Development and internal validation of nomograms for the prediction of postoperative survival of patients with grade 4 renal cell carcinoma (RCC)

BackgroundTo develop successful prognostic models for grade 4 renal cell carcinoma (RCC) following partial nephrectomy and radical nephrectomy.MethodsThe nomograms were established based on a retrospective study of 135 patients who underwent partial and radical nephrectomy for grade 4 RCC at the Department of Urology, Peking University First Hospital from January 2013 to October 2018. The predictive performance of the nomograms was assessed by the calibration plot and C-index. The results were validated using bootstrap resampling.ResultsAspartate transaminase (AST), the maximum diameter of tumor (cutoff value =7 cm), lymph node metastasis, and the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) risk group were independent factors for determining the overall survival (OS) and cancer-specific survival (CSS) in multivariate analysis. AST, the maximum diameter of the tumor (cutoff value =7 cm), and lymph node metastasis were found to be independent variables for progression-free survival (PFS) in multivariate analysis. These variables were used for the studies to establish nomograms. All calibration plots revealed excellent predictive accuracy of the models. The C-indexes of the nomograms for predicting OS, CSS and PFS were 0.729 (95% CI, 0.659–0.799), 0.725 (95% CI, 0.654–0.796) and 0.702 (95% CI, 0.626–0.778), respectively. Moreover, the recurrence rate was not associated with open or laparoscopic radical nephrectomy in our cohort (P=0.126).ConclusionsWe have developed easy-to-use models that are internally validated to predict postoperative 1-, 3-, and 5-year OS, CSS, and PFS rates of grade 4 RCC patients. The new models could aid in identifying high-risk patients, making postoperative therapeutic and follow-up strategies as well as predicting patients’ survival after externally validated. Besides, our study shows that the recurrence rate is not associated with open or laparoscopic radical nephrectomy.     

Development and validation of prognostic nomogram based on log odds of positive lymph nodes for patients with gastric signet ring cell carcinoma

ObjectiveOur aims were to establish novel nomogram models, which directly targeted patients with signet ring cell carcinoma (SRC), for individualized prediction of overall survival (OS) rate and cancer-specific survival (CSS).MethodsWe selected 1,365 SRC patients diagnosed from 2010 to 2015 from Surveillance, Epidemiology and End Results (SEER) database, and then randomly partitioned them into a training cohort and a validation cohort. Independent predicted indicators, which were identified by using univariate testing and multivariate analyses, were used to construct our prognostic nomogram models. Three methods, Harrell concordance index (C-index), receiver operating characteristics (ROC) curve and calibration curve, were used to assess the ability of discrimination and predictive accuracy. Integrated discrimination improvement (IDI), net reclassification improvement (NRI) and decision curve analysis (DCA) were used to assess clinical utility of our nomogram models.ResultsSix independent predicted indicators, age, race, log odds of positive lymph nodes (LODDS), T stage, M stage and tumor size, were associated with OS rate. Nevertheless, only five independent predicted indicators were associated with CSS except race. The developed nomograms based on those independent predicted factors showed reliable discrimination. C-index of our nomogram for OS and CSS was 0.760 and 0.763, which were higher than American Joint Committee on Cancer (AJCC) 8th edition tumor-node-metastasis (TNM) staging system (0.734 and 0.741, respectively). C-index of validation cohort for OS was 0.757 and for CSS was 0.773. The calibration curves also performed good consistency. IDI, NRI and DCA showed the nomograms for both OS and CSS had a comparable clinical utility than the TNM staging system.ConclusionsThe novel nomogram models based on LODDS provided satisfying predictive ability of SRC both in OS and CSS than AJCC 8th edition TNM staging system alone.     

Development and validation of prediction models for neurocognitive disorders in adult patients admitted to the ICU with sleep disturbance

