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1.
J Refract Surg ; 38(1): 35-42, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-35020538

RESUMO

PURPOSE: To develop a novel index that combines the locations and magnitudes of corneal alterations to improve discrimination of eyes with subclinical keratoconus from normal eyes. METHODS: A Scheimpflug-based tomography system was used to image 252 eyes (normal: 78 eyes, subclinical keratoconus: 71 eyes, and keratoconus: 103 eyes) of 252 patients from two clinical centers. Coordinates and magnitudes of the maximum corneal protrusion alterations were extracted from curvature, elevation, and pachymetry maps. A location consistency index (LCI) was calculated from the Euclidean distances among these locations. A logistic regression model, named the location consistency enhanced score (LCES), which combined the LCI and the magnitudes of these maximum alterations, was trained and tested in two different datasets. RESULTS: The LCI in eyes with subclinical keratoconus was 7.8 ± 2.6 µm, which was significantly different from that in normal eyes (11.8 ± 3.9 µm) and eyes with keratoconus (5.8 ± 2.4 µm) (all P < .001). The LCI could differentiate eyes with subclinical keratoconus from normal eyes with a sensitivity of 67.6%, specificity of 83.3%, and area under the receiver operating characteristic curve (AUC) of 0.81. Combining the magnitudes of these maximum alterations with the LCI for the LCES yielded a sensitivity of 90.0% and a specificity of 74.4% for differentiating eyes with subclinical keratoconus from normal eyes (AUC: 0.91). CONCLUSIONS: The LCI can assist in differentiating eyes with subclinical keratoconus from normal eyes. The LCES is a potential new index to assist in a confirmatory test of eyes with subclinical keratoconus. [J Refract Surg. 2022;38(1):35-42.].


Assuntos
Ceratocone , Córnea , Paquimetria Corneana , Topografia da Córnea , Humanos , Ceratocone/diagnóstico , Curva ROC , Estudos Retrospectivos
2.
PLoS One ; 17(1): e0262193, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-34986168

RESUMO

OBJECTIVE: To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED). METHODS: We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict "severe" COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance. RESULTS: The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed "severe" COVID-19. Patients in the highest quintile developed "severe" COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05). CONCLUSION: A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation.


Assuntos
COVID-19/diagnóstico , Sistemas de Apoio a Decisões Clínicas , Modelos Logísticos , Aprendizado de Máquina , Triagem/métodos , COVID-19/fisiopatologia , Serviço Hospitalar de Emergência , Humanos , Curva ROC , Índice de Gravidade de Doença
3.
Zhonghua Yi Xue Za Zhi ; 102(1): 62-66, 2022 Jan 04.
Artigo em Chinês | MEDLINE | ID: mdl-34991239

RESUMO

Objective: To investigate the value of ischemia modified albumin (IMA) level for predicting in-hospital mortality in patients with acute aortic dissection (AAD). Methods: A total of 195 patients with AAD from the Department of Cardio-Vascular Surgery of Affiliated Hospital of North Sichuan Medical College from January 2017 to November 2019 were consecutively collected, with 126 males and 69 females. Based on whether they died during hospitalization or not, these patients were divided into 2 groups: survival group and mortality group. The baseline data and IMA levels at admission of the two groups were recorded. Univariate logistic regression analysis was used to identify the independent risk factors, and multivariate logistic regression analysis was further performed on variables with statistical significance in univariate analysis. The area under the receiver operating characteristic (ROC) curve was calculated to determine the value of IMA for predicting in-hospital mortality in patients with AAD. Results: Forty-two AAD patients died and 153 survived, and the mortality rate was 21.5%. Logistic regression analysis showed that age (OR=2.143,95%CI:1.247-4.826,P=0.011), Stanford type A (OR=6.751,95%CI:3.189-14.291,P<0.001), drug therapy (OR=5.133,95%CI:2.463-10.700,P<0.001), IMA level (OR=4.452,95%CI:2.231-8.953,P=0.004) were independent risk factors for in-hospital mortality in patients with AAD, however surgery was a protective factor (OR=0.195,95%CI:0.093-0.406,P<0.001). The area under the ROC curve for IMA level in predicting in-hospital mortality with AAD was 0.838 (95%CI: 0.774-0.901, P<0.001), with a cut-off value of 86.55 U/ml, and the sensitivity and specificity were 83.3% and 75.2%, respectively. Conclusions: IMA may serve as a simple risk assessment indicator for patients with AAD. IMA level at admission is an independent predictor of in-hospital mortality. For patients with higher IMA level, early surgical intervention should be performed.


