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1.
Nutr Diabetes ; 12(1): 36, 2022 Aug 05.
Artigo em Inglês | MEDLINE | ID: mdl-35931671

RESUMO

OBJECTIVE: Early identification of diabetic retinopathy (DR) is key to prioritizing therapy and preventing permanent blindness. This study aims to propose a machine learning model for DR early diagnosis using metabolomics and clinical indicators. METHODS: From 2017 to 2018, 950 participants were enrolled from two affiliated hospitals of Wenzhou Medical University and Anhui Medical University. A total of 69 matched blocks including healthy volunteers, type 2 diabetes, and DR patients were obtained from a propensity score matching-based metabolomics study. UPLC-ESI-MS/MS system was utilized for serum metabolic fingerprint data. CART decision trees (DT) were used to identify the potential biomarkers. Finally, the nomogram model was developed using the multivariable conditional logistic regression models. The calibration curve, Hosmer-Lemeshow test, receiver operating characteristic curve, and decision curve analysis were applied to evaluate the performance of this predictive model. RESULTS: The mean age of enrolled subjects was 56.7 years with a standard deviation of 9.2, and 61.4% were males. Based on the DT model, 2-pyrrolidone completely separated healthy controls from diabetic patients, and thiamine triphosphate (ThTP) might be a principal metabolite for DR detection. The developed nomogram model (including diabetes duration, systolic blood pressure and ThTP) shows an excellent quality of classification, with AUCs (95% CI) of 0.99 (0.97-1.00) and 0.99 (0.95-1.00) in training and testing sets, respectively. Furthermore, the predictive model also has a reasonable degree of calibration. CONCLUSIONS: The nomogram presents an accurate and favorable prediction for DR detection. Further research with larger study populations is needed to confirm our findings.


Assuntos
Diabetes Mellitus Tipo 2 , Retinopatia Diabética , Diabetes Mellitus Tipo 2/complicações , Diabetes Mellitus Tipo 2/diagnóstico , Retinopatia Diabética/diagnóstico , Diagnóstico Precoce , Feminino , Humanos , Aprendizado de Máquina , Masculino , Metabolômica , Pessoa de Meia-Idade , Nomogramas , Espectrometria de Massas em Tandem
2.
J Immunol Res ; 2022: 2148215, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35935576

RESUMO

Methods: Datasets containing RNA sequencing and corresponding clinical data of cervical cancer patients were obtained from searching publicly accessible databases. The "NMF" R package was conducted to calculate the matrix of the screened prognosis gene expression. Ferroptosis-related differential genes in cervical cancer were detected using the "limma" R function and WGCNA. The least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression analysis were conducted to develop a novel prognostic signature. The prediction model was verified by the nomogram integrating clinical characteristics; the GSE44001 dataset was used as an external verification. Then, the immune status and tumor mutation load were explored. Finally, immunohistochemistry as well as quantitative polymerase chain reaction (RT-qPCR) was utilized to ascertain the expression of FRGs. Results: Two molecular subgroups (cluster 1 and cluster 2) with different FRG expression patterns were recognized. A ferroptosis-related model based on 4 genes (VEGFA, CA9, DERL3, and RNF130) was developed through TCGA database to identify the unfavorable prognosis cases. Patients in cluster 1 showed significantly decreased overall survival in contrast with those in cluster 2 (P < 0.05). The LASSO technique and Cox regression analysis were both utilized to establish the independence of the prognostic model. The validity of nomogram prognostic predictions has been well demonstrated for 3- and 5-year survival in both internal and external data validation cohorts. These two subgroups showed striking differences in tumor-infiltrating leukocytes and tumor mutation burden. The low-risk subgroup showed a longer overall survival time with a higher immune cell score and higher tumor mutation rate. Gene functional enrichment analyses revealed predominant enrichment in various tumor-associated signaling pathways. Finally, the expression of each gene was confirmed by immunohistochemistry and RT-qPCR. Conclusion: A novel and comprehensive ferroptosis-related gene model was proposed for cervical cancer which was capable of distinguishing the patients independently with high risk for poor survival, and targeting ferroptosis may represent a promising approach for the treatment of CC.


Assuntos
Ferroptose , Neoplasias do Colo do Útero , Feminino , Ferroptose/genética , Regulação Neoplásica da Expressão Gênica , Humanos , Nomogramas , Prognóstico , Neoplasias do Colo do Útero/diagnóstico , Neoplasias do Colo do Útero/genética
3.
Front Immunol ; 13: 907729, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35935983

RESUMO

Objective: To search for the immunological risk factors of Psoriatic arthritis (PsA) combined with nonalcoholic fatty liver disease (NAFLD), development and assessment of predictive nomograms for NAFLD risk in patients with PsA, and to further explore the correlation between risk factors and dyslipidemia. Methds: A total of 127 patients with PsA (46 with NAFLD and 81 without NAFLD) were included in this retrospective study. The clinical and serological parameters of the patients were collected. The percentage and the absolute number of lymphocytes and CD4+T cells were determined by Flow cytometry. Univariate and multivariate binary logistic regression analysis was used to screen independent risk factors of PsA complicated with NAFLD in the model population, and a nomogram prediction model was developed and assessed. Results: (1) Univariate and multivariate logistic regression analysis of the modeling population showed that the percentage of peripheral blood T helper 1 cells (Th1%) (OR=1.12, P=0.001), body mass index (BMI) (OR=1.22, P=0.005) and triglycerides (TG) (OR=4.78, P=0.003) were independent risk factors for NAFLD in patients with PsA, which were incorporated and established a nomogram prediction model. The model has good discrimination and calibration, and also has certain clinical application value. (2) The number of peripheral blood NK cells in PsA patients was significantly positively correlated with serum triglyceride (TG) (r=0.489, P<0.001), cholesterol (CHOL) (r=0.314, P=0.003) and low-density lipoprotein (LDL) (r=0.362, P=0.001) levels. Conclusions: Our study shows that the novel NAFLD nomogram could assess the risk of NAFLD in PsA patients with good efficiency. In addition, peripheral blood NK cell levels may be associated with dyslipidemia in patients with PsA.