BackgroundNeurocognitive disorders (NCDs) and sleep disturbance are highly prevalent in the perioperative period and intensive care unit (ICU). There has been a lack of individualized evaluation tools designed for the high‐risk NCDs in critically ill patients with sleep disturbance.ObjectivesThe aim of this study was to develop and validate prediction models for NCDs among adult patients with sleep disturbance.MethodsThe R software was used to analyze the dataset of adult patients admitted to the ICU with sleep disturbance, who were diagnosed following the codes of the International Classification of Diseases, 9th Revision (ICD‐9) and 10th Revision (ICD‐10) using the MIMIC‐IV database. We used logistic regression and LASSO analyses to identify important risk factors associated with NCDs and develop nomograms for NCDs predictions. We measured the performances of the nomograms using the bootstrap resampling procedure, sensitivity, specificity of the receiver operating characteristic (ROC), area under the ROC curves (AUC), and decision curve analysis (DCA).ResultsThe prediction models shared the 10 risk factors (age, gender, midazolam, morphine, glucose, diabetes diseases, potassium, international normalized ratio, partial thromboplastin time, and respiratory rate). Cardiovascular diseases were included in the logistic regression, the sensitivity was 74.1%, and specificity was 64.6%. When platelet and Glasgow Coma Score (GCS) were included and cardiovascular diseases were removed in the LASSO prediction model, the sensitivity was 86.1% and specificity was 82.8%. Discriminative abilities of the logistic prediction and LASSO prediction models for NCDs in the validation set were evaluated as the AUC scores, which were 0.730 (95% CI 0.716–0.743) and 0.920 (95% CI 0.912–0.927). Net benefits of the prediction models were observed at threshold probabilities of 0.567 and 0.914.ConclusionsThe LASSO prediction model showed better performance than the logistic prediction model and should be preferred for nomogram‐assisted decisions on clinical risk management of NCDs among adult patients with sleep disturbance in the ICU.     

老年脓毒性休克患者进展为慢重症的列线图预测模型的构建及验证

目的 探讨老年脓毒性休克患者进展为慢重症的相关危险因素,并在此基础上构建与验证预测慢重症发生风险的列线图模型。方法 将纳入研究的252例年龄≥65岁的脓毒性休克患者作为训练集,并根据是否进展为慢重症将其分为慢重症组（86例）和非慢重症组（166例）。统计2组患者入EICU 24 h内一般资料、查尔森合并症指数（CCI）评分、序贯器官衰竭评估（SOFA）评分、腹内压（IAP）、机械通气（MV）和连续肾脏替代治疗（CRRT）比例以及血清乳酸（Lac）、降钙素原（PCT）水平的差异。通过多因素Logistic回归确定老年脓毒性休克患者进展为慢重症的独立危险因素,并以此构建预测慢重症发生风险的列线图模型。分别通过校准曲线和受试者工作特征（ROC）曲线验证模型的校准度和区分度,并采用决策曲线分析法（DCA）确定模型的临床实用性。另外选取74例老年脓毒性休克患者作为验证集对预测模型进行外部验证。结果 训练集老年脓毒性休克患者慢重症发生率为34.13%。与非慢重症组相比,慢重症组年龄≥75岁,CCI评分≥3分,CRRT比例、MV比例、SOFA评分、IAP水平较高（P<0.05）。多因素Logi...     

Accuracy of the Kattan nomogram across prostate cancer risk-groups

Study Type – Diagnostic (exploratory cohort)
Level of Evidence 2b What’s known on the subject? and What does the study add? The Kattan nomogram is one of the most commonly used preoperative prediction tools for estimating individualized risk of biochemical recurrence after radical prostatectomy. However, little is known about this nomogram’s accuracy for patients at the extremes of the risk spectra, as only a small fraction of such patients comprised the cohort used in its development. We examined the accuracy of the Kattan nomogram across various risk groups, and confirmed its ability to accurately estimate risk of recurrence, even for patients with high and low‐risk prostate cancer.

OBJECTIVE

? To investigate the predictive ability of nomograms at the extremes of preoperative clinical parameters by examining the predictive ability across all prostate cancer risk groups.

PATIENTS AND METHODS

? The Columbia University Urologic Oncology Database was reviewed: 3663 patients underwent radical prostatectomy from 1988 to 2008. Patients who had received neoadjuvant or adjuvant therapy, or had insufficient clinical parameters for estimation of 5‐year progression‐free probability using the preoperative Kattan nomogram were excluded. ? A total of 1877 patients were included and stratified by D’Amico risk criteria. Mean estimated nomogram progression rates were compared with actuarial Kaplan–Meier survival statistics. ? A regression model to predict progression‐free survival was fitted with estimated nomogram score and concordance indices were calculated for the entire model and subsequently for each risk group.

