented in the HL7 Fast Healthcare Interoperability Resources (FHIR) standard into the clinical quality language (CQL). A minimal NLP data model with 11 NLP-specific data elements was created, including six FHIR NLP extensions. All 11 data elements were identified from their usage in real-world phenotyping algorithms. An NLP ruleset generation mechanism was integrated into the NLP2FHIR pipeline and the NLP rulesets enabled comparable performance for a case study with the identification of obesity comorbidities. The NLP ruleset generation mechanism created a reproducible process for defining the NLP components of a phenotyping algorithm and its execution.


Assuntos
Registros Eletrônicos de Saúde , Processamento de Linguagem Natural , Algoritmos , Comorbidade , Humanos , Idioma
9.
PLoS One ; 16(8): e0255261, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34339438

RESUMO

RATIONALE: Clinical decision support (CDS) tools leveraging electronic health records (EHRs) have been an approach for addressing challenges in asthma care but remain under-studied through clinical trials. OBJECTIVES: To assess the effectiveness and efficiency of Asthma-Guidance and Prediction System (A-GPS), an Artificial Intelligence (AI)-assisted CDS tool, in optimizing asthma management through a randomized clinical trial (RCT). METHODS: This was a single-center pragmatic RCT with a stratified randomization design conducted for one year in the primary care pediatric practice of the Mayo Clinic, MN. Children (<18 years) diagnosed with asthma receiving care at the study site were enrolled along with their 42 primary care providers. Study subjects were stratified into three strata (based on asthma severity, asthma care status, and asthma diagnosis) and were blinded to the assigned groups. MEASUREMENTS: Intervention was a quarterly A-GPS report to clinicians including relevant clinical information for asthma management from EHRs and machine learning-based prediction for risk of asthma exacerbation (AE). Primary endpoint was the occurrence of AE within 1 year and secondary outcomes included time required for clinicians to review EHRs for asthma management. MAIN RESULTS: Out of 555 participants invited to the study, 184 consented for the study and were randomized (90 in intervention and 94 in control group). Median age of 184 participants was 8.5 years. While the proportion of children with AE in both groups decreased from the baseline (P = 0.042), there was no difference in AE frequency between the two groups (12% for the intervention group vs. 15% for the control group, Odds Ratio: 0.82; 95%CI 0.374-1.96; P = 0.626) during the study period. For the secondary end points, A-GPS intervention, however, significantly reduced time for reviewing EHRs for asthma management of each participant (median: 3.5 min, IQR: 2-5), compared to usual care without A-GPS (median: 11.3 min, IQR: 6.3-15); p<0.001). Mean health care costs with 95%CI of children during the trial (compared to before the trial) in the intervention group were lower than those in the control group (-$1,036 [-$2177, $44] for the intervention group vs. +$80 [-$841, $1000] for the control group), though there was no significant difference (p = 0.12). Among those who experienced the first AE during the study period (n = 25), those in the intervention group had timelier follow up by the clinical care team compared to those in the control group but no significant difference was found (HR = 1.93; 95% CI: 0.82-1.45, P = 0.10). There was no difference in the proportion of duration when patients had well-controlled asthma during the study period between the intervention and the control groups. CONCLUSIONS: While A-GPS-based intervention showed similar reduction in AE events to usual care, it might reduce clinicians' burden for EHRs review resulting in efficient asthma management. A larger RCT is needed for further studying the findings. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02865967.

