scholarly journals Predictive nomogram for postoperative pancreatic fistula following pancreaticoduodenectomy: a retrospective study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jian Shen ◽  
Feng Guo ◽  
Yan Sun ◽  
Jingyuan Zhao ◽  
Jin Hu ◽  
...  
Keyword(s):  
Pancreatic Fistula ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Postoperative Pancreatic Fistula ◽  
Individual Risk ◽  
Calibration Plot ◽  
Multivariable Logistic Regression Analysis ◽  
Lasso Regression ◽  
Training Cohort ◽  
Significant Difference

Abstract Background Postoperative pancreatic fistula (POPF) represents the most common complication following pancreaticoduodenectomy (PD). Predictive models are needed to select patients with a high risk of POPF. This study was aimed to establish an effective predictive nomogram for POPF following PD. Methods Consecutive patients who had undergone PD between January 2016 and May 2020 at a single institution were analysed retrospectively. A predictive nomogram was established based on a training cohort, and Lasso regression and multivariable logistic regression analysis were used to evaluate predictors. The predictive abilities of the predicting model were assessed for internal validation by the area under the receiver operating characteristic curve (AUC) and calibration plot using bootstrap resampling. The performance of the nomogram was compared with that of the currently used a-FRS model. Results A total of 459 patients were divided into a training cohort (n = 302) and a validation cohort (n = 157). No significant difference was observed between the two groups with respect to clinicopathological characteristics. The POPF rate was 16.56%. The risk factors of POPF POPF were albumin difference, drain amylase value on postoperative day 1, pancreas texture, and BMI, which were all selected into a nomogram. Nomogram application revealed good discrimination (AUC = 0.87, 95% CI: 0.81–0.94, P <  0.001) as well as calibration abilities in the validation cohort. The predictive value of the nomogram was better than that of the a-FRS model (AUC: 0.87 vs 0.62, P <  0.001). Conclusions This predictive nomogram could be used to evaluate the individual risk of POPF in patients following PD, and albumin difference is a new, accessible predictor of POPF after PD. Trial registration This study was registered in the Chinese Clinical Trial Register (ChiCTR2000034435).

scholarly journals Development of a nomogram for predicting the presence of combined pulmonary fibrosis and emphysema

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xueting Yuan ◽  
Jin Jin ◽  
Xiaomao Xu
Keyword(s):  
Risk Factors ◽  
Pulmonary Fibrosis ◽  
Early Recognition ◽  
Pulmonary Function Tests ◽  
Characteristic Curve ◽  
Calibration Plot ◽  
Multivariable Logistic Regression Analysis ◽  
Significant Difference ◽  
Lemeshow Test ◽  
Good Concordance

Abstract Background In the clinical management of patients with combined pulmonary fibrosis and emphysema (CPFE), early recognition and appropriate treatment is essential. This study was designed to develop an accurate prognostic nomogram model to predict the presence of CPFE. Methods We retrospectively enrolled 85 patients with CPFE and 128 patients with idiopathic pulmonary fibrosis (IPF) between January 2015 and January 2020. Clinical characteristics were compared between groups. A multivariable logistic regression analysis was performed to identify risk factors for CPFE. Then, and a nomogram to predict the presence of CPFE was constructed for clinical use. Concordance index (C-index), area under the receiver operating characteristic curve (AUC), and calibration plot was used to evaluate the efficiency of the nomogram. Results Compared to the IPF group, the proportion of patients with male, smoking and allergies were significantly higher in the CPFE group. In terms of pulmonary function tests, patients with CPFE had lower FEV1/FVC%, DLCO/VA% pred, and higher RV, RV%pred, VC, VC%pred, TLC%pred, VA, TLC, TLC%pred, FVC, FVC%pred and FEV1 with significant difference than the other group. Positive correlation was found between DLCO and VA%, RV%, TLC% in patients with IPF but not in patients with CPFE. By multivariate analysis, male, smoking, allergies, FEV1/FVC% and DLCO/VA%pred were identified as independent predictors of the presence of CPFE. The nomogram was then developed using these five variables. After 1000 internal validations of bootstrap resampling, the C-index of the nomogram was 0.863 (95% CI 0.795–0.931) and the AUC was 0.839 (95% CI 0.764–0.913). Moreover, the calibration plot showed good concordance of incidence of CPFE between nomogram prediction and actual observation (Hosmer–Lemeshow test: P = 0.307). Conclusions Patients of CPFE have a characteristic lung function profile including relatively preserved lung volumes and ventilating function, contrasting with a disproportionate reduction of carbon monoxide transfer. By incorporating clinical risk factors, we created a nomogram to predict the presence of CPFE, which may serve as a potential tool to guide personalized treatment.