RESULTS

? Of 1877 patients, 857 (45.6%) were low risk, 704 (37.5%) were intermediate risk, and 316 (16.8%) were high risk by D’Amico criteria. ? Mean estimated nomogram survival and actuarial Kaplan–Meier survival at 5 years were 90.5% and 92.2% (95% CI 89.2–94.3) for low‐risk, 76.7% and 77.8% (73.3–81.7) for intermediate‐risk, and 65.8% and 60.4% (52.0–67.7) for high‐risk groups, respectively. Using nomogram score in the regression model, the c‐index for the full model was 0.61. ? For low‐, intermediate‐ and high‐risk patients independently the c‐index was 0.60, 0.59 and 0.57, respectively. When low‐, intermediate‐ and high‐risk patients were independently removed from the model the c‐index was 0.64, 0.65 and 0.55, respectively. ? The c‐index for the full model using the categorical nomogram risk scores was 0.67. Similar to the D’Amico model, the c‐index improved to 0.69 when intermediate‐risk patients were removed from the model.

CONCLUSIONS

? The study confirms the ability of preoperative nomograms to accurately predict actuarial survival across all risk groups. ? The predictive ability of the nomogram varies by risk group, yet even at the extremes of high‐risk and low‐risk prostate cancer the nomogram accurately predicts outcome.     

Predictors and Dynamic Nomogram to Determine the Individual Risk of Malignant Brain Edema After Endovascular Thrombectomy in Acute Ischemic Stroke

Background and PurposeThis study aimed to construct an optimal dynamic nomogram for predicting malignant brain edema (MBE) in acute ischemic stroke (AIS) patients after endovascular thrombectomy (ET).MethodsWe enrolled AIS patients after ET from May 2017 to April 2021. MBE was defined as a midline shift of >5 mm at the septum pellucidum or pineal gland based on follow-up computed tomography within 5 days after ET. Multivariate logistic regression and LASSO (least absolute shrinkage and selection operator) regression were used to construct the nomogram. The area under the receiver operating characteristic curve (AUC) and decision-curve analysis were used to compare our nomogram with two previous risk models for predicting brain edema after ET.ResultsMBE developed in 72 (21.9%) of the 329 eligible patients. Our dynamic web-based nomogram (https://successful.shinyapps.io/DynNomapp/) consisted of five parameters: basal cistern effacement, postoperative National Institutes of Health Stroke Scale (NIHSS) score, brain atrophy, hypoattenuation area, and stroke etiology. The nomogram showed good discrimination ability, with a C-index (Harrell’s concordance index) of 0.925 (95% confidence interval=0.890–0.961), and good calibration (Hosmer-Lemeshow test, p=0.386). All variables had variance inflation factors of <1.5 and tolerances of >0.7, suggesting no significant collinearity among them. The AUC of our nomogram (0.925) was superior to those of Xiang-liang Chen and colleagues (0.843) and Ming-yang Du and colleagues (0.728).ConclusionsOur web-based dynamic nomogram reliably predicted the risk of MBE in AIS patients after ET, and hence is worthy of further evaluation.     

Nomograms Predict Cancer‐Specific and Overall Survival of Patients With Primary Limb Leiomyosarcoma

To date, there have been no data to predict the survival of patients with leiomyosarcoma from soft limb tissue because of the rarity of this disease. Nomograms have been widely applied in clinical oncology to precisely predict the survival of individual patients. This was a retrospective study to construct and validate nomograms to predict the cancer‐specific survival (CSS) and overall survival (OS) of patients with primary limb leiomyosarcoma (PL‐LMS). A total of 1,208 patients with LMS from limb soft tissue were collected from the Surveillance, Epidemiology, and End Results database from 1975 to 2015. We identified independent prognostic factors using univariate and multivariate Cox analyses. These prognostic factors were then included in the nomograms to predict 3‐ and 5‐year CSS and OS rates. Finally, we validated the nomograms internally and externally. A total of 1208 patients were collected and divided into validation (N = 604) and training (N = 604) groups. Age, race, grade, tumor size, stage, and surgical types were demonstrated as independent prognostic factors for CSS and OS (all p < 0.05) and further used to construct the nomograms. The concordance index (C‐index) for CSS was 0.857 for internal validation and 0.727 for external validation. The C‐index for OS and CSS both demonstrated that the nomogram prediction agreed perfectly with actual survival. We developed nomograms to predict CSS and OS in PL‐LMS patients and can benefit from using them to identify patients’ mortality risk and make more precise assessments regarding survival. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:1649–1657, 2019.