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1.
World J Surg Oncol ; 19(1): 297, 2021 Oct 13.
Artículo en Inglés | MEDLINE | ID: mdl-34645481

RESUMEN

BACKGROUND: Inflammation markers have an important effect on tumor proliferation, invasion, and metastasis. Oligometastatic disease (OMD) is an intermediate state between widespread metastases and locally confined disease, where curative strategies may be effective for some patients. We aimed to explore the predictive value of inflammatory markers in patients with oligometastatic colorectal cancer (OMCC) and build a nomogram to predict the prognosis of these patients. METHODS: Two hundred nine patients with OMCC were retrospectively collected in this study. The Kaplan-Meier survival curves and Cox regression analysis were used to estimate overall survival (OS) and progression-free survival (PFS). A multivariate Cox analysis model was utilized to establish the nomogram. The concordance index (C-index), calibration curve, and receiver operating characteristics (ROC) were established to verify the validity and accuracy of the prediction model. RESULTS: According to the multivariate analysis, decreased platelet-to-lymphocyte ratio (PLR) might independently improve OS in patients with OMCC (HR = 2.396, 95% CI 1.391-4.126, P = 0.002). Metastases of extra-regional lymph nodes indicated poor OS (HR = 2.472, 95% CI 1.247-4.903, P = 0.010). While the patients with early N stage had better OS (HR = 4.602, 95% CI 2.055-10.305, P = 0.001) and PFS (HR = 2.100, 95% CI 1.364-3.231, P = 0.007). Primary tumor resection (HR = 0.367, 95% CI 0.148-0.908, P = 0.030) and lower fibrinogen (HR = 2.254, 95% CI 1.246-4.078, P = 0.007) could significantly prolong the OS in patients with OMCC. PLR, metastases of extra-regional lymph nodes, N stage, primary tumor resection, and fibrinogen were used to make up the nomogram. The C-index and area under the curve (AUC) of the ROC in nomogram were 0.721 and 0.772 respectively for OS, showed good consistency between predictive probability of OS and actual survival. CONCLUSIONS: Decreased PLR could predict a good prognosis in patients with OMCC. The nomogram including inflammatory factors and clinicopathological markers was credible and accurate to predict survivals in patients with OMCC.


Asunto(s)
Neoplasias Colorrectales , Linfocitos , Plaquetas , Humanos , Nomogramas , Pronóstico , Estudios Retrospectivos
2.
Medicine (Baltimore) ; 100(40): e27308, 2021 Oct 08.
Artículo en Inglés | MEDLINE | ID: mdl-34622829

RESUMEN

ABSTRACT: Various researches demonstrated that transcription factors (TFs) played a crucial role in the progression and prognosis of cancer. However, few studies indicated that TFs were independent biomarkers for the prognosis of thyroid papillary carcinoma (TPC). Our aim was to establish and validate a novel TF signature for the prediction of TPC patients' recurrence-free survival (RFS) from The Cancer Genome Atlas (TCGA) database to improve the prediction of survival in TPC patients.The genes expression data and corresponding clinical information for TPC were obtained from TCGA database. In total, 722 TFs and 545 TPC patients with eligible clinical information were determined to build a novel TF signature. All TFs were included in a univariate Cox regression model. Then, the least absolute shrinkage and selection operator Cox regression model was employed to identify candidate TFs relevant to TPC patients' RFS. Finally, multivariate Cox regression was conducted via the candidate TFs for the selection of the TF signatures in the RFS assessment of TPC patients.We identified 6 TFs that were related to TPC patients' RFS. Receiver operating characteristic analysis was performed in training, validation, and whole datasets, we verified the high capacity of the 6-TF panel for predicting TPC patients' RFS (AUC at 1, 3, and 5 years were 0.880, 0.934, and 0.868, respectively, in training dataset; 0.760, 0.737, and 0.726, respectively, in validation dataset; and 0.777, 0.776, and 0.761, respectively, in entire dataset). The result of Kaplan-Meier analysis suggested that the TPC patients with low scores had longer RFS than the TPC patients with high score (P = .003). A similar outcome was displayed in the validation dataset (P = .001) and the entire dataset (P = 2e-05). In addition, a nomogram was conducted through risk score, cancer status, C-index, receiver operating characteristic, and the calibration plots analysis implied good value and clinical utility of the nomogram.We constructed and validated a novel 6-TF signature-based nomogram for predicting the RFS of TPC patients.


Asunto(s)
Carcinoma Papilar/genética , Nomogramas , Neoplasias de la Tiroides/genética , Factores de Transcripción/genética , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Biomarcadores de Tumor/genética , Bases de Datos Factuales , Supervivencia sin Enfermedad , Femenino , Regulación Neoplásica de la Expresión Génica , Humanos , Masculino , Persona de Mediana Edad , Curva ROC , Estudios Retrospectivos , Medición de Riesgo , Adulto Joven
3.
Nan Fang Yi Ke Da Xue Xue Bao ; 41(9): 1358-1365, 2021 Aug 31.
Artículo en Chino | MEDLINE | ID: mdl-34658350