Assuntos
Artrite Psoriásica , Dislipidemias , Hepatopatia Gordurosa não Alcoólica , Artrite Psoriásica/complicações , Humanos , Células Matadoras Naturais , Nomogramas , Hepatopatia Gordurosa não Alcoólica/etiologia , Estudos Retrospectivos , Fatores de Risco , Triglicerídeos
4.
Comput Math Methods Med ; 2022: 5002681, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35936364

RESUMO

The risk factors of upper respiratory tract infection (URI) within 6 months after diagnosis in patients with idiopathic thrombocytopenic purpura (ITP) were analyzed, and the nomogram model was established and verified, with 242 and 50 ITP patients as the training and validation set, respectively. The patients were followed up for six months after the diagnosis of ITP. The clinical data of patients were collected, and the risk factors of URI in ITP patients within six months after diagnosis were analyzed using univariable, followed by multivariable logistic regression. Among the 242 ITP patients in the training set, 52 cases (21.49%) had URI, including 24 cases of viral infection, 11 cases of Mycoplasma pneumoniae infection, and 17 cases of bacterial infection. Logistic regression analysis showed that advanced age, use of glucocorticoid, smoking history, platelet count, serum CRP level, and lymphocyte subsets CD4 + and CD8 + were all risk factors for ITP patients to develop symptoms within six months after diagnosis (P < 0.05). Using the above five indicators, a nomogram prediction model was built for URI occurrence in patients with ITP within half a year after diagnosis, and the results showed an AUC, a sensitivity, and a specificity of 0.936 (95% CI: 0.878-0.983), 0.942, and 0.865, respectively. The nomogram model was internally verified by the bootstrap method for 500 self-sampling times, and the prediction of the calibration curve was in high consistency with the real results. External validation of the nomogram model resulted in an AUC, a sensitivity, and a specificity of 0.890 (95% CI: 0.757-0.975), 0.949, and 0.727, respectively. The nomogram model of URI in ITP patients within half a year after diagnosis based on logistic regression analysis has good discrimination and prediction accuracy. This provides important guidance value for individualized prediction of URI in ITP patients.


Assuntos
Púrpura Trombocitopênica Idiopática , Infecções Respiratórias , Humanos , Lactente , Nomogramas , Contagem de Plaquetas , Púrpura Trombocitopênica Idiopática/diagnóstico , Infecções Respiratórias/diagnóstico , Estudos Retrospectivos
5.
Sci Rep ; 12(1): 13290, 2022 Aug 02.
Artigo em Inglês | MEDLINE | ID: mdl-35918354

RESUMO

Necroptosis, a type of programmed cell death, has become a potential therapeutic target for solid tumors. Nevertheless, the potential roles of necroptosis-related genes (NRGs) in gastric cancer (GC) remain unknown. The objective of the present study was to create a necroptosis-related prognostic signature that can provide more accurate assessment of prognosis in GC. Using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data, we identified differentially expressed NRGs. Univariate analysis and Lasso regression were performed to determine the prognostic signature. Risk scores were calculated and all GC patients were divided into high- and low-risk score group according to the median risk score value. The robustness of this signature was externally validated with data from GSE84437 cohort (n = 431). Survival analysis revealed high-risk score patients had a worse prognosis. Results evidenced that the signature was an independent prognosis factor for survival. Single-sample sequence set enrichment analysis (ssGSEA) exhibited different enrichment of immune cells and immune-related pathways in the two risk groups. Furthermore, a predictive nomogram was generated and showed excellent predictive performance based on discrimination and calibration. In addition, the risk score positively correlated with tumor mutational burden and was associated with sensitivity to multiple anti-cancer drugs. Overall, our work demonstrates a close relationship between necroptosis and the prognosis of GC. The signature we constructed with potential clinical application value, can be used for prognosis prediction and being a potential therapeutic responses indicator in GC patients.


