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1.
J Infect Chemother ; 28(1): 10-18, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34535404

RESUMEN

INTRODUCTION: Although several models to predict intensive care unit (ICU) mortality are available, their performance decreases in certain subpopulations because specific factors are not included. Moreover, these models often involve complex techniques and are not applicable in low-resource settings. We developed a prediction model and simplified risk score to predict 14-day mortality in ICU patients infected with Klebsiella pneumoniae. METHODOLOGY: A retrospective cohort study was conducted using data of ICU patients infected with Klebsiella pneumoniae at the largest tertiary hospital in Northern Vietnam during 2016-2018. Logistic regression was used to develop our prediction model. Model performance was assessed by calibration (area under the receiver operating characteristic curve-AUC) and discrimination (Hosmer-Lemeshow goodness-of-fit test). A simplified risk score was also constructed. RESULTS: Two hundred forty-nine patients were included, with an overall 14-day mortality of 28.9%. The final prediction model comprised six predictors: age, referral route, SOFA score, central venous catheter, intracerebral haemorrhage surgery and absence of adjunctive therapy. The model showed high predictive accuracy (AUC = 0.83; p-value Hosmer-Lemeshow test = 0.92). The risk score has a range of 0-12 corresponding to mortality risk 0-100%, which produced similar predictive performance as the original model. CONCLUSIONS: The developed prediction model and risk score provide an objective quantitative estimation of individual 14-day mortality in ICU patients infected with Klebsiella pneumoniae. The tool is highly applicable in practice to help facilitate patient stratification and management, evaluation of further interventions and allocation of resources and care, especially in low-resource settings where electronic systems to support complex models are missing.


Asunto(s)
Cuidados Críticos , Klebsiella pneumoniae , Mortalidad Hospitalaria , Humanos , Unidades de Cuidados Intensivos , Pronóstico , Curva ROC , Estudios Retrospectivos
2.
Neurosurg Focus ; 51(5): E7, 2021 11.
Artículo en Inglés | MEDLINE | ID: mdl-34724640

RESUMEN

OBJECTIVE: The overuse of head CT examinations has been much discussed, especially those for minor traumatic brain injury (TBI). In the disruptive era, machine learning (ML) is one of the prediction tools that has been used and applied in various fields of neurosurgery. The objective of this study was to compare the predictive performance between ML and a nomogram, which is the other prediction tool for intracranial injury following cranial CT in children with TBI. METHODS: Data from 964 pediatric patients with TBI were randomly divided into a training data set (75%) for hyperparameter tuning and supervised learning from 14 clinical parameters, while the remaining data (25%) were used for validation purposes. Moreover, a nomogram was developed from the training data set with similar parameters. Therefore, models from various ML algorithms and the nomogram were built and deployed via web-based application. RESULTS: A random forest classifier (RFC) algorithm established the best performance for predicting intracranial injury following cranial CT of the brain. The area under the receiver operating characteristic curve for the performance of RFC algorithms was 0.80, with 0.34 sensitivity, 0.95 specificity, 0.73 positive predictive value, 0.80 negative predictive value, and 0.79 accuracy. CONCLUSIONS: The ML algorithms, particularly the RFC, indicated relatively excellent predictive performance that would have the ability to support physicians in balancing the overuse of head CT scans and reducing the treatment costs of pediatric TBI in general practice.


Asunto(s)
Lesiones Traumáticas del Encéfalo , Nomogramas , Algoritmos , Lesiones Traumáticas del Encéfalo/diagnóstico por imagen , Niño , Humanos , Aprendizaje Automático , Curva ROC
3.
BMC Bioinformatics ; 22(1): 546, 2021 Nov 10.
Artículo en Inglés | MEDLINE | ID: mdl-34758743

RESUMEN

BACKGROUND: Host population structure is a key determinant of pathogen and infectious disease transmission patterns. Pathogen phylogenetic trees are useful tools to reveal the population structure underlying an epidemic. Determining whether a population is structured or not is useful in informing the type of phylogenetic methods to be used in a given study. We employ tree statistics derived from phylogenetic trees and machine learning classification techniques to reveal an underlying population structure. RESULTS: In this paper, we simulate phylogenetic trees from both structured and non-structured host populations. We compute eight statistics for the simulated trees, which are: the number of cherries; Sackin, Colless and total cophenetic indices; ladder length; maximum depth; maximum width, and width-to-depth ratio. Based on the estimated tree statistics, we classify the simulated trees as from either a non-structured or a structured population using the decision tree (DT), K-nearest neighbor (KNN) and support vector machine (SVM). We incorporate the basic reproductive number ([Formula: see text]) in our tree simulation procedure. Sensitivity analysis is done to investigate whether the classifiers are robust to different choice of model parameters and to size of trees. Cross-validated results for area under the curve (AUC) for receiver operating characteristic (ROC) curves yield mean values of over 0.9 for most of the classification models. CONCLUSIONS: Our classification procedure distinguishes well between trees from structured and non-structured populations using the classifiers, the two-sample Kolmogorov-Smirnov, Cucconi and Podgor-Gastwirth tests and the box plots. SVM models were more robust to changes in model parameters and tree size compared to KNN and DT classifiers. Our classification procedure was applied to real -world data and the structured population was revealed with high accuracy of [Formula: see text] using SVM-polynomial classifier.


