Nomograms and risk scores for predicting the risk of oral cancer in different sexes: a large-scale case-control study
Fa ChenLisong LinLingjun YanFengqiong LiuYu QiuJing WangZhijian HuJunfeng WuXiaodan BaoLiangkun LinRui WangGuoxi CaiKiyoshi AoyagiLin CaiBaochang He
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Background: Although previous studies have explored the associations of modifiable lifestyle factors with oral cancer risk, few studies integrated these factors and established predictive tools for oral cancer risk in different sexes. Methods: Using a case-control study design, a total of 978 oral cancer cases and 2646 healthy controls were recruited in this study. Nomograms were constructed according to significant factors in multivariable logistic regression. Risk scores were calculated based on the nomograms and quantified the risk of oral cancer using restricted cubic spline. Results: Multivariate analyses demonstrated that smoking, alcohol drinking, tea, intake of fish, seafood, vegetables, fruits, teeth loss, regular dental visits and repetitive dental ulcer were independent factors for male oral cancer. Passive smoking, age at first intercourse, cooking oil fumes exposure, tea, intake of beans, vegetables, fruits, teeth loss, regular dental visits and repetitive dental ulcer were associated with female oral cancer. Then, two nomograms were developed for predicting the probability of oral cancer in men and women with the C-index of 0.768 (95% CI: 0.723-0.813) and 0.700 (95% CI: 0.635-0.765), respectively. Restricted cubic splines graphically revealed the risk of oral cancer in individuals with different risk scores. Moreover, the risk escalated continuously with the increasing number of the risk scores among both sexes. Conclusions: Combining nomograms with risk scores developed in this study could precisely predict oral cancer occurrence and provide an accurate risk assessment.Keywords:
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Prediction is central to the management of prostate cancer. Nomograms are devices that make predictions. We organized many nomograms for prostate cancer.Using MEDLINE a literature search was performed on prostate cancer nomograms from January 1966 to February 2000. We recorded input variables, prediction form, the number of patients used to develop the nomogram and the outcome being predicted. We also recorded the accuracy measures reported by the original authors and whether the nomograms have withstood validation. In addition, we noted whether the nomograms were proprietary or in the public domain. Each nomogram was classified into patient clinical disease state and the outcome being predicted.The literature search generated 42 published nomograms that may be applied to patients in various clinical stages of disease. Of the 42 nomograms only 18 had undergone validation, of which 2 partially failed. Few nomograms have been compared for predictive superiority and none appears to have been compared with clinical judgment alone.Patients with prostate cancer need accurate predictions. Prognostic nomograms are available for many clinical states and outcomes, and may provide the most accurate predictions currently available. Selection among them and progress in this field are hampered by the lack of comparisons for predictive accuracy.
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Historical nomograms for the prediction of cancer on prostate biopsy, developed in the sextant biopsy era are no more accurate today. The aim of this study was an independent external validation of a 10-core biopsy nomogram by Chun et al. (2007).A total of 322 patients who presented for their initial biopsy in a tertiary care center and had all the necessary data available were included in the retrospective analysis. To validate the nomogram, receiver operator characteristic (ROC) curves and calibration plots were constructed.Area under the ROC curve calculated for our data using the nomogram was 0.773, similar to that reported originally. However, the nomogram systematically overestimated prostate cancer risk, which, for our data, could be resolved by subtracting 24 points from the total number of points of the nomogram.The nomogram yielded overall good predictive accuracy as measured by the area under the ROC curve, but it systematically overestimated PC probability in individual patients. However, we showed how the nomogram could easily be adapted to our patient sample, resolving the bias issue.
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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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