scholarly journals Establishment and validation of prognosis model for patients with cerebral contusion

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yufeng Zhu ◽  
Xiaoqing Jin ◽  
Lulu Xu ◽  
Pei Han ◽  
Shengwu Lin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Third Ventricle ◽  
Curve Analysis ◽  
Imaging Features ◽  
Lasso Regression ◽  
Cerebral Contusion ◽  
Decision Curve Analysis ◽  
Training Cohort

Abstract Background And Objective Cerebral Contusion (CC) is one of the most serious injury types in patients with traumatic brain injury (TBI). In this study, the baseline data, imaging features and laboratory examinations of patients with CC were summarized and analyzed to develop and validate a prediction model of nomogram to evaluate the clinical outcomes of patients. Methods A total of 426 patients with cerebral contusion (CC) admitted to the People’s Hospital of Qinghai Province and Affiliated Hospital of Qingdao University from January 2018 to January 2021 were included in this study, We randomly divided the cohort into a training cohort (n = 284) and a validation cohort (n = 142) with a ratio of 2:1.At Least absolute shrinkage and selection operator (Lasso) regression were used for screening high-risk factors affecting patient prognosis and development of the predictive model. The identification ability and clinical application value of the prediction model were analyzed through the analysis of receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). Results Twelve independent prognostic factors, including age, Glasgow Coma Score (GCS), Basal cistern status, Midline shift (MLS), Third ventricle status, intracranial pressure (ICP) and CT grade of cerebral edema,etc., were selected by Lasso regression analysis and included in the nomogram. The model showed good predictive performance, with a C index of (0.87, 95% CI, 0.026–0.952) in the training cohort and (0.93, 95% CI, 0.032–0.965) in the validation cohort. Clinical decision curve analysis (DCA) also showed that the model brought high clinical benefits to patients. Conclusion This study established a high accuracy of nomogram model to predict the prognosis of patients with CC, its low cost, easy to promote, is especially applicable in the acute environment, at the same time, CSF-glucose/lactate ratio(C-G/L), volume of contusion, and mean CT values of edema zone, which were included for the first time in this study, were independent predictors of poor prognosis in patients with CC. However, this model still has some limitations and deficiencies, which require large sample and multi-center prospective studies to verify and improve our results.

scholarly journals POS1439 A MULTI-PREDICTOR MODEL TO PREDICT RISK OF SCLERODERMA RENAL CRISIS IN SYSTEMIC SCLEROSIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1004.1-1004
Author(s):  
D. Xu ◽  
R. Mu
Keyword(s):  
Systemic Sclerosis ◽  
Prediction Model ◽  
Renal Involvement ◽  
Area Under The Curve ◽  
Individual Risk ◽  
Multivariable Logistic Regression Analysis ◽  
Scleroderma Renal Crisis ◽  
Lasso Regression ◽  
Patients At Risk ◽  
Renal Crisis

Background:Scleroderma renal crisis (SRC) is a life-threatening syndrome. The early identification of patients at risk is essential for timely treatment to improve the outcome[1].Objectives:We aimed to provide a personalized tool to predict risk of SRC in systemic sclerosis (SSc).Methods:We tried to set up a SRC prediction model based on the PKUPH-SSc cohort of 302 SSc patients. The least absolute shrinkage and selection operator (Lasso) regression was used to optimize disease features. Multivariable logistic regression analysis was applied to build a SRC prediction model incorporating the features of SSc selected in the Lasso regression. Then, a multi-predictor nomogram combining clinical characteristics was constructed and evaluated by discrimination and calibration.Results:A multi-predictor nomogram for evaluating the risk of SRC was successfully developed. In the nomogram, four easily available predictors were contained including disease duration <2 years, cardiac involvement, anemia and corticosteroid >15mg/d exposure. The nomogram displayed good discrimination with an area under the curve (AUC) of 0.843 (95% CI: 0.797-0.882) and good calibration.Conclusion:The multi-predictor nomogram for SRC could be reliably and conveniently used to predict the individual risk of SRC in SSc patients, and be a step towards more personalized medicine.References:[1]Woodworth TG, Suliman YA, Li W, Furst DE, Clements P (2016) Scleroderma renal crisis and renal involvement in systemic sclerosis. Nat Rev Nephrol 12 (11):678-91.Disclosure of Interests:None declared