Asunto(s)
Aprendizaje Automático , Máquina de Vectores de Soporte , Algoritmos , Filogenia , Curva ROC
4.
Zhonghua Yi Xue Za Zhi ; 101(42): 3495-3500, 2021 Nov 16.
Artículo en Chino | MEDLINE | ID: mdl-34775708

RESUMEN

Objective: To explore risk factors for hyperkalemia in hemodialysis (HD) patients, and establish and verify a risk assessment model of hyperkalemia in HD patients. Methods: The clinical data of HD patients who were admitted to the Department of Nephrology of the First Affiliated Hospital of Zhengzhou University between April 2020 and January 2021 were retrospectively collected and divided into training dataset and validation dataset by using the conversion-random number generator. In the training dataset, multivariate logistic regression analysis was used to screen the risk factors for hyperkalemia in HD patients and the factors were scored to establish the risk assessment model. The validation dataset was substituted into the model and the receiver operating characteristic (ROC) curve was drawn and the area under the curve (AUC) was calculated to verify the effectiveness of the risk prediction model in predicting hyperkalemia. Results: A total of 502 HD patients were enrolled and further divided into training dataset (n=372) and validation dataset (n=130). There were 268 males and 234 females, with a mean age of (54±13) years. Multivariate logistic regression analysis showed that metabolic acidosis, high potassium diet, history of hyperkalemia, the change of electrocardiogram (ECG), disfunction of vascular access and time interval from last dialysis were risk factors for causing hyperkalemia in patients undergoing HD. Risk assessment model was established based on these risk factors. The AUC of the ROC curve was 0.799. Using 5 as the cut-off value, the sensitivity and specificity for predicting hyperkalemia events was 61.4% and 86.3%, respectively. Conclusion: The current study preliminarily established a risk assessment model for hyperkalemia in HD patients, which can help clinicians manage the potassium level of HD patients.


Asunto(s)
Hiperpotasemia , Adulto , Anciano , Femenino , Humanos , Hiperpotasemia/epidemiología , Masculino , Persona de Mediana Edad , Curva ROC , Diálisis Renal , Estudios Retrospectivos , Medición de Riesgo , Factores de Riesgo
5.
Nan Fang Yi Ke Da Xue Xue Bao ; 41(10): 1569-1576, 2021 Oct 20.
Artículo en Chino | MEDLINE | ID: mdl-34755674

RESUMEN

OBJECTIVE: To explore the value of CT-based radiomics in differential diagnosis of retroperitoneal neuroblastoma (NB) and ganglioneuroblastoma (GNB) in children. METHODS: A total of 172 children with NB and 48 children with GNB were assigned into the training set and testing set at the ratio of 7∶3 using a random stratified sampling method. Radiomics features were extracted and selected from non-enhanced and post-enhanced CT images. Based on the subset of optimal features, a multivariate regression model was used to establish the radiomics models for each phase and the combined radiomics models. The ROC curves of the models were drawn, and the evaluation indexes such as AUC, accuracy, sensitivity and specificity of these models were calculated and compared. RESULTS: A total of 1218 radiomics features were extracted from the CT images acquired in non-enhanced (NP), arterial (AP) and venous phases (VP), from which 4 features from the NP model, 3 features from the AP model, 2 features from the VP model and 5 features from the combined model were selected. The AUC of the NP model in the training set and testing set was 0.840 (95% CI: 0.778-0.902) and 0.804 (95% CI: 0.699-0.899), respectively, as compared with 0.819 (95%CI: 0.759-0.877) and 0.815 (95%CI: 0.697-0.915) for the AP model, 0.730 (95%CI: 0.649-0.803) and 0.751 (95%CI: 0.619-0.869) for the VP model, and 0.861 (95%CI: 0.809-0.910) and 0.827 (95%CI: 0.726-0.915) for the combined model. CONCLUSION: Radiomics signature based on non-enhanced and post-enhanced CT images can be helpful for distinguishing retroperitoneal NB and GNB in children. Compared with the first-order histogram features, textural features can better reflect the difference of the lesions. NP, AP and VP models have similar classification efficacy in differentiating retroperitoneal NB and GNB. The efficacy of the combined model is similar to that of the NP and AP models, but superior to that of the VP model.


Asunto(s)
Ganglioneuroblastoma , Neuroblastoma , Niño , Diagnóstico Diferencial , Ganglioneuroblastoma/diagnóstico por imagen , Humanos , Neuroblastoma/diagnóstico por imagen , Curva ROC , Estudios Retrospectivos , Tomografía Computarizada por Rayos X
6.
Scand J Trauma Resusc Emerg Med ; 29(1): 160, 2021 Nov 13.
Artículo en Inglés | MEDLINE | ID: mdl-34774074

