Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 41.399
Filtrar
1.
BMC Infect Dis ; 21(1): 264, 2021 Mar 16.
Artículo en Inglés | MEDLINE | ID: mdl-33726688

RESUMEN

BACKGROUND: Rapid identification of pathogenic Mycobacterium species is critical for a successful treatment. However, traditional method is time-consuming and cannot discriminate isolated non-tuberculosis mycobacteria (NTM) at species level. In the retrospective study, we evaluated the clinical applicability of PCR-reverse blot hybridization assay (PCR-REBA Myco-ID) with clinical specimens for rapid detection and differentiation of mycobacterial species. METHODS: A total of 334 sputum and 362 bronchial alveolar lavage fluids (BALF) from 696 patients with mycobacterium pulmonary disease (MPD) and 210 patients with non-mycobacterium pulmonary disease used as controls were analyzed. Sputum or BALF were obtained for MGIT 960-TBc ID test and PCR-REBA Myco-ID assay. High resolution melt analysis (HRM) was used to resolve inconsistent results of MGIT 960-TBc ID test and PCR-REBA Myco-ID assay. RESULTS: A total of 334 sputum and 362 BALF specimens from 696 MPD patients (292 MTB and 404 NTM) were eventually analyzed. In total, 292 MTBC and 436 NTM isolates (mixed infection of two species in 32 specimens) across 10 Mycobacterium species were identified. The most frequently isolated NTM species were M. intracellulare (n = 236, 54.1%), followed by M. abscessus (n = 106, 24.3%), M. kansasii (n = 46, 10.6%), M. avium (n = 36, 8.3%). Twenty-two cases had M. intracellulare and M. abscessus mixed infection and ten cases had M. avium and M. abscessus mixed infection. A high level of agreement (n = 696; 94.5%) was found between MGIT 960-TBc ID and PCR-REBA Myco-ID (k = 0.845, P = 0.000). PCR-REBA Myco-ID assay had higher AUC for both MTBC and NTM than MGIT 960-TBc ID test. CONCLUSION: PCR-REBA Myco-ID has the advantages of rapid, comparatively easy to perform, relatively low cost and superior accuracy in mycobacterial species identification compared with MGIT 960-TBc ID. We recommend it into workflow of mycobacterial laboratories especially in source-limited countries.


Asunto(s)
Infecciones por Mycobacterium/diagnóstico , Mycobacterium tuberculosis/aislamiento & purificación , Micobacterias no Tuberculosas/aislamiento & purificación , Hibridación de Ácido Nucleico/métodos , Adulto , Anciano , Anciano de 80 o más Años , Área Bajo la Curva , Líquido del Lavado Bronquioalveolar/microbiología , ADN Bacteriano/metabolismo , Femenino , Infecciones por VIH/diagnóstico , Humanos , Masculino , Persona de Mediana Edad , Infecciones por Mycobacterium/microbiología , Mycobacterium tuberculosis/genética , Micobacterias no Tuberculosas/genética , Reacción en Cadena de la Polimerasa , Curva ROC , Estudios Retrospectivos , Esputo/microbiología , Adulto Joven
2.
Anal Chem ; 93(11): 4782-4787, 2021 03 23.
Artículo en Inglés | MEDLINE | ID: mdl-33656857

RESUMEN

The outbreak of coronavirus disease 2019 (COVID-19) caused by SARS CoV-2 is ongoing and a serious threat to global public health. It is essential to detect the disease quickly and immediately to isolate the infected individuals. Nevertheless, the current widely used PCR and immunoassay-based methods suffer from false negative results and delays in diagnosis. Herein, a high-throughput serum peptidome profiling method based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is developed for efficient detection of COVID-19. We analyzed the serum samples from 146 COVID-19 patients and 152 control cases (including 73 non-COVID-19 patients with similar clinical symptoms, 33 tuberculosis patients, and 46 healthy individuals). After MS data processing and feature selection, eight machine learning methods were used to build classification models. A logistic regression machine learning model with 25 feature peaks achieved the highest accuracy (99%), with sensitivity of 98% and specificity of 100%, for the detection of COVID-19. This result demonstrated a great potential of the method for screening, routine surveillance, and diagnosis of COVID-19 in large populations, which is an important part of the pandemic control.


Asunto(s)
/diagnóstico , Péptidos/sangre , Espectrometría de Masa por Láser de Matriz Asistida de Ionización Desorción/métodos , Área Bajo la Curva , /virología , Estudios de Casos y Controles , Análisis Discriminante , Ensayos Analíticos de Alto Rendimiento , Humanos , Análisis de los Mínimos Cuadrados , Aprendizaje Automático , Análisis de Componente Principal , Curva ROC , Sensibilidad y Especificidad , Tuberculosis/metabolismo , Tuberculosis/patología
3.
Methods Mol Biol ; 2266: 141-154, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33759125

RESUMEN

Molecular docking produces often lackluster results in real-life virtual screening assays that aim to discover novel drug candidates or hit compounds. The problem lies in the inability of the default docking scoring to properly estimate the Gibbs free energy of binding, which impairs the recognition of the best binding poses and the separation of active ligands from inactive compounds. Negative image-based rescoring (R-NiB) provides both effective and efficient way for re-ranking the outputted flexible docking poses to improve the virtual screening yield. Importantly, R-NiB has been shown to work with multiple genuine drug targets and six popular docking algorithms using demanding benchmark test sets. The effectiveness of the R-NiB methodology relies on the shape/electrostatics similarity between the target protein's ligand-binding cavity and the docked ligand poses. In this chapter, the R-NiB method is described with practical usability in mind.