Assuntos
Neoplasias Gástricas , Biomarcadores Tumorais/genética , Biomarcadores Tumorais/metabolismo , Humanos , Necroptose/genética , Nomogramas , Prognóstico , Neoplasias Gástricas/patologia
6.
World J Surg Oncol ; 20(1): 249, 2022 Aug 03.
Artigo em Inglês | MEDLINE | ID: mdl-35922824

RESUMO

BACKGROUND: Prolonged air leak (PAL) remains one of the most frequent postoperative complications after pulmonary resection. This study aimed to develop a predictive nomogram to estimate the risk of PAL for individual patients after minimally invasive pulmonary resection. METHODS: Patients who underwent minimally invasive pulmonary resection for either benign or malignant lung tumors between January 2020 and December 2021 were included. All eligible patients were randomly assigned to the training cohort or validation cohort at a 3:1 ratio. Univariate and multivariate logistic regression were performed to identify independent risk factors. All independent risk factors were incorporated to establish a predictive model and nomogram, and a web-based dynamic nomogram was then built based on the logistic regression model. Nomogram discrimination was assessed using the receiver operating characteristic (ROC) curve. The calibration power was evaluated using the Hosmer-Lemeshow test and calibration curves. The nomogram was also evaluated for clinical utility by the decision curve analysis (DCA). RESULTS: A total of 2213 patients were finally enrolled in this study, among whom, 341 cases (15.4%) were confirmed to have PAL. The following eight independent risk factors were identified through logistic regression: age, body mass index (BMI), smoking history, percentage of the predicted value for forced expiratory volume in 1 second (FEV1% predicted), surgical procedure, surgical range, operation side, operation duration. The area under the ROC curve (AUC) was 0.7315 [95% confidence interval (CI): 0.6979-0.7651] for the training cohort and 0.7325 (95% CI: 0.6743-0.7906) for the validation cohort. The P values of the Hosmer-Lemeshow test were 0.388 and 0.577 for the training and validation cohorts, respectively, with well-fitted calibration curves. The DCA demonstrated that the nomogram was clinically useful. An operation interface on a web page ( https://lirongyangql.shinyapps.io/PAL_DynNom/ ) was built to improve the clinical utility of the nomogram. CONCLUSION: The nomogram achieved good predictive performance for PAL after minimally invasive pulmonary resection. Patients at high risk of PAL could be identified using this nomogram, and thus some preventive measures could be adopted in advance.


Assuntos
Nomogramas , Pneumonectomia , Estudos de Coortes , Humanos , Pneumonectomia/efeitos adversos , Pneumonectomia/métodos , Curva ROC , Estudos Retrospectivos
7.
BMC Pregnancy Childbirth ; 22(1): 629, 2022 Aug 08.
Artigo em Inglês | MEDLINE | ID: mdl-35941542

RESUMO

BACKGROUND: This study aimed to identify multiple endometrial receptivity related factors by applying non-invasive, repeatable multimodal ultrasound methods. Combined with basic clinical data, we further established a practical prediction model for early clinical outcomes in Freeze-thawed Embryo Transfer (FET). METHODS: Retrospective analysis of clinical data of infertility patients undergoing FET cycle in our Center from January 2017 to September 2019. Receiver operating characteristic (ROC) curve and decision curve analyses were performed by 500 bootstrap resamplings to assess the determination and clinical value of the nomogram, respectively. RESULTS: A total of 2457 FET cycles were included. We developed simple nomograms that predict the early clinical outcomes in FET cycles by using the parameters of age, BMI, type and number of embryos transferred, endometrial thickness, FI, RI, PI and number of endometrial and sub-endometrial blood flow. In the training cohort, the area under the ROC curve (AUC) showed statistical accuracy (AUC = 0.698), and similar results were shown in the subsequent validation cohort (AUC = 0.699). Decision curve analysis demonstrated the clinical value of this nomogram. CONCLUSIONS: Our nomogram can predict clinical outcomes and it can be used as a simple, affordable and widely implementable tool to provide guidance and treatment recommendations for FET patients.


Assuntos
Criopreservação , Nomogramas , Criopreservação/métodos , Transferência Embrionária/métodos , Endométrio/diagnóstico por imagem , Feminino , Humanos , Gravidez , Taxa de Gravidez , Estudos Retrospectivos
8.
Comput Math Methods Med ; 2022: 3436631, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35912147

RESUMO

Objective: To develop and authenticate a risk stratification framework and nomogram for ascertaining cancer-specific survival (CSS) among the pediatric brainstem gliomas. Methods: For patients less than 12 years, according to Surveillance, Epidemiology, and End Results (SEER), information from 1998 to 2016 is found in their databases. The survival outcomes, treatments, and demographic clinicopathologic conditions are scrutinized per the database validation, and training cohorts are divided and validated using multivariate Cox regression analysis. A nomogram was designed, and predominantly, the risk stratification conceptualization engaged selected tenets according to the multivariate analysis. The model's authenticity was substantiated through C-index measure and calibration curves. Results: There are 806 pediatric concerns of histologically concluded brainstem glioma in the research. According to multivariate analysis, age, grade, radiotherapy, and race (with P value < 0.05) depicted independent prognostic variations of the pediatric gliomas. The nomogram's C-index was approximately 0.75 and an accompanied predictive capability for CSS. Conclusion: The nomogram constructed in this glioma's context is the primary predictor of using risk stratification. A combination of nomograms with the risk stratification mechanism assists clinicians in monitoring high-risk individuals and engage targeted accessory treatment.