Development and Validation of a Nomogram for Predicting Bladder Calculi Risk in Patients With Benign Prostatic Hyperplasia

2021 ◽  
Author(s):  
Euxu Xie ◽  
Xuelian Gu ◽  
Chen Ma ◽  
Li Guo ◽  
Man Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Benign Prostatic Hyperplasia ◽  
Characteristic Curve ◽  
Prostatic Hyperplasia ◽  
Calibration Plot ◽  
Lasso Regression ◽  
Net Benefit ◽  
Bladder Calculi ◽  
Validation Set ◽  
Selection Operator

Abstract Objective To develop and validate a nomogram for predicting bladder calculi risk in patients with benign prostatic hyperplasia (BPH).Methods A total of 368 patients who underwent transurethral resection of the prostate (TURP) and had histologically proven BPH from January 2018 to January 2021 were retrospectively collected. Eligible patients were randomly assigned to the training and validation datasets. Least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal risk factors. A prediction model was established based on the selected characteristics. The performance of the nomogram was assessed by calibration plots and the area under the receiver operating characteristic curve (AUROC). Furthermore, decision curve analysis (DCA) was used to determine the net benefit rate of of the nomogram. Results Among 368 patients who met the inclusion criteria, older age, a history of diabetes and hyperuricemia, longer intravesical prostatic protrusion (IPP)and larger prostatic urethral angulation (PUA) were independent risk factors for bladder calculi in patients with BPH. These factors were used to develop a nomogram, which had a good identification ability in predicting the risk of bladder calculi in patients, with AUROCs of 0.911 (95% CI: 0.876–0.945) in the training set and 0.884 (95% CI: 0.820–0.948) in the validation set. The calibration plot showed that the model had good calibration. Moreover, DCA indicated that the model had a goodclinical benefit. Conclusion We developed and internally validated the first nomogram to date to help physicians assess the risk of bladder calculi in patients with BPH, which may help physicians improve individual interventions and make better clinical decisions.

scholarly journals Comparison of prognostic impact of anatomic location of the pancreas on postoperative pancreatic fistula in laparoscopic and open gastrectomy

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Jun Kinoshita ◽  
Takahisa Yamaguchi ◽  
Hiroto Saito ◽  
Hideki Moriyama ◽  
Mari Shimada ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Pancreatic Fistula ◽  
Left Gastric Artery ◽  
Postoperative Pancreatic Fistula ◽  
Open Gastrectomy ◽  
Amylase Level ◽  
Prognostic Impact ◽  
Anatomic Location ◽  
Significant Difference ◽  
Preoperative Ct

Abstract Background Postoperative pancreatic fistula (POPF) is a serious complication after gastric cancer surgery. The current study aimed to investigate the significance of the anatomic location of the pancreas as a predictor for POPF in both laparoscopic gastrectomy (LG) and open gastrectomy (OG). Methods In total, 233 patients with gastric cancer were assessed retrospectively. We measured the maximum vertical (P-L height; PLH) and horizontal length (P-L depth; PLD) between the upper border of pancreas and the root of left gastric artery on a preoperative CT in the sagittal direction. The maximum length of the vertical line between the surface of the pancreas and the aorta (P-A length), previously reported as prognostic factor of POPF, was also measured. We investigated the correlations between these parameters and the incidence of POPF in LG and OG groups. Results Among the patients in this study, 118 underwent OG and 115 underwent LG. In LG, the median PLH and P-A length in patients with POPF were significantly longer compared with those without POPF (p = 0.026, 0.034, respectively), but not in OG. There was no significant difference in the median PLD between the patients with or without POPF in both LG and OG. The multivariate analysis demonstrated that PLH (odds ratio [OR] 4.19, 95% confidence interval [CI] 1.57–11.3, P = 0.004) and P-A length (OR 4.06, 95%CI 1.05–15.7, P = 0.042] were independent factors for predicting POPF in LG. However, intraoperative blood loss (OR 2.55, 95%CI 1.05–6.18, P = 0.038) was extracted as an independent factor in OG. The median amylase level in the drained fluid (D-Amy) were significantly higher in patients with high PLH(≥12.4 mm) or high P-A length (≥45 mm) compared with those with low PLH or low P-A length in LG. However, there were no differences in the D-Amy levels by PLH or P-A length in OG patients. Conclusions The anatomic location of the pancreas is a specific and independent predictor of POPF in LG but not in OG. PLH is a simple parameter that can evaluate the anatomic position of the pancreas, and it may be useful for preventing POPF after LG.