Assuntos
Aneurisma Dissecante , Albumina Sérica , Biomarcadores , Feminino , Mortalidade Hospitalar , Humanos , Isquemia , Masculino , Prognóstico , Curva ROC , Estudos Retrospectivos
4.
Afr J Paediatr Surg ; 19(2): 89-96, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35017378

RESUMO

Aim: The aim of this study was to evaluate the mortality and morbidity of infants <1 year of age with intestinal obstruction requiring surgical intervention and to investigate the factors affecting mortality and hospital length of stay in paediatric surgery, including albumin-haemoglobin index. Patients and Methods: The records of gastrointestinal paediatric surgeries in the past 10 years of patients who were <1-year-old at Baskent University Konya Hospital were obtained from the hospital and retrospectively studied. Patient characteristics, especially the relationship between albumin haemoglobin index (AHI) and hospital duration and mortality, were examined. According to the surgical areas, it also subjected this relationship to further analysed in subgroups. Results: There were 144 cases who fulfilled the inclusion criteria. Pre-operative serum AHI was analysed using receiver operating characteristics (ROC) curve analyzes. In the ROC analysis, AHI had a diagnostic value in predicting case discharge rates (area under the curve: 0.755, P = 0.001). When the cut-off point was set at 46.18, the sensitivity of the test was 57.5% and the sensitivity for predicting survival was 84%. In the logistic regression model to estimate survival, the odds ratio of AHI was 1.063 (confidence interval: 1.020-1.108, P = 0.004). In subgroup analyzes, AHI positively predicted survival in the NEC group and in the other group. In a linear regression model analysing the effect of AHI on hospital stay of length, AHI explained 10% of the variance in the hospital stay of length variable and significantly and negatively influenced the hospital length variable (ß = -0.319, P = 0.05). In the linear regression model for subgroup analyzes, AHI significantly and negatively predicted hospital length of stay in the NEC and pyloric surgery groups, but positively predicted hospital length of stay in the perforation group. Conclusion: The AHI can be used as a valuable marker to predict the likelihood of discharge and length of hospital stay in paediatric surgical cases <1-year-old.


Assuntos
Albuminas , Hemoglobinas , Criança , Mortalidade Hospitalar , Humanos , Lactente , Tempo de Internação , Prognóstico , Curva ROC , Estudos Retrospectivos
5.
J Coll Physicians Surg Pak ; 32(1): 4-8, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34983139

RESUMO

OBJECTIVE: To find out whether there is any correlation between the fractional flow reserve (FFR) that indicates the severity of coronary artery disease (CAD), and the HbA1c value in non-diabetic patients. STUDY DESIGN: Observational study. PLACE AND DURATION OF STUDY: Department of Cardiology, Dicle University, Turkey, from September 2015 to November 2019. METHODOLOGY: Patients who underwent elective FFR procedure were included in the study. There were two groups formed according to FFR lesion severity: FFR <0.8 group (75 patients), FFR >0.8 group (39 patients). HbA1c was compared between the two groups. The relationship between categorical variables was examined with Pearson Chi-square and Fisher's Exact test. ROC (Receiver operating characteristic) analysis was performed for the HbA1c the cut-off value. RESULTS: The two groups were similar in terms of mean age and male gender ratios (58.4±9.6 vs. 57.9 ± 10.8 years, p=0.794; 64% vs. 74.4%, respectively, p=0.262). HbA1c value was statistically higher in the group with FFR value <0.8 [(5.8 (IQR: 5.7-6.0)] compared to the group with FFR value ≥0.8 [(5.5 (IQR: 5.2-6.0, p = 0.002)]. The HbA1c cut-off value was determined as 5.55. The ideal HbA1c threshold value calculated by the Youden index had 88% sensitivity, and 53.85% specificity. CONCLUSION: HbA1c, which shows the long-term glycemic index in non-diabetic individuals, is associated with the severity of CAD determined by the fractional flow reserve. Key Words: Coronary artery disease, fractional flow reserve, HbA1c.


Assuntos
Doença da Artéria Coronariana , Estenose Coronária , Reserva Fracionada de Fluxo Miocárdico , Angiografia Coronária , Vasos Coronários , Hemoglobina A Glicada , Humanos , Masculino , Valor Preditivo dos Testes , Curva ROC , Índice de Gravidade de Doença
6.
BMC Med Inform Decis Mak ; 22(1): 2, 2022 01 04.
Artigo em Inglês | MEDLINE | ID: mdl-34983496