Asunto(s)
Descubrimiento de Drogas/métodos , Simulación del Acoplamiento Molecular/métodos , Proteínas/química , Algoritmos , Área Bajo la Curva , Sitios de Unión , Cristalografía por Rayos X , Ciclooxigenasa 2/química , Bases de Datos de Proteínas , Ligandos , Conformación Molecular , Neuraminidasa/química , Unión Proteica , Programas Informáticos , Electricidad Estática
4.
Lancet Digit Health ; 3(1): e10-e19, 2021 01.
Artículo en Inglés | MEDLINE | ID: mdl-33735063

RESUMEN

BACKGROUND: Diabetic retinopathy screening is instrumental to preventing blindness, but scaling up screening is challenging because of the increasing number of patients with all forms of diabetes. We aimed to create a deep-learning system to predict the risk of patients with diabetes developing diabetic retinopathy within 2 years. METHODS: We created and validated two versions of a deep-learning system to predict the development of diabetic retinopathy in patients with diabetes who had had teleretinal diabetic retinopathy screening in a primary care setting. The input for the two versions was either a set of three-field or one-field colour fundus photographs. Of the 575 431 eyes in the development set 28 899 had known outcomes, with the remaining 546 532 eyes used to augment the training process via multitask learning. Validation was done on one eye (selected at random) per patient from two datasets: an internal validation (from EyePACS, a teleretinal screening service in the USA) set of 3678 eyes with known outcomes and an external validation (from Thailand) set of 2345 eyes with known outcomes. FINDINGS: The three-field deep-learning system had an area under the receiver operating characteristic curve (AUC) of 0·79 (95% CI 0·77-0·81) in the internal validation set. Assessment of the external validation set-which contained only one-field colour fundus photographs-with the one-field deep-learning system gave an AUC of 0·70 (0·67-0·74). In the internal validation set, the AUC of available risk factors was 0·72 (0·68-0·76), which improved to 0·81 (0·77-0·84) after combining the deep-learning system with these risk factors (p<0·0001). In the external validation set, the corresponding AUC improved from 0·62 (0·58-0·66) to 0·71 (0·68-0·75; p<0·0001) following the addition of the deep-learning system to available risk factors. INTERPRETATION: The deep-learning systems predicted diabetic retinopathy development using colour fundus photographs, and the systems were independent of and more informative than available risk factors. Such a risk stratification tool might help to optimise screening intervals to reduce costs while improving vision-related outcomes. FUNDING: Google.


Asunto(s)
Aprendizaje Profundo , Retinopatía Diabética/diagnóstico , Anciano , Área Bajo la Curva , Técnicas de Diagnóstico Oftalmológico , Femenino , Humanos , Estimación de Kaplan-Meier , Masculino , Persona de Mediana Edad , Fotograbar , Pronóstico , Curva ROC , Reproducibilidad de los Resultados , Medición de Riesgo/métodos
5.
Lancet Digit Health ; 3(1): e29-e40, 2021 01.
Artículo en Inglés | MEDLINE | ID: mdl-33735066

RESUMEN

BACKGROUND: In current approaches to vision screening in the community, a simple and efficient process is needed to identify individuals who should be referred to tertiary eye care centres for vision loss related to eye diseases. The emergence of deep learning technology offers new opportunities to revolutionise this clinical referral pathway. We aimed to assess the performance of a newly developed deep learning algorithm for detection of disease-related visual impairment. METHODS: In this proof-of-concept study, using retinal fundus images from 15 175 eyes with complete data related to best-corrected visual acuity or pinhole visual acuity from the Singapore Epidemiology of Eye Diseases Study, we first developed a single-modality deep learning algorithm based on retinal photographs alone for detection of any disease-related visual impairment (defined as eyes from patients with major eye diseases and best-corrected visual acuity of <20/40), and moderate or worse disease-related visual impairment (eyes with disease and best-corrected visual acuity of <20/60). After development of the algorithm, we tested it internally, using a new set of 3803 eyes from the Singapore Epidemiology of Eye Diseases Study. We then tested it externally using three population-based studies (the Beijing Eye study [6239 eyes], Central India Eye and Medical study [6526 eyes], and Blue Mountains Eye Study [2002 eyes]), and two clinical studies (the Chinese University of Hong Kong's Sight Threatening Diabetic Retinopathy study [971 eyes] and the Outram Polyclinic Study [1225 eyes]). The algorithm's performance in each dataset was assessed on the basis of the area under the receiver operating characteristic curve (AUC). FINDINGS: In the internal test dataset, the AUC for detection of any disease-related visual impairment was 94·2% (95% CI 93·0-95·3; sensitivity 90·7% [87·0-93·6]; specificity 86·8% [85·6-87·9]). The AUC for moderate or worse disease-related visual impairment was 93·9% (95% CI 92·2-95·6; sensitivity 94·6% [89·6-97·6]; specificity 81·3% [80·0-82·5]). Across the five external test datasets (16 993 eyes), the algorithm achieved AUCs ranging between 86·6% (83·4-89·7; sensitivity 87·5% [80·7-92·5]; specificity 70·0% [66·7-73·1]) and 93·6% (92·4-94·8; sensitivity 87·8% [84·1-90·9]; specificity 87·1% [86·2-88·0]) for any disease-related visual impairment, and the AUCs for moderate or worse disease-related visual impairment ranged between 85·9% (81·8-90·1; sensitivity 84·7% [73·0-92·8]; specificity 74·4% [71·4-77·2]) and 93·5% (91·7-95·3; sensitivity 90·3% [84·2-94·6]; specificity 84·2% [83·2-85·1]). INTERPRETATION: This proof-of-concept study shows the potential of a single-modality, function-focused tool in identifying visual impairment related to major eye diseases, providing more timely and pinpointed referral of patients with disease-related visual impairment from the community to tertiary eye hospitals. FUNDING: National Medical Research Council, Singapore.