RESUMEN

OBJECTIVE: To explore the preoperative radiomics features (RFs) and construct a nomogram for predicting postoperative recurrence of stage Ⅰ-Ⅲ clear cell renal carcinoma (ccRCC). METHODS: The clinicopathological data and preoperative enhanced CT images collected from 256 patients with ccRCC were used as the training dataset (175 patients) and test dataset (81 patients). The enhanced CT images of the tumor were segmented using ITK-SNAP software, and the RFs were extracted using the PyRadiomics computing platform. In the training dataset, the RFs were screened based on Lasso-CV algorithm, and the Rad_score was calculated. The Clinic factors were screened by univariate and multivariate logistic regression analysis of the clinical and pathological factors and CT characteristics. The Rad_score, Clinic、Rad_score + Clinic nomograms were constructed and verified using the test dataset. The performance, discrimination power and calibration of the nomograms were compared, and their clinical value was evaluated using decision curve analysis. RESULTS: Six RFs were retained to calculate the Rad_score. The Clinic factors included Rad_score, KPS score, platelet, calcification and TNM clinical stage. In terms of discrimination, the Rad_score + Clinic nomogram showed better performance (AUC=0.84 for training set; AUC=0.85 for test set) than the Rad_score nomogram (AUC=0.78 for training set, P=0.029; AUC=0.77 for Test set, P=0.025) and Clinic nomogram (AUC=0.77 for training set, P=0.014; AUC=0.77 for test set, P=0.011). In terms of calibration, the P value for goodness of fit test of the Rad_score+Clinic nomogram was 0.065 for the training set and 0.628 for the test set. Decision curve analysis showed a greater clinical value of the Rad_score+Clinic nomogram with Rad_score than the Clinic nomogram without Rad_score. CONCLUSION: The nomogram based on preoperative CT RFs has a high value for predicting postoperative recurrence of stage Ⅰ-Ⅲ ccRCC to facilitate individualized treatment of RCC.


Asunto(s)
Carcinoma de Células Renales , Neoplasias Renales , Carcinoma de Células Renales/diagnóstico por imagen , Carcinoma de Células Renales/cirugía , Humanos , Neoplasias Renales/diagnóstico por imagen , Neoplasias Renales/cirugía , Nomogramas , Estudios Retrospectivos , Tomografía Computarizada por Rayos X
4.
Zhonghua Wei Chang Wai Ke Za Zhi ; 24(10): 883-888, 2021 Oct 25.
Artículo en Chino | MEDLINE | ID: mdl-34674463

RESUMEN

Objective: To establish a novel nomogram to predict overall survival of patients with gastric neuroendocrine neoplasms (g-NEN). Methods: A case control study was conducted. Clinicopathological and follow-up data of patients with g-NEN who were treated in two academic medical centers in Southern China between July 2008 and June 2018 were retrospectively collected, including 174 patients from Sun Yat-sen University Cancer Center and 102 patients from the First Affiliated Hospital of Sun Yat-sen University. Univariate survival analysis using Kaplan-Meier method and multivariate analysis using Cox regression were performed to identify prognostic factors. A nomogram was subsequently established based on prognostic factors. Harrell's concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were used to verify the performance of the model according to differentiation, calibration and clinical utility. Results: A total of 276 patients were enrolled in the study, of whom 189 patients were male and 87 were female. The age at diagnosis was below 60 years old in 150 patients and 60 years or older in 126 patients. There were patients diagnosed with gastric neuroendocrine carcinoma (g-NEC) and 101 patients with gastric neuroendocrine tumor (g-NET). The number of patients with primary tumor locating at upper, middle and lower parts of stomach was 131, 98 and 47, respectively. As for TNM stage, 72 patients were categorized as stage I, 26 patients stage II, 93 patients stage III, and 85 patients stage IV. Univariate analysis indicated that age, pathological type, primary site, Ki-67 index, T stage, N stage, and M stage were associated with overall survival of g-NEN patients (all P<0.05). Multivariate regression analysis testified that high Ki-67 index, advanced T stage and advanced M stage were independent prognostic factors (all P<0.05). The C-index of the nomogram was 0.806 (95%CI: 0.769-0.863). The calibration curve of the nomogram showed that the predicted survival rate was consistent with the actual survival rate in g-NEN patients. The ROC curves and DCA showed that the nomogram had better differentiation and clinical utility than the American Joint Committee on Cancer (AJCC) 8th TNM staging system (the area under the ROC curve was 0.862 vs. 0.792). Conclusion: The first nomogram to predict overall survival of patients with g-NEN is established and verified in this study, which provides individual prediction of 3-year overall survival rate and is applicable to both g-NET and g-NEC patients.


Asunto(s)
Tumores Neuroendocrinos , Nomogramas , Estudios de Casos y Controles , Femenino , Humanos , Masculino , Persona de Mediana Edad , Estadificación de Neoplasias , Pronóstico , Estudios Retrospectivos
5.
BMC Infect Dis ; 21(1): 1085, 2021 Oct 21.
Artículo en Inglés | MEDLINE | ID: mdl-34674642

RESUMEN

BACKGROUND: Early prediction of bronchitis obliterans (BO) is of great significance to the improvement of the long-term prognosis of children caused by refractory Mycoplasma pneumoniae pneumonia (RMPP). This study aimed to establish a nomogram model to predict the risk of BO in children due to RMPP. METHODS: A retrospective observation was conducted to study the clinical data of children with RMPP (1-14 years old) during acute infection. According to whether there is BO observed in the bronchoscope, children were divided into BO and the non-BO groups. The multivariate logistic regression model was used to construct the nomogram model. RESULTS: One hundred and forty-one children with RMPP were finally included, of which 65 (46.0%) children with RMPP were complicated by BO. According to the multivariate logistic regression analysis, WBC count, ALB level, consolidation range exceeding 2/3 of lung lobes, timing of macrolides, glucocorticoids or fiber bronchoscopy and plastic bronchitis were independent influencing factors for the occurrence of BO and were incorporated into the nomogram. The area under the receiver operating characteristic curve (AUC-ROC) value of nomogram was 0.899 (95% confidence interval [CI] 0.848-0.950). The Hosmer-Lemeshow test showed good calibration of the nomogram (p = 0.692). CONCLUSION: A nomogram model found by seven risk factor was successfully constructed and can use to early prediction of children with BO due to RMPP.