RESUMO

BACKGROUND: The coronavirus disease (COVID-19) hospitalized patients are always at risk of death. Machine learning (ML) algorithms can be used as a potential solution for predicting mortality in COVID-19 hospitalized patients. So, our study aimed to compare several ML algorithms to predict the COVID-19 mortality using the patient's data at the first time of admission and choose the best performing algorithm as a predictive tool for decision-making. METHODS: In this study, after feature selection, based on the confirmed predictors, information about 1500 eligible patients (1386 survivors and 144 deaths) obtained from the registry of Ayatollah Taleghani Hospital, Abadan city, Iran, was extracted. Afterwards, several ML algorithms were trained to predict COVID-19 mortality. Finally, to assess the models' performance, the metrics derived from the confusion matrix were calculated. RESULTS: The study participants were 1500 patients; the number of men was found to be higher than that of women (836 vs. 664) and the median age was 57.25 years old (interquartile 18-100). After performing the feature selection, out of 38 features, dyspnea, ICU admission, and oxygen therapy were found as the top three predictors. Smoking, alanine aminotransferase, and platelet count were found to be the three lowest predictors of COVID-19 mortality. Experimental results demonstrated that random forest (RF) had better performance than other ML algorithms with accuracy, sensitivity, precision, specificity, and receiver operating characteristic (ROC) of 95.03%, 90.70%, 94.23%, 95.10%, and 99.02%, respectively. CONCLUSION: It was found that ML enables a reasonable level of accuracy in predicting the COVID-19 mortality. Therefore, ML-based predictive models, particularly the RF algorithm, potentially facilitate identifying the patients who are at high risk of mortality and inform proper interventions by the clinicians.


Assuntos
COVID-19 , Algoritmos , Feminino , Humanos , Aprendizado de Máquina , Masculino , Pessoa de Meia-Idade , Curva ROC , Estudos Retrospectivos , SARS-CoV-2
7.
Korean J Radiol ; 23(1): 139-149, 2022 01.
Artigo em Inglês | MEDLINE | ID: mdl-34983100

RESUMO

OBJECTIVE: To compare the effects of bone suppression imaging using deep learning (BSp-DL) based on a generative adversarial network (GAN) and bone subtraction imaging using a dual energy technique (BSt-DE) on radiologists' performance for pulmonary nodule detection on chest radiographs (CXRs). MATERIALS AND METHODS: A total of 111 adults, including 49 patients with 83 pulmonary nodules, who underwent both CXR using the dual energy technique and chest CT, were enrolled. Using CT as a reference, two independent radiologists evaluated CXR images for the presence or absence of pulmonary nodules in three reading sessions (standard CXR, BSt-DE CXR, and BSp-DL CXR). Person-wise and nodule-wise performances were assessed using receiver-operating characteristic (ROC) and alternative free-response ROC (AFROC) curve analyses, respectively. Subgroup analyses based on nodule size, location, and the presence of overlapping bones were performed. RESULTS: BSt-DE with an area under the AFROC curve (AUAFROC) of 0.996 and 0.976 for readers 1 and 2, respectively, and BSp-DL with AUAFROC of 0.981 and 0.958, respectively, showed better nodule-wise performance than standard CXR (AUAFROC of 0.907 and 0.808, respectively; p ≤ 0.005). In the person-wise analysis, BSp-DL with an area under the ROC curve (AUROC) of 0.984 and 0.931 for readers 1 and 2, respectively, showed better performance than standard CXR (AUROC of 0.915 and 0.798, respectively; p ≤ 0.011) and comparable performance to BSt-DE (AUROC of 0.988 and 0.974; p ≥ 0.064). BSt-DE and BSp-DL were superior to standard CXR for detecting nodules overlapping with bones (p < 0.017) or in the upper/middle lung zone (p < 0.017). BSt-DE was superior (p < 0.017) to BSp-DL in detecting peripheral and sub-centimeter nodules. CONCLUSION: BSp-DL (GAN-based bone suppression) showed comparable performance to BSt-DE and can improve radiologists' performance in detecting pulmonary nodules on CXRs. Nevertheless, for better delineation of small and peripheral nodules, further technical improvements are required.


Assuntos
Neoplasias Pulmonares , Nódulos Pulmonares Múltiplos , Nódulo Pulmonar Solitário , Adulto , Humanos , Neoplasias Pulmonares/diagnóstico por imagem , Curva ROC , Radiografia Torácica , Sensibilidade e Especificidade , Nódulo Pulmonar Solitário/diagnóstico por imagem
8.
J Coll Physicians Surg Pak ; 32(1): 37-41, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34983145