Asunto(s)
Algoritmos , Aprendizaje Profundo , Oftalmopatías/complicaciones , Trastornos de la Visión/diagnóstico , Trastornos de la Visión/etiología , Anciano , Área Bajo la Curva , Grupo de Ascendencia Continental Asiática , Femenino , Humanos , Masculino , Persona de Mediana Edad , Fotograbar/métodos , Prueba de Estudio Conceptual , Curva ROC , Sensibilidad y Especificidad , Singapur/epidemiología
6.
PLoS One ; 16(3): e0248891, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33740030

RESUMEN

BACKGROUND: Identifying factors that can predict severe disease in patients needing hospitalization for COVID-19 is crucial for early recognition of patients at greatest risk. OBJECTIVE: (1) Identify factors predicting intensive care unit (ICU) transfer and (2) develop a simple calculator for clinicians managing patients hospitalized with COVID-19. METHODS: A total of 2,685 patients with laboratory-confirmed COVID-19 admitted to a large metropolitan health system in Georgia, USA between March and July 2020 were included in the study. Seventy-five percent of patients were included in the training dataset (admitted March 1 to July 10). Through multivariable logistic regression, we developed a prediction model (probability score) for ICU transfer. Then, we validated the model by estimating its performance accuracy (area under the curve [AUC]) using data from the remaining 25% of patients (admitted July 11 to July 31). RESULTS: We included 2,014 and 671 patients in the training and validation datasets, respectively. Diabetes mellitus, coronary artery disease, chronic kidney disease, serum C-reactive protein, and serum lactate dehydrogenase were identified as significant risk factors for ICU transfer, and a prediction model was developed. The AUC was 0.752 for the training dataset and 0.769 for the validation dataset. We developed a free, web-based calculator to facilitate use of the prediction model (https://icucovid19.shinyapps.io/ICUCOVID19/). CONCLUSION: Our validated, simple, and accessible prediction model and web-based calculator for ICU transfer may be useful in assisting healthcare providers in identifying hospitalized patients with COVID-19 who are at high risk for clinical deterioration. Triage of such patients for early aggressive treatment can impact clinical outcomes for this potentially deadly disease.


Asunto(s)
/patología , Enfermedad Crítica , Hospitalización/estadística & datos numéricos , Adulto , Anciano , Área Bajo la Curva , Proteína C-Reactiva/análisis , Comorbilidad , Femenino , Humanos , Unidades de Cuidados Intensivos , L-Lactato Deshidrogenasa/sangre , Modelos Logísticos , Masculino , Persona de Mediana Edad , Curva ROC , Estudios Retrospectivos , Factores de Riesgo , /aislamiento & purificación
7.
PLoS One ; 16(3): e0248230, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33740793

RESUMEN

BACKGROUND: There is limited data on the markers of coagulation and hemostatic activation (MOCHA) profile in Coronavirus disease 2019 (COVID-19) and its ability to identify COVID-19 patients at risk for thrombotic events and other complications. METHODS: Hospitalized patients with confirmed SARS-COV-2 from four Atlanta hospitals were included in this observational cohort study and underwent admission testing of MOCHA parameters (plasma d-dimer, prothrombin fragment 1.2, thrombin-antithrombin complex, fibrin monomer). Clinical outcomes included deep vein thrombosis, pulmonary embolism, myocardial infarction, ischemic stroke, access line thrombosis, ICU admission, intubation and mortality. MAIN RESULTS: Of 276 patients (mean age 59 ± 6.4 years, 47% female, 62% African American), 45 (16%) had a thrombotic endpoint. Each MOCHA parameter was independently associated with a thrombotic event (p<0.05) and ≥ 2 abnormalities was associated with thrombotic endpoints (OR 3.3, 95% CI 1.2-8.8) as were admission D-dimer ≥ 2000 ng/mL (OR 3.1, 95% CI 1.5-6.6) and ≥ 3000 ng/mL (OR 3.6, 95% CI 1.6-7.9). However, only ≥ 2 MOCHA abnormalities were associated with ICU admission (OR 3.0, 95% CI 1.7-5.2) and intubation (OR 3.2, 95% CI 1.6-6.4). MOCHA and D-dimer cutoffs were not associated with mortality. MOCHA with <2 abnormalities (26% of the cohort) had 89% sensitivity and 93% negative predictive value for a thrombotic endpoint. CONCLUSIONS: An admission MOCHA profile is useful to risk-stratify COVID-19 patients for thrombotic complications and more effective than isolated d-dimer for predicting risk of ICU admission and intubation.


Asunto(s)
Antitrombina III/análisis , Productos de Degradación de Fibrina-Fibrinógeno/análisis , Fragmentos de Péptidos/análisis , Péptido Hidrolasas/análisis , Protrombina/análisis , Trombosis/diagnóstico , Anciano , Área Bajo la Curva , /mortalidad , Estudios de Cohortes , Femenino , Humanos , Unidades de Cuidados Intensivos , Masculino , Persona de Mediana Edad , Oportunidad Relativa , Admisión del Paciente , Curva ROC , Factores de Riesgo , Tasa de Supervivencia , Trombosis/complicaciones
8.
AAPS PharmSciTech ; 22(3): 111, 2021 Mar 21.
Artículo en Inglés | MEDLINE | ID: mdl-33748928

RESUMEN

Dihydromyricetin (DMY) is highly effective in counteracting acute alcohol intoxication. However, its poor aqueous solubility and permeability lead to the low oral bioavailability and limit its clinic application. The aim of this work is to use Solutol®HS15 (HS 15) as surfactant to develop novel micelle to enhance the oral bioavailability of DMY by improving its solubility and permeability. The DMY-loaded Solutol®HS15 micelles (DMY-Ms) were prepared by the thin-film hydration method. The particle size of DMY-Ms was 13.97 ± 0.82 nm with an acceptable polydispersity index of 0.197 ± 0.015. Upon entrapped in micelles, the solubility of DMY in water was increased more than 25-fold. The DMY-Ms had better sustained release property than that of pure DMY. In single-pass intestinal perfusion models, the absorption rate constant (Ka) and permeability coefficient (Papp) of DMY-Ms were 5.5-fold and 3.0-fold than that of pure DMY, respectively. The relative bioavailability of the DMY-Ms (AUC0-∞) was 205% compared with that of pure DMY (AUC0-∞), indicating potential for clinical application. After administering DMY-Ms, there was much lower blood alcohol level and shorter duration of the loss of righting relax (LORR) in drunk animals compared with that treated by pure DMY. In addition, the oral administration of DMY-Ms greatly reduced oxidative stress, and significantly defended liver and gastric mucosa from alcoholic damages in mice with alcohol-induced tissue injury. Taken together, HS 15-based micelle system greatly improves the bioavailability of DMY and represents a promising strategy for the management of acute alcoholism. Graphical abstract.