Asunto(s)
Bronquitis , Neumonía por Mycoplasma , Adolescente , Niño , Preescolar , Humanos , Lactante , Mycoplasma pneumoniae , Nomogramas , Neumonía por Mycoplasma/complicaciones , Neumonía por Mycoplasma/diagnóstico , Neumonía por Mycoplasma/epidemiología , Estudios Retrospectivos , Factores de Riesgo
6.
BMC Cancer ; 21(1): 1072, 2021 Sep 30.
Artículo en Inglés | MEDLINE | ID: mdl-34592957

RESUMEN

OBJECTIVE: To investigate the predictive value of preoperative complete blood count for the survival of patients with esophageal squamous cell carcinoma. METHODS: A total of 1587 patients with pathologically confirmed esophageal squamous cell carcinoma who underwent esophagectomy in the Cancer Hospital Affiliated to Xinjiang Medical University from January 2010 to December 2019 were collected by retrospective study. A total of 359 patients were as the validation cohort from January 2015 to December 2016, and the remaining 1228 patients were as the training cohort. The relevant clinical data were collected by the medical record system, and the patients were followed up by the hospital medical record follow-up system. The follow-up outcome was patient death. The survival time of all patients was obtained. The Cox proportional hazards regression model and nomogram were established to predict the survival prognosis of esophageal squamous cell carcinoma by the index, their cut-off values obtained the training cohort by the ROC curve. The Kaplan-Meier survival curve was established to express the overall survival rate. The 3-year and 5-year calibration curves and C-index were used to determine the accuracy and discrimination of the prognostic model. The decision curve analysis was used to predict the potential of clinical application. Finally, the validation cohort was used to verify the results of the training cohort. RESULTS: The cut-off values of NLR, NMR, LMR, RDW and PDW in complete blood count of the training cohort were 3.29, 12.77, 2.95, 15.05 and 13.65%, respectively. All indicators were divided into high and low groups according to cut-off values. Univariate Cox regression analysis model showed that age (≥ 60), NLR (≥3.29), LMR (< 2.95), RDW (≥15.05%) and PDW (≥13.65%) were risk factors for the prognosis of esophageal squamous cell carcinoma; multivariate Cox regression analysis model showed that age (≥ 60), NLR (≥3.29) and LMR (< 2.95) were independent risk factors for esophageal squamous cell carcinoma. Kaplan-Meier curve indicated that age <  60, NLR < 3.52 and LMR ≥ 2.95 groups had higher overall survival (p <  0.05). The 3-year calibration curve indicated that its predictive probability overestimate the actual probability. 5-year calibration curve indicated that its predictive probability was consistent with the actual probability. 5 c-index was 0.730 and 0.737, respectively, indicating that the prognostic model had high accuracy and discrimination. The decision curve analysis indicated good potential for clinical application. The validation cohort also proved the validity of the prognostic model. CONCLUSION: NLR and LMR results in complete blood count results can be used to predict the survival prognosis of patients with preoperative esophageal squamous cell carcinoma.


Asunto(s)
Recuento de Células Sanguíneas , Neoplasias Esofágicas/sangre , Carcinoma de Células Escamosas de Esófago/sangre , Factores de Edad , Neoplasias Esofágicas/mortalidad , Neoplasias Esofágicas/cirugía , Carcinoma de Células Escamosas de Esófago/mortalidad , Carcinoma de Células Escamosas de Esófago/cirugía , Esofagectomía/estadística & datos numéricos , Femenino , Humanos , Estimación de Kaplan-Meier , Masculino , Persona de Mediana Edad , Nomogramas , Cuidados Preoperatorios , Curva ROC , Estudios Retrospectivos , Tasa de Supervivencia
7.
Can J Gastroenterol Hepatol ; 2021: 4073503, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-34616695

RESUMEN

Objectives: Alcohol-related liver disease is an increasing public health burden in China, but there is a lack of models to predict its prognosis. This study established a nomogram for predicting the survival of Chinese patients with alcohol-related liver disease (ALD). Methods: Hospitalized alcohol-related liver disease patients were retrospectively enrolled from 2015 to 2018 and followed up for 24 months to evaluate survival profiles. A total of 379 patients were divided into a training cohort (n = 265) and validation cohort (n = 114). Cox proportional hazard survival analysis identified survival factors of the patients in the training cohort. A nomogram was built and internally validated. Results: The 3-month, 6-month, 12-month, and 24-month survival rates for the training cohort were 82.6%, 81.1%, 74.3%, and 64.5%, respectively. The Cox analysis showed relapse (P=0.001), cirrhosis (P=0.044), liver cancer (P < 0.001), and a model for end-stage liver diseases score of ≥21 (P=0.041) as independent prognostic factors. A nomogram was built, which predicted the survival of patients in the training cohort with a concordance index of 0.749 and in the internal validation cohort with a concordance index of 0.756. Conclusion: The long-term survival of Chinese alcohol-related liver disease patients was poor with a 24-month survival rate of 64.5%. Relapse, cirrhosis, liver cancer, and a model for end-stage liver disease score of ≥21 were independent risk factors for those patients. A nomogram was developed and internally validated for predicting the probability of their survival at different time points.


Asunto(s)
Enfermedad Hepática en Estado Terminal , Nomogramas , China/epidemiología , Humanos , Recurrencia Local de Neoplasia , Pronóstico , Estudios Retrospectivos , Índice de Severidad de la Enfermedad
8.
Medicine (Baltimore) ; 100(39): e27374, 2021 Oct 01.
Artículo en Inglés | MEDLINE | ID: mdl-34596153

RESUMEN

ABSTRACT: Increasing evidence has shown that hypoxia is closely related to the development, progression, and prognosis of clear cell renal cell carcinoma (ccRCC). Nevertheless, reliable prognostic signatures based on hypoxia have not been well-established. This study aimed to establish a hypoxia-related prognostic signature and construct an optimized nomogram for patients with ccRCC.We accessed hallmark gene sets of hypoxia, including 200 genes, and an original RNA seq dataset of ccRCC cases with integrated clinical information obtained by mining the Cancer Genome Atlas database and the International Cancer Genome Consortium (ICGC) database. Univariate Cox regression analysis and multivariate Cox proportional hazards regression were performed to identify prognostic hub genes and further established prognostic model as well as visualized the nomogram. External validation of the optimized nomogram was performed in independent cohorts from the ICGC database.ANKZF1, ETS1, PLAUR, SERPINE1, FBP1, and PFKP were selected as prognostic hypoxia-related hub genes, and the prognostic model effectively distinguishes high-risk and low-risk patients with ccRCC. The results of receiver operating characteristic curve, risk plots, survival analysis, and independent analysis suggested that RiskScore was a useful tool and independent predictive factor. A novel prognosis nomogram optimized via RiskScore showed its promising performance in both the Cancer Genome Atlas-ccRCC cohort and an ICGC-ccRCC cohort.Our study reveals that the differential expressions of hypoxia-related genes are associated with the overall survival of patients with ccRCC. The prognostic model we established showed a good predictive and discerning ability in ccRCC patients. The novel nomogram optimized via RiskScore exhibited a promising predictive ability. It may be able to serve as a visualized tool for guiding clinical decisions and selecting effective individualized treatments.