Assuntos
Neoplasias Encefálicas/mortalidade , Tronco Encefálico/patologia , Glioma/mortalidade , Neoplasias Encefálicas/terapia , Criança , Pré-Escolar , Estudos de Coortes , Glioma/terapia , Humanos , Lactente , Análise Multivariada , Nomogramas , Prognóstico , Análise de Regressão , Medição de Risco/métodos , Programa de SEER
9.
Technol Cancer Res Treat ; 21: 15330338221110673, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35929137

RESUMO

Background: This study aimed to develop a prognostic model based on the Surveillance, Epidemiology, and End Results (SEER) database to predict the overall survival (OS) of small cell carcinoma of the uterine cervix (SmCC). Methods: Between 1975 and 2016, a total of 401 patients were included, and their comprehensive sociodemographic and clinicopathological characteristics were collected. Univariate and multivariate Cox regression models were used to screen for independent prognostic factors. The identified factors were used to conduct a nomogram for predicting the OS of SmCC. The performance of the nomogram was determined using area under the receiver operating characteristic curve (AUC), concordance index (C-index), calibration curve, and decision curve analysis (DCA) metrics. Results: The median survival time of all patients was about 24 months (95% confidence interval [95% CI] [1.50-2.17]). Age (hazard ratio [HR] = 1.693 for 45-59 vs 21-34, 95% CI [1.140-2.513], P = .009; HR = 2.836 for 60-92 vs 21-34, 95% CI [1.851-4.345], P < .001), positive nodes (HR = 2.384, 95% CI [1.437-3.955], P < .001), regional nodes number ≥12 (HR = 0.500, 95% CI [0.282-0.886], P = .018), and treatment method (HR = 0.409 for surgery vs no, 95% CI [0.267-0.628], P < .001; HR = 0.649 for chemotherapy vs no, 95% CI [0.478-0.881)], P = .006) were independent factors of OS. Young patients who had surgical resection or chemotherapy, negative lymph nodes, and regional lymph nodes ≥12 had a longer survival time. These clinical factors were utilized to construct a nomogram for predicting OS. The AUC and C-index were higher than 0.7, indicating the good discriminating ability of the nomogram. The calibrations were all around the 45-degree line, indicating excellent consistency between the prediction of the model and actual observations. The DCA plots supported the clinical utility of the nomogram. Conclusion: The constructed nomogram is expected to help predict the prognosis of SmCC and guide patient treatment.


Assuntos
Carcinoma de Células Pequenas , Neoplasias do Colo do Útero , Fatores Etários , Carcinoma de Células Pequenas/epidemiologia , Carcinoma de Células Pequenas/terapia , Feminino , Humanos , Estadiamento de Neoplasias , Nomogramas , Prognóstico , Programa de SEER , Neoplasias do Colo do Útero/epidemiologia , Neoplasias do Colo do Útero/terapia
10.
Hematology ; 27(1): 840-848, 2022 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-35924822

RESUMO

BACKGROUND: Acute myeloid leukemia (AML) is the most common acute blood malignancy in adults. The complicated and dynamic genomic instability (GI) is the most prominent feature of AML. Our study aimed to explore the prognostic value of GI-related genes in AML patients. METHODS: The mRNA data and mutation data were downloaded from the TCGA and GEO databases. Differential expression analyses were completed in limma package. GO and KEGG functional enrichment was conducted using clusterProfiler function of R. Univariate Cox and LASSO Cox regression analyses were performed to screen key genes for Risk score model construction. Nomogram was built with rms package. RESULTS: We identified 114 DEGs between high TMB patients and low TMB AML patients, which were significantly enriched in 429 GO terms and 13 KEGG pathways. Based on the univariate Cox and LASSO Cox regression analyses, seven optimal genes were finally applied for Risk score model construction, including SELE, LGALS1, ITGAX, TMEM200A, SLC25A21, S100A4 and CRIP1. The Risk score could reliably predict the prognosis of AML patients. Age and Risk score were both independent prognostic indicators for AML, and the Nomogram based on them could also reliably predict the OS of AML patients. CONCLUSIONS: A prognostic signature based on seven GI-related genes and a predictive Nomogram for AML patients are finally successfully constructed.


Assuntos
Leucemia Mieloide Aguda , Adulto , Instabilidade Genômica , Humanos , Leucemia Mieloide Aguda/diagnóstico , Leucemia Mieloide Aguda/genética , Leucemia Mieloide Aguda/patologia , Mutação , Nomogramas , Prognóstico
11.
Front Immunol ; 13: 953403, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35911714

RESUMO

Objective: Low-intensity shockwave therapy (LiST) has been applied in the clinical treatment of chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS), but few studies have focused on the prediction of its therapeutic effect before treatment. Methods: Seventy-five CP/CPPS patients from our institute between July 2020 and May 2021 were enrolled and received 3 Hz, 0.25 mJ/mm2 LiST once a week over the course of four weeks. The scores of the NIH-CPSI, IPSS questionnaire and demographic features before treatment were recorded. The plasma before LiST treatment was also collected, while liquid chromatography-tandem mass spectrometry was used to detect the metabolites. Least absolute shrinkage and selection operator (LASSO) regression analysis was employed to identify the prediction metabolites and generate the metabolism score. Receiver operating characteristic curves and calibration curves were drawn to assess the prediction accuracy of the nomogram. Results: Twelve metabolites were identified at incomparable levels before and after LiST treatment. The metabolism score generated by LASSO analysis presented a perfect prediction value (AUC: 0.848, 95% CI: 0.719-0.940) in the training cohort and further increased to 0.892 (95% CI: 0.802-0.983) on the nomogram, which accompanied with the NIH-CPSI scores and age. Similar results of the metabolism score (AUC: 0.732, 95% CI: 0.516-0.889) and total nomogram (AUC: 0.968, 95% CI: 0.909-1.000) were obtained in the testing cohort. Further enrichment of the 12 metabolites indicated that the glycine and serine metabolism pathway was involved in the LiST treatment. Conclusion: We used our system to accurately and quantitatively measure plasma metabolites and establish a predictive model to identify suitable patients for LiST treatment.