10.
J Am Med Inform Assoc ; 28(10): 2193-2201, 2021 Sep 18.
Artigo em Inglês | MEDLINE | ID: mdl-34272955

RESUMO

OBJECTIVE: : Developing clinical natural language processing systems often requires access to many clinical documents, which are not widely available to the public due to privacy and security concerns. To address this challenge, we propose to develop methods to generate synthetic clinical notes and evaluate their utility in real clinical natural language processing tasks. MATERIALS AND METHODS: : We implemented 4 state-of-the-art text generation models, namely CharRNN, SegGAN, GPT-2, and CTRL, to generate clinical text for the History and Present Illness section. We then manually annotated clinical entities for randomly selected 500 History and Present Illness notes generated from the best-performing algorithm. To compare the utility of natural and synthetic corpora, we trained named entity recognition (NER) models from all 3 corpora and evaluated their performance on 2 independent natural corpora. RESULTS: : Our evaluation shows GPT-2 achieved the best BLEU (bilingual evaluation understudy) score (with a BLEU-2 of 0.92). NER models trained on synthetic corpus generated by GPT-2 showed slightly better performance on 2 independent corpora: strict F1 scores of 0.709 and 0.748, respectively, when compared with the NER models trained on natural corpus (F1 scores of 0.706 and 0.737, respectively), indicating the good utility of synthetic corpora in clinical NER model development. In addition, we also demonstrated that an augmented method that combines both natural and synthetic corpora achieved better performance than that uses the natural corpus only. CONCLUSIONS: : Recent advances in text generation have made it possible to generate synthetic clinical notes that could be useful for training NER models for information extraction from natural clinical notes, thus lowering the privacy concern and increasing data availability. Further investigation is needed to apply this technology to practice.

11.
Mayo Clin Proc ; 96(7): 1890-1895, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-34218862

RESUMO

Predictive models have played a critical role in local, national, and international response to the COVID-19 pandemic. In the United States, health care systems and governmental agencies have relied on several models, such as the Institute for Health Metrics and Evaluation, Youyang Gu (YYG), Massachusetts Institute of Technology, and Centers for Disease Control and Prevention ensemble, to predict short- and long-term trends in disease activity. The Mayo Clinic Bayesian SIR model, recently made publicly available, has informed Mayo Clinic practice leadership at all sites across the United States and has been shared with Minnesota governmental leadership to help inform critical decisions during the past year. One key to the accuracy of the Mayo Clinic model is its ability to adapt to the constantly changing dynamics of the pandemic and uncertainties of human behavior, such as changes in the rate of contact among the population over time and by geographic location and now new virus variants. The Mayo Clinic model can also be used to forecast COVID-19 trends in different hypothetical worlds in which no vaccine is available, vaccinations are no longer being accepted from this point forward, and 75% of the population is already vaccinated. Surveys indicate that half of American adults are hesitant to receive a COVID-19 vaccine, and lack of understanding of the benefits of vaccination is an important barrier to use. The focus of this paper is to illustrate the stark contrast between these 3 scenarios and to demonstrate, mathematically, the benefit of high vaccine uptake on the future course of the pandemic.


Assuntos
Vacinas contra COVID-19 , COVID-19/prevenção & controle , COVID-19/epidemiologia , Previsões , Hospitalização/estatística & dados numéricos , Hospitalização/tendências , Humanos , Estados Unidos/epidemiologia
12.
J Cardiovasc Electrophysiol ; 32(9): 2504-2514, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34260141

RESUMO

INTRODUCTION: The efficacy of cardiac resynchronization therapy (CRT) has been widely studied in the medical literature; however, about 30% of candidates fail to respond to this treatment strategy. Smart computational approaches based on clinical data can help expose hidden patterns useful for identifying CRT responders. METHODS: We retrospectively analyzed the electronic health records of 1664 patients who underwent CRT procedures from January 1, 2002 to December 31, 2017. An ensemble of ensemble (EoE) machine learning (ML) system composed of a supervised and an unsupervised ML layers was developed to generate a prediction model for CRT response. RESULTS: We compared the performance of EoE against traditional ML methods and the state-of-the-art convolutional neural network (CNN) model trained on raw electrocardiographic (ECG) waveforms. We observed that the models exhibited improvement in performance as more features were incrementally used for training. Using the most comprehensive set of predictors, the performance of the EoE model in terms of the area under the receiver operating characteristic curve and F1-score were 0.76 and 0.73, respectively. Direct application of the CNN model on the raw ECG waveforms did not generate promising results. CONCLUSION: The proposed CRT risk calculator effectively discriminates which heart failure (HF) patient is likely to respond to CRT significantly better than using clinical guidelines and traditional ML methods, thus suggesting that the tool can enhanced care management of HF patients by helping to identify high-risk patients.