Asunto(s)
Carcinoma de Células Renales/genética , Regulación Neoplásica de la Expresión Génica , Hipoxia/genética , Neoplasias Renales/genética , Anciano , Biomarcadores de Tumor/genética , Carcinoma de Células Renales/mortalidad , Femenino , Humanos , Neoplasias Renales/mortalidad , Masculino , Persona de Mediana Edad , Nomogramas , Modelos de Riesgos Proporcionales , Estudios Retrospectivos , Medición de Riesgo/métodos
9.
World J Gastroenterol ; 27(33): 5610-5621, 2021 Sep 07.
Artículo en Inglés | MEDLINE | ID: mdl-34588755

RESUMEN

BACKGROUND: Perineural invasion (PNI), as a key pathological feature of tumor spread, has emerged as an independent prognostic factor in patients with rectal cancer (RC). The preoperative stratification of RC patients according to PNI status is beneficial for individualized treatment and improved prognosis. However, the preoperative evaluation of PNI status is still challenging. AIM: To establish a radiomics model for evaluating PNI status preoperatively in RC patients. METHODS: This retrospective study enrolled 303 RC patients in a single institution from March 2018 to October 2019. These patients were classified as the training cohort (n = 242) and validation cohort (n = 61) at a ratio of 8:2. A large number of intra- and peritumoral radiomics features were extracted from portal venous phase images of computed tomography (CT). After deleting redundant features, we tested different feature selection (n = 6) and machine-learning (n = 14) methods to form 84 classifiers. The best performing classifier was then selected to establish Rad-score. Finally, the clinicoradiological model (combined model) was developed by combining Rad-score with clinical factors. These models for predicting PNI were compared using receiver operating characteristic curve (ROC) analysis and area under the ROC curve (AUC). RESULTS: One hundred and forty-four of the 303 patients were eventually found to be PNI-positive. Clinical factors including CT-reported T stage (cT), N stage (cN), and carcinoembryonic antigen (CEA) level were independent risk factors for predicting PNI preoperatively. We established Rad-score by logistic regression analysis after selecting features with the L1-based method. The combined model was developed by combining Rad-score with cT, cN, and CEA. The combined model showed good performance to predict PNI status, with an AUC of 0.828 [95% confidence interval (CI): 0.774-0.873] in the training cohort and 0.801 (95%CI: 0.679-0.892) in the validation cohort. For comparison of the models, the combined model achieved a higher AUC than the clinical model (cT + cN + CEA) achieved (P < 0.001 in the training cohort, and P = 0.045 in the validation cohort). CONCLUSION: The combined model incorporating Rad-score and clinical factors can provide an individualized evaluation of PNI status and help clinicians guide individualized treatment of RC patients.


Asunto(s)
Nomogramas , Neoplasias del Recto , Humanos , Estadificación de Neoplasias , Pronóstico , Neoplasias del Recto/diagnóstico por imagen , Neoplasias del Recto/cirugía , Estudios Retrospectivos
10.
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue ; 33(8): 967-972, 2021 Aug.
Artículo en Chino | MEDLINE | ID: mdl-34590565

RESUMEN

OBJECTIVE: To establish a nomogram model for predicting the risk of coronary artery disease in elderly patients with acute myocardial infarction (AMI). METHODS: The clinical data of elderly patients with AMI who underwent coronary angiography in the department of cardiology of Cangzhou Central Hospital from July 2015 to March 2020 were analyzed, including age, gender, smoking history, underlying diseases, family history, blood pressure, left ventricular ejection fraction (LVEF), and several biochemical indicators at admission, such as total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), lipoprotein [Lp(a)], apolipoproteins (ApoA, ApoB), ApoA/B ratio, total bilirubin (TBil), direct bilirubin (DBil), indirect bilirubin (IBil), fasting blood glucose (FBG) and uric acid (UA). Patients were divided into model group (2 484 cases) and validation group (683 cases) according to the ratio of 8:2. According to Gensini score, the model group and validation group were divided into mild lesion group (0-20 points) and severe lesion group (≥ 81 points). The differences of each index between different coronary lesion degree groups were compared. Lasso regression and Logistic regression were used to analyze the risk factors of aggravating coronary lesion risk in elderly patients with AMI, and then the nomogram prediction model was established for evaluation and external validation. RESULTS: (1) In the model group, there were significant differences in the family history of coronary heart disease, FBG and HDL-C between the mild lesion group (411 cases) and the severe lesion group (417 cases) [family history of coronary heart disease: 3.6% vs. 7.7%, FBG (mmol/L): 5.88±1.74 vs. 6.43±2.06, HDL-C (mmol/L): 1.48±0.69 vs. 1.28±0.28, all P < 0.05]. In the validation group, there were significant differences between the mild lesion group (153 cases) and the severe lesion group [132 cases; FBG (mmol/L): 5.58±0.88 vs. 6.85±0.79, HDL-C (mmol/L): 1.59±0.32 vs. 1.16±0.21, both P < 0.05]. (2) Lasso regression analysis showed that family history of coronary heart disease, FBG, and HDL-C were risk factors of coronary artery disease in elderly patients with AMI, with coefficients 0.118, 0.767, and -0.558, respectively. Logistic regression analysis showed that FBG [odds ratio (OR) = 1.479, 95% confidence interval (95%CI) was 1.051-2.082, P = 0.025] and HDL-C (OR = 0.386, 95%CI was 0.270-0.553, P < 0.001] were independent risk factors of coronary artery disease in elderly patients with AMI. (3) According to the rank score of FBG and HDL-C, the nomogram prediction risk model of aggravating coronary artery disease degree was established for each patient. It was concluded that the risk of coronary artery disease in elderly people with higher FBG level and (or) lower HDL-C level was significantly increased. (4) The nomogram model constructed with the model group data predicted the risk concordance index (C-index) was 0.689, and the C-index of the external validation group was 0.709. CONCLUSIONS: FBG and HDL-C are independent risk factors for aggravating coronary artery disease in elderly patients with AMI. The nomogram model of aggravating coronary artery disease in elderly patients with AMI has good predictive ability, which can provide more intuitive research methods and clinical value for preventing the aggravation of coronary artery disease in elderly patients.