RESUMO

OBJECTIVE: To determine the efficacy and cut-off values of C-reactive protein (CRP), lactate dehydrogenase (LDH), serum ferritin, and D-dimer for predicting mortality of COVID-19 infection. STUDY DESIGN: Observational study. PLACE AND DURATION OF STUDY: Department of Medicine, Jinnah Hospital, Lahore from January to May 2021. METHODOLOGY: Serum CRP, LDH, ferritin, and D-dimer were measured in patients with moderate to severe COVID-19 infection at admission. Patients were followed for in-hospital disease outcome. ROC curve was used to determine area under curve (AUC) and cut-off values of biomarkers, followed by multi-variate analysis by logistic regression. RESULTS: In 386 patients, male to female ratio was 1.47/1 (230/156); and mean age was 54.03 ± 16.2 years. Disease was fatal in 135 (35%) patients. AUC for mortality was 0.730 for LDH, 0.737 for CRP, 0.747 for ferritin and 0.758 for D-dimer. Mortality was higher with LDH ≥400 U/ml, Odds Ratio (OR) 5.37 (95% CI 3.01-9.57: p = 0.001), CRP ≥30 ng/L, OR 4.30 (95% CI 2.11-8.74: p = <0.001), serum ferritin ≥200 ng/ml, OR 4.13 (95% CI 1.05-16.2: p = 0.02), and D-dimer ≥400 ng/ml, OR 2.72 (95% CI 1.06-7.01: p = 0.03) with 2 log likelihood of 131.54 for predicting disease outcome with 71.7% accuracy in multi-variate analysis. CONCLUSION: Elevated serum CRP, LDH, ferritin and D-dimer are associated with higher mortality in patients of COVID-19 infection. Serum CRP ≥30ng/ml, LDH ≥400 U/L, ferritin ≥200 ng/ml and D-dimer ≥400 ng/ml can predict fatal outcome in COVID-19 patients. Key Words: C-reactive protein (CRP), COVID-19 infection, D-dimer, Ferritin, Lactate dehydrogenase (LDH), Mortality.


Assuntos
Biomarcadores/sangue , COVID-19 , Adulto , Idoso , Proteína C-Reativa/análise , COVID-19/mortalidade , Feminino , Ferritinas/sangue , Produtos de Degradação da Fibrina e do Fibrinogênio/análise , Humanos , L-Lactato Desidrogenase/sangue , Masculino , Pessoa de Meia-Idade , Prognóstico , Curva ROC , Estudos Retrospectivos
9.
In Vivo ; 36(1): 398-408, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-34972741

RESUMO

BACKGROUND/AIM: To provide data regarding relationships between quantitative dynamic contrast enhanced magnetic resonance imaging (DCE MRI) and prognostic factors in breast cancer (BC). PATIENTS AND METHODS: Data from 4 Centers (200 female patients, mean age, 51.2±11.5 years) were acquired. The following data were collected: histopathological diagnosis, tumor grade, stage, hormone receptor status, KI 67, and DCE MRI values including Ktrans (volume transfer constant), Ve (volume of the extravascular extracellular leakage space (EES) and Kep (diffusion of contrast medium from the EES back to the plasma). DCE MRI values between different groups were compared using the Mann-Whitney U-test and by the Kruskal-Wallis H test. The association between DCE MRI and Ki 67 values was calculated by the Spearman's rank correlation coefficient. RESULTS: DCE MRI values of different tumor subtypes overlapped significantly. There were no statistically significant differences of DCE MRI values between different tumor grades. All DCE MRI parameters correlated with KI-67: Ktrans, r=0.44, p=0.0001; Ve, r=0.34, p=0.0001; Kep, r=0.28, p=0.002. ROC analysis identified a Ktrans threshold of 0.3 min-1 for discrimination of tumors with low KI-67 expression (<25%) and high KI-67 expression (≥25%): sensitivity, 75.5%, specificity, 73.0%, accuracy, 74.0%, AUC, 0.78. DCE MRI values overlapped between tumors with different T and N stages. CONCLUSION: Ktrans, Kep, and Ve cannot be used as reliable a surrogate marker for hormone receptor status, tumor stage and grade in BC. Ktrans may discriminate lesions with high and lower proliferation activity.


Assuntos
Neoplasias da Mama , Adulto , Biomarcadores , Neoplasias da Mama/diagnóstico por imagem , Meios de Contraste , Feminino , Humanos , Imageamento por Ressonância Magnética , Pessoa de Meia-Idade , Curva ROC
10.
J Card Surg ; 37(1): 29-38, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34796544

RESUMO

OBJECTIVE: Model for end-stage liver disease (MELD) likely has nonlinear effects on operative outcomes. We use machine learning to evaluate the nonlinear (dependent variable may not correlate one to one with an increased risk in the outcome) relationship between MELD and outcomes of cardiac surgery. METHODS: Society of Thoracic Surgery indexed elective cardiac operations between 2011 and 2018 were included. MELD was retrospectively calculated. Logistic regression models and an imbalanced random forest classifier were created on operative mortality. Cox regression models and random forest survival models evaluated survival. Variable importance analysis (VIMP) ranked variables by predictive power. Linear and machine-learned models were compared with receiver operator characteristic (ROC) and Brier score. RESULTS: We included 3872 patients. Operative mortality was 1.7% and 5-year survival was 82.1%. MELD was the fourth largest positive predictor on VIMP analysis for operative long-term survival and the strongest negative predictor for operative mortality. MELD was not a significant predictor for operative mortality or long-term survival in the logistic or Cox regressions. The logistic model ROC area was 0.762, compared to the random forest classifier ROC of 0.674. The Brier score of the random forest survival model was larger than the Cox regression starting at 2 years and continuing throughout the study period. Bootstrap estimation on linear regression demonstrated machine-learned models were superior. CONCLUSIONS: MELD and mortality are nonlinear. MELD was insignificant in the Cox multivariable regression but was strongly important in the random forest survival model and when using bootstrapping, the superior utility was demonstrated of the machine-learned models.