Asunto(s)
Intoxicación Alcohólica/tratamiento farmacológico , Flavonoles/administración & dosificación , Flavonoles/uso terapéutico , Intoxicación Alcohólica/patología , Animales , Área Bajo la Curva , Disponibilidad Biológica , Depresores del Sistema Nervioso Central/sangre , Etanol/sangre , Excipientes , Flavonoles/farmacocinética , Mucosa Gástrica/patología , Hepatitis Alcohólica/prevención & control , Masculino , Ratones , Ratones Endogámicos C57BL , Micelas , Nanopartículas , Equilibrio Postural/efectos de los fármacos , Ratas , Ratas Sprague-Dawley , Tensoactivos
9.
Medicine (Baltimore) ; 100(11): e24805, 2021 Mar 19.
Artículo en Inglés | MEDLINE | ID: mdl-33725946

RESUMEN

BACKGROUND: The main purpose of this study is to systematically evaluate the diagnostic value of long-chain non-coding RNA urothelial carcinoembryonic antigen 1 (lncRNA-UCA1) for bladder cancer, and to provide a scientific basis for the diagnosis of bladder cancer. METHODS: By searching PubMed, Web of Science, EMBASE, CNKI, Wanfang, Weipu and other databases, in order to collect relevant literature of lncRNA-UCA1 for diagnosis of bladder cancer. The starting and ending time of the search is from the establishment of the database to December 31, 2019. Screen documents and extract data according to inclusion and exclusion criteria. QUADAS entry tool was used to evaluate the quality of literature. Meta-Disc 1.4 and Stata 12.0 software were used for statistical analysis, and UCA1 was combined for the statistics of bladder cancer diagnosis. RESULTS: A total of 7 articles were included in this study, including 954 cases of bladder cancer patients and 482 cases of non-bladder cancer patients. The receiver operating characteristic curve (ROC) curve AUC of lncRNA-UCA1 used to diagnose bladder cancer was 0.86. The sensitivity was 0.83 (95% CI: 0.80-0.85), and the specificity was 0.86 (95% CI: 0.82-0.89). The positive likelihood ratio is 6.38 (95% CI: 3.01-13.55), and the negative likelihood ratio is 0.20 (95% CI: 0.13-0.31). The diagnostic odds ratio is 33.13 (95% CI: 11.16-98.33). CONCLUSION: lncRNA-UCA1 has a high value of clinical auxiliary diagnosis for bladder cancer, and it can be further promoted and applied clinically.


Asunto(s)
ARN Largo no Codificante/análisis , Neoplasias de la Vejiga Urinaria/diagnóstico , Adulto , Área Bajo la Curva , Biomarcadores de Tumor/análisis , Estudios de Casos y Controles , Femenino , Humanos , Masculino , Persona de Mediana Edad , Oportunidad Relativa , Valor Predictivo de las Pruebas , Curva ROC , Sensibilidad y Especificidad , Neoplasias de la Vejiga Urinaria/genética
10.
Medicine (Baltimore) ; 100(11): e25081, 2021 Mar 19.
Artículo en Inglés | MEDLINE | ID: mdl-33725985

RESUMEN

ABSTRACT: This work aims to explore risk factors for ischemic stroke in young adults and analyze the Traditional Vascular Risk Factors Model based on age, hypertension, diabetes, smoking history, and drinking history. Further, the Lipid Metabolism Model was analyzed based on lipoprotein a [LP (a)], high-density lipoprotein (HDL), low-density lipoprotein (LDL), apolipoprotein AI (apo AI), apolipoprotein B (apo B), and the Early Renal Injury Model based on urinary microalbuminuria/creatinine ratio (UACR). Besides, we estimated glomerular filtration rate (eGFR), cystatin C (Cys-C), homocysteine (Hcy), ß2 microglobulin (ß2m), and validated their predictive efficacy and clinical value for the development of ischemic stroke in young adults.We selected and retrospectively analyzed the clinical data of 565 young inpatients admitted to Zhejiang Provincial Hospital of Chinese Medicine between 2010 and 2020, 187 of whom were young stroke patients. A single-factor analysis was used to analyze the risk factors for stroke in young people and developed a traditional vascular risk factors model, a lipid metabolism model, and an early kidney injury model based on backpropagation (BP) neural networks technology to predict early stroke occurrence. Moreover, the prediction performance by the area under the receiver operating characteristics (ROC) curve (AUC) was assessed to further understand the risk factors for stroke in young people and apply their predictive role in the clinical setting.Single-factor analysis showed that ischemic stroke in young adults was associated with hypertension, diabetes, smoking history, drinking history, LP(a), HDL, LDL, apo AI, apo B, eGFR, Cys-C, and ß2m (P < .05). The BP neural networks technique was used to plot the ROC curves for the Traditional Vascular Risk Factors Model, the Lipid Metabolism Model, and the Early Kidney Injury Model in enrolled patients, and calculated AUC values of 0.7915, 0.8387, and 0.9803, respectively.The early kidney injury model precisely predicted the risk of ischemic stroke in young adults and exhibited a certain clinical value as a reference for morbidity assessment. Whereas the prediction performance of the Traditional Vascular Risk Factors Model and the Lipid Metabolism Model were inferior to that of the early kidney injury model.


