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The Lancet. Oncology Apr 2015Nomograms are widely used as prognostic devices in oncology and medicine. With the ability to generate an individual probability of a clinical event by integrating... (Review)
Review
Nomograms are widely used as prognostic devices in oncology and medicine. With the ability to generate an individual probability of a clinical event by integrating diverse prognostic and determinant variables, nomograms meet our desire for biologically and clinically integrated models and fulfill our drive towards personalised medicine. Rapid computation through user-friendly digital interfaces, together with increased accuracy, and more easily understood prognoses compared with conventional staging, allow for seamless incorporation of nomogram-derived prognosis to aid clinical decision making. This has led to the appearance of many nomograms on the internet and in medical journals, and an increase in nomogram use by patients and physicians alike. However, the statistical foundations of nomogram construction, their precise interpretation, and evidence supporting their use are generally misunderstood. This issue is leading to an under-appreciation of the inherent uncertainties regarding nomogram use. We provide a systematic, practical approach to evaluating and comprehending nomogram-derived prognoses, with particular emphasis on clarifying common misconceptions and highlighting limitations.
Topics: Disease-Free Survival; Humans; Neoplasms; Nomograms; Prognosis
PubMed: 25846097
DOI: 10.1016/S1470-2045(14)71116-7 -
Cancer Communications (London, England) Jul 2020Low-grade endometrial stromal sarcoma (LG-ESS) is a rare tumor that lacks a prognostic prediction model. Our study aimed to develop a nomogram to predict overall...
BACKGROUND
Low-grade endometrial stromal sarcoma (LG-ESS) is a rare tumor that lacks a prognostic prediction model. Our study aimed to develop a nomogram to predict overall survival of LG-ESS patients.
METHODS
A total of 1172 patients confirmed to have LG-ESS between 1988 and 2015 were selected from the Surveillance, Epidemiology and End Results (SEER) database. They were further divided into a training cohort and a validation cohort. The Akaike information criterion was used to select variables for the nomogram. The discrimination and calibration of the nomogram were evaluated using concordance index (C-index), area under time-dependent receiver operating characteristic curve (time-dependent AUC), and calibration plots. The net benefits of the nomogram at different threshold probabilities were quantified and compared with those of the International Federation of Gynecology and Obstetrics (FIGO) criteria-based tumor staging using decision curve analysis (DCA). Net reclassification index (NRI) and integrated discrimination improvement (IDI) were also used to compare the nomogram's clinical utility with that of the FIGO criteria-based tumor staging. The risk stratifications of the nomogram and the FIGO criteria-based tumor staging were compared.
RESULTS
Seven variables were selected to establish the nomogram for LG-ESS. The C-index (0.814 for the training cohort and 0.837 for the validation cohort) and the time-dependent AUC (> 0.7) indicated satisfactory discriminative ability of the nomogram. The calibration plots showed favorable consistency between the prediction of the nomogram and actual observations in both the training and validation cohorts. The NRI values (training cohort: 0.271 for 5-year and 0.433 for 10-year OS prediction; validation cohort: 0.310 for 5-year and 0.383 for 10-year OS prediction) and IDI (training cohort: 0.146 for 5-year and 0.185 for 10-year OS prediction; validation cohort: 0.177 for 5-year and 0.191 for 10-year OS prediction) indicated that the established nomogram performed significantly better than the FIGO criteria-based tumor staging alone (P < 0.05). Furthermore, DCA showed that the nomogram was clinically useful and had better discriminative ability to recognize patients at high risk than the FIGO criteria-based tumor staging.
CONCLUSIONS
A prognostic nomogram was developed and validated to assist clinicians in evaluating prognosis of LG-ESS patients.
Topics: Endometrial Neoplasms; Female; Humans; Nomograms; SEER Program; Sarcoma, Endometrial Stromal; Survival Rate
PubMed: 32558385
DOI: 10.1002/cac2.12067 -
BMC Gastroenterology Oct 2020This study aimed to establish nomogram models of overall survival (OS) and cancer-specific survival (CSS) in elderly colorectal cancer (ECRC) patients (Age ≥ 70).
BACKGROUND
This study aimed to establish nomogram models of overall survival (OS) and cancer-specific survival (CSS) in elderly colorectal cancer (ECRC) patients (Age ≥ 70).
METHODS
The clinical variables of patients confirmed as ECRC between 2004 and 2016 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate analysis were performed, followed by the construction of nomograms in OS and CSS.