13.
Mol Genet Genomic Med ; 9(8): e1743, 2021 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-34264011

RESUMO

BACKGROUND: Oocytes have a lot of maternal RNAs and proteins, which are used by the early embryo before zygotic genome activation. Transducin-like enhancer of split 6 (TLE6) is a component of a subcortical maternal complex which plays a critical role in early embryonic development. METHODS: The patient had been diagnosed with primary infertility for 6 years and had undergone multiple failed in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) cycles. Genomic DNA samples were extracted from her parents' peripheral blood as well as hers. Whole-exome sequencing and Sanger validation were performed to identify candidate variants. RESULTS: We identified a novel transducin-like enhancer of split 6 (TLE6) gene mutations in the female patient with recurrent IVF/ICSI failure. The patient carried a homozygous mutation (NM_001143986.1(TLE6): c.541+1G>A) and had viable but low-quality embryos. Her parents both had heterozygous mutations at this locus. CONCLUSION: Our study expands the mutational and phenotypic spectrum of TLE6 and suggests the important role of TLE6 during embryonic development. Our findings have implications for the genetic diagnosis of female infertility with recurrent IVF/ICSI failure.

14.
Artigo em Inglês | MEDLINE | ID: mdl-34196524

RESUMO

In this study, Cu-MOF/rGO/CuO/α-MnO2 nanocomposites have been fabricated by a one-step hydrothermal method and used in the voltammetric detection of resorcinol (RS). The poor conductivity of MOFs in the field of electrochemical sensing is still a major challenge. A series of Cu-MOF/rGO/CuO/α-MnO2 nanocomposites have been synthesized with varying fractions of rGO and with a fixed amount of α-MnO2 via a facile method. These nanocomposites are well characterized using some sophisticated characterization techniques. The as-prepared nanohybrids have strongly promoted the redox reactions at the electrode surface due to their synergistic effects of improved conductivity, high electrocatalytic activity, an enlarged specific surface area, and a plethora of nanoscale level interfacial collaborations. The electrode modified with Cu-MOF/rGO/CuO/α-MnO2 has revealed superior electrochemical properties demonstrating linear differential pulse voltammetry (DPV) responses from a 0.2 to 22 µM RS concentration range (R2 = 0.999). The overall results of this sensing podium have shown excellent stability, good recovery, and a low detection limit of 0.2 µM. With excellent sensing performance achieved, the practicability of the sensor has been evaluated to detect RS in commercial hair color samples as well as in tap water and river water samples. Therefore, we envision that our hybrid nanostructures synthesized by the structural integration strategy will open new horizons in material synthesis and biosensing platforms.

15.
Clin Res Hepatol Gastroenterol ; : 101779, 2021 Jul 28.
Artigo em Inglês | MEDLINE | ID: mdl-34332125

RESUMO

BACKGROUND: The poor outcomes in advanced gastric cancer (GC) necessitate alternative therapeutic strategy. Ubiquitin-specific protease 11 (USP11) has recently garnered attention as a therapeutic target in cancer because of its important regulatory role in cancer cell functions. Here, we revealed the expression, function and underlying molecular interactions of USP11 in gastric cancer. METHODS: The expression of USP11 was analyzed using immunohistochemistry and ELISA. The loss-of function and gain-of function analysis of USP11 was performed using siRNA knockdown and plasmid overexpression approaches. The downstream molecules regulated by USP11 were determined using immunoblotting analysis. RESULTS: USP11 was upregulated in ∼80% of gastric cancer patients, and the upregulation was associated with HER3 overexpression. In addition, USP11 level was not regulated by HER3 and vice versa. Functional studies demonstrated that USP11 overexpression promoted gastric cancer growth and migration, and alleviated toxicity-induced by chemotherapeutic drug. In contrast, USP11 depletion significantly inhibited gastric cancer growth, migration and survival, and augmented chemotherapeutic drug's efficacy. Gastric cancer cells with higher USP11 levels were more sensitive to USP11 inhibitions than cells with lower USP11 levels. Mechanism studies showed that USP11 depletion suppressed migration via RhoA-mediated pathway and inhibited growth and survival likely via Ras-mediated pathway. CONCLUSIONS: Our work highlights the important role of USP11 in gastric cancer and therapeutic value of inhibiting USP11 to sensitize gastric cancer to chemotherapy.