Asunto(s)
Reglas de Decisión Clínica , Pruebas de Función Renal/estadística & datos numéricos , Redes Neurales de la Computación , Medición de Riesgo/estadística & datos numéricos , Lesión Renal Aguda/complicaciones , Lesión Renal Aguda/diagnóstico , Adolescente , Adulto , Factores de Edad , Consumo de Bebidas Alcohólicas/efectos adversos , Área Bajo la Curva , Diabetes Mellitus Tipo 2/complicaciones , Diabetes Mellitus Tipo 2/diagnóstico , Análisis Factorial , Femenino , Humanos , Hipertensión/complicaciones , Hipertensión/diagnóstico , Pruebas de Función Renal/métodos , Lipoproteínas/sangre , Masculino , Persona de Mediana Edad , Valor Predictivo de las Pruebas , Curva ROC , Medición de Riesgo/métodos , Factores de Riesgo , Fumar/efectos adversos , Adulto Joven
11.
Medicine (Baltimore) ; 100(11): e25222, 2021 Mar 19.
Artículo en Inglés | MEDLINE | ID: mdl-33726019

RESUMEN

ABSTRACT: The reasons for readmission of children with Hirschsprung disease (HD) are multiple. The study aims to predict the relevant factors for the readmission of children with HD by collecting and analyzing the relevant data of the child's admission to the hospital at the time of surgery.A retrospective review was performed including all patients with surgical treatment of HD at our institution between the years of 2011 to 2020. Univariate and multivariate Logistic regression analysis were performed to obtain the independent risk factor for this study. The receiver operating characteristic curve (ROC) were used to assess the performance of derived models.A total of 162 patients were identified. The average presurgery weights were 6.93 ±â€Š1.78 kg in the readmission group and 8.38 ±â€Š3.17 kg in the non-readmission group. Six children were classified as a low-weight in the readmission group, and 11 children classified as low-weight in the non-readmission group. The length of the intestinal tube after resection was 25.25 ±â€Š15.21 cm in the readmission group, and 16.23 ±â€Š4.10 cm in the non-readmission group. The ROC for the prediction model of readmission after HD surgery (AUC = 0.811).In children undergoing the HD surgery, we showed preoperative low body weight and long intra-operative bowel resection significantly increase the probability of readmission due to complications.


Asunto(s)
Peso Corporal , Enfermedad de Hirschsprung/cirugía , Admisión del Paciente/estadística & datos numéricos , Readmisión del Paciente/estadística & datos numéricos , Complicaciones Posoperatorias/epidemiología , Área Bajo la Curva , Femenino , Enfermedad de Hirschsprung/fisiopatología , Humanos , Lactante , Modelos Logísticos , Masculino , Tempo Operativo , Complicaciones Posoperatorias/etiología , Periodo Preoperatorio , Curva ROC , Estudios Retrospectivos , Factores de Riesgo
12.
BMC Med Imaging ; 21(1): 31, 2021 02 17.
Artículo en Inglés | MEDLINE | ID: mdl-33596844

RESUMEN

BACKGROUND: In this COVID-19 pandemic, the differential diagnosis of viral pneumonia is still challenging. We aimed to assess the classification performance of computed tomography (CT)-based CT signs and radiomics features for discriminating COVID-19 and influenza pneumonia. METHODS: A total of 154 patients with confirmed viral pneumonia (COVID-19: 89 cases, influenza pneumonia: 65 cases) were collected retrospectively in this study. Pneumonia signs and radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models. The predictive performance of the radiomics model, CT sign model, the combined model was constructed based on the whole dataset and internally invalidated by using 1000-times bootstrap. Diagnostic performance of the models was assessed via receiver operating characteristic (ROC) analysis. RESULTS: The combined models consisted of 4 significant CT signs and 7 selected features and demonstrated better discrimination performance between COVID-19 and influenza pneumonia than the single radiomics model. For the radiomics model, the area under the ROC curve (AUC) was 0.888 (sensitivity, 86.5%; specificity, 78.4%; accuracy, 83.1%), and the AUC was 0.906 (sensitivity, 86.5%; specificity, 81.5%; accuracy, 84.4%) in the CT signs model. After combining CT signs and radiomics features, AUC of the combined model was 0.959 (sensitivity, 89.9%; specificity, 90.7%; accuracy, 90.3%). CONCLUSIONS: CT-based radiomics combined with signs might be a potential method for distinguishing COVID-19 and influenza pneumonia with satisfactory performance.


Asunto(s)
/diagnóstico por imagen , Gripe Humana/diagnóstico por imagen , Neumonía Viral/etiología , Interpretación de Imagen Radiográfica Asistida por Computador/métodos , Tomografía Computarizada por Rayos X/métodos , Adulto , Área Bajo la Curva , Diagnóstico Diferencial , Femenino , Humanos , Masculino , Persona de Mediana Edad , Modelos Teóricos , Neumonía Viral/diagnóstico por imagen , Valor Predictivo de las Pruebas , Estudios Retrospectivos
13.
Medicine (Baltimore) ; 100(7): e24756, 2021 Feb 19.
Artículo en Inglés | MEDLINE | ID: mdl-33607821

RESUMEN

ABSTRACT: This study was conducted to develop a convolutional neural network (CNN)-based model to predict the sex and age of patients by identifying unique unknown features from paranasal sinus (PNS) X-ray images.We employed a retrospective study design and used anonymized patient imaging data. Two CNN models, adopting ResNet-152 and DenseNet-169 architectures, were trained to predict sex and age groups (20-39, 40-59, 60+ years). The area under the curve (AUC), algorithm accuracy, sensitivity, and specificity were assessed. Class-activation map (CAM) was used to detect deterministic areas. A total of 4160 PNS X-ray images were collected from 4160 patients. The PNS X-ray images of patients aged ≥20 years were retrieved from the picture archiving and communication database system of our institution. The classification performances in predicting the sex (male vs female) and 3 age groups (20-39, 40-59, 60+ years) for each established CNN model were evaluated.For sex prediction, ResNet-152 performed slightly better (accuracy = 98.0%, sensitivity = 96.9%, specificity = 98.7%, and AUC = 0.939) than DenseNet-169. CAM indicated that maxillary sinuses (males) and ethmoid sinuses (females) were major factors in identifying sex. Meanwhile, for age prediction, the DenseNet-169 model was slightly more accurate in predicting age groups (77.6 ±â€Š1.5% vs 76.3 ±â€Š1.1%). CAM suggested that the maxillary sinus and the periodontal area were primary factors in identifying age groups.Our deep learning model could predict sex and age based on PNS X-ray images. Therefore, it can assist in reducing the risk of patient misidentification in clinics.