Asunto(s)
Enfermedad de la Arteria Coronaria , Infarto del Miocardio , Anciano , HDL-Colesterol , Enfermedad de la Arteria Coronaria/diagnóstico por imagen , Humanos , Nomogramas , Factores de Riesgo , Volumen Sistólico , Función Ventricular Izquierda
11.
BMC Infect Dis ; 21(1): 1004, 2021 Sep 25.
Artículo en Inglés | MEDLINE | ID: mdl-34563117

RESUMEN

BACKGROUND: Early identification of severe COVID-19 patients who will need intensive care unit (ICU) follow-up and providing rapid, aggressive supportive care may reduce mortality and provide optimal use of medical resources. We aimed to develop and validate a nomogram to predict severe COVID-19 cases that would need ICU follow-up based on available and accessible patient values. METHODS: Patients hospitalized with laboratory-confirmed COVID-19 between March 15, 2020, and June 15, 2020, were enrolled in this retrospective study with 35 variables obtained upon admission considered. Univariate and multivariable logistic regression models were constructed to select potential predictive parameters using 1000 bootstrap samples. Afterward, a nomogram was developed with 5 variables selected from multivariable analysis. The nomogram model was evaluated by Area Under the Curve (AUC) and bias-corrected Harrell's C-index with 95% confidence interval, Hosmer-Lemeshow Goodness-of-fit test, and calibration curve analysis. RESULTS: Out of a total of 1022 patients, 686 cases without missing data were used to construct the nomogram. Of the 686, 104 needed ICU follow-up. The final model includes oxygen saturation, CRP, PCT, LDH, troponin as independent factors for the prediction of need for ICU admission. The model has good predictive power with an AUC of 0.93 (0.902-0.950) and a bias-corrected Harrell's C-index of 0.91 (0.899-0.947). Hosmer-Lemeshow test p-value was 0.826 and the model is well-calibrated (p = 0.1703). CONCLUSION: We developed a simple, accessible, easy-to-use nomogram with good distinctive power for severe illness requiring ICU follow-up. Clinicians can easily predict the course of COVID-19 and decide the procedure and facility of further follow-up by using clinical and laboratory values of patients available upon admission.


Asunto(s)
COVID-19 , Nomogramas , Cuidados Críticos , Estudios de Seguimiento , Humanos , Unidades de Cuidados Intensivos , Estudios Retrospectivos , SARS-CoV-2
12.
J Int Med Res ; 49(9): 3000605211044892, 2021 Sep.
Artículo en Inglés | MEDLINE | ID: mdl-34586931

RESUMEN

OBJECTIVE: To construct a nomogram based on the Sequential Organ Failure Assessment (SOFA) that is more accurate in predicting 30-, 60-, and 90-day mortality risk in patients with sepsis. METHODS: Data from patients with sepsis were retrospectively collected from the Medical Information Mart for Intensive Care (MIMIC) database. Included patients were randomly divided into training and validation cohorts. Variables were selected using a backward stepwise selection method with Cox regression, then used to construct a prognostic nomogram. The nomogram was compared with the SOFA model using the concordance index (C-index), area under the time-dependent receiver operating characteristics curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration plotting, and decision-curve analysis (DCA). RESULTS: A total of 5240 patients were included in the study. Patient's age, SOFA score, metastatic cancer, SpO2, lactate, body temperature, albumin, and red blood cell distribution width were included in the nomogram. The C-index, AUC, NRI, IDI, and DCA of the nomogram showed that it performs better than the SOFA alone. CONCLUSION: A nomogram was established that performed better than the SOFA in predicting 30-, 60-, and 90-day mortality risk in patients with sepsis.


Asunto(s)
Nomogramas , Sepsis , Humanos , Unidades de Cuidados Intensivos , Pronóstico , Curva ROC , Estudios Retrospectivos , Programa de VERF , Sepsis/diagnóstico
13.
J Int Med Res ; 49(9): 3000605211042502, 2021 Sep.
Artículo en Inglés | MEDLINE | ID: mdl-34551601

RESUMEN

OBJECTIVE: To investigate the risk factors of medication nonadherence in patients with type 2 diabetes mellitus (T2DM) and to establish a risk nomogram model. METHODS: This retrospective study enrolled patients with T2DM, which were divided into two groups based on their scores on the Morisky Medication Adherence scale. Univariate and multivariate logistic regression analyses were used to screen for independent risk factors for medication nonadherence. A risk model was then established using a nomogram. The accuracy of the prediction model was evaluated using centrality measurement index and receiver operating characteristic curves. Internal verification was evaluated using bootstrapping validation. RESULTS: A total of 338 patients with T2DM who included in the analysis. Logistic regression analysis showed that the educational level, monthly per capita income, drug affordability, the number of drugs used, daily doses of drugs and the time spent taking medicine were all independent risk factors for medication nonadherence. Based on these six risk factors, a nomogram model was established to predict the risk of medication nonadherence, which was shown to be very reliable. Bootstrapping validated the nonadherence nomogram model for patients with T2DM. CONCLUSIONS: This nomogram model could be used to evaluate the risks of drug nonadherence in patients with T2DM.