RESULTS
A total of 44,761 cases were finally included in this study. Both C-index and calibration plots indicated noticeable performance of newly established nomograms. Moreover, nomograms also showed higher outcomes of decision curve analysis (DCA) and the area under the curve (AUC) compared to American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) stage and SEER stage.
CONCLUSIONS
This study established nomograms of elderly colorectal cancer patients with distinct clinical values compared to AJCC TNM and SEER stages regarding both OS and CSS.
Topics: Aged; Colorectal Neoplasms; Humans; Neoplasm Staging; Nomograms; Prognosis; SEER Program
PubMed: 33081695
DOI: 10.1186/s12876-020-01464-z -
Frontiers in Immunology 2022To investigate the risk factors for recurrence in patients with early-stage hepatocellular carcinoma (HCC) after minimally invasive treatment with curative intent, then...
PURPOSE
To investigate the risk factors for recurrence in patients with early-stage hepatocellular carcinoma (HCC) after minimally invasive treatment with curative intent, then to construct a prediction model based on Lasso-Cox regression and visualize the model built.
METHODS
Clinical data were collected from 547 patients that received minimally invasive treatment in our hospital from January 1, 2012, to December 31, 2016. Lasso regression was used to screen risk factors for recurrence. Then we established Cox proportional hazard regression model and random survival forest model including several parameters screened by Lasso regression. An optimal model was selected by comparing the values of C-index, then the model was visualized and the nomogram was finally plotted.
RESULTS
The variables screened by Lasso regression including age, gender, cirrhosis, tumor number, tumor size, platelet-albumin-bilirubin index (PALBI), and viral load were incorporated in the Cox model and random survival forest model (P<0.05). The C-index of these two models in the training sets was 0.729 and 0.708, and was 0.726 and 0.700 in the validation sets, respectively. So we finally chose Lasso-Cox regression model, and the calibration curve in the validation set performed well, indicating that the model built has a better predictive ability. And then a nomogram was plotted based on the model chosen to visualize the results.
CONCLUSIONS
The present study established a nomogram for predicting recurrence in patients with early-stage HCC based on the Lasso-Cox regression model. This nomogram was of some guiding significance for screening populations at high risk of recurrence after treatment, by which doctors can formulate individualized follow-up strategies or treatment protocols according to the predicted risk of relapse for patients to improve the long-term prognosis.
Topics: Humans; Carcinoma, Hepatocellular; Liver Neoplasms; Neoplasm Recurrence, Local; Nomograms; Liver Cirrhosis
PubMed: 36505501
DOI: 10.3389/fimmu.2022.1019638 -
World Journal of Surgical Oncology Dec 2021The prognosis of obstructive colorectal cancer (oCRC) is worse than that of nonobstructive colorectal cancer. However, no previous study has established an...
BACKGROUND
The prognosis of obstructive colorectal cancer (oCRC) is worse than that of nonobstructive colorectal cancer. However, no previous study has established an individualized prediction model for the prognosis of patients with oCRC. We aimed to screen the factors that affect the prognosis of oCRC and to use these findings to establish a nomogram model that predicts the individual prognosis of patients with oCRC.
METHODS
This retrospective study collected data of 181 patients with oCRC from three medical hospitals between February 2012 and December 2017. Among them, 129 patients from one hospital were used as the training cohort. Univariate and multivariate analyses were used in this training cohort to select independent risk factors that affect the prognosis of oCRC, and a nomogram model was established. The other 52 patients from two additional hospitals were used as the validation cohort to verify the model.
RESULTS
Multivariate analysis showed that carcinoembryonic antigen level (p = 0.037, hazard ratio [HR] = 2.872 [1.065-7.740]), N stage (N1 vs. N0, p = 0.028, HR = 3.187 [1.137-8.938]; N2 vs. N0, p = 0.010, HR = 4.098 [1.393-12.051]), and surgical procedures (p = 0.002, HR = 0.299 [0.139-0.643]) were independent prognostic factors of overall survival in patients with oCRC. These factors were used to construct the nomogram model, which showed good concordance and accuracy.
CONCLUSION
Carcinoembryonic antigen, N stage, and surgical method are independent prognostic factors for overall survival in patients with oCRC, and the nomogram model can visually display these results.