RESUMEN

BACKGROUND: While there are clear national resuscitation room admission guidelines for major trauma patients, there are no comparable alarm criteria for critically ill nontrauma (CINT) patients in the emergency department (ED). The aim of this study was to define and validate specific trigger factor cut-offs for identification of CINT patients in need of a structured resuscitation management protocol. METHODS: All CINT patients at a German university hospital ED for whom structured resuscitation management would have been deemed desirable were prospectively enrolled over a 6-week period (derivation cohort, n = 108). The performance of different thresholds and/or combinations of trigger factors immediately available during triage were compared with the National Early Warning Score (NEWS) and Quick Sequential Organ Failure Assessment (qSOFA) score. Identified combinations were then tested in a retrospective sample of consecutive nontrauma patients presenting at the ED during a 4-week period (n = 996), and two large external datasets of CINT patients treated in two German university hospital EDs (validation cohorts 1 [n = 357] and 2 [n = 187]). RESULTS: The any-of-the-following trigger factor iteration with the best performance in the derivation cohort included: systolic blood pressure < 90 mmHg, oxygen saturation < 90%, and Glasgow Coma Scale score < 15 points. This set of triggers identified > 80% of patients in the derivation cohort and performed better than NEWS and qSOFA scores in the internal validation cohort (sensitivity = 98.5%, specificity = 98.6%). When applied to the external validation cohorts, need for advanced resuscitation measures and hospital mortality (6.7 vs. 28.6%, p < 0.0001 and 2.7 vs. 20.0%, p < 0.012) were significantly lower in trigger factor-negative patients. CONCLUSION: Our simple, any-of-the-following decision rule can serve as an objective trigger for initiating resuscitation room management of CINT patients in the ED.


Asunto(s)
Puntuaciones en la Disfunción de Órganos , Sepsis , Servicio de Urgencia en Hospital , Mortalidad Hospitalaria , Humanos , Curva ROC , Resucitación , Estudios Retrospectivos , Triaje
7.
BMC Geriatr ; 21(1): 619, 2021 11 02.
Artículo en Inglés | MEDLINE | ID: mdl-34724908

RESUMEN

BACKGROUND: Frailty is a kind of geriatric syndrome, which is very common in the elderly. Patients with malnutrition are at higher risk of frailty. This study explored the correlation between nutrition and frailty and compared the receiver operating characteristic curve of different nutritional indexes for frailty. METHODS: This cross-sectional study included 179 inpatients aged ≥65 years old. Frailty was measured using Fried Frailty Phenotype, handgrip strength was measured using JAMAR@Plus and the 4.57 m usual gait speed was measured using a stopwatch. Comprehensive nutritional assessment refers to the application of Mini Nutritional Assessment (MNA) to assess the nutritional status of patients. RESULTS: Compared with the non-frailty group, the upper arm circumference, calf circumference, hemoglobin, albumin, prealbumin, cholesterol and low density lipoprotein in the frailty group were lower (P < 0.05). Comprehensive nutritional assessment, whether as a categorical variable or a continuous variable, was significantly correlated with frailty (P < 0.05). Model1 showed that the risk of frailty in malnourished patients was 3.381 times higher than that in well nourished patients (P = 0.036). Model2 showed that the risk of frailty decreased by 13.8% for every 1 point increase in MNA score (P = 0.009). The area under the curves of albumin, prealbumin and hemoglobin was larger (AUC > 0.65), AUC was 0.718, 0.693 and 0.743, respectively. CONCLUSIONS: Our results suggest that malnutrition is closely related to frailty. As for single nutritional indexes, albumin, prealbumin and hemoglobin were found to be associated with frailty. Further cohort studies are needed to verify their ability to screen for frailty.


Asunto(s)
Fragilidad , Desnutrición , Anciano , Estudios Transversales , Fragilidad/diagnóstico , Fragilidad/epidemiología , Evaluación Geriátrica , Fuerza de la Mano , Humanos , Desnutrición/diagnóstico , Desnutrición/epidemiología , Evaluación Nutricional , Estado Nutricional , Curva ROC
8.
BMC Med Inform Decis Mak ; 21(1): 303, 2021 11 01.
Artículo en Inglés | MEDLINE | ID: mdl-34724933

RESUMEN

BACKGROUND: Accurately predicting which patients with chronic heart failure (CHF) are particularly vulnerable for adverse outcomes is of crucial importance to support clinical decision making. The goal of the current study was to examine the predictive value on long term heart failure (HF) hospitalisation and all-cause mortality in CHF patients, by exploring and exploiting machine learning (ML) and traditional statistical techniques on a Dutch health insurance claims database. METHODS: Our study population consisted of 25,776 patients with a CHF diagnosis code between 2012 and 2014 and one year and three years follow-up HF hospitalisation (1446 and 3220 patients respectively) and all-cause mortality (2434 and 7882 patients respectively) were measured from 2015 to 2018. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated after modelling the data using Logistic Regression, Random Forest, Elastic Net regression and Neural Networks. RESULTS: AUC rates ranged from 0.710 to 0.732 for 1-year HF hospitalisation, 0.705-0.733 for 3-years HF hospitalisation, 0.765-0.787 for 1-year mortality and 0.764-0.791 for 3-years mortality. Elastic Net performed best for all endpoints. Differences between techniques were small and only statistically significant between Elastic Net and Logistic Regression compared with Random Forest for 3-years HF hospitalisation. CONCLUSION: In this study based on a health insurance claims database we found clear predictive value for predicting long-term HF hospitalisation and mortality of CHF patients by using ML techniques compared to traditional statistics.