Assuntos
Procedimentos Cirúrgicos Cardíacos , Doença Hepática Terminal , Cirurgia Torácica , Doença Hepática Terminal/cirurgia , Humanos , Aprendizado de Máquina , Prognóstico , Curva ROC , Estudos Retrospectivos , Índice de Gravidade de Doença
11.
Acta Neurochir Suppl ; 134: 115-118, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-34862535

RESUMO

Decision curve analysis is an increasingly popular method to assess the impact of a prediction model on medical decision making. The analysis provides a graphical summary. A basic understanding of a decision curve is needed to interpret these graphics. This short introduction addresses the common features of a decision curve. Furthermore, using a glioblastoma patient set provided by the Machine Intelligence in Clinical Neuroscience Lab from the Department of Neurosurgery and Clinical Neuroscience Center, University Hospital Zurich a decision curve is plotted for two prediction models. The corresponding R code is provided.


Assuntos
Modelos Estatísticos , Neurocirurgia , Tomada de Decisão Clínica , Humanos , Prognóstico , Curva ROC
12.
Gene ; 808: 145966, 2022 Jan 15.
Artigo em Inglês | MEDLINE | ID: mdl-34530089

RESUMO

This study was designed to construct a prognostic risk model to predict prognosis and immunotherapy response of bladder cancer (BCa) patinets. 350 differential expressed immune-related genes (DEIRGs) were obtained according to the transcriptome profiling and immune-related genes from the Cancer Genome Atlas (TCGA) database and ImmPort database, respectively. A prognostic risk model was constructed based on 15 hub genes through univariate, multivariate, and LASSO Cox regression analyses. The area under the receiver operating characteristic (ROC) curve was 0.743, indicating the superiority of the model. The scatter plot showed that as the risk score increased, the overall survival decreased significantly. In addition, all results were internally verified by the TCGA cohort. The model showed that the higher the grade, clinical stage, and TNM stage of BCa, the higher the risk score of patients. The tumor mutation burden of the low-risk group was generally higher than that of the high-risk group. Immune cell infiltration analysis showed that CD8 T cells, naive CD4 T cells, follicular helper T cells and M0 Macrophage were significantly different between the two groups. Several key immune checkpoint genes were found to be significantly different between the two groups, such as CTLA4, PD-L1, CD47, CD276, CXCL8, and HAVCR2/TIM3. Finally, the analysis of immunotherapy revealed that the efficacy of CTLA4 or PD1 blockers alone was better in the low-risk group than in the high-risk group. Taken together, we developed and validated a prognostic risk model based on 15 hub genes, which performed well in predicting prognosis and immunotherapy response of BCa patients.


Assuntos
Neoplasias da Bexiga Urinária/genética , Neoplasias da Bexiga Urinária/imunologia , Antígenos B7/genética , Biomarcadores Farmacológicos , Biomarcadores Tumorais/genética , Antígeno CTLA-4/genética , Expressão Gênica/genética , Perfilação da Expressão Gênica/métodos , Regulação Neoplásica da Expressão Gênica/genética , Humanos , Estimativa de Kaplan-Meier , Modelos Teóricos , Nomogramas , Prognóstico , Curva ROC , Fatores de Risco , Transcriptoma/genética , Microambiente Tumoral/genética , Bexiga Urinária/patologia
13.
Nutrition ; 93: 111443, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34563934

RESUMO

OBJECTIVES: The aim of this study was to investigate the association of the Metabolic Score for Visceral Fat (METS-VF) with the risk for hypertension and to compare the ability of the METS-VF, the metabolic score for insulin resistance, visceral adiposity index, waist-to-height ratio, waist circumference, and body mass index to predict hypertension incidence based on a large prospective study of rural Chinese individuals. METHODS: In all, 10 297 non-hypertensive adults (≥18 y of age) from a rural Chinese cohort study in 2007 and 2008 were included at baseline and followed up in 2013 and 2014. Multivariable logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between baseline METS-VF and hypertension risk. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the ability of METS-VF to predict hypertension incidence. RESULTS: We identified 2071 hypertension cases during follow-up. After adjusting for multivariable confounding factors, the adjusted ORs (95% CIs) for the highest versus lowest METS-VF quartile overall and for men and women were 3.84 (3.23-4.56), 3.25 (2.48-4.24), and 4.14 (3.30-5.20), respectively. Also, per-SD increase in METS-VF was positively associated with hypertension risk overall and for men and women. Similar results were found in the sensitivity and subgroup analyses. Finally, the AUC value for hypertension was higher for METS-VF than the other five indices overall and for men and women. CONCLUSIONS: The present study indicated that METS-VF was positively associated with hypertension incidence and performed better in predicting hypertension risk than five other indices, which suggests that METS-VF is a reliable predictor of hypertension in the Chinese population.