Topics: Biomarkers, Tumor; Colorectal Neoplasms; Humans; Nomograms; Prognosis; Retrospective Studies
PubMed: 34857001
DOI: 10.1186/s12957-021-02445-6 -
The Journal of Thoracic and... Apr 2018
Topics: Carcinoma, Non-Small-Cell Lung; Humans; Logistic Models; Lung Neoplasms; Nomograms; ROC Curve
PubMed: 29370910
DOI: 10.1016/j.jtcvs.2017.12.107 -
Annals of Medicine Dec 2023Delayed extubation was commonly associated with increased adverse outcomes. This study aimed to explore the incidence and predictors and to construct a nomogram for... (Review)
Review
OBJECTIVE
Delayed extubation was commonly associated with increased adverse outcomes. This study aimed to explore the incidence and predictors and to construct a nomogram for delayed extubation after thoracoscopic lung cancer surgery.
METHODS
We reviewed medical records of 8716 consecutive patients undergoing this surgical treatment from January 2016 to December 2017. Using potential predictors to develop a nomogram and using a bootstrap-resampling approach to conduct internal validation. For external validation, we additionally pooled 3676 consecutive patients who underwent this procedure between January 2018 and June 2018. Extubation performed outside the operating room was defined as delayed extubation.
RESULTS
The rate of delayed extubation was 1.60%. Multivariate analysis identified age, BMI, FEV/FVC, lymph nodes calcification, thoracic paravertebral blockade (TPVB) usage, intraoperative transfusion, operative time and operation later than 6 p.m. as independent predictors for delayed extubation. Using these eight candidates to develop a nomogram, with a concordance statistic (C-statistic) value of 0.798 and good calibration. After internal validation, similarly good calibration and discrimination (C-statistic, 0.789; 95%CI, 0.748 to 0.830) were observed. The decision curve analysis (DCA) indicated the positive net benefit with the threshold risk range of 0 to 30%. Goodness-of-fit test and discrimination in the external validation were 0.113 and 0.785, respectively.
CONCLUSION
The proposed nomogram can reliably identify patients at high risk for the decision to delayed extubation after thoracoscopic lung cancer surgery. Optimizing four modifiable factors including BMI, FEV/FVC, TPVB usage, and operation later than 6 p.m. may reduce the risk of delayed extubation.Key Messages:This study identified eight independent predictors for delayed extubation, among which lymph node calcification and anaesthesia type were not commonly reported.Using these eight candidates to develop a nomogram, we could reliably identify high-risk patients for the decision to delayed extubation.Optimizing four modifiable factors, including BMI, FEV/FVC, TPVB usage, and operation later than 6 p.m. may reduce the risk of delayed extubation.
Topics: Humans; Airway Extubation; Nomograms; Multivariate Analysis; Operative Time; Lung Neoplasms
PubMed: 36869647
DOI: 10.1080/07853890.2022.2160490 -
BMC Nephrology Nov 2022Hyperkalemia increases the risk of mortality and cardiovascular-related hospitalizations in patients with hemodialysis. Predictors of hyperkalemia are yet to be... (Randomized Controlled Trial)
Randomized Controlled Trial
BACKGROUND
Hyperkalemia increases the risk of mortality and cardiovascular-related hospitalizations in patients with hemodialysis. Predictors of hyperkalemia are yet to be identified. We aimed at developing a nomogram able to predict hyperkalemia in patients with hemodialysis.
METHODS
We retrospectively screened patients with end-stage renal disease (ESRD) who had regularly received hemodialysis between Jan 1, 2017, and Aug 31, 2021, at Lishui municipal central hospital in China. The outcome for the nomogram was hyperkalemia, defined as serum potassium [K] ≥ 5.5 mmol/L. Data were collected from hemodialysis management system. Least Absolute Shrinkage Selection Operator (LASSO) analysis selected predictors preliminarily. A prediction model was constructed by multivariate logistic regression and presented as a nomogram. The performance of nomogram was measured by the receiver operating characteristic (ROC) curve, calibration diagram, and decision curve analysis (DCA). This model was validated internally by calculating the performance on a validation cohort.
RESULTS
A total of 401 patients were enrolled in this study. 159 (39.65%) patients were hyperkalemia. All participants were divided into development (n = 256) and validation (n = 145) cohorts randomly. Predictors in this nomogram were the number of hemodialysis session, blood urea nitrogen (BUN), serum sodium, serum calcium, serum phosphorus, and diabetes. The ROC curve of the training set was 0.82 (95%CI 0.77, 0.88). Similar ROC curve was achieved at validation set 0.81 (0.74, 0.88). The calibration curve demonstrated that the prediction outcome was correlated with the observed outcome.