Asunto(s)
Insuficiencia Cardíaca , Hospitalización , Humanos , Modelos Logísticos , Aprendizaje Automático , Curva ROC
9.
Clin Lab ; 67(11)2021 Nov 01.
Artículo en Inglés | MEDLINE | ID: mdl-34758216

RESUMEN

BACKGROUND: An increasing number of studies have indicated that uncomplicated acute appendicitis can be cured with antibiotics alone. Reducing the hazards of appendicitis in infants and young children is a priority problem. It is necessary to search for potential biomarkers for early diagnosis of appendicitis in infants and young children. METHODS: A retrospective cohort study, including 366 infants and young children treated in the pediatric surgery department, was conducted. Complete blood count, C-reactive protein, and procalcitonin were measured at admission and 24 hours after operation. RESULTS: The median of PCT, CRP, and WBC in the acute appendicitis group and other diseases group were 1.20, 0.11 - 4.06; 16.50, 0.81 - 76.21; 13.51, 7.53 - 26.30 and 0.03, 0.01 - 0.13; 3.35, 0.92 - 6.33; 14.34, 8.84 - 17.23 at the admission, respectively. PCT and CRP were found higher in the acute appendicitis group than that in other abdominal pain diseases group (p < 0.05). WBC is not a specific indicator for identifying acute appendicitis and other abdominal pain diseases (p > 0.05). In different acute appendicitis cases, PCT and CRP significantly increased in complicated appendicitis (p < 0.05). Data showed that WBC mildly increased in complicated appendicitis compared to acute simple appendicitis (p < 0.05). ROC curves showed that PCT was a specific indicator for identifying acute appendicitis and other abdominal pain diseases, AUCPCT = 1.000 (95% CI, 0.999 - 1.000). The median of antibiotic treatment is 4.0 d (95% CI 3.0 - 5.0) in acute appendicitis with PCT results versus 7.0 d (95% CI 5.0 - 9.0) in acute appendicitis without PCT result. CONCLUSIONS: PCT shows a high diagnostic ability for appendicitis in infants and young children at admission and assists pediatricians in management of pediatric appendicitis. The combination of these biomarkers is highly recommended. Further studies are needed to confirm our findings.


Asunto(s)
Apendicitis , Polipéptido alfa Relacionado con Calcitonina , Apendicitis/diagnóstico , Apendicitis/cirugía , Biomarcadores , Proteína C-Reactiva/análisis , Niño , Preescolar , Humanos , Lactante , Recuento de Leucocitos , Curva ROC , Estudios Retrospectivos
10.
Clin Lab ; 67(11)2021 Nov 01.
Artículo en Inglés | MEDLINE | ID: mdl-34758223

RESUMEN

BACKGROUND: Cirrhosis is often an asymptomatic disease. Its early diagnosis before the development of life-threatening complications is an important step to prevent the progression of the disease. The aim of the present study was the identification of parameters that are significantly changed in cirrhosis, are not affected by the cause of cirrhosis, and are associated with fatal complications of cirrhosis. METHODS: Demographic and pre-transplant ultrasound and laboratory findings were reviewed in patients with viral- (n = 27), autoimmune hepatitis- (n = 27), alcohol- (n = 18), primary sclerosing cholangitis- (PSC) (n = 36), and nonalcoholic steatohepatitis-related cirrhosis (n = 42). RESULTS: Among laboratory findings, only the aspartate aminotransferase-to-platelet ratio index (APRI) value in cirrhotic patients was significantly higher than that of healthy individuals (p < 0.001) and, meanwhile, its value was not different among cirrhotic patients with various etiologies (p = 0.240) but was associated with the ascites, as a cirrhosis life-threatening complication (p < 0.001). CONCLUSIONS: The APRI has acceptable potential to predict prognosis in cirrhosis. So, it can be a possible parameter to the prediction of the lethal complications of cirrhosis.


Asunto(s)
Cirrosis Hepática , Aspartato Aminotransferasas , Humanos , Cirrosis Hepática/diagnóstico , Recuento de Plaquetas , Pronóstico , Curva ROC , Estudios Retrospectivos , Ultrasonografía
11.
BMC Bioinformatics ; 22(Suppl 5): 93, 2021 Nov 08.
Artículo en Inglés | MEDLINE | ID: mdl-34749631

RESUMEN

BACKGROUND: Atrial fibrillation is a paroxysmal heart disease without any obvious symptoms for most people during the onset. The electrocardiogram (ECG) at the time other than the onset of this disease is not significantly different from that of normal people, which makes it difficult to detect and diagnose. However, if atrial fibrillation is not detected and treated early, it tends to worsen the condition and increase the possibility of stroke. In this paper, P-wave morphology parameters and heart rate variability feature parameters were simultaneously extracted from the ECG. A total of 31 parameters were used as input variables to perform the modeling of artificial intelligence ensemble learning model. RESULTS: This paper applied three artificial intelligence ensemble learning methods, namely Bagging ensemble learning method, AdaBoost ensemble learning method, and Stacking ensemble learning method. The prediction results of these three artificial intelligence ensemble learning methods were compared. As a result of the comparison, the Stacking ensemble learning method combined with various models finally obtained the best prediction effect with the accuracy of 92%, sensitivity of 88%, specificity of 96%, positive predictive value of 95.7%, negative predictive value of 88.9%, F1 score of 0.9231 and area under receiver operating characteristic curve value of 0.911. CONCLUSION: In feature extraction, this paper combined P-wave morphology parameters and heart rate variability parameters as input parameters for model training, and validated the value of the proposed parameters combination for the improvement of the model's predicting effect. In the calculation of the P-wave morphology parameters, the hybrid Taguchi-genetic algorithm was used to obtain more accurate Gaussian function fitting parameters. The prediction model was trained using the Stacking ensemble learning method, so that the model accuracy had better results, which can further improve the early prediction of atrial fibrillation.


