Risk factors for and predictive nomogram of overall survival in adult patients with craniopharyngiomas: A SEER population-based study
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Studies with relatively large sample size as well as long-term follow-up focusing on adult craniopharyngioma (CP) patients are still lacking. We attempted to identify independent prognostic factors and establish a nomogram model to estimate survival rates for adult CP patients. The Surveillance, Epidemiology, and End Results database was used to obtain data on patients with CP. Univariable and multivariable Cox analyses were utilized to identify the prognostic factors of adult CP patients. A survival prediction model was constructed and its predictive performance was also assessed. A total of 991 patients (695 in training group and 296 in validation group) were eligible for final inclusion. Multivariate Cox analysis presented that age at diagnosis, marital status, race, tumor size, and surgery type were statistically significant prognostic factors for overall survival (all P < .05). A graphical predicting nomogram model was developed to calculate the predicted patients' survival probabilities at 1, 2, 5, and 10 years. The concordance indexes were 0.708 ± 0.019 and 0.750 ± 0.025 for the training and validation samples, respectively, demonstrating favorable discrimination abilities. Similarly, the time-dependent area under curve also showed overall satisfactory discrimination ability. Favorable consistencies between the predicted and actual survival were presented according to the calibration curves. An easy-to-use nomogram, being proven to be with reliable discrimination ability and accuracy, was established to help predict overall survival for adult patients with CP using the identified significant prognostic factors.Keywords:
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This paper analyzes the nuptiality of Zhejiang Province, China, using data from the 1982 census and the 1-per-1000-population fertility survey. In 1982, the unmarried population in Zhejiang Province accounted for 29.42% of the population aged 15 and over. Unmarried men made up 34.62% of the male population aged 15 and over, while unmarried women made up 23.81% of the female population aged 15 and over. The urban unmarried population is larger than the rural one. The unmarried population is concentrated in the age group 15-27. In 1982, married people accounted for 62.67% of the population aged 15 and over, with 60.27% of the men and 65.26% of the women being married. The marriage age for men is concentrated in the age group 24-29, while the marriage age for women is concentrated in the age group 21-25. In 1982, the widowed population accounted for 7.17% of the population aged 15 and over (3.85% for men and 10.75% for women). In 1982, the divorced population accounted for .74% of the population aged 15 and over (1.26% for men and .18% for women). Remarried women accounted for 6.17% of the married women in the province.
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Abstract This 1:5 case‐control study aimed to identify the risk factors of hospital‐acquired pressure injuries (HAPIs) and to develop a mathematical model of nomogram for the risk prediction of HAPIs. Data for 370 patients with HAPIs and 1971 patients without HAPIs were extracted from the adverse events and the electronic medical systems. They were randomly divided into two sets: training (n = 1951) and validation (n = 390). Significant risk factors were identified by univariate and multivariate analyses in the training set, followed by a nomogram constructed. Age, independent movement, sensory perception and response, moisture, perfusion, use of medical devices, compulsive position, hypoalbuminaemia, an existing pressure injury or scarring from a previous pressure injury, and surgery sufferings were considered significant risk factors and were included to construct a nomogram. In both of the training and validation sets, the areas of 0.90 under the receiver operating characteristic curves showed excellent discrimination of the nomogram; calibration plots demonstrated a good consistency between the observed probability and the nomogram's prediction; decision curve analyses exhibited preferable net benefit along with the threshold probability in the nomogram. The excellent performance of the nomogram makes it a convenient and reliable tool for the risk prediction of HAPIs.
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Objective: To compare the diagnostic accuracy of various transcutaneous bilirubin (TcB) nomograms for predischarge screening. Methods: The paired total serum bilirubin (TSB) and TcB measurements collected in neonates ≥35 weeks and ≥2000 g birth weight were analyzed. BiliCare™ bilirubinometer was used for TcB measurement. We chose the following nomograms for the study: Bhutani nomogram, Maisel's nomogram, Agarwal nomogram, Thakkar nomogram, American Academy of Pediatrics (AAP) nomogram within 3 mg/dl of phototherapy cutoff, AAP nomogram >70% of phototherapy cutoff and if TcB value is above 13 mg/dl. The diagnostic accuracy of these nomograms for TcB was compared with TSB plotted in the Bhutani nomogram. Results: TcB showed a positive correlation with TSB (Pearson correlation coefficient = 0.783). Bhutani nomogram, Maisel's nomogram and AAP (using within 3 mg/dL cutoff) nomogram showed good sensitivity and low false-negative rate while avoiding blood draws in most neonates. Conclusion: Bhutani nomogram, Maisel's nomogram, and AAP (using within 3 mg/dL of phototherapy cutoff) nomograms have comparable diagnostic accuracy for predischarge bilirubin screening in neonates.
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Abstract Background: The aim of the study was to establish and validate nomograms to predict the mortality risk of patients with COVID-19 using routine clinical indicators. Method: This retrospective study included a development cohort enrolled 2119 hospitalized COVID-19 patients and a validation cohort included 1504 COVID-19 patients. The demographics, clinical manifestations, vital signs and laboratory test results of the patients at admission and outcome of in-hospital death were recorded. The independent factors associated with death were identified by a forward stepwise multivariate logistic regression analysis and used to construct two prognostic nomograms. The models were then tested in an external dataset. Results: Nomogram 1 is a full model included nine factors identified in the multivariate logistic regression and nomogram 2 is built by selecting four factors from nine to perform as a reduced model. Nomogram 1 and nomogram 2 established showed better performance in discrimination and calibration than the MuLBSTA score in training. In validation, Nomogram 1 performed better than nomogram 2 for calibration. Conclusion: Nomograms we established performed better than the MuLBSTA score. We recommend the application of nomogram 1 in general hospital which provide robust prognostic performance but more cumbersome; nomogram 2 in mobile cabin hospitals which depend on less laboratory examinations and more convenient. Both nomograms can help clinicians in identifying patients at risk of death with routine clinical indicators at admission, which may reduce the overall mortality of COVID-19.
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