Assuntos
Dor Crônica , Doenças dos Genitais Femininos , Prostatite , Doenças Vasculares , Doença Crônica , Dor Crônica/complicações , Feminino , Humanos , Masculino , Nomogramas , Dor Pélvica/complicações , Dor Pélvica/diagnóstico , Dor Pélvica/terapia , Prostatite/complicações , Síndrome , Doenças Vasculares/complicações
12.
Bone Joint J ; 104-B(8): 963-971, 2022 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-35909382

RESUMO

AIMS: Heterotopic ossification (HO) is a common complication after elbow trauma and can cause severe upper limb disability. Although multiple prognostic factors have been reported to be associated with the development of post-traumatic HO, no model has yet been able to combine these predictors more succinctly to convey prognostic information and medical measures to patients. Therefore, this study aimed to identify prognostic factors leading to the formation of HO after surgery for elbow trauma, and to establish and validate a nomogram to predict the probability of HO formation in such particular injuries. METHODS: This multicentre case-control study comprised 200 patients with post-traumatic elbow HO and 229 patients who had elbow trauma but without HO formation between July 2019 and December 2020. Features possibly associated with HO formation were obtained. The least absolute shrinkage and selection operator regression model was used to optimize feature selection. Multivariable logistic regression analysis was applied to build the new nomogram: the Shanghai post-Traumatic Elbow Heterotopic Ossification Prediction model (STEHOP). STEHOP was validated by concordance index (C-index) and calibration plot. Internal validation was conducted using bootstrapping validation. RESULTS: Male sex, obesity, open wound, dislocations, late definitive surgical treatment, and lack of use of non-steroidal anti-inflammatory drugs were identified as adverse predictors and incorporated to construct the STEHOP model. It displayed good discrimination with a C-index of 0.80 (95% confidence interval 0.75 to 0.84). A high C-index value of 0.77 could still be reached in the internal validation. The calibration plot showed good agreement between nomogram prediction and observed outcomes. CONCLUSION: The newly developed STEHOP model is a valid and convenient instrument to predict HO formation after surgery for elbow trauma. It could assist clinicians in counselling patients regarding treatment expectations and therapeutic choices. Cite this article: Bone Joint J 2022;104-B(8):963-971.


Assuntos
Traumatismos do Braço , Articulação do Cotovelo , Ossificação Heterotópica , Estudos de Casos e Controles , China/epidemiologia , Cotovelo , Articulação do Cotovelo/lesões , Articulação do Cotovelo/cirurgia , Humanos , Masculino , Nomogramas , Ossificação Heterotópica/diagnóstico , Ossificação Heterotópica/etiologia , Ossificação Heterotópica/cirurgia , Prognóstico , Estudos Retrospectivos , Fatores de Risco
13.
Front Endocrinol (Lausanne) ; 13: 890057, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35909507

RESUMO

Aims: To develop and validate a nomogram prediction model for the risk of diabetic foot in patients with type 2 diabetes mellitus (T2DM) and evaluate its clinical application value. Methods: We retrospectively collected clinical data from 1,950 patients with T2DM from the Second Affiliated Hospital of Xi'an Jiaotong University between January 2012 and June 2021. The patients were divided into training cohort and validation cohort according to the random number table method at a ratio of 7:3. The independent risk factors for diabetic foot among patients with T2DM were identified by multivariate logistic regression analysis. Then, a nomogram prediction model was developed using the independent risk factors. The model performances were evaluated by the area under the receiver operating characteristic curve (AUC), calibration plot, Hosmer-Lemeshow test, and the decision curve analysis (DCA). Results: Multivariate logistic regression analysis indicated that age, hemoglobin A1c (HbA1c), low-density lipoprotein (LDL), total cholesterol (TC), smoke, and drink were independent risk factors for diabetic foot among patients with T2DM (P < 0.05). The AUCs of training cohort and validation cohort were 0.806 (95% CI: 0.775∼0.837) and 0.857 (95% CI: 0.814∼0.899), respectively, suggesting good discrimination of the model. Calibration curves of training cohort and validation cohort showed a favorable consistency between the predicted probability and the actual probability. In addition, the P values of Hosmer-Lemeshow test for training cohort and validation cohort were 0.826 and 0.480, respectively, suggesting a high calibration of the model. When the threshold probability was set as 11.6% in the DCA curve, the clinical net benefits of training cohort and validation cohort were 58% and 65%, respectively, indicating good clinical usefulness of the model. Conclusion: We developed and validated a user-friendly nomogram prediction model for the risk of diabetic foot in patients with T2DM. Nomograms may help clinicians early screen and identify patients at high risk of diabetic foot.