Asunto(s)
Diabetes Mellitus Tipo 2 , Nomogramas , China , Diabetes Mellitus Tipo 2/tratamiento farmacológico , Humanos , Cumplimiento de la Medicación , Estudios Retrospectivos
14.
J Int Med Res ; 49(9): 3000605211047771, 2021 Sep.
Artículo en Inglés | MEDLINE | ID: mdl-34590874

RESUMEN

OBJECTIVE: To identify risk factors and develop predictive web-based nomograms for the early death of patients with bone metastasis of lung adenocarcinoma (LUAD). METHODS: Patients in the Surveillance, Epidemiology, and End Results database diagnosed with bone metastasis of LUAD between 2010 and 2016 were included and randomly divided into training and validation sets. Early death-related risk factors (survival time ≤7 months) were evaluated by logistic regression. Two predictive nomograms were established and validated by calibration curves, receiver operating characteristic curves, and decision curve analysis. RESULTS: A total of 9189 patients (56.59%) died from all causes within 7 months of being diagnosed, including 8585 patients (56.67%) who died from cancer-specific causes. Age >65 years, sex (men), T stage (T3 and T4), N stage (N2 and N3), brain metastasis, and liver metastasis were risk factors for all-cause and cancer-specific early death. The area under the curves of the nomograms for all-cause and cancer-specific early death prediction were 0.754 and 0.753 (training set) and 0.747 and 0.754 (validation set), respectively. Further analysis showed that the two nomograms performed well. CONCLUSIONS: Our two web-based nomograms for all-cause and cancer-specific early death provide valuable tools for predicting early death in these patients.


Asunto(s)
Adenocarcinoma del Pulmón , Neoplasias Pulmonares , Anciano , Humanos , Internet , Masculino , Nomogramas , Pronóstico , Programa de VERF
15.
Chin Med J (Engl) ; 134(19): 2306-2315, 2021 Sep 21.
Artículo en Inglés | MEDLINE | ID: mdl-34561337

RESUMEN

BACKGROUND: Existing clinical prediction models for in vitro fertilization are based on the fresh oocyte cycle, and there is no prediction model to evaluate the probability of successful thawing of cryopreserved mature oocytes. This research aims to identify and study the characteristics of pre-oocyte-retrieval patients that can affect the pregnancy outcomes of emergency oocyte freeze-thaw cycles. METHODS: Data were collected from the Reproductive Center, Peking University Third Hospital of China. Multivariable logistic regression model was used to derive the nomogram. Nomogram model performance was assessed by examining the discrimination and calibration in the development and validation cohorts. Discriminatory ability was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration plots. RESULTS: The predictors in the model of "no transferable embryo cycles" are female age (odds ratio [OR] = 1.099, 95% confidence interval [CI] = 1.003-1.205, P = 0.0440), duration of infertility (OR = 1.140, 95% CI = 1.018-1.276, P = 0.0240), basal follicle-stimulating hormone (FSH) level (OR = 1.205, 95% CI = 1.051-1.382, P = 0.0084), basal estradiol (E2) level (OR = 1.006, 95% CI = 1.001-1.010, P = 0.0120), and sperm from microdissection testicular sperm extraction (MESA) (OR = 7.741, 95% CI = 2.905-20.632, P < 0.0010). Upon assessing predictive ability, the AUC for the "no transferable embryo cycles" model was 0.799 (95% CI: 0.722-0.875, P < 0.0010). The Hosmer-Lemeshow test (P = 0.7210) and calibration curve showed good calibration for the prediction of no transferable embryo cycles. The predictors in the cumulative live birth were the number of follicles on the day of human chorionic gonadotropin (hCG) administration (OR = 1.088, 95% CI = 1.030-1.149, P = 0.0020) and endometriosis (OR = 0.172, 95% CI = 0.035-0.853, P = 0.0310). The AUC for the "cumulative live birth" model was 0.724 (95% CI: 0.647-0.801, P < 0.0010). The Hosmer-Lemeshow test (P = 0.5620) and calibration curve showed good calibration for the prediction of cumulative live birth. CONCLUSIONS: The predictors in the final multivariate logistic regression models found to be significantly associated with poor pregnancy outcomes were increasing female age, duration of infertility, high basal FSH and E2 level, endometriosis, sperm from MESA, and low number of follicles with a diameter >10 mm on the day of hCG administration.


Asunto(s)
Nomogramas , Resultado del Embarazo , Transferencia de Embrión , Femenino , Fertilización In Vitro , Humanos , Oocitos , Inducción de la Ovulación , Embarazo , Índice de Embarazo , Estudios Retrospectivos
16.
BMC Cancer ; 21(1): 1030, 2021 Sep 16.
Artículo en Inglés | MEDLINE | ID: mdl-34525956

RESUMEN

BACKGROUND: Fluoropyrimidine plus platinum chemotherapy remains the standard first line treatment for gastric cancer (GC). Guidelines exist for the clinical interpretation of four DPYD genotypes related to severe fluoropyrimidine toxicity within European populations. However, the frequency of these single nucleotide polymorphisms (SNPs) in the Latin American population is low (< 0.7%). No guidelines have been development for platinum. Herein, we present association between clinical factors and common SNPs in the development of grade 3-4 toxicity. METHODS: Retrospectively, 224 clinical records of GC patient were screened, of which 93 patients were incorporated into the study. Eleven SNPs with minor allelic frequency above 5% in GSTP1, ERCC2, ERCC1, TP53, UMPS, SHMT1, MTHFR, ABCC2 and DPYD were assessed. Association between patient clinical characteristics and toxicity was estimated using logistic regression models and classification algorithms. RESULTS: Reported grade ≤ 2 and 3-4 toxicities were 64.6% (61/93) and 34.4% (32/93) respectively. Selected DPYD SNPs were associated with higher toxicity (rs1801265; OR = 4.20; 95% CI = 1.70-10.95, p = 0.002), while others displayed a trend towards lower toxicity (rs1801159; OR = 0.45; 95% CI = 0.19-1.08; p = 0.071). Combination of paired SNPs demonstrated significant associations in DPYD (rs1801265), UMPS (rs1801019), ABCC2 (rs717620) and SHMT1 (rs1979277). Using multivariate logistic regression that combined age, sex, peri-operative chemotherapy, 5-FU regimen, the binary combination of the SNPs DPYD (rs1801265) + ABCC2 (rs717620), and DPYD (rs1801159) displayed the best predictive performance. A nomogram was constructed to assess the risk of developing overall toxicity. CONCLUSION: Pending further validation, this model could predict chemotherapy associated toxicity and improve GC patient quality of life.