Asunto(s)
Fibrilación Atrial , Algoritmos , Inteligencia Artificial , Fibrilación Atrial/diagnóstico , Electrocardiografía , Humanos , Aprendizaje Automático , Curva ROC
12.
Zhongguo Dang Dai Er Ke Za Zhi ; 23(10): 1021-1026, 2021 Oct 15.
Artículo en Inglés, Chino | MEDLINE | ID: mdl-34719417

RESUMEN

OBJECTIVES: To study the value of serum miR-922 and miR-506 expression levels in the diagnosis and prognostic assessment of childhood acute lymphoblastic leukemia (ALL). METHODS: A total of 132 children with ALL (ALL group) and 80 healthy children (healthy control group) were prospectively selected in this study. Quantitative real-time polymerase chain reaction was used to measure the expression levels of serum miR-922 and miR-506 in both groups. Receiver operating characteristic (ROC) curves were plotted to analyze the diagnostic value of miR-922 and miR-506 for childhood ALL. The Kaplan-Meier method was used to plot survival curves, and multivariate COX regression models were used to analyze the risk factors for poor prognosis in children with ALL. RESULTS: The ALL group had significantly higher expression levels of serum miR-922 and miR-506 than the control group (P<0.001). The ROC curve analysis showed that the optimal cut-off values of miR-922 and miR-506 for the diagnosis of childhood ALL were 1.46 and 2.17, respectively. The high miR-922 expression (≥1.46) group and high miR-506 expression (≥2.17) group had significantly higher incidence rates of lymph node enlargement, leukocyte count ≥50×109/L, medium-high risk stratification, mixed-lineage leukemia (MLL) gene rearrangement, and karyotype abnormality than the low miR-922 expression group and low miR-506 expression group (P<0.05). The Kaplan-Meier analysis showed that high expression of miR-922 and miR-506 was associated with short survival time in children with ALL (P<0.05). The multivariate COX regression analysis showed that leukocyte count ≥50×109/L, medium-high risk stratification, MLL gene rearrangement, miR-922≥1.46, and miR-506≥2.17 could indicate poor prognosis in children with ALL (P<0.05). CONCLUSIONS: The expression levels of miR-922 and miR-506 are of good value in the diagnosis and prognostic assessment of childhood ALL.


Asunto(s)
MicroARNs , Leucemia-Linfoma Linfoblástico de Células Precursoras , Biomarcadores de Tumor , Niño , Humanos , Estimación de Kaplan-Meier , MicroARNs/sangre , MicroARNs/genética , Leucemia-Linfoma Linfoblástico de Células Precursoras/diagnóstico , Leucemia-Linfoma Linfoblástico de Células Precursoras/genética , Pronóstico , Curva ROC
13.
Front Cell Infect Microbiol ; 11: 654272, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-34722325

RESUMEN

Introduction: Asymptomatic coronavirus disease 2019 (COVID-19) and moderate COVID-19 may be the most common COVID-19 cases. This study was designed to develop a diagnostic model for patients with asymptomatic and moderate COVID-19 based on demographic, clinical, and laboratory variables. Methods: This retrospective study divided the subjects into 2 groups: asymptomatic COVID-19 (without symptoms, n = 15) and moderate COVID-19 (with symptoms, n = 57). Demographic characteristics, clinical data, routine blood tests, other laboratory tests, and inpatient data were collected and analyzed to compare patients with asymptomatic COVID-19 and moderate COVID-19. Results: Comparison of the asymptomatic COVID-19 group with the moderate COVID-19 group yielded the following results: the patients were younger (P = 0.045); the cluster of differentiation (CD)8+ (cytotoxic) T cell level was higher (P = 0.017); the C-reactive protein (CRP) level was lower (P = 0.001); the white blood cell (WBC, P < 0.001), neutrophil (NEU, P = 0.036), lymphocyte (LYM, P = 0.009), and eosinophil (EOS, P = 0.036) counts were higher; and the serum iron level (P = 0.049) was higher in the asymptomatic COVID-19 group. The multivariate analysis showed that the NEU count (odds ratio [OR] = 2.007, 95% confidence interval (CI): 1.162 - 3.715, P = 0.014) and LYM count (OR = 9.380, 95% CI: 2.382 - 36.934, P = 0.001) were independent factors for the presence of clinical symptoms after COVID-19 infection. The NEU count and LYM count were diagnostic predictors of asymptomatic COVID-19. This diagnostic prediction model showed high discriminatory power, consistency, and net clinical benefits. Conclusions: The proposed model can distinguish asymptomatic COVID-19 from moderate COVID-19, thereby helping clinicians identify and distinguish patients with potential asymptomatic COVID-19 from those with moderate COVID-19.


Asunto(s)
COVID-19 , Neutrófilos , Humanos , Linfocitos , Curva ROC , Estudios Retrospectivos , SARS-CoV-2
14.
World J Gastroenterol ; 27(38): 6465-6475, 2021 Oct 14.
Artículo en Inglés | MEDLINE | ID: mdl-34720535