16.
Bioelectrochemistry ; 141: 107880, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34229181

RESUMO

Microbiologically influenced corrosion (MIC) is one of the reasons leading to the service failure of pipelines buried in the soil. The effects of sulfate-reducing bacteria (SRB) on steel corrosion without organic carbon are not clear. In this work, SRB cells were enriched in the simulated soil solution, aiming to study SRB corrosion behavior without organic carbon source using weight loss, electrochemical measurements, and surface analysis. Effects of DO on SRB corrosion were also studied. Results indicate that SRB can survive after 14 days of incubation without organic carbon source, but approximately 90% SRB have died. SRB without organic carbon source could inhibit the uniform corrosion but enhance the pitting corrosion compared with the control specimen. The corrosion rate of the control calculated from weight loss is highest with a value of (0.081 ± 0.013) mm/y. The highest localized corrosion rate of (0.306 ± 0.006) mm/y is obtained with an initial SRB count of 107 cells/mL. The presence of DO influences the steel corrosion process. Oxygen corrosion dominates for the specimens in the absence and presence of SRB with an initial count of 103 cells/mL, while SRB MIC is primary for the specimens with high SRB counts.

17.
BMJ Open ; 11(6): e044353, 2021 06 08.
Artigo em Inglês | MEDLINE | ID: mdl-34103314

RESUMO

PURPOSE: The depth and breadth of clinical data within electronic health record (EHR) systems paired with innovative machine learning methods can be leveraged to identify novel risk factors for complex diseases. However, analysing the EHR is challenging due to complexity and quality of the data. Therefore, we developed large electronic population-based cohorts with comprehensive harmonised and processed EHR data. PARTICIPANTS: All individuals 30 years of age or older who resided in Olmsted County, Minnesota on 1 January 2006 were identified for the discovery cohort. Algorithms to define a variety of patient characteristics were developed and validated, thus building a comprehensive risk profile for each patient. Patients are followed for incident diseases and ageing-related outcomes. Using the same methods, an independent validation cohort was assembled by identifying all individuals 30 years of age or older who resided in the largely rural 26-county area of southern Minnesota and western Wisconsin on 1 January 2013. FINDINGS TO DATE: For the discovery cohort, 76 255 individuals (median age 49; 53% women) were identified from which a total of 9 644 221 laboratory results; 9 513 840 diagnosis codes; 10 924 291 procedure codes; 1 277 231 outpatient drug prescriptions; 966 136 heart rate measurements and 1 159 836 blood pressure (BP) measurements were retrieved during the baseline time period. The most prevalent conditions in this cohort were hyperlipidaemia, hypertension and arthritis. For the validation cohort, 333 460 individuals (median age 54; 52% women) were identified and to date, a total of 19 926 750 diagnosis codes, 10 527 444 heart rate measurements and 7 356 344 BP measurements were retrieved during baseline. FUTURE PLANS: Using advanced machine learning approaches, these electronic cohorts will be used to identify novel sex-specific risk factors for complex diseases. These approaches will allow us to address several challenges with the use of EHR.


Assuntos
Registros Eletrônicos de Saúde , Aprendizado de Máquina , Estudos de Coortes , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Minnesota/epidemiologia , Wisconsin
18.
Mayo Clin Proc ; 2021 Jun 22.
Artigo em Inglês | MEDLINE | ID: mdl-34172290

RESUMO

The publisher regrets that this article has been temporarily removed. A replacement will appear as soon as possible in which the reason for the removal of the article will be specified, or the article will be reinstated. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.