Assuntos
Diabetes Mellitus Tipo 2 , Pé Diabético , Diabetes Mellitus Tipo 2/complicações , Pé Diabético/diagnóstico , Pé Diabético/epidemiologia , Pé Diabético/etiologia , Humanos , Nomogramas , Curva ROC , Estudos Retrospectivos
14.
Front Endocrinol (Lausanne) ; 13: 937049, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35909521

RESUMO

Objective: Preoperative evaluation of cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) has been one of the serious clinical challenges. The present study aims at understanding the relationship between preoperative serum thyroglobulin (PS-Tg) and LNM and intends to establish nomogram models to predict cervical LNM. Methods: The data of 1,324 PTC patients were retrospectively collected and randomly divided into training cohort (n = 993) and validation cohort (n = 331). Univariate and multivariate logistic regression analyses were performed to determine the risk factors of central lymph node metastasis (CLNM) and lateral lymph node metastasis (LLNM). The nomogram models were constructed and further evaluated by 1,000 resampling bootstrap analyses. The receiver operating characteristic curve (ROC curve), calibration curve, and decision curve analysis (DCA) of the nomogram models were carried out for the training, validation, and external validation cohorts. Results: Analyses revealed that age, male, maximum tumor size >1 cm, PS-Tg ≥31.650 ng/ml, extrathyroidal extension (ETE), and multifocality were the significant risk factors for CLNM in PTC patients. Similarly, such factors as maximum tumor size >1 cm, PS-Tg ≥30.175 ng/ml, CLNM positive, ETE, and multifocality were significantly related to LLNM. Two nomogram models predicting the risk of CLNM and LLNM were established with a favorable C-index of 0.801 and 0.911, respectively. Both nomogram models demonstrated good calibration and clinical benefits in the training and validation cohorts. Conclusion: PS-Tg level is an independent risk factor for both CLNM and LLNM. The nomogram based on PS-Tg and other clinical characteristics are effective for predicting cervical LNM in PTC patients.


Assuntos
Metástase Linfática , Nomogramas , Tireoglobulina , Neoplasias da Glândula Tireoide , Humanos , Masculino , Estudos Retrospectivos , Câncer Papilífero da Tireoide/diagnóstico , Câncer Papilífero da Tireoide/patologia , Câncer Papilífero da Tireoide/cirurgia , Neoplasias da Glândula Tireoide/diagnóstico , Neoplasias da Glândula Tireoide/patologia , Neoplasias da Glândula Tireoide/cirurgia
15.
BMC Cancer ; 22(1): 872, 2022 Aug 10.
Artigo em Inglês | MEDLINE | ID: mdl-35945526

RESUMO

BACKGROUND: The determination of HER2 expression status contributes significantly to HER2-targeted therapy in breast carcinoma. However, an economical, efficient, and non-invasive assessment of HER2 is lacking. We aimed to develop a clinicoradiomic nomogram based on radiomics scores extracted from multiparametric MRI (mpMRI, including ADC-map, T2W1, DCE-T1WI) and clinical risk factors to assess HER2 status. METHODS: We retrospectively collected 214 patients with pathologically confirmed invasive ductal carcinoma between January 2018 to March 2021 from Fudan University Shanghai Cancer Center, and randomly divided this cohort into training set (n = 128, 42 HER2-positive and 86 HER2-negative cases) and validation set (n = 86, 28 HER2-positive and 58 HER2-negative cases) at a ratio of 6:4. The original and transformed pretherapy mpMRI images were treated by semi-automated segmentation and manual modification on the DeepWise scientific research platform v1.6 ( http://keyan.deepwise.com/ ), then radiomics feature extraction was implemented with PyRadiomics library. Recursive feature elimination (RFE) based on logistic regression (LR) and LASSO regression were adpoted to identify optimal features before modeling. LR, Linear Discriminant Analysis (LDA), support vector machine (SVM), random forest (RF), naive Bayesian (NB) and XGBoost (XGB) algorithms were used to construct the radiomics signatures. Independent clinical predictors were identified through univariate logistic analysis (age, tumor location, ki-67 index, histological grade, and lymph node metastasis). Then, the radiomics signature with the best diagnostic performance (Rad score) was further combined with significant clinical risk factors to develop a clinicoradiomic model (nomogram) using multivariate logistic regression. The discriminative power of the constructed models were evaluated by AUC, DeLong test, calibration curve, and decision curve analysis (DCA). RESULTS: 70 (32.71%) of the enrolled 214 cases were HER2-positive, while 144 (67.29%) were HER2-negative. Eleven best radiomics features were retained to develop 6 radiomcis classifiers in which RF classifier showed the highest AUC of 0.887 (95%CI: 0.827-0.947) in the training set and acheived the AUC of 0.840 (95%CI: 0.758-0.922) in the validation set. A nomogram that incorporated the Rad score with two selected clinical factors (Ki-67 index and histological grade) was constructed and yielded better discrimination compared with Rad score (p = 0.374, Delong test), with an AUC of 0.945 (95%CI: 0.904-0.987) in the training set and 0.868 (95%CI: 0.789-0.948; p = 0.123) in the validation set. Moreover, calibration with the p-value of 0.732 using Hosmer-Lemeshow test demonstrated good agreement, and the DCA verified the benefits of the nomogram. CONCLUSION: Post largescale validation, the clinicoradiomic nomogram may have the potential to be used as a non-invasive tool for determination of HER2 expression status in clinical HER2-targeted therapy prediction.