RESUMEN

BACKGROUND: Synchronous liver metastasis (SLM) is an indicator of poor prognosis for colorectal cancer (CRC). Nearly 50% of CRC patients develop hepatic metastasis, with 15%-25% of them presenting with SLM. The evaluation of SLM in CRC is crucial for precise and personalized treatment. It is beneficial to detect its response to chemotherapy and choose an optimal treatment method. AIM: To construct prediction models based on magnetic resonance imaging (MRI)-radiomics and clinical parameters to evaluate the chemotherapy response in SLM of CRC. METHODS: A total of 102 CRC patients with 223 SLM lesions were identified and divided into disease response (DR) and disease non-response (non-DR) to chemotherapy. After standardizing the MRI images, the volume of interest was delineated and radiomics features were calculated. The MRI-radiomics logistic model was constructed after methods of variance/Mann-Whitney U test, correlation analysis, and least absolute shrinkage and selection operator in feature selecting. The radiomics score was calculated. The receiver operating characteristics curves by the DeLong test were analyzed with MedCalc software to compare the validity of all models. Additionally, the area under curves (AUCs) of DWI, T2WI, and portal phase of contrast-enhanced sequences radiomics model (Ra-DWI, Ra-T2WI, and Ra-portal phase of contrast-enhanced sequences) were calculated. The radiomics-clinical nomogram was generated by combining radiomics features and clinical characteristics of CA19-9 and clinical N staging. RESULTS: The AUCs of the MRI-radiomics model were 0.733 and 0.753 for the training (156 lesions with 68 non-DR and 88 DR) and the validation (67 lesions with 29 non-DR and 38 DR) set, respectively. Additionally, the AUCs of the training and the validation set of Ra-DWI were higher than those of Ra-T2WI and Ra-portal phase of contrast-enhanced sequences (training set: 0.652 vs 0.628 and 0.633, validation set: 0.661 vs 0.575 and 0.543). After chemotherapy, the top four of twelve delta-radiomics features of Ra-DWI in the DR group belonged to gray-level run-length matrices radiomics parameters. The radiomics-clinical nomogram containing radiomics score, CA19-9, and clinical N staging was built. This radiomics-clinical nomogram can effectively discriminate the patients with DR from non-DR with a higher AUC of 0.809 (95% confidence interval: 0.751-0.858). CONCLUSION: MRI-radiomics is conducive to predict chemotherapeutic response in SLM patients of CRC. The radiomics-clinical nomogram, involving radiomics score, CA19-9, and clinical N staging is more effective in predicting chemotherapeutic response.


Asunto(s)
Neoplasias Colorrectales , Neoplasias Hepáticas , Neoplasias Colorrectales/diagnóstico por imagen , Neoplasias Colorrectales/tratamiento farmacológico , Humanos , Neoplasias Hepáticas/diagnóstico por imagen , Neoplasias Hepáticas/tratamiento farmacológico , Imagen por Resonancia Magnética , Nomogramas , Curva ROC , Estudios Retrospectivos
15.
Sci Rep ; 11(1): 21519, 2021 11 02.
Artículo en Inglés | MEDLINE | ID: mdl-34728719

RESUMEN

A high neutrophil to lymphocyte ratio (NLR) is considered an unfavorable prognostic factor in various diseases, including COVID-19. The prognostic value of NLR in other respiratory viral infections, such as Influenza, has not hitherto been extensively studied. We aimed to compare the prognostic value of NLR in COVID-19, Influenza and Respiratory Syncytial Virus infection (RSV). A retrospective cohort of COVID-19, Influenza and RSV patients admitted to the Tel Aviv Medical Center from January 2010 to October 2020 was analyzed. Laboratory, demographic, and clinical parameters were collected. Two way analyses of variance (ANOVA) was used to compare the association between NLR values and poor outcomes among the three groups. ROC curve analyses for each virus was applied to test the discrimination ability of NLR. 722 COVID-19, 2213 influenza and 482 RSV patients were included. Above the age of 50, NLR at admission was significantly lower among COVID-19 patients (P < 0.001). NLR was associated with poor clinical outcome only in the COVID-19 group. ROC curve analysis was performed; the area under curve of poor outcomes for COVID-19 was 0.68, compared with 0.57 and 0.58 for Influenza and RSV respectively. In the COVID-19 group, multivariate logistic regression identified a high NLR (defined as a value above 6.82) to be a prognostic factor for poor clinical outcome, after adjusting for age, sex and Charlson comorbidity score (odds ratio of 2.9, P < 0.001). NLR at admission is lower and has more prognostic value in COVID-19 patients, when compared to Influenza and RSV.


Asunto(s)
COVID-19/patología , Gripe Humana/patología , Infecciones por Virus Sincitial Respiratorio/patología , Adulto , Anciano , Anciano de 80 o más Años , Área Bajo la Curva , COVID-19/inmunología , COVID-19/virología , Femenino , Humanos , Gripe Humana/inmunología , Linfocitos/citología , Linfocitos/metabolismo , Masculino , Persona de Mediana Edad , Neutrófilos/citología , Neutrófilos/metabolismo , Pronóstico , Curva ROC , Infecciones por Virus Sincitial Respiratorio/inmunología , Estudios Retrospectivos , SARS-CoV-2/aislamiento & purificación
16.
Chin Med J (Engl) ; 134(21): 2535-2543, 2021 Oct 14.
Artículo en Inglés | MEDLINE | ID: mdl-34748524

