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【Objective】 To establish and verify a nomogram model of overall survival (OS) of prostate cancer patients based on the SEER data. 【Methods】 A total of 12 642 patients diagnosed with prostate cancer during 2010 and 2015 were extracted from the SEER database. Patients were randomly divided into the model group (n=8 850) and validation group (n=3 792). The independent risk factors for OS were analyzed with univariate Cox proportional risk regression, lasso regression and multivariate Cox proportional risk regression. A nomogram was constructed to predict the 1-year, 3-year and 5-year OS. The prediction potential of the model was evaluated with the consistency index (C-index), calibration curve and receiver operating characteristic (ROC) curve. 【Results】 Multivariate Cox regression analysis showed that age, T stage, N stage, M stage, bone metastasis, liver metastasis and regional lymphadenectomy were independent risk factors for OS (P<0.05). The seven factors were used to construct an OS nomogram model. The C-index of the modeling set was 0.750, and the area under the ROC curve (AUC) at 1, 3 and 5 years were 0.77, 0.77 and 0.76, respectively;the C-index of the validation set was 0.765, and the AUC at 1, 3 and 5 years were 0.83, 0.79 and 0.76, respectively. The calibration curves of the modelling set and validation set showed a good agreement with the actual survival prediction rate. Risk stratification of patients based on the nomogram model showed that the OS of patients in the high-risk group was significantly lower than that in the low-risk group (P<0.001). 【Conclusion】 The nomogram can be used to predict the prognosis of prostate cancer patients, and is important for individualized treatment plans.
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@#Objective To investigate the influencing factors for the clinical remission of advanced esophageal squamous cell carcinoma (ESCC) after neoadjuvant chemotherapy, establish an individualized nomogram model to predict the clinical remission of advanced ESCC with neoadjuvant chemotherapy and evaluate its efficacy, providing serve for the preoperative adjuvant treatment of ESCC. Methods The clinical data of patients with esophageal cancer who underwent neoadjuvant chemotherapy (nedaplatin 80 mg/m2, day 3+docetaxel 75 mg/m2, day 1, 2 cycles, 21 days per cycle interval) in the Department of Thoracic Surgery, Affiliated Hospital of North Sichuan Medical College from February 2016 to August 2020 were analyzed retrospectively. According to the WHO criteria for efficacy assessment of solid tumors, tumors were divided into complete remission (CR), partial remission (PR), stable disease (SD) and progressive disease (PD). CR and PR were defined as effective neoadjuvant chemotherapy, and SD and PD were defined as ineffective neoadjuvant chemotherapy. Univariate and multivariate analyses were used to analyze the influencing factors for the short-term efficacy of neoadjuvant chemotherapy. The R software was used to establish a nomogram model for predicting of the model. C-index, calibration curve and receiver operating characteristic (ROC) curve were used to evaluate the predictive performance of the nomogram. Results Finally 115 patients were enrolled, including 93 males and 22 females, aged 40-75 (64.0±8.0) years. After receiving docetaxel+nedaplatin neoadjuvant chemotherapy for 2 cycles, there were 9 patients with CR, 56 patients with PR, 43 patients with SD and 7 patients with PD. Among them, chemotherapy was effective (CR+PR) in 65 patients and ineffective (SD+PD) in 50 patients, with the clinical effective rate of about 56.5%(65/115). Univariate analysis showed that there were statistical differences in smoking history, alcoholism history, tumor location, tumor differentiation degree, and cN stage before chemotherapy between the effective neoadjuvant chemotherapy group and the ineffective neoadjuvant chemotherapy group (P<0.05). Logistic regression analysis showed that low-differentiation advanced ESCC had the worst clinical response to neoadjuvant chemotherapy, moderately-highly differentiated ESCC responded better (P<0.05). Stage cN0 advanced ESCC responded better to neoadjuvant chemotherapy than stage cN1 and cN2 (P<0.05). The C-index and the area under the ROC curve of the nomogram were both 0.763 (95%CI 0.676-0.850), the calibration curve fit well, the best critical value of the nomogram calculated by the Youden index was 70.04 points, and the sensitivity and specificity of the critical value were 80.0% and 58.0%, respectively. Conclusion The established clinical prediction model has good discrimination and accuracy, and can provide a reference for individualized analysis of the clinical remission of advanced ESCC with neoadjuvant chemotherapy and the screening of new adjuvant treatment subjects.
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Objective:To establish a risk prediction model for the occurrence of low 1 min Apgar scores in extremely premature infants (EPIs).Methods:From January 2017 to December 2021, EPIs delivered at our hospital were retrospectively analyzed and randomly assigned into training set group and validation set group in a 7∶3 ratio. 17 clinical indicators were selected as predictive variables and low Apgar scores after birth as outcome variables. Lasso regression and multi-factor logistic regression were used within the training set group to select the final predictors for the final model, and the calibration, distinguishability and clinical decision making curves of the final model were evaluated in the validation set group.Results:A total of 169 EPIs were enrolled, including 117 in the training set group and 52 in the validation set group. 4 indicators including gender, fetal distress, assisted conception and delivery time were selected as the final predictors in the final model. Both the training set group and the validation set group had good calibration curves. The area under the receiver operating characteristic curve (AUC) of the prediction model was 0.731, the sensitivity was 72.2%, the specificity was 60.5% and the AUC of the external validation curve was 0.704. The clinical decision making curve showed that the model had a greater benefit in predicting the occurrence of low Apgar score in EPIs within the threshold of 2% to 75%.Conclusions:The clinical prediction model established in this study has good distinguishability, calibration and clinical accessibility and can be used as a reference tool to predict low Apgar scores in EPIs.