Asunto(s)
Aprendizaje Profundo/estadística & datos numéricos , Senos Paranasales/diagnóstico por imagen , Radiografía/métodos , Adulto , Anciano , Algoritmos , Área Bajo la Curva , Manejo de Datos , Bases de Datos Factuales , Femenino , Humanos , Masculino , Seno Maxilar/diagnóstico por imagen , Persona de Mediana Edad , Redes Neurales de la Computación , Valor Predictivo de las Pruebas , Estudios Retrospectivos , Sensibilidad y Especificidad
14.
BMC Infect Dis ; 21(1): 153, 2021 Feb 06.
Artículo en Inglés | MEDLINE | ID: mdl-33549035

RESUMEN

BACKGROUND: This systematic review and meta-analysis explored the relationship between vancomycin (VCM) monitoring strategies and VCM effectiveness and safety. METHODS: We conducted our analysis using the MEDLINE, Web of Sciences, and Cochrane Register of Controlled Trials electronic databases searched on August 9, 2020. We calculated odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Adult patients with methicillin-resistant Staphylococcus aureus (MRSA) bacteraemia with VCM trough concentrations ≥15 µg/mL had significantly lower treatment failure rates (OR 0.63, 95% CI 0.47-0.85). The incidence of acute kidney injury (AKI) increased with increased trough concentrations and was significantly higher for trough concentrations ≥20 µg/mL compared to those at 15-20 µg/mL (OR 2.39, 95% CI 1.78-3.20). Analysis of the target area under the curve/minimum inhibitory concentration ratios (AUC/MIC) showed significantly lower treatment failure rates for high AUC/MIC (cut-off 400 ± 15%) (OR 0.28, 95% CI 0.18-0.45). The safety analysis revealed that high AUC value (cut-off 600 ± 15%) significantly increased the risk of AKI (OR 2.10, 95% CI 1.13-3.89). Our meta-analysis of differences in monitoring strategies included four studies. The incidence of AKI tended to be lower in AUC-guided monitoring than in trough-guided monitoring (OR 0.54, 95% CI 0.28-1.01); however, it was not significant in the analysis of mortality. CONCLUSIONS: We identified VCM trough concentrations and AUC values that correlated with effectiveness and safety. Furthermore, compared to trough-guided monitoring, AUC-guided monitoring showed potential for decreasing nephrotoxicity.


Asunto(s)
Antibacterianos/uso terapéutico , Monitoreo de Drogas/métodos , Vancomicina/uso terapéutico , Lesión Renal Aguda/inducido químicamente , Lesión Renal Aguda/epidemiología , Adulto , Antibacterianos/farmacología , Área Bajo la Curva , Bacteriemia/tratamiento farmacológico , Humanos , Staphylococcus aureus Resistente a Meticilina/efectos de los fármacos , Pruebas de Sensibilidad Microbiana , Oportunidad Relativa , Seguridad , Infecciones Estafilocócicas/tratamiento farmacológico , Insuficiencia del Tratamiento , Vancomicina/farmacología
15.
Korean J Radiol ; 22(3): 442-453, 2021 03.
Artículo en Inglés | MEDLINE | ID: mdl-33629545

RESUMEN

Artificial intelligence (AI) will likely affect various fields of medicine. This article aims to explain the fundamental principles of clinical validation, device approval, and insurance coverage decisions of AI algorithms for medical diagnosis and prediction. Discrimination accuracy of AI algorithms is often evaluated with the Dice similarity coefficient, sensitivity, specificity, and traditional or free-response receiver operating characteristic curves. Calibration accuracy should also be assessed, especially for algorithms that provide probabilities to users. As current AI algorithms have limited generalizability to real-world practice, clinical validation of AI should put it to proper external testing and assisting roles. External testing could adopt diagnostic case-control or diagnostic cohort designs. A diagnostic case-control study evaluates the technical validity/accuracy of AI while the latter tests the clinical validity/accuracy of AI in samples representing target patients in real-world clinical scenarios. Ultimate clinical validation of AI requires evaluations of its impact on patient outcomes, referred to as clinical utility, and for which randomized clinical trials are ideal. Device approval of AI is typically granted with proof of technical validity/accuracy and thus does not intend to directly indicate if AI is beneficial for patient care or if it improves patient outcomes. Neither can it categorically address the issue of limited generalizability of AI. After achieving device approval, it is up to medical professionals to determine if the approved AI algorithms are beneficial for real-world patient care. Insurance coverage decisions generally require a demonstration of clinical utility that the use of AI has improved patient outcomes.


Asunto(s)
Inteligencia Artificial , Aprobación de Recursos , Cobertura del Seguro , Área Bajo la Curva , Estudios de Casos y Controles , Toma de Decisiones , Prestación de Atención de Salud , Humanos , Neoplasias Pulmonares/diagnóstico , Curva ROC
16.
JAMA Netw Open ; 4(2): e2036220, 2021 02 01.
Artículo en Inglés | MEDLINE | ID: mdl-33630084

RESUMEN

Importance: The Undiagnosed Diseases Network (UDN) is a national network that evaluates individual patients whose signs and symptoms have been refractory to diagnosis. Providing reliable estimates of admission outcomes may assist clinical evaluators to distinguish, prioritize, and accelerate admission to the UDN for patients with undiagnosed diseases. Objective: To develop computational models that effectively predict admission outcomes for applicants seeking UDN evaluation and to rank the applications based on the likelihood of patient admission to the UDN. Design, Setting, and Participants: This prognostic study included all applications submitted to the UDN from July 2014 to June 2019, with 1209 applications accepted and 1212 applications not accepted. The main inclusion criterion was an undiagnosed condition despite thorough evaluation by a health care professional; the main exclusion criteria were a diagnosis that explained the objective findings or a review of the records that suggested a diagnosis. A classifier was trained using information extracted from application forms, referral letters from health care professionals, and semantic similarity between referral letters and textual description of known mendelian disorders. The admission labels were provided by the case review committee of the UDN. In addition to retrospective analysis, the classifier was prospectively tested on another 288 applications that were not evaluated at the time of classifier development. Main Outcomes and Measures: The primary outcomes were whether a patient was accepted or not accepted to the UDN and application order ranked based on likelihood of admission. The performance of the classifier was assessed by comparing its predictions against the UDN admission outcomes and by measuring improvement in the mean processing time for accepted applications. Results: The best classifier obtained sensitivity of 0.843, specificity of 0.738, and area under the receiver operating characteristic curve of 0.844 for predicting admission outcomes among 1212 accepted and 1210 not accepted applications. In addition, the classifier can decrease the current mean (SD) UDN processing time for accepted applications from 3.29 (3.17) months to 1.05 (3.82) months (68% improvement) by ordering applications based on their likelihood of acceptance. Conclusions and Relevance: A classification system was developed that may assist clinical evaluators to distinguish, prioritize, and accelerate admission to the UDN for patients with undiagnosed diseases. Accelerating the admission process may improve the diagnostic journeys for these patients and serve as a model for partial automation of triaging or referral for other resource-constrained applications. Such classification models make explicit some of the considerations that currently inform the use of whole-genome sequencing for undiagnosed disease and thereby invite a broader discussion in the clinical genetics community.