scholarly journals Comparison of prognostic impact of anatomic location of the pancreas on postoperative pancreatic fistula in laparoscopic and open gastrectomy

2020 ◽  
Author(s):  
Jun Kinoshita ◽  
Takahisa Yamaguchi ◽  
Hiroto Saito ◽  
Hideki Moriyama ◽  
Mari Shimada ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Pancreatic Fistula ◽  
Left Gastric Artery ◽  
Postoperative Pancreatic Fistula ◽  
Open Gastrectomy ◽  
Amylase Level ◽  
Prognostic Impact ◽  
Anatomic Location ◽  
Significant Difference ◽  
Preoperative Ct

Abstract Background: Postoperative pancreatic fistula (POPF) is a serious complication after gastric cancer surgery. The current study aimed to investigate the significance of the anatomic location of the pancreas as a predictor for POPF in both laparoscopic gastrectomy (LG) and open gastrectomy (OG). Methods: In total, 233 patients with gastric cancer were assessed retrospectively. We measured the maximum vertical (P-L height; PLH) and horizontal length (P-L depth; PLD) between the upper border of pancreas and the root of left gastric artery on a preoperative CT in the sagittal direction. The maximum length of the vertical line between the surface of the pancreas and the aorta (P-A length), previously reported as prognostic factor of POPF, was also measured. We investigated the correlations between these parameters and the incidence of POPF in LG and OG groups. Results: Among the patients in this study, 118 underwent OG and 115 underwent LG. In LG, the median PLH and P-A length in patients with POPF were significantly longer compared with those without POPF (p=0.026, 0.034, respectively), but not in OG. There was no significant difference in the median PLD between the patients with or without POPF in both LG and OG. The multivariate analysis demonstrated that PLH (odds ratio [OR] 4.19, 95% confidence interval [CI] 1.57–11.3, P=0.004) and P-A length (OR 4.06, 95%CI 1.05–15.7, P=0.042] were independent factors for predicting POPF in LG. However, intraoperative blood loss (OR 2.55, 95%CI 1.05–6.18, P=0.038) was extracted as an independent factor in OG. The median amylase level in the drained fluid (D-Amy) were significantly higher in patients with high PLH(≥12.4 mm) or high P-A length (≥45 mm) compared with those with low PLH or low P-A length in LG. However, there were no differences in the D-Amy levels by PLH or P-A length in OG patients. Conclusions: The anatomic location of the pancreas is a specific and independent predictor of POPF in LG but not in OG. PLH is a simple parameter that can evaluate the anatomic position of the pancreas, and it may be useful for preventing POPF after LG.

Development and validation of a radiomics nomogram to discriminate advanced pancreatic cancer patients with liver metastases or other metastatic patterns.

2021 ◽  
Vol 39 (3_suppl) ◽  
pp. 435-435
Author(s):  
Junjie Hang ◽  
Lixia Wu
Keyword(s):  
Pancreatic Cancer ◽  
Liver Metastases ◽  
Cancer Patients ◽  
Validation Cohort ◽  
Advanced Pancreatic Cancer ◽  
Region Of Interest ◽  
Calibration Plot ◽  
Training Cohort ◽  
Kaplan Meier ◽  
Metastatic Patterns