19.
BMC Neurol ; 21(1): 189, 2021 May 11.
Artigo em Inglês | MEDLINE | ID: mdl-33975556

RESUMO

BACKGROUND: There are numerous barriers to identifying patients with silent brain infarcts (SBIs) and white matter disease (WMD) in routine clinical care. A natural language processing (NLP) algorithm may identify patients from neuroimaging reports, but it is unclear if these reports contain reliable information on these findings. METHODS: Four radiology residents reviewed 1000 neuroimaging reports (RI) of patients age > 50 years without clinical histories of stroke, TIA, or dementia for the presence, acuity, and location of SBIs, and the presence and severity of WMD. Four neuroradiologists directly reviewed a subsample of 182 images (DR). An NLP algorithm was developed to identify findings in reports. We assessed interrater reliability for DR and RI, and agreement between these two and with NLP. RESULTS: For DR, interrater reliability was moderate for the presence of SBIs (k = 0.58, 95 % CI 0.46-0.69) and WMD (k = 0.49, 95 % CI 0.35-0.63), and moderate to substantial for characteristics of SBI and WMD. Agreement between DR and RI was substantial for the presence of SBIs and WMD, and fair to substantial for characteristics of SBIs and WMD. Agreement between NLP and DR was substantial for the presence of SBIs (k = 0.64, 95 % CI 0.53-0.76) and moderate (k = 0.52, 95 % CI 0.39-0.65) for the presence of WMD. CONCLUSIONS: Neuroimaging reports in routine care capture the presence of SBIs and WMD. An NLP can identify these findings (comparable to direct imaging review) and can likely be used for cohort identification.


Assuntos
Infarto Encefálico/diagnóstico por imagem , Interpretação de Imagem Assistida por Computador/métodos , Leucoencefalopatias/diagnóstico por imagem , Processamento de Linguagem Natural , Neuroimagem/métodos , Idoso , Estudos de Coortes , Feminino , Humanos , Imageamento por Ressonância Magnética/métodos , Masculino , Pessoa de Meia-Idade , Reprodutibilidade dos Testes
20.
Eur Urol ; 2021 Apr 03.
Artigo em Inglês | MEDLINE | ID: mdl-33824031

RESUMO

CONTEXT: Identifying the most effective first-line treatment for metastatic renal cell carcinoma (mRCC) is challenging as rapidly evolving data quickly outdate the existing body of evidence, and current approaches to presenting the evidence in user-friendly formats are fraught with limitations. OBJECTIVE: To maintain living evidence for contemporary first-line treatment for previously untreated mRCC. EVIDENCE ACQUISITION: We have created a living, interactive systematic review (LISR) and network meta-analysis for first-line treatment of mRCC using data from randomized controlled trials comparing contemporary treatment options with single-agent tyrosine kinase inhibitors. We applied an advanced programming and artificial intelligence-assisted framework for evidence synthesis to create a living search strategy, facilitate screening and data extraction using a graphical user interface, automate the frequentist network meta-analysis, and display results in an interactive manner. EVIDENCE SYNTHESIS: As of October 22, 2020, the LISR includes data from 14 clinical trials. Baseline characteristics are summarized in an interactive table. The cabozantinib + nivolumab combination (CaboNivo) is ranked the highest for the overall response rate, progression-free survival, and overall survival, whereas ipilimumab + nivolumab (NivoIpi) is ranked the highest for achieving a complete response (CR). NivoIpi, and atezolizumab + bevacizumab (AteBev) were ranked highest (lowest toxicity) and CaboNivo ranked lowest for treatment-related adverse events (AEs). Network meta-analysis results are summarized as interactive tables and plots, GRADE summary-of-findings tables, and evidence maps. CONCLUSIONS: This innovative living and interactive review provides the best current evidence on the comparative effectiveness of multiple treatment options for patients with untreated mRCC. Trial-level comparisons suggest that CaboNivo is likely to cause more AEs but is ranked best for all efficacy outcomes, except NivoIpi offers the best chance of CR. Pembrolizumab + axitinib and NivoIpi are acceptable alternatives, except NivoIpi may not be preferred for patients with favorable risk. Although network meta-analysis provides rankings with statistical adjustments, there are inherent biases in cross-trial comparisons with sparse direct evidence that does not replace randomized comparisons. PATIENT SUMMARY: It is challenging to decide the best option among the several treatment combinations of immunotherapy and targeted treatments for newly diagnosed metastatic kidney cancer. We have created interactive evidence summaries of multiple treatment options that present the benefits and harms and evidence certainty for patient-important outcomes. This evidence is updated as soon as new studies are published.

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