Asunto(s)
Aprendizaje Automático , Selección de Paciente , Enfermedades Raras/diagnóstico , Derivación y Consulta , Enfermedades no Diagnosticadas/diagnóstico , Adolescente , Adulto , Área Bajo la Curva , Niño , Preescolar , Simulación por Computador , Femenino , Humanos , Lactante , Recién Nacido , Masculino , Persona de Mediana Edad , Admisión del Paciente , Estudios Prospectivos , Curva ROC , Enfermedades Raras/genética , Reproducibilidad de los Resultados , Estudios Retrospectivos , Triaje , Enfermedades no Diagnosticadas/genética , Secuenciación Completa del Genoma , Adulto Joven
17.
Circulation ; 143(13): 1274-1286, 2021 03 30.
Artículo en Inglés | MEDLINE | ID: mdl-33517677

RESUMEN

BACKGROUND: Heart rate-corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome, and/or systemic diseases including SARS-CoV-2-mediated coronavirus disease 2019 (COVID-19), can predispose to ventricular arrhythmias and sudden cardiac death. Currently, QTc assessment and monitoring relies largely on 12-lead electrocardiography. As such, we sought to train and validate an artificial intelligence (AI)-enabled 12-lead ECG algorithm to determine the QTc, and then prospectively test this algorithm on tracings acquired from a mobile ECG (mECG) device in a population enriched for repolarization abnormalities. METHODS: Using >1.6 million 12-lead ECGs from 538 200 patients, a deep neural network (DNN) was derived (patients for training, n = 250 767; patients for testing, n = 107 920) and validated (n = 179 513 patients) to predict the QTc using cardiologist-overread QTc values as the "gold standard". The ability of this DNN to detect clinically-relevant QTc prolongation (eg, QTc ≥500 ms) was then tested prospectively on 686 patients with genetic heart disease (50% with long QT syndrome) with QTc values obtained from both a 12-lead ECG and a prototype mECG device equivalent to the commercially-available AliveCor KardiaMobile 6L. RESULTS: In the validation sample, strong agreement was observed between human over-read and DNN-predicted QTc values (-1.76±23.14 ms). Similarly, within the prospective, genetic heart disease-enriched dataset, the difference between DNN-predicted QTc values derived from mECG tracings and those annotated from 12-lead ECGs by a QT expert (-0.45±24.73 ms) and a commercial core ECG laboratory [10.52±25.64 ms] was nominal. When applied to mECG tracings, the DNN's ability to detect a QTc value ≥500 ms yielded an area under the curve, sensitivity, and specificity of 0.97, 80.0%, and 94.4%, respectively. CONCLUSIONS: Using smartphone-enabled electrodes, an AI DNN can predict accurately the QTc of a standard 12-lead ECG. QTc estimation from an AI-enabled mECG device may provide a cost-effective means of screening for both acquired and congenital long QT syndrome in a variety of clinical settings where standard 12-lead electrocardiography is not accessible or cost-effective.


Asunto(s)
Inteligencia Artificial , Electrocardiografía/métodos , Cardiopatías/diagnóstico , Frecuencia Cardíaca/fisiología , Adulto , Anciano , Área Bajo la Curva , /virología , Electrocardiografía/instrumentación , Femenino , Cardiopatías/fisiopatología , Humanos , Síndrome de QT Prolongado/diagnóstico , Síndrome de QT Prolongado/fisiopatología , Masculino , Persona de Mediana Edad , Estudios Prospectivos , Curva ROC , Sensibilidad y Especificidad , Teléfono Inteligente
18.
Int J Mol Sci ; 22(3)2021 Jan 26.
Artículo en Inglés | MEDLINE | ID: mdl-33530402

RESUMEN

Blood-based protein biomarkers are increasingly being explored as supplementary or efficient alternatives for population-based screening of colorectal cancer (CRC). The objective of the current study was to compare the diagnostic potential of proteins measured with different proteomic technologies. The concentrations of protein biomarkers were measured using proximity extension assays (PEAs), liquid chromatography/multiple reaction monitoring-mass spectrometry (LC/MRM-MS) and quantibody microarrays (QMAs) in plasma samples of 56 CRC patients and 99 participants free of neoplasms. In another approach, proteins were measured in serum samples of 30 CRC cases and 30 participants free of neoplasm using immunome full-length functional protein arrays (IpAs). From all the measurements, 9, 6, 35 and 14 protein biomarkers overlapped for comparative evaluation of (a) PEA and LC/MRM-MS, (b) PEA and QMA, (c) PEA and IpA, and (d) LC/MRM-MS and IpA measurements, respectively. Correlation analysis was performed, along with calculation of the area under the curve (AUC) for assessing the diagnostic potential of each biomarker. DeLong's test was performed to assess the differences in AUC. Evaluation of the nine biomarkers measured with PEA and LC/MRM-MS displayed correlation coefficients >+0.6, similar AUCs and DeLong's p-values indicating no differences in AUCs for biomarkers like insulin-like growth factor binding protein 2 (IGFBP2), matrix metalloproteinase 9 (MMP9) and serum paraoxonase lactonase 3 (PON3). Comparing six proteins measured with PEA and QMA showed good correlation and similar diagnostic performance for only one protein, growth differentiation factor 15 (GDF15). The comparison of 35 proteins measured with IpA and PEA and 14 proteins analyzed with IpA and LC/MRM-MS revealed poor concordance and comparatively better AUCs when measured with PEA and LC/MRM-MS. The comparison of different proteomic technologies suggests the superior performance of novel technologies like PEA and LC/MRM-MS over the assessed array-based technologies in blood-protein-based early detection of CRC.