435 Background: Pancreatic cancer patients with liver metastases had much poorer prognosis than those with other metastatic patterns. This study aimed to develop and validate a radiomics model to discriminate pancreatic cancer patients with liver metastases from patients with other metastatic patterns. Methods: We evaluated 77 patients advanced pancreatic cancer (APC) with different metastatic patterns and performed texture analysis on the region of interest (ROI). 58 patients and 19 patients were allocated randomly into the training cohort and the validation cohort with almost the same proportion of patients with liver metastases. An independent samples t-test was used for initial feature selection in the training cohort. Random Forest Classifier (RFC) was used to construct models based on these features in both cohorts and a radiomics signature (RS) was derived from the model. Then a nomogram was constructed based on RS and CA19-9, and validated with calibration plot and decision curve. The prognostic value of RS was evaluated by Kaplan-Meier methods. Results: A nomogram based on the RS and CA19-9 was constructed and it demonstrated good discrimination in the training cohort (AUC = 0.93) and validation cohort (AUC = 0.81). Kaplan-meier methods showed that patients with RS>0.61 had much poorer OS than patients with RS < 0.61 in both cohorts. Conclusions:This study presents a radiomics nomogram incorporating both RS and CA19-9, which can be used to discriminate advanced pancreatic cancer patients with liver metastases from patients with other metastatic patterns.

scholarly journals Nomogram to predict primary non-response to infliximab in patients with Crohn’s disease: a multicenter study

2020 ◽  
Author(s):  
Xiao-Qi Ye ◽  
Jing Cai ◽  
Qiao Yu ◽  
Xiao-Cang Cao ◽  
Yan Chen ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Mucosal Healing ◽  
Clinical Usefulness ◽  
Secondary Outcome ◽  
Training Cohort ◽  
Graphical Tool ◽  
Therapeutic Decision Making

Abstract Background Infliximab (IFX) is effective at inducing and maintaining clinical remission and mucosal healing in patients with Crohn’s disease (CD); however, 9%–40% of patients do not respond to primary IFX treatment. This study aimed to construct and validate nomograms to predict IFX response in CD patients. Methods A total of 343 patients diagnosed with CD who had received IFX induction from four tertiary centers between September 2008 and September 2019 were enrolled in this study and randomly classified into a training cohort (n = 240) and a validation cohort (n = 103). The primary outcome was primary non-response (PNR) and the secondary outcome was mucosal healing (MH). Nomograms were constructed from the training cohort using multivariate logistic regression. Performance of nomograms was evaluated by area under the receiver-operating characteristic curve (AUC) and calibration curve. The clinical usefulness of nomograms was evaluated by decision-curve analysis. Results The nomogram for PNR was developed based on four independent predictors: age, C-reactive protein (CRP) at week 2, body mass index, and non-stricturing, non-penetrating behavior (B1). AUC was 0.77 in the training cohort and 0.76 in the validation cohort. The nomogram for MH included four independent factors: baseline Crohn’s Disease Endoscopic Index of Severity, CRP at week 2, B1, and disease duration. AUC was 0.79 and 0.72 in the training and validation cohorts, respectively. The two nomograms showed good calibration in both cohorts and were superior to single factors and an existing matrix model. The decision curve indicated the clinical usefulness of the PNR nomogram. Conclusions We established and validated nomograms for the prediction of PNR to IFX and MH in CD patients. This graphical tool is easy to use and will assist physicians in therapeutic decision-making.

A urine exosomal circRNA classifier for detection of high-grade prostate cancer at initial biopsy: A multicenter, retrospective study.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 5522-5522
Author(s):  
Liaoyuan Li ◽  
Wen Tao ◽  
Yadi He ◽  
Tao He ◽  
Qing Li ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Circular Rna ◽  
High Grade ◽  
Training Cohort ◽  
Potential Biomarker ◽  
Initial Biopsy

