BackgroundLymphovascular invasion (LVI) is a high-risk factor for testicular germ-cell tumors (TGCT), but a prognostic model for TGCT-LVI patients is lacking. This study aimed to develop a nomogram for predicting the overall survival (OS) of TGCT-LVI patients.MethodsA complete cohort of 3288 eligible TGCG-LVI patients (training cohort, 2300 cases; validation cohort, 988 cases) were obtained from the Surveillance, Epidemiology, and End Results database. Variables screened by multivariate Cox regression analysis were used to construct a nomogram, which was subsequently evaluated using the consistency index (C-index), time-dependent receiver operating characteristic curve (ROC), and calibration plots. The advantages and disadvantages of the American Joint Committee on Cancer (AJCC) staging system and the nomogram were assessed by integrated discrimination improvement (IDI) and net reclassification improvement (NRI). Decision-analysis curve (DCA) was used to measure the net clinical benefit of the nomogram versus the AJCC staging system. Finally, Kaplan–Meier curves were used to evaluate the ability to identify different risk groups between the traditional AJCC staging system and the new risk-stratification system built on the nomogram.ResultsNine variables were screened by multivariate Cox regression analysis to construct the nomogram. The C-index (training cohort, 0.821; validation cohort, 0.819) and time-dependent ROC of 3-, 5-, and 9-year OS between the two cohorts suggested that the nomogram had good discriminatory ability. Calibration curves showed good consistency of the nomogram. The NRI values of 3-, 5-, and 9-year OS were 0.308, 0.274, and 0.295, respectively, and the corresponding values for the validation cohort were 0.093, 0.093, and 0.099, respectively (P<0.01). Additionally, the nomogram had more net clinical benefit as shown by the DCA curves, and the new risk-stratification system provided better differentiation than the AJCC staging system.ConclusionsA prognostic nomogram and new risk-stratification system were developed and validated to assist clinicians in assessing TGCT-LVI patients.
Background The outbreak of coronavirus disease 2019 (COVID-19) has rapidly spread all over the world. The specific information about immunity of non-survivors with COVID-19 is scarce. We aimed to describe the clinical characteristics and abnormal immunity of the confirmed COVID-19 non-survivors.Methods In this single-centered, retrospective, observational study, we enrolled 125 patients with COVID-19 who were died between Jan, 13 and Mar 4, 2020 from Renmin Hospital of Wuhan University. 414 randomly recruited patients with confirmed COVID-19 who were discharged from the same hospital during the same period served as control. Demographic and clinical characteristics, laboratory findings and chest computed tomograph results at admission, and treatment were collected. The immunity-related risk factors associated with in-hospital death were detected.Results Non-survivors were older than survivors. More than half of non-survivors was male. Nearly half of the patients had chronic medical illness. The common signs and symptoms at admission of non-survivors were fever. Non-survivors had higher white blood cell (WBC) count, more elevated neutrophil count, lower lymphocytes and platelete count, raised concentration of procalcitonin and C-reactive protein (CRP) than survivors. The levels of CD3+ T cells, CD4+ T cells, CD8+ T cells, CD19+ T cells, and CD16+56+T cells were significantly decreased in non-survivors when compared with survivors. The concentrations of immunoglobulins (Ig) G, IgA and IgE were increased, whereas the levels of complement proteins (C)3 and C4 were decreased in non-survivors when compared with survivors. Non-survivors presented lower levels of oximetry saturation at rest and lactate. Old age, comorbidity of malignant tumour, neutrophilia, lymphocytopenia, low CD4+ T cells, decreased C3, and low oximetry saturation were the risk factors of death in patients with confirmed COVID-19. The frequency of CD4+ T cells positively correlated with the numbers of lymphocytes and the level of oximetry saturation, whereas CD4+ T cells were negatively correlated with age and the numbers of neutrophils.Conclusion Abnormal cellular immunity and humoral immunity were considerable in non-survivors with COVID-19. Neutrophilia, lymphocytopenia, low CD4+ T cells, and decreased C3 were the immunity-related risk factors predicting mortality of patients with COVID-19.
BackgroundThe identification of uropathogens (UPBs) and urinary tract colonizing bacteria (UCB) conduces to guide the antimicrobial therapy to reduce resistant bacterial strains and study urinary microbiota. This study established a nomogram based on the nanopore-targeted sequencing (NTS) and other infectious risk factors to distinguish UPB from UCB.MethodsBasic information, medical history, and multiple urine test results were continuously collected and analyzed by least absolute shrinkage and selection operator (LASSO) regression, and multivariate logistic regression was used to determine the independent predictors and construct nomogram. Receiver operating characteristics, area under the curve, decision curve analysis, and calibration curves were used to evaluate the performance of the nomogram.ResultsIn this study, the UPB detected by NTS accounted for 74.1% (401/541) of all urinary tract microorganisms. The distribution of ln(reads) between UPB and UCB groups showed significant difference (OR = 1.39; 95% CI, 1.246–1.551, p < 0.001); the reads number in NTS reports could be used for the preliminary determination of UPB (AUC=0.668) with corresponding cutoff values being 7.042. Regression analysis was performed to determine independent predictors and construct a nomogram, with variables ranked by importance as ln(reads) and the number of microbial species in the urinary tract of NTS, urine culture, age, urological neoplasms, nitrite, and glycosuria. The calibration curve showed an agreement between the predicted and observed probabilities of the nomogram. The decision curve analysis represented that the nomogram would benefit clinical interventions. The performance of nomogram with ln(reads) (AUC = 0.767; 95% CI, 0.726–0.807) was significantly better (Z = 2.304, p-value = 0.021) than that without ln(reads) (AUC = 0.727; 95% CI, 0.681–0.772). The rate of UPB identification of nomogram was significantly higher than that of ln(reads) only (χ2 = 7.36, p-value = 0.009).ConclusionsNTS is conducive to distinguish uropathogens from colonizing bacteria, and the nomogram based on NTS and multiple independent predictors has better prediction performance of uropathogens.
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