Assuntos
Hipertensão , Síndrome Metabólica , Adiposidade , Adulto , Índice de Massa Corporal , Estudos de Coortes , Feminino , Humanos , Hipertensão/epidemiologia , Gordura Intra-Abdominal , Masculino , Obesidade Abdominal/complicações , Obesidade Abdominal/epidemiologia , Estudos Prospectivos , Curva ROC , Fatores de Risco , Circunferência da Cintura
14.
Sci Total Environ ; 806(Pt 2): 150674, 2022 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-34597539

RESUMO

BACKGROUND: With dramatically increasing prevalence, diabetes mellitus has imposed a tremendous toll on individual well-being. Humans are exposed to various environmental chemicals, which have been postulated as underappreciated but potentially modifiable diabetes risk factors. OBJECTIVES: To determine the utility of environmental chemical exposure in predicting diabetes mellitus. METHODS: A total of 8501 eligible participants from NHANES 2005-2016 were randomly assigned to a discovery (N = 5953) set and a validation (N = 2548) set. We applied random forest (RF) and least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation in the discovery set to select features, and built an optimal model to predict diabetes mellitus, blood insulin, fasting plasma glucose (FPG) and 2-h plasma glucose after oral glucose tolerance test (2-h PG after OGTT). RESULTS: The machine learning model using LASSO regression predicted diabetes with an area under the receiver operating characteristics (AUROC) of 0.80 and 0.78 in the discovery set and validation set, respectively. The linear model predicted blood insulin level with an R2 of 0.42 and 0.40 in the discovery set and validation set, respectively. For FPG, the discovery set and validation set yielded an R2 of 0.16 and 0.15, respectively. For 2-h PG after OGTT, the discovery set and validation set yielded an R2 of 0.18 and 0.17, respectively. CONCLUSION: We used environmental chemical exposure, constructed machine learning models and achieved relatively accurate prediction for diabetes, emphasizing the predictive value of widespread environmental chemicals for complicated diseases.


Assuntos
Diabetes Mellitus , Diabetes Mellitus/epidemiologia , Jejum , Humanos , Aprendizado de Máquina , Inquéritos Nutricionais , Curva ROC
15.
Int J Gynaecol Obstet ; 156(1): 139-144, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33715162

RESUMO

OBJECTIVE: To explore the ability of albumin concentration to predict calf venous thromboembolism (cVTE) in gynecologic diseases. METHODS: We analyzed data from 761 patients from the gynecology department. We screened the serum albumin concentration as an important indicator for predicting cVTE through logistic analysis. The data were divided into albumin below 35 and 35 g/L or more. Receiver operating characteristics analysis was used to compare the predictive ability of albumin, D-dimer, and a combination of these parameters as indicators for cVTE risk in different groups and subgroups. RESULTS: In gynecologic diseases, the albumin concentrations were lower in the surgery and malignancy group than in the chemotherapy and benign disease group. Albumin concentration had a predictive ability for cVTE risk. In ovarian cancer patients with albumin concentrations less than 35 g/L, albumin was better than D-dimer at predicting cVTE (area under the curve [AUC] 0.79, 95% confidence interval [CI] 0.70-0.87, P < 0.001 versus AUC 0.65, 95% CI 0.54-0.77, P = 0.016). CONCLUSION: The albumin concentration was a candidate indicator for predicting cVTE in surgical patients in the gynecology department, especially in ovarian cancer patients with albumin concentrations less than 35 g/L. A combination of the albumin and D-dimer parameters may improve the predictive ability for cVTE.


Assuntos
Tromboembolia Venosa , Albuminas , Feminino , Procedimentos Cirúrgicos em Ginecologia/efeitos adversos , Humanos , Curva ROC , Fatores de Risco , Tromboembolia Venosa/diagnóstico , Tromboembolia Venosa/epidemiologia , Tromboembolia Venosa/etiologia
16.
Ann Pharmacother ; 56(1): 83-92, 2022 01.
Artigo em Inglês | MEDLINE | ID: mdl-33829897