Asunto(s)
Protocolos de Quimioterapia Combinada Antineoplásica/efectos adversos , Compuestos de Platino/administración & dosificación , Polimorfismo de Nucleótido Simple , Neoplasias Gástricas/tratamiento farmacológico , Neoplasias Gástricas/genética , Anciano , Capecitabina/efectos adversos , Estudios de Casos y Controles , Intervalos de Confianza , Proteínas de Unión al ADN/genética , Dihidrouracilo Deshidrogenasa (NADP)/genética , Endonucleasas/genética , Femenino , Fluorouracilo/efectos adversos , Frecuencia de los Genes , Genes p53 , Genotipo , Gutatión-S-Transferasa pi/genética , Glicina Hidroximetiltransferasa/genética , Humanos , Leucovorina/efectos adversos , Modelos Logísticos , Masculino , Metilenotetrahidrofolato Reductasa (NADPH2)/genética , Persona de Mediana Edad , Proteínas Asociadas a Resistencia a Múltiples Medicamentos/genética , Complejos Multienzimáticos/genética , Nomogramas , Oportunidad Relativa , Compuestos Organoplatinos/efectos adversos , Orotato Fosforribosiltransferasa/genética , Orotidina-5'-Fosfato Descarboxilasa/genética , Pirimidinas , Calidad de Vida , Estudios Retrospectivos , Neoplasias Gástricas/patología , Proteína de la Xerodermia Pigmentosa del Grupo D/genética
17.
BMC Cancer ; 21(1): 977, 2021 Aug 31.
Artículo en Inglés | MEDLINE | ID: mdl-34465283

RESUMEN

BACKGROUND: There is a lack of useful diagnostic tools to identify EGFR mutated NSCLC patients with long-term survival. This study develops a prognostic model using real world data to assist clinicians to predict survival beyond 24 months. METHODS: EGFR mutated stage IIIB and IV NSCLC patients diagnosed between January 2009 and December 2017 included in the Spanish Lung Cancer Group (SLCG) thoracic tumor registry. Long-term survival was defined as being alive 24 months after diagnosis. A multivariable prognostic model was carried out using binary logistic regression and internal validation through bootstrapping. A nomogram was developed to facilitate the interpretation and applicability of the model. RESULTS: 505 of the 961 EGFR mutated patients identified in the registry were included, with a median survival of 27.73 months. Factors associated with overall survival longer than 24 months were: being a woman (OR 1.78); absence of the exon 20 insertion mutation (OR 2.77); functional status (ECOG 0-1) (OR 4.92); absence of central nervous system metastases (OR 2.22), absence of liver metastases (OR 1.90) or adrenal involvement (OR 2.35) and low number of metastatic sites (OR 1.22). The model had a good internal validation with a calibration slope equal to 0.781 and discrimination (optimism corrected C-index 0.680). CONCLUSIONS: Survival greater than 24 months can be predicted from six pre-treatment clinicopathological variables. The model has a good discrimination ability. We hypothesized that this model could help the selection of the best treatment sequence in EGFR mutation NSCLC patients.


Asunto(s)
Supervivientes de Cáncer/estadística & datos numéricos , Carcinoma de Pulmón de Células no Pequeñas/mortalidad , Quimioradioterapia/mortalidad , Neoplasias Pulmonares/mortalidad , Mutación , Nomogramas , Anciano , Biomarcadores de Tumor/genética , Carcinoma de Pulmón de Células no Pequeñas/genética , Carcinoma de Pulmón de Células no Pequeñas/patología , Carcinoma de Pulmón de Células no Pequeñas/terapia , Receptores ErbB/genética , Femenino , Estudios de Seguimiento , Humanos , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/patología , Neoplasias Pulmonares/terapia , Masculino , Persona de Mediana Edad , Modelos Estadísticos , Pronóstico , Estudios Retrospectivos , Tasa de Supervivencia
18.
BMC Cancer ; 21(1): 986, 2021 Sep 03.
Artículo en Inglés | MEDLINE | ID: mdl-34479488

RESUMEN

BACKGROUND: Prosthesis-related complications, after knee reconstruction with endoprosthesis during operation for tumors around the knee, remain an unresolved problem which necessitate a revision or even an amputational surgery. The purpose of the current study was to identify significant risk factors associated with implant failure, and establish a novel model to predict survival of the prosthesis in patients operated with endoprostheses for tumor around knee. METHODS: We retrospectively reviewed the clinical database of our institution for patients who underwent knee reconstruction due to tumors. A total of 203 patients were included, including 123 males (60.6%) and 80 (39.4%) females, ranging in age from 14 to 77 years (mean: 34.3 ± 17.3 years). The cohort was randomly divided into training (n = 156) and validation (n = 47) samples. Univariable COX analysis was used for initially identifying potential independent predictors of prosthesis survival with the training group (p < 0.150). Multivariate COX proportional hazard model was selected to identify final significant prognostic factors. Using these significant predictors, a graphic nomogram, and an online dynamic nomogram were generated for predicting the prosthetic survival. C-index and calibration curve were used for evaluate the discrimination ability and accuracy of the novel model, both in the training and validation groups. RESULTS: The 1-, 5-, and 10-year prosthetic survival rates were 94.0, 90.8, and 83.0% in training sample, and 96.7, 85.8, and 76.9% in validation sample, respectively. Anatomic sites, length of resection and length of prosthetic stem were independently associated with the prosthetic failure according to multivariate COX regression model (p<0.05). Using these three significant predictors, a graphical nomogram and an online dynamic nomogram model were generated. The C-indexes in training and validation groups were 0.717 and 0.726 respectively, demonstrating favourable discrimination ability of the novel model. And the calibration curve at each time point showed favorable consistency between the predicted and actual survival rates in training and validation samples. CONCLUSIONS: The length of resection, anatomical location of tumor, and length of prosthetic stem were significantly associated with prosthetic survival in patients operated for tumor around knee. A user-friendly novel online model model, with favorable discrimination ability and accuracy, was generated to help surgeons predict the survival of the prosthesis.


