Nomogram to predict cause‐specific mortality in extensive‐stage small cell lung cancer: A competing risk analysis
Jia ZhongQiwen ZhengTongtong AnJun ZhaoMeina WuYuyan WangMinglei ZhuoJianjie LiXinghui ZhaoXue YangBo JiaHanxiao ChenZhi DongJingjing WangYujia ChiXiaoyu ZhaiZiping Wang
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Background Small‐cell lung cancer (SCLC) is one of the most aggressive types of lung cancer. The prognosis for SCLC patients depends on many factors. The intent of this study was to construct a nomogram model to predict mortality for extensive‐stage SCLC. Methods Original data was collected from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute in the United States. A nomogram prognostic model was constructed to predict death probability for extensive‐stage SCLC. Results A total of 16 554 extensive‐stage SCLC patients from 2004 to 2014 in the SEER database were included in this study. Gender, race, age, TNM staging (including tumor extent, nodal status, and metastasis), and treatment (surgery, chemotherapy, and radiotherapy) were identified as independent predictors for lung cancer‐specific death for extensive‐stage SCLC patients. A nomogram model was constructed based on multivariate models for lung cancer related death and other cause related death. Performance of the two models was validated by calibration and discrimination, with C‐index values of 0.714 and 0.638, respectively. Conclusion A prognostic nomogram model was established to predict death probability for extensive‐stage SCLC. This validated prognostic model may be beneficial for treatment strategy choice and survival prediction.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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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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