RESUMO

OBJECTIVES: To review the clinical usefulness of the biomarker TIMP-2•IGFBP7 in adult, general medical-surgical intensive care unit (ICU) settings. DATA SOURCES: PubMed (1946 to mid-February 2021) and EMBASE (1947 to mid-February 2021) with bibliographies of retrieved articles reviewed for additional articles. STUDY SELECTION AND DATA EXTRACTION: Studies evaluating use of the urinary TIMP-2•IGFBP7 assay in adult patients in ICU settings. DATA SYNTHESIS: Studies published after investigations leading to TIMP-2•IGFBP7 assay approval confirm the appropriateness of considerations discussed in product labeling, such as use of the test within 12 hours of assessment, use of a dichotomous 0.3 (ng/mL)2/1000 cutoff, and use only in combination with other assessments of acute kidney injury (AKI). However, as a biomarker routinely used for early identification of patients at risk for AKI in mixed ICU populations, the additional resources required for TIMP-2•IGFBP monitoring are difficult to justify because of limited data demonstrating usefulness in preventing or ameliorating AKI and its attendant complications. RELEVANCE TO PATIENT CARE AND CLINICAL PRACTICE: Biomarkers are potentially useful not only for assessment and diagnosis of AKI, but also for practitioners involved in the management of nephrotoxic medications and medications needing adjustment for decreased kidney function. CONCLUSIONS: Although there is evidence to suggest that the urinary TIMP-2•IGFBP7 biomarker is helpful in predicting AKI progression in general medical-surgical ICU patients when used within 12 hours of patient assessment in combination with routine testing, including serum creatinine and urine output, there is little evidence that its use leads to improvements in clinically important patient outcomes.


Assuntos
Injúria Renal Aguda , Estado Terminal , Injúria Renal Aguda/diagnóstico , Adulto , Biomarcadores , Humanos , Proteínas de Ligação a Fator de Crescimento Semelhante a Insulina , Curva ROC , Inibidor Tecidual de Metaloproteinase-2
17.
J Infect Chemother ; 28(1): 10-18, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34535404

RESUMO

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.


Assuntos
Cuidados Críticos , Klebsiella pneumoniae , Mortalidade Hospitalar , Humanos , Unidades de Terapia Intensiva , Prognóstico , Curva ROC , Estudos Retrospectivos
18.
Br J Radiol ; 95(1129): 20210279, 2022 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-34813375

RESUMO

OBJECTIVES: To investigate the value of 18F-fluorodeoxyglucose (FDG) positron-emission tomography (PET)/computed tomography (CT) combined with the platelet-lymphocyte ratio (PLR) in predicting the prognosis of nasopharyngeal carcinoma (NPC). METHODS: This was a retrospective analysis of the data of 73 patients with NPC who underwent 18F-FDG PET/CT before treatment from January 2010 to December 2014. The maximum standard uptake value (SUVmax) of NPC and the PLR within 1 week before treatment were both measured. The Mann-Whitney U-test was used to compare the differences between the SUVmax and PLR among the different clinical characteristics of patients with NPC and the 5-year progression-free survival (PFS) rate; according to the receiver operating characteristic (ROC) curve, the best cutoff values of the SUVmax and PLR were obtained and used to group patients. The Kaplan-Meier method and Log-rank test were used to conduct univariate analysis of 5-year PFS in patients with NPC, and Cox regression was used to conduct multivariate analysis; differences in the 5-year PFS of patients with different SUVmax values combined with the PLR were compared. RESULTS: The SUVmax and PLR of patients with disease progression within 5 years were higher than those of patients without disease progression (p = 0.006 and p = 0.026). SUVmax = 9.7 and PLR = 132.98 had the best prognostic diagnostic efficiency for patients. Cox multivariate analysis showed that the SUVmax and PLR are independent factors affecting the prognosis of NPC. The 5-year PFS of patients with SUVmax <9.7 was significantly higher than that of patients with SUVmax ≥9.7 in the high PLR group (PLR ≥132.98) and in the low PLR group (PLR <132.98) (59.3% vs 29.4%, p = 0.033 and 90.9% vs 42.9%, p = 0.006, respectively). For patients with SUVmax <9.7, the 5-year PFS of the high PLR group was significantly lower than the low PLR group (59.3% vs 90.9%, p = 0.016); for patients with SUVmax ≥9.7, there was no significant difference in 5-year PFS between the high PLR group and the low PLR group (29.4% vs 42.9%, p = 0.406). CONCLUSIONS: Both the SUVmax of the primary tumor and the PLR before treatment have an important influence on the prognosis of NPC. Combining the SUVmax and the PLR can more accurately predict the prognosis of patients with NPC. ADVANCES IN KNOWLEDGE: In this study, we evaluated the prognostic value of combining pretreatment tumor 18F-FDG uptake on PET/CT imaging and PLR in NPC patients. We found that both SUVmax and PLR are independent factors for the PFS of NPC patients, and a low SUVmax (SUVmax <9.7) combined with a low PLR (PLR <132.98) revealed significant PFS benefit.