Asunto(s)
Prótesis de la Rodilla/estadística & datos numéricos , Neoplasias/cirugía , Nomogramas , Prótesis e Implantes/estadística & datos numéricos , Falla de Prótesis/tendencias , Procedimientos Quirúrgicos Reconstructivos/métodos , Adolescente , Adulto , Anciano , Femenino , Estudios de Seguimiento , Humanos , Masculino , Persona de Mediana Edad , Neoplasias/patología , Pronóstico , Estudios Retrospectivos , Factores de Riesgo , Adulto Joven
19.
BMC Cancer ; 21(1): 999, 2021 Sep 07.
Artículo en Inglés | MEDLINE | ID: mdl-34493229

RESUMEN

BACKGROUND: There are differences in survival between high-and low-grade Upper Tract Urothelial Carcinoma (UTUC). Our study aimed to develop a nomogram to predict overall survival (OS) of patients with high- and low-grade UTUC after tumor resection, and to explore the difference between high- and low-grade patients. METHODS: Patients confirmed to have UTUC between 2004 and 2015 were selected from the Surveillance, Epidemiology and End Results (SEER) database. The UTUCs were identified and classified as high- and low-grade, and 1-, 3- and 5-year nomograms were established. The nomogram was then validated using the Chinese multicenter dataset (patients diagnosed in Shandong, China between January 2010 and October 2020). RESULTS: In the high-grade UTUC patients, nine important factors related to survival after tumor resection were identified to construct nomogram. The C index of training dataset was 0.740 (95% confidence interval [CI]: 0.727-0.754), showing good calibration. The C index of internal validation dataset was 0.729(95% CI:0.707-0.750). On the other hand, Two independent predictors were identified to construct nomogram of low-grade UTUC. The C index was 0.714 (95% CI: 0.671-0.758) for the training set,0.731(95% CI:0.670-0.791) for the internal validation dataset. Encouragingly, the nomogram was clinically useful and had a good discriminative ability to identify patients at high risk. CONCLUSION: We constructed a nomogram and a corresponding risk classification system predicting the OS of patients with an initial diagnosis of high-and low-grade UTUC.


Asunto(s)
Modelos Estadísticos , Nomogramas , Programa de VERF/estadística & datos numéricos , Neoplasias de la Vejiga Urinaria/mortalidad , Factores de Edad , Anciano , Anciano de 80 o más Años , Femenino , Estudios de Seguimiento , Humanos , Masculino , Estadificación de Neoplasias , Estudios Retrospectivos , Tasa de Supervivencia , Neoplasias de la Vejiga Urinaria/patología , Neoplasias de la Vejiga Urinaria/cirugía
20.
BMC Cancer ; 21(1): 1057, 2021 Sep 26.
Artículo en Inglés | MEDLINE | ID: mdl-34563149

RESUMEN

BACKGROUND: Brain metastases were rare in esophageal cancer patients. Using the Surveillance, Epidemiology, and End Results (SEER) database, the present study investigated the incidence, risk and prognostic factors of brain metastases in esophageal cancer patients. METHODS: Retrieving esophageal cancer patients diagnosed between 2010 and 2018 from the SEER database, univariable and multivariable logistic and cox regression models were used to investigate the risk factors for brain metastases development and prognosis, respectively. The brain metastases predicting nomogram was constructed, evaluated and validated. The overall survival (OS) of patients with brain metastases was analyzed by Kaplan-Meier method. RESULTS: A total of 34,107 eligible esophageal cancer patients were included and 618 of them were diagnosed with brain metastases (1.8%). The median survival of the brain metastatic esophageal cancer patients was 5 (95% CI: 5-7) months. The presence of bone metastases and lung metastases were the homogeneously associated factors for the development and prognosis of brain metastases in esophageal cancer patients. Patients younger than 65 years, American Indian/Alaska Native race (vs. White), overlapping lesion (vs. Upper third), esophageal adenocarcinoma histology subtype, higher N stage, and liver metastases were positively associated with brain metastases occurrence. The calibration curve, ROC curve, and C-index exhibited good performance of the nomogram for predicting brain metastases. CONCLUSIONS: Homogeneous and heterogeneous factors were found for the development and prognosis of brain metastases in esophageal cancer patients. The nomogram had good calibration and discrimination for predicting brain metastases.


Asunto(s)
Neoplasias Encefálicas/secundario , Neoplasias Esofágicas/patología , Nomogramas , Programa de VERF , Adenocarcinoma/secundario , Anciano , Neoplasias Óseas/epidemiología , Neoplasias Óseas/secundario , Neoplasias Encefálicas/epidemiología , Neoplasias Encefálicas/etnología , Neoplasias Encefálicas/mortalidad , Intervalos de Confianza , Neoplasias Esofágicas/epidemiología , Neoplasias Esofágicas/etnología , Neoplasias Esofágicas/mortalidad , Femenino , Humanos , Incidencia , Estimación de Kaplan-Meier , Neoplasias Hepáticas/epidemiología , Neoplasias Hepáticas/secundario , Modelos Logísticos , Neoplasias Pulmonares/epidemiología , Neoplasias Pulmonares/secundario , Masculino , Persona de Mediana Edad , Pronóstico , Factores de Riesgo
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