5522 Background: The low specificity of prostate-specific antigen (PSA) has resulted in the overdiagnosis and overtreatment of clinically indolent prostate cancer (PCa). We aimed to identify a urine exosomal circular RNA (circRNA) classifier that could detect high-grade (Gleason score [GS]7 or greater) PCa. Methods: We did a three-stage study that enrolled eligible participants, including PCa-free men, 45 years or older, scheduled for an initial prostate biopsy due to suspicious digital rectal examination findings and/or PSA levels (limit range, 2.0-20.0 ng/mL), from four hospitals in China. We used RNA sequencing and digital droplet polymerase chain reaction to identify 18 candidate urine exosomal circRNAs that were increased in 11 patients with high-grade PCa compared with 11 case-matched patients with benign prostatic hyperplasia. Using a training cohort of eligible participants, we built a urine exosomal circRNA classifier (Ccirc) to detect high-grade PCa. We then evaluated the classifier in discrimination of GS7 or greater from GS6 and benign disease on initial biopsy in two independent cohorts. We used the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) to evaluate diagnostic performance, and compared Ccirc with standard of care (SOC) (ie, PSA level, age, race, and family history). Results: Between June 1, 2016, and July 31, 2019, we recruited 356 participants to the training cohort, and 442 and 325 participants to the two independent validation cohorts. We identified a Ccirc containing five differentially expressed circRNAs (circ_0049335, circ_0056536, circ_0004028, circ_0008475, and circ_0126027) that could detect high-grade PCa. Ccirc showed higher accuracy than SOC to distinguish individuals with high-grade PCa from controls in both the training cohort and the validation cohorts. (AUC 0.831 [95% CI 0.765-0.883] vs 0.724 [0.705-0.852], P = 0.032 in the training cohort; 0.823 [0.762-0.871] vs 0.706 [0.649-0.762], P = 0.007 in validation cohort 1; and 0.878 [0.802-0.943] vs 0.785 [0.701-0.890], P = 0.021 for validation cohort 2). In all three cohorts, Ccirc had higher sensitivity (range 71.6-87.2%) and specificity (82.3-90.7%) than did SOC (sensitivity, 42.3-68.2%; specificity, 40.1-62.3%) to detect high-grade PCa. Using a predefined cut point, 202 of 767 (26.3%) biopsies would have been avoided, missing only 6% of patients with dominant pattern 4 high-risk GS 7 disease. Conclusions: Ccirc is a potential biomarker for high-grade PCa among suspicious men.

scholarly journals Prediction of distant metastasis of spinal and pelvic osteosarcoma by constructing and validating nomogram:a retrospective study based on SEER database

2022 ◽  
Author(s):  
Yu Lin ◽  
Zhenyu Wang ◽  
Gang Chen ◽  
Wenge Liu
Keyword(s):  
Risk Factor ◽  
Receiver Operating Characteristic ◽  
Survival Time ◽  
Distant Metastasis ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Seer Database ◽  
Lasso Regression ◽  
Training Cohort ◽  
Kaplan Meier

Abstract Background:Spinal and pelvic osteosarcoma is a rare type of all osteosarcomas,and distant metastasis is an important factor for poor prognosis of this disease. There are no similar studies on prediction of distant metastasis of spinal and pelvic osteosarcoma. We aim to construct and validate a nomogram to predict the risk of distant metastasis of spinal and pelvic osteosarcoma.Methods:We collected the data on patients with spinal and pelvic osteosarcoma from the Surveillance, Epidemiology, and End Results(SEER) database retrospectively. The Kaplan-Meier curve was used to compare differences in survival time between patients with metastasis and non-metastasis. Total patients were randomly divided into training cohort and validation cohort. The risk factor of distant metastasis were identified via the least absolute shrinkage and selection operator(LASSO) regression and multivariate logistic analysis. The nomogram we constructed were validated internally and externally by C-index, calibration curves,receiver operating characteristic(ROC) curve and Decision curve analysis (DCA).Results:The Kaplan-Meier curve showed that the survival time of non-metastatic patients was longer than that of metastatic patients(P＜0.001).All patients(n=358) were divided into training cohort(n=269) and validation cohort(n=89).The LASSO regression selected five meaningful variables in the training cohort. The multivariate logistic regression analysis demonstrated that surgery(yes,OR=0.175, 95%CI=0.095-0.321,p=0.000) was the independent risk factors for distant metastasis of patients with spinal and pelvic osteosarcoma. The C-index and calibration curves showed the good agreement between the predicted results and the actual results. The area under the receiver operating characteristic curve(AUC) values were 0.748(95%CI=0.687-0.817) and 0.758(95%CI=0.631-0.868) in the training and validation cohorts respectively. The DCA showed that the nomogram has a good clinical usefulness and net benefit.Conclusion:No surgery is the independent risk factor of distant metastasis of spinal and pelvic osteosarcoma. The nomogram we constructed to predict the probability of distant metastasis of patients with spinal and pelvic osteosarcoma is reliable and effective by internal and external verification.

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