A creatinine-based model to predict recurrent bleeding after modified percutaneous transhepatic variceal embolization in cirrhotic patients
Zhen LiKun JiHanlong ZhuSi ZhaoXin LiPengchao ZhanYang ShiShuwen YeBingcan XieYuyuan ZhangYu PengZhigang RenJuan DingXinwei Han
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Abstract Background: Patients who survived the initial esophagogastric variceal bleeding (EVB) experienced an increased risk of recurrent bleeding and death, but a reliable predictive model is lacking. We aim to develop and validate a model for rebleeding prediction in cirrhotic patients with EVB after modified percutaneous transhepatic variceal embolization (PTVE) with cyanoacrylate. Methods: A total of 122 patients with EVB who underwent PTVE from January 2015 to March 2020 were enrolled in this study . The multivariate logistic analyses were conducted to determine independent risk factors for nomogram construction. The discrimination, calibration and clinical utility of the nomogram were compared with the Model for End-stage Liver Disease (MELD) and Child–Pugh model. Additionally, a risk stratification was developed according to nomogram. Results: Rebleeding within 3 months after PTVE occurred in 32 patients (26.2%). The independent rebleeding indicators included the history of endoscopic therapy, Child–Pugh score, partial splenic embolization and creatinine level. The nomogram incorporating these four predictors achieved excellent calibration and discriminatory abilities with a concordance index of 0.85 which was confirmed to be 0.83 through bootstrapping validation. The nomogram harbored better discrimination and clinical applicability than the MELD and Child–Pugh model. As shown in the Kaplan-Meier curves, the patients at high risk were significantly associated with the high probability of rebleeding ( P < 0.001). Conclusions: We created a creatinine-based nomogram to predict rebleeding in cirrhotic patients received PTVE, which could help clinicians to discern the high-risk patients to perform more aggressive treatments earlier and formulate intensive follow-up plan for them.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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