Asunto(s)
Biomarcadores de Tumor/sangre , Proteínas Sanguíneas , Neoplasias Colorrectales/sangre , Neoplasias Colorrectales/diagnóstico , Proteómica/métodos , Anciano , Anciano de 80 o más Años , Área Bajo la Curva , Cromatografía Liquida , Colonoscopía , Neoplasias Colorrectales/etiología , Femenino , Humanos , Masculino , Persona de Mediana Edad , Estadificación de Neoplasias , Pronóstico , Espectrometría de Masas en Tándem
19.
JAMA Netw Open ; 4(2): e2036733, 2021 02 01.
Artículo en Inglés | MEDLINE | ID: mdl-33538826

RESUMEN

Importance: Posttraumatic stress disorder (PTSD) is a serious mental health disorder that can be effectively treated with empirically based practices. PTSD screening is essential for identifying undetected cases and providing patients with appropriate care. Objective: To determine whether the Primary Care PTSD screen for the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (PC-PTSD-5) is a diagnostically accurate and acceptable measure for use in Veterans Affairs (VA) primary care clinics. Design, Setting, and Participants: This cross-sectional, diagnostic accuracy study enrolled participants from May 19, 2017, to September 26, 2018. Participants were recruited from primary care clinics across 2 VA Medical Centers. Session 1 was conducted in person, and session 2 was completed within 30 days via telephone. A consecutive sample of 1594 veterans, aged 18 years or older, who were scheduled for a primary care visit was recruited. Data analysis was performed from March 2019 to August 2020. Exposures: In session 1, participants completed a battery of questionnaires. In session 2, a research assistant administered the PC-PTSD-5 to participants, and then a clinician assessor blind to PC-PTSD-5 results conducted a structured diagnostic interview for PTSD. Main Outcomes and Measures: The range of PC-PTSD-5 cut points overall and across gender was assessed, and diagnostic performance was evaluated by calculating weighted κ values. Results: In total, 495 of 1594 veterans (31%) participated, and 396 completed all measures and were included in the analyses. Participants were demographically similar to the VA primary care population (mean [SD] age, 61.4 [15.5] years; age range, 21-93 years) and were predominantly male (333 participants [84.1%]) and White (296 of 394 participants [75.1%]). The PC-PTSD-5 had high levels of diagnostic accuracy for the overall sample (area under the receiver operating characteristic curve [AUC], 0.927; 95% CI, 0.896-0.959), men (AUC, 0.932; 95% CI, 0.894-0.969), and women (AUC, 0.899, 95% CI, 0.824-0.974). A cut point of 4 ideally balanced false negatives and false positives for the overall sample and for men. However, for women, this cut point resulted in high numbers of false negatives (6 veterans [33.3%]). A cut point of 3 fit better for women, despite increasing the number of false positives. Participants rated the PC-PTSD-5 as highly acceptable. Conclusions and Relevance: The PC-PTSD-5 is an accurate and acceptable screening tool for use in VA primary care settings. Because performance parameters will change according to sample, clinicians should consider sample characteristics and screening purposes when selecting a cut point.


Asunto(s)
Aceptación de la Atención de Salud , Atención Primaria de Salud , Trastornos por Estrés Postraumático/diagnóstico , Veteranos/psicología , Adulto , Anciano , Anciano de 80 o más Años , Área Bajo la Curva , Manual Diagnóstico y Estadístico de los Trastornos Mentales , Femenino , Humanos , Masculino , Tamizaje Masivo/métodos , Persona de Mediana Edad , Cuestionario de Salud del Paciente , Curva ROC , Sensibilidad y Especificidad , Factores Sexuales , Estados Unidos , United States Department of Veterans Affairs , Adulto Joven
20.
Cell Prolif ; 54(4): e12989, 2021 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-33609051

RESUMEN

OBJECTIVES: Our aim was to investigate the prevalence and predictive variables of sarcopenia. METHODS: We recruited participants from the Peking Union Medical College Hospital Multicenter Prospective Longitudinal Sarcopenia Study (PPLSS). Muscle mass was quantified using bioimpedance, and muscle function was quantified using grip strength and gait speed. Logistic regression revealed the relationships between sarcopenia and nutritional, lifestyle, disease, psychosocial and physical variables. RESULTS: The prevalence of sarcopenia and sarcopenic obesity was 9.2%-16.2% and 0.26%-9.1%, respectively. Old age, single status, undernourishment, higher income, smoking, low physical activity, poor appetite and low protein diets were significantly associated with sarcopenia. Multiple logistic regression analysis showed that age was a risk factor for all stages of sarcopenia, and participants above 80 years were greater than fivefold more susceptible to sarcopenia, while lower physical activity was an independent risk factor. The optimal cut-off value for age was 71 years, which departs from the commonly accepted cut-off of 60 years. Female participants were greater than twofold less susceptible to sarcopenia than male participants. The sterol derivative 25-hydroxyvitamin D was associated with fourfold lower odds of sarcopenia in male participants. Several protein intake variables were also correlated with sarcopenia. Based on these parameters, we defined a highly predictive index for sarcopenia. CONCLUSIONS: Our findings support a predictive index of sarcopenia, which agglomerates the complex influences that sterol metabolism and nutrition exert on male vs female participants.


Asunto(s)
Proteínas/metabolismo , Sarcopenia/patología , Esteroles/metabolismo , Anciano , Anciano de 80 o más Años , Área Bajo la Curva , Calcifediol/metabolismo , China/epidemiología , Ejercicio Físico , Femenino , Humanos , Modelos Logísticos , Estudios Longitudinales , Masculino , Persona de Mediana Edad , Estudios Prospectivos , Curva ROC , Factores de Riesgo , Sarcopenia/epidemiología , Factores Sexuales , Testosterona/análisis
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA
...