RESUMEN

BACKGROUND: It is crucial to differentiate accurately glioma recurrence and pseudoprogression which have entirely different prognosis and require different treatment strategies. This study aimed to assess the diagnostic accuracy of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as a tool for distinguishing glioma recurrence and pseudoprogression. METHODS: According to particular criteria of inclusion and exclusion, related studies up to May 1, 2019, were thoroughly searched from several databases including PubMed, Embase, Cochrane Library, and Chinese biomedical databases. The quality assessment of diagnostic accuracy studies was applied to evaluate the quality of the included studies. By using the "mada" package in R, the heterogeneity, overall sensitivity, specificity, and diagnostic odds ratio were calculated. Moreover, funnel plots were used to visualize and estimate the publication bias in this study. The area under the summary receiver operating characteristic (SROC) curve was computed to display the diagnostic efficiency of DCE-MRI. RESULTS: In the present meta-analysis, a total of 11 studies covering 616 patients were included. The results showed that the pooled sensitivity, specificity, and diagnostic odds ratio were 0.792 (95% confidence interval [CI] 0.707-0.857), 0.779 (95% CI 0.715-0.832), and 16.219 (97.5% CI 9.123-28.833), respectively. The value of the area under the SROC curve was 0.846. In addition, the SROC curve showed high sensitivities (>0.6) and low false positive rates (<0.5) from most of the included studies, which suggest that the results of our study were reliable. Furthermore, the funnel plot suggested the existence of publication bias. CONCLUSIONS: While the DCE-MRI is not the perfect diagnostic tool for distinguishing glioma recurrence and pseudoprogression, it was capable of improving diagnostic accuracy. Hence, further investigations combining DCE-MRI with other imaging modalities are required to establish an efficient diagnostic method for glioma patients.


Asunto(s)
Glioma , Recurrencia Local de Neoplasia , Glioma/diagnóstico por imagen , Humanos , Imagen por Resonancia Magnética , Recurrencia Local de Neoplasia/diagnóstico por imagen , Curva ROC , Sensibilidad y Especificidad
17.
Medicine (Baltimore) ; 100(41): e27507, 2021 Oct 15.
Artículo en Inglés | MEDLINE | ID: mdl-34731135

RESUMEN

BACKGROUND: The study was conducted to investigate the value of Positron emission tomography computed tomography (PET/CT) in predicting invasiveness of ground glass nodule (GGN) by the method of meta-analysis. METHODS: Two researchers independently searched for published literature on PET/CT diagnosis of GGN as of November 30, 2020. After extracting the data, RevMan5.3 was used to evaluate the quality of the included literature. The Stata14 software was used to test the heterogeneity of the original study that met the inclusion criteria, to calculate the combined sensitivity, specificity, positive likelihood ratio and negative likelihood ratio, the prior probability and posttest probability. The summary receiver operator characteristic curve was drawn and the area under the curve was calculated. Using Deeks funnel plot to evaluate publication bias. RESULTS: Five studies were finally included, including 298 GGN cases. The included studies had no obvious heterogeneity and publication bias. The combined sensitivity and specificity of PET/CT for predicting invasive adenocarcinoma presenting as GGN were 0.74 (95% confidence interval [CI]: 0.68-0.79), 0.82 (95% CI: 0.71-0.90), positive likelihood ratio and negative likelihood ratio were 4.1 (95% CI: 2.5-6.9), 0.32 (95% CI: 0.25-0.40), and the diagnostic odds ratio was 13 (95% CI: 7-26). The prior probability is 20%, the probability of GGN being invasive adenocarcinoma when PET/CT was negative was reduced to 7%, and the probability of GGN being invasive adenocarcinoma when PET/CT was positive was increased to 51%. The area under the curve of the summary receiver operator characteristic curve was 0.85. CONCLUSION: PET/CT has high diagnostic accuracy for invasive adenocarcinoma presenting as GGN.


Asunto(s)
Neoplasias Pulmonares/patología , Nódulos Pulmonares Múltiples/diagnóstico por imagen , Tomografía Computarizada por Tomografía de Emisión de Positrones/métodos , Adenocarcinoma/diagnóstico , Adenocarcinoma/mortalidad , Área Bajo la Curva , Humanos , Nódulos Pulmonares Múltiples/mortalidad , Nódulos Pulmonares Múltiples/patología , Invasividad Neoplásica/patología , Valor Predictivo de las Pruebas , Curva ROC , Sensibilidad y Especificidad , Tasa de Supervivencia
18.
J Healthc Eng ; 2021: 1713363, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-34733452

RESUMEN

This study was to preview the risk of 30-day mortality in sepsis patients using sentiment analysis. The clinical data of patients and nursing notes were collected from the Medical Information Mart for Intensive Care (MIMIC-III) database. The factors influencing 30-day mortality were analyzed using the Cox regression model. And, the prognostic index (PI) was estimated. The receiver operating characteristic (ROC) curve was used to determine the PI cut-off point and assess the prediction ability of the model. In total, 1844 of 3560 patients were eligible for the study, with a 30-day mortality of 37.58%. Multivariate Cox analysis showed that sentiment polarity scores, sentiment subjectivity scores, simplified acute physiology score (SAPS)-II, age, and intensive care unit (ICU) types were all associated with the risk of 30-day mortality (P < 0.05). In the preview of 30-day mortality, the area under the curve (AUC) of ROC was 0.78 (95%CI: 0.74-0.81,P < 0.001) when the cut-off point of PI was 0.467. The documented notes from nurses were described for the first time. Sentiment scores measured in nursing notes are associated with the risk of 30-day mortality in sepsis patients and may improve the preview of 30-day mortality.