Assuntos
Fluordesoxiglucose F18 , Contagem de Linfócitos , Carcinoma Nasofaríngeo/sangue , Carcinoma Nasofaríngeo/diagnóstico por imagem , Contagem de Plaquetas , Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada/métodos , Compostos Radiofarmacêuticos , Quimiorradioterapia , Feminino , Humanos , Estimativa de Kaplan-Meier , Masculino , Pessoa de Meia-Idade , Carcinoma Nasofaríngeo/mortalidade , Carcinoma Nasofaríngeo/terapia , Prognóstico , Intervalo Livre de Progressão , Modelos de Riscos Proporcionais , Curva ROC , Estudos Retrospectivos
19.
Cytokine ; 149: 155751, 2022 01.
Artigo em Inglês | MEDLINE | ID: mdl-34739899

RESUMO

BACKGROUND: New biomarkers for diagnosis and monitoring the COVID-19 disease are the most important topics to be studied recently. We aimed to investigate the association between midkine levels and disease severity in pregnant women with COVID-19. METHODS: Totally 186 pregnant women were participated in this study. 96 of them were healthy pregnant women, 90 of them were pregnant women with COVID19. Pregnant women were evaluated according to their trimesters. Serum midkine level, biochemical profile clinical and disease severity outcomes of pregnant women were obtained. RESULTS: Our results showed that pregnant women with COVID19 have significantly increased serum midkine level compared to healthy pregnant women (1.801 ± 0.977 vs 0.815 ± 0.294 ng/dL). According to the data among each trimester, it was shown that there were significant increase in serum midkine level during all pregnancy trimesters (1st trimester Control Group: 0.714 ± 0.148, COVID-19 group 1.623 ± 0.824, p < 0.0001; 2nd trimester Control Group: 0.731 ± 0.261, COVID-19 group 2.059 ± 1.146, p < 0.0001; 3rd trimester Control Group: 1.0 ± 0.35, COVID-19 group 1.723 ± 0.907, p = 0.001). Serum midkine levels were significantly different between disease severity subgroups of pregnant women with COVID19; moderate and severe/critic groups had significantly higher serum midkine level than mild group. There was also significant correlation between serum midkine level and severity status (p:0.0001, r: 0.468). The most striking results of serum midkine levels were corelation between length of hospitalization (p: 0.01, r: 0.430) and O2 saturation (p < 0.0001, r: -0.521). ROC curve analysis showed that serum midkine level might be a tool for predicting COVID-19 in pregnant women with COVID-19 (AUC: 0.912, 95% CI: [0.871, 0.952], p < 0.0001) CONCLUSION: Our data showed that there is an obvious relation between COVID19 progression and serum midkine level for the first time which might be used for monitoring the disease process.


Assuntos
COVID-19/sangue , COVID-19/diagnóstico , Midkina/sangue , Adulto , Biomarcadores/sangue , COVID-19/patologia , Estudos de Casos e Controles , Progressão da Doença , Feminino , Hospitalização , Humanos , Gravidez , Trimestres da Gravidez , Curva ROC , Índice de Gravidade de Doença , Adulto Jovem
20.
Anal Biochem ; 637: 114474, 2022 01 15.
Artigo em Inglês | MEDLINE | ID: mdl-34801482

RESUMO

Yaobitong capsule (YBTC) has been used for the prevention and treatment of inflammation-related lumbago and leg pain. However, its intervention mechanism still remains unclear. This study was aimed to evaluate the control efficiency of YBTC on adjuvant-induced rheumatoid arthritis (RA) rats by metabonomic method and to explore its possible anti-arthritis mechanism. Taking into account the complexity of endogenous metabolites in serum samples, an integrated metabolomics method based on RP/HILIC-UHPLC-Q-TOF MS was developed, to overcome the limitations of a single chromatographic in this study. The results showed that 32 potential biomarkers of arthritis were identified, primarily related to amino acid metabolism, glucose metabolism, lipid metabolism and nucleotide metabolism. Further receiver operating characteristic analysis revealed that the area under the curve of two down-regulated metabolites (3-Hydroxy-hexadecanoic acid, 2-Oxoarginine) and one up-regulated metabolite (l-Glutamic acid) among 32 biomarkers were 0.906, 0.969 and 1.000, respectively, indicating that high predictive ability of this method for RA. In this study, an integrated serum metabolomics method based on high-resolution mass spectrometry was successfully established for the first time to study the intervention mechanism of YBTC in RA, providing evidence regarding the clinical application of YBTC and a new insight for the prevention of RA in the future.


Assuntos
Artrite Reumatoide/tratamento farmacológico , Medicamentos de Ervas Chinesas/farmacologia , Metabolômica/métodos , Animais , Artrite Reumatoide/metabolismo , Biomarcadores/sangue , Cromatografia Líquida de Alta Pressão/métodos , Citocinas/sangue , Modelos Animais de Doenças , Ácido Glutâmico/metabolismo , Masculino , Espectrometria de Massas/métodos , Ácido Palmítico/metabolismo , Curva ROC , Ratos , Ratos Sprague-Dawley
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