Assuntos
Neoplasias da Mama , Carcinoma Ductal , Teorema de Bayes , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/genética , China , Feminino , Humanos , Antígeno Ki-67 , Nomogramas , Estudos Retrospectivos
16.
BMC Pulm Med ; 22(1): 306, 2022 Aug 09.
Artigo em Inglês | MEDLINE | ID: mdl-35945553

RESUMO

BACKGROUND: Acute exacerbation of Chronic Obstructive Pulmonary Disease (AECOPD) contributes significantly to mortality among patients with COPD in Intensive care unit (ICU). This study aimed to develop a nomogram to predict 30-day mortality among AECOPD patients in ICU. METHODS: In this retrospective cohort study, we extracted AECOPD patients from Medical Information Mart for Intensive Care III (MIMIC-III) database. Multivariate logistic regression based on Akaike information criterion (AIC) was used to establish the nomogram. Internal validation was performed by a bootstrap resampling approach with 1000 replications. The discrimination and calibration of the nomogram were evaluated by Harrell's concordance index (C-index) and Hosmer-Lemeshow (HL) goodness-of-fit test. Decision curve analysis (DCA) was performed to evaluate its clinical application. RESULTS: A total of 494 patients were finally included in the study with a mean age of 70.8 years old. 417 (84.4%) patients were in the survivor group and 77 (15.6%) patients were in the non-survivor group. Multivariate logistic regression analysis based on AIC included age, pO2, neutrophil-to-lymphocyte ratio (NLR), prognostic nutritional index (PNI), invasive mechanical ventilation and vasopressor use to construct the nomogram. The adjusted C-index was 0.745 (0.712, 0.778) with good calibration (HL test, P = 0.147). The Kaplan-Meier survival curves revealed a significantly lower survival probability in the high-risk group than that in the low-risk group (P < 0.001). DCA showed that nomogram was clinically useful. CONCLUSION: The nomogram developed in this study could help clinicians to stratify AECOPD patients and provide appropriate care in clinical setting.


Assuntos
Nomogramas , Doença Pulmonar Obstrutiva Crônica , Idoso , Humanos , Unidades de Terapia Intensiva , Prognóstico , Estudos Retrospectivos
17.
PLoS One ; 17(8): e0272877, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35947625

RESUMO

INTRODUCTION: Multi-drug resistant tuberculosis has impeded tuberculosis prevention and control due to its low treatment efficiency and prolonged infectious periods. Early culture conversion status has long been used as a predictor of good treatment outcomes and an important infection control metric, as culture-negative patients are less likely to spread tuberculosis. There is also evidence that suggests that delayed sputum conversion is linked to negative outcomes. Therefore, this study was aimed at developing a nomogram to predict the risk of late culture conversion in patients with multi-drug resistant tuberculosis using readily available predictors. OBJECTIVE: The objective of this study was to develop and validate a risk prediction nomogram for the prediction of late culture conversion among multi-drug resistant tuberculosis patients in North-West Ethiopia. METHODS: Multi-drug resistant tuberculosis data from the University of Gondar and the Debre Markos referral hospitals have been used and a total of 316 patients were involved. The analysis was carried out using STATA version 16 and R version 4.0.5 statistical software. Based on the binomial logistic regression model, a validated simplified risk prediction model (nomogram) was built, and its performance was evaluated by assessing its discriminatory power and calibration. Finally, decision curve analysis (DCA) was used to assess the generated model's clinical and public health impact. RESULTS: Registration group, HIV co-infection, baseline BMI, baseline sputum smear grade, and radiological abnormalities were prognostic determinants used in the construction of the nomogram. The model has a discriminating power of 0.725 (95% CI: 0.669, 0.781) and a P-value of 0.665 in the calibration test. It was internally validated using the bootstrapping method, and it was found to perform similarly to the model developed on the entire dataset. The decision curve analysis revealed that the model has better clinical and public health impact than other strategies specified. CONCLUSION: The developed nomogram, which has a satisfactory level of accuracy and good calibration, can be utilized to predict late culture conversion in MDR-TB patients. The model has been found to be useful in clinical practice and is clinically interpretable.


Assuntos
Infecções por HIV , Tuberculose Resistente a Múltiplos Medicamentos , Tuberculose , Etiópia/epidemiologia , Humanos , Nomogramas , Escarro , Tuberculose Resistente a Múltiplos Medicamentos/diagnóstico , Tuberculose Resistente a Múltiplos Medicamentos/tratamento farmacológico , Tuberculose Resistente a Múltiplos Medicamentos/epidemiologia
18.
Clin Appl Thromb Hemost ; 28: 10760296221117991, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35942697

RESUMO

Objectives: To develop a nomogram for predicting calf muscle veins thrombosis (CMVT) in stroke patients during rehabilitation. Methods: We enrolled 360 stroke patients from the Rehabilitation Medicine Center from December 2015 to February 2019. Of the participants, 123 were included in the CMVT group and 237 in the no CMVT group. The least absolute shrinkage and selection operator (LASSO) regression model was applied to optimize feature selection for the model. Multivariable logistic regression analysis was applied to construct a predictive model. Performance and clinical utility of the nomogram were generated using the Harrell's concordance index, calibration curve, and decision curve analysis (DCA). Results: Age, Brunnstrom stage (lower extremity), D-dimer, and antiplatelet therapy were associated with the occurrence of CMVT. The prediction nomogram showed satisfactory performance with a concordance index of 0.718 (95% CI: 0.663-0.773) in internal verification. The Hosmer-Lemeshow test, P = .217, suggested that the model was of goodness-of-fit. In addition, the DCA demonstrated that the CMVT nomogram had a good clinical net benefit. Conclusions: We developed a nomogram that could help clinicians identify high-risk groups of CMVT in stroke patients during rehabilitation for early intervention.