CONCLUSION
This nomogram helps clinicians in predicting the risk of PEW and managing serum potassium in the patients with hemodialysis.
Topics: Humans; Nomograms; Retrospective Studies; Hyperkalemia; Cohort Studies; Renal Dialysis; Potassium
PubMed: 36319967
DOI: 10.1186/s12882-022-02976-4 -
Pediatrics Jan 2015The majority of newborns are exclusively breastfed during the birth hospitalization, and weight loss is nearly universal for these neonates. The amount of weight lost...
BACKGROUND
The majority of newborns are exclusively breastfed during the birth hospitalization, and weight loss is nearly universal for these neonates. The amount of weight lost varies substantially among newborns with higher amounts of weight loss increasing risk for morbidity. No hour-by-hour newborn weight loss nomogram exists to assist in early identification of those on a trajectory for adverse outcomes.
METHODS
For 161 471 term, singleton neonates born at ≥36 weeks' gestation at Northern California Kaiser Permanente hospitals in 2009-2013, data were extracted from the birth hospitalization regarding delivery mode, race/ethnicity, feeding type, and weights from electronic records. Quantile regression was used to create nomograms stratified by delivery mode that estimated percentiles of weight loss as a function of time among exclusively breastfed neonates. Weights measured subsequent to any nonbreastmilk feeding were excluded.
RESULTS
Among this sample, 108 907 newborns had weights recorded while exclusively breastfeeding with 83 433 delivered vaginally and 25 474 delivered by cesarean. Differential weight loss by delivery mode was evident 6 hours after delivery and persisted over time. Almost 5% of vaginally delivered newborns and >10% of those delivered by cesarean had lost ≥10% of their birth weight 48 hours after delivery. By 72 hours, >25% of newborns delivered by cesarean had lost ≥10% of their birth weight.
CONCLUSIONS
These newborn weight loss nomograms demonstrate percentiles for weight loss by delivery mode for those who are exclusively breastfed. The nomograms can be used for early identification of neonates on a trajectory for greater weight loss and related morbidities.
Topics: Age Factors; Birth Weight; Breast Feeding; Humans; Infant, Newborn; Nomograms; Weight Loss
PubMed: 25554815
DOI: 10.1542/peds.2014-1532 -
Frontiers in Public Health 2022Osteosarcoma (OSC) and Ewing's sarcoma (EWS) are children's most common primary bone tumors. The purpose of the study is to develop and validate a new nomogram to... (Randomized Controlled Trial)
Randomized Controlled Trial
BACKGROUND
Osteosarcoma (OSC) and Ewing's sarcoma (EWS) are children's most common primary bone tumors. The purpose of the study is to develop and validate a new nomogram to predict the cancer-specific survival (CSS) of childhood OSC and EWS.
METHODS
The clinicopathological information of all children with OSC and EWS from 2004 to 2018 was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analyses were used to screen children's independent risk factors for CSS. These risk factors were used to construct a nomogram to predict the CSS of children with OSC and EWS. A series of validation methods, including calibration plots, consistency index (C-index), and area under the receiver operating characteristic curve (AUC), were used to validate the accuracy and reliability of the prediction model. Decision curve analysis (DCA) was used to validate the clinical application efficacy of predictive models. All patients were divided into low- and high-risk groups based on the nomogram score. Kaplan-Meier curve and log-rank test were used to compare survival differences between the two groups.
RESULTS
A total of 2059 children with OSC and EWS were included. All patients were randomly divided into training cohort 60% ( = 1215) and validation cohort 40% ( = 844). Univariate and multivariate analysis suggested that age, surgery, stage, primary site, tumor size, and histological type were independent risk factors. Nomograms were established based on these factors to predict 3-, 5-, and 8-years CSS of children with OSC and EWS. The calibration plots showed that the predicted value was highly consistent with the actual value. In the training cohort and validation cohort, the C-index was 0.729 (0.702-0.756) and 0.735 (0.702-0.768), respectively. The AUC of the training cohort and the validation cohort also showed similar results. The DCA showed that the nomogram had good clinical value.
CONCLUSION
We constructed a new nomogram to predict the CSS of OSC and EWS in children. This predictive model has good accuracy and reliability and can help doctors and patients develop clinical strategies.
Topics: Bone Neoplasms; Child; Humans; Nomograms; Osteosarcoma; Reproducibility of Results; Sarcoma, Ewing
PubMed: 35178367
DOI: 10.3389/fpubh.2022.837506