Asunto(s)
Sepsis , Cuidados Críticos , Humanos , Unidades de Cuidados Intensivos , Pronóstico , Curva ROC , Estudios Retrospectivos
19.
Eur Rev Med Pharmacol Sci ; 25(21): 6767-6774, 2021 11.
Artículo en Inglés | MEDLINE | ID: mdl-34787881

RESUMEN

OBJECTIVE: We aimed to test the efficiency of CHA2DS2-VASc, CHA2DS2-VASc-HS, R2CHA2DS2-VASc score systems on the prediction of mortality in the patients with COVID-19. PATIENTS AND METHODS: The data were collected from 508 hospitalized patients with COVID-19. Comorbidity features including coronary artery disease, peripheral arterial disease, congestive heart failure, hypertension, atrial fibrillation, diabetes mellitus, hyperlipidemia, smoking, chronic obstructive pulmonary disease, cerebrovascular event, cancer status, and renal disease were recorded. The patients were divided as surviving group (n=440) and non-survivors (n=68). RESULTS: The in-hospital mortality rate of the patients with COVID-19 was 13.4%. Factors found to be associated with mortality in univariate analysis were CHA2DS2-VASc, CHA2DS2-VASc-HS, R2CHA2DS2-VASc, cancer state, atrial fibrillation, hemoglobin, lymphocyte count, CRP, albumin and ferritin. Model 1 multivariate cox regression analysis revealed CHA2DS2-VASc, hemoglobin, CRP and ferritin levels to be independently associated with mortality. Factors that were found to be independently associated with in-hospital mortality in Model 2 analysis were CHA2DS2-VASc-HS, R2CHA2DS2-VASc, hemoglobin, CRP and ferritin whereas except hemoglobin in Model 3 analysis, the other variables had been the same. Predictive power of R2CHA2DS2-VASc was better than of both CHA2DS2-VASc (p=0.002) and CHA2DS2-VASc-HS (p=0.034) in determining the in-hospital mortality. Patients with higher R2CHA2DS2-VASc (> 3 points), CHA2DS2-VASc-HS (> 3 points) and CHA2DS2-VASc (> 2 points) scores exhibited the highest mortality rate in survival analysis by using Kaplan-Meier and long-rank tests. CONCLUSIONS: CHA2DS2-VASc, CHA2DS2-VASc-HS, and R2CHA2DS2-VASc were found to be independent predictors of mortality in hospitalized COVID-19 patients. The current study revealed that the predictive ability of R2CHA2DS2-VASc was better than the both of CHA2DS2-VASc and CHA2DS2-VASc-HS score.


Asunto(s)
COVID-19/mortalidad , Comorbilidad , Índice de Severidad de la Enfermedad , Adulto , Anciano , Anciano de 80 o más Años , Área Bajo la Curva , COVID-19/patología , COVID-19/virología , Femenino , Hemoglobinas/análisis , Mortalidad Hospitalaria , Hospitalización , Humanos , Estimación de Kaplan-Meier , Recuento de Linfocitos , Masculino , Persona de Mediana Edad , Modelos de Riesgos Proporcionales , Curva ROC , SARS-CoV-2/aislamiento & purificación
20.
Eur Rev Med Pharmacol Sci ; 25(21): 6731-6740, 2021 Nov.
Artículo en Inglés | MEDLINE | ID: mdl-34787878

RESUMEN

OBJECTIVE: The aim of the study was to determine the association between platelet indices and disease severity, and outcomes of the patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a secondary hospital. PATIENTS AND METHODS: 722 hospitalized patients who had positive rRT-PCR for SARS-CoV-2 and/or typical findings of COVID-19 at chest computed tomography (CT) were enrolled in this study. Initial platelet count (PLT) and indices, including mean platelet volume (MPV), platelet distribution width (PDW), plateletcrit (PCT), MPV/PCT, MPV/PLT, PDW/PLT, PDW/PCT on admission and the third day of hospitalization, and their relationship with disease severity and outcomes were evaluated retrospectively. RESULTS: The mean age of the patients was 57.2±15.6 years (range: 16-94) and male/female ratio was 1.22. 81.9% of the patients had moderate and 11.8% had severe disease. 1.8% of the patients had thrombocytopenia at admission. The patients transferred to the intensive care unit (ICU) had significantly lower baseline lymphocyte counts, PLT, PCT, and 3rd day lymphocyte counts when compared with the patients in wards. ICU patients also had higher baseline CRP, LDH, ferritin, MPV/PCT, MPV/PLT, PDW/PLT, PDW/PCT ratios, and 3rd day PDW, CRP, LDH, and ferritin levels than the patients in wards. Mortality was associated with lower baseline lymphocyte counts, PLT, PCT, 3rd day lymphocyte counts and PCT. Higher baseline CRP, LDH, ferritin, MPV/PCT, PDW/PLT, PDW/PCT and 3rd day CRP, LDH, ferritin, procalcitonin, PDW, MPV/PCT, PDW/PLT, and PDW/PCT ratios were also associated with poor prognosis. CONCLUSIONS: Platelet count and ratios were significantly associated with mortality in patients with COVID-19.


Asunto(s)
Plaquetas/citología , COVID-19/patología , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Área Bajo la Curva , COVID-19/mortalidad , COVID-19/virología , Femenino , Humanos , Unidades de Cuidados Intensivos , Masculino , Persona de Mediana Edad , Recuento de Plaquetas , Pronóstico , Curva ROC , Estudios Retrospectivos , SARS-CoV-2/aislamiento & purificación , Índice de Severidad de la Enfermedad , Análisis de Supervivencia , Adulto Joven
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