Assuntos
Acidente Vascular Cerebral , Trombose Venosa , Humanos , Nomogramas , Estudos Retrospectivos , Acidente Vascular Cerebral/complicações , Trombose Venosa/etiologia
19.
Technol Cancer Res Treat ; 21: 15330338221117405, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35950233

RESUMO

Background : The major salivary gland squamous cell carcinoma is a rare head and neck tumor, often accompanied by lymph node metastasis. Even if the patient undergoes surgery, the prognosis remains unsatisfactory. To explore the prognostic factors of postoperative major salivary gland squamous cell carcinoma to establish a prognostic risk stratification model to guide clinical practice. Methods: Patients' information was retrieved from the Surveillance, Epidemiology, and End Results database from 2004 to 2018. Optimal cutoff points were determined using X-tile software, and overall survival and disease-specific survival were calculated by the Kaplan-Meier method. Independent prognostic factors affecting the overall survival and disease-specific survival were identified by multivariate analysis, and corresponding 2 nomogram models were constructed. The discriminative ability and calibration of nomograms were evaluated by the Concordance index, area under curves, and calibration plots. Results: A total of 815 patients with postoperative major salivary gland squamous cell carcinoma were enrolled. The cutoff values for the number of lymph nodes were 2, and the cutoff values for the lymph node ratio were 0.11 and 0.5, respectively. Age, T stage, tumor size, lymph nodes, lymph node ratio, and radiotherapy were prognostic factors for overall survival and disease-specific survival. Nomograms for disease-specific survival and overall survival were established and showed favorable performance with a higher Concordance index and area under curves than that of the tumor-node-metastasis stage and Surveillance, Epidemiology, and End Results stage. The calibration plots of 1-, 3-, and 5-year overall survival and disease-specific survival also exhibited good consistency. What's more, patients were divided into low-, moderate-, and high-risk groups according to the scores calculated by the models. The overall survival and disease-specific survival of patients in the high-risk group were significantly worse than those in the moderate- and low-risk group. Conclusions: Our nomogram integrated clinicopathological features and treatment modality to demonstrate excellent performance in risk stratification and prediction of survival outcomes in patients with major salivary gland squamous cell carcinoma after surgery, with important clinical value.


Assuntos
Carcinoma de Células Escamosas , Nomogramas , Carcinoma de Células Escamosas/patologia , Humanos , Estadiamento de Neoplasias , Prognóstico , Programa de SEER , Glândulas Salivares/patologia
20.
Zhonghua Yi Xue Za Zhi ; 102(26): 2018-2025, 2022 Jul 12.
Artigo em Chinês | MEDLINE | ID: mdl-35817727

RESUMO

Objective: To explore the risk factors of colorectal advanced adenomas (AA) and construct a nomogram to predict the risk of colorectal AAs. Methods: Clinical data of patients were retrospectively collected who underwent their first colonoscopy from January 2017 to December 2020 in the First Affiliated Hospital of Nanjing Medical University and were pathologically confirmed harboring colorectal polyps. A credible random split-sample method was used to divide data into training and validation cohorts (split ratio=7∶3). Univariate and multivariate logistic regression analysis were used to identify the predictors of colorectal advanced adenomas, and a nomogram was developed based on the above results. The validation cohort was used for internal validation of the nomogram. The discriminatory value of the nomogram was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). The consistency between actual outcomes and predicted probabilities was evaluated by the calibration curve. The clinical validity of the model was evaluated by the decision analysis curve (DCA). Results: A total of 1 936 patients with colorectal polyps were eligible. Including 1 356 patients in the training cohort (840 males and 516 females), and 580 patients in the validation cohort (379 males and 201 females), with the mean ages of (57.4±9.8) and (57.6±9.7) years, respectively. There were 1 502 (77.6%) patients without AAs and 434 [22.4%,1-9 mm 73(16.8%) cases、>9-<20 mm 271(62.5%) cases、≥20 mm 90(20.7%) cases] patients with AAs. The regression analysis found that age (OR=1.018, 95%CI:1.003-1.033), fatty liver (OR=1.870, 95%CI:1.274-2.744), low-density lipoprotein (LDL) (OR=1.378, 95%CI:1.159-1.637), fecal occult blood test (FOBT) (OR=2.597, 95%CI:1.857-3.631), and location of adenomas [proximal (OR=2.869, 95%CI:1.727-4.764), distal (OR=2.791, 95%CI:1.721-4.527)] were identified as predictors of colorectal AAs. The AUC of the nomogram was 0.664 (95%CI:0.630-0.698) in the training cohort and 0.640 (95%CI:0.587-0.693) in the validation cohort. The calibration curve showed good consistency between the predicted and actual risk, and the Hosmer-Lemeshow (H-L) test P value was 0.830 and 0.150 in the training cohort and the validation cohort. DCA demonstrated that the nomogram had a better clinical application value. Conclusions: A nomogram with five predictors, including age, fatty liver, LDL, FOBT, and location of adenomas, helped predict the risk of colorectal AAs in patients with polyps and implemented colorectcal cancer stratified screening strategy for colonoscopy in the high-risk population.


Assuntos
Adenoma , Pólipos do Colo , Neoplasias Colorretais , Fígado Gorduroso , Adenoma/diagnóstico , Neoplasias Colorretais/diagnóstico , Feminino , Humanos , Masculino , Nomogramas , Estudos Retrospectivos , Fatores de Risco
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