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Objective:To explore the predictive value of peripheral blood cytokine models on organ functional impairment after chimeric antigen receptor T(CAR-T) cell therapy in children with B-lineage lymphocytic leukemia.Methods:The clinical data of 44 children with acute B-lineage lymphoblastic leukemia who received CAR-T cell therapy at Children′s Hospital of Soochow University from September 2018 to October 2020 were retrospectively analyzed.Peripheral blood cytokines, including interleukin(IL)-2, IL-4, IL-6, IL-10, tumor necrosis factor-α, interferon(IFN)-γ and IL-17A, were measured daily for 14 days after receiving CAR-T cell therapy.The trend of peripheral blood cytokine levels was analyzed at the endpoint of organ function recovery or death within 14 days after CAR-T cell treatment.Receiver operating characteristic curve was used to establish a mathematical prediction model to predict the occurrence of organ damage in the children.Results:Of the 44 children, 31 cases were boys and 13 cases were girls, with a median age of 7.96 (5.19, 11.48)years.Cytokine release syndrome(CRS) response occurred in 95.5% (42/44) children, with 88.1% (37/42) had a grade 1-3 CRS response, and 16.7% (7/42) had a severe grade 4-5 CRS response.Using IL-6>3 892.95 pg/mL as cut-off value, the area under the curve(AUC) for predicting acute respiratory failure was 0.818, with a sensitivity of 0.8 and a specificity of 0.735, while combining IFN-γ>414.4 pg/mL, IL-6>3 892.95 pg/mL and IL-2>27.05 pg/mL were the three cut-off values, with an AUC of 0.741, sensitivity of 0.6 and specificity of 0.912 for predicting acute respiratory failure. Using IFN-γ>1 699.5 pg/mL as cut-off value, the AUC for predicting shock was 0.908, with a sensitivity of 0.722 and a specificity of 1.With IL-6>4 607.3 pg/mL as cut-off value, the AUC for predicting liver injury was 0.964, with a sensitivity of 1 and a specificity of 0.906, while combining both IL-6>4 607.3 pg/mL and IFN-γ>1 446.2 pg/mL as cut-off values, the AUC for predicting liver injury was 0.977, with a sensitivity of 1 and a specificity of 0.906.Combining both IL-6>6 972.2 pg/mL and IFN-γ>3 981.5 pg/mL predicted a positive predictive value of 62.5% and a negative predictive value of 94.4% for grade 4-5 CRS response, with an AUC of 0.846, a predictive sensitivity of 0.714 and a specificity of 0.838, and all children had a combination of two or more organ function injuries.Conclusion:The combination of IL-6 and IFN-γ can effectively predict the incidence of liver injury and cytokine release syndrome.The combination of peripheral blood cytokines IFN-γ, IL-6 and IL-2 can be used to predict the incidence of acute respiratory failure after the treatment of CAR-T cells in children with acute B-lineage lymphoblastic leukaemia.IFN-γ single index can be used to predict the incidence of shock.The combination of IL-6 and IFN-γ can be used to predict the incidence of liver injury and the severity of CRS.
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Objective:To retrospectively analyze the independent risk factors of complicated appendicitis(CA)in children under five years old and establish a clinical prediction model, and to evaluate the clinical application of this model.Methods:A retrospective analysis was performed on children under five years old who underwent appendectomy at Children′s Hospital of Shanghai Jiao Tong University School of Medicine from January 2018 to December 2021.The children were divided into CA group and uncomplicated appendicitis group according to whether there was sign of perforation or gangrene in appendiceal tissue after operation.The differences in clinical features and preoperative laboratory test results between two groups were compared.The independent risk factors of CA were identified and a clinical prediction model was established.The clinical prediction model was verified by receiver operating characteristic curve.Results:A total of 140 children were enrolled in this study, including 84 cases in the CA group and 56 cases in uncomplicated appendicitis group.Univariate and binary Logistic regression analysis showed that the duration of symptoms>23.5 h( OR=6.650, 95% CI 2.469-17.912, P<0.05), abdominal muscle tension( OR=3.082, 95% CI 1.190-7.979, P<0.05) and C-reactive protein>41 mg/L ( OR=3.287, 95% CI 1.274-8.480, P<0.05) were independent risk factors for CA( P<0.05). The clinical prediction model of CA was constructed by the above mentioned three independent risk factors.The area under the receiver operating characteristic curve of the clinical prediction model was 0.881(95% CI 0.825-0.936), the sensitivity was 77.4%, the specificity was 87.5%, the positive predictive value was 91.3% and the negative predictive value was 70.0%. Conclusion:Acute appendicitis in children under five years old is more likely to progress to CA if the duration of symptoms>23.5 h, the level of C-reactive protein is increased, and the abdominal muscle tension is accompanied.The clinical prediction model of CA constructed by common clinical information in pediatric clinics has good prediction efficiency, which provides a simple and feasible reference method for clinicians to distinguish CA from uncomplicated appendicitis.
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Postoperative complications of colorectal cancer (CRC) are the main cause of postoperative death and seriously affect the quality of life and survival time of patients. The application of a clinical prediction model for postoperative complications of CRC can help promptly identify high-risk patients. Accordingly, reasonable intervention measures can be actively taken to reduce the incidence of postoperative complications of CRC. A scientific basis can also be provided to improve the prognosis of patients. In this work, literature on the risk-factor analysis and prediction-model construction of postoperative complications of CRC at home and abroad in recent years was collected and reviewed. The evaluation content and efficiency of the clinical prediction models in postoperative complications of CRC were summarized. Their advantages and disadvantages were also analyzed. The purpose of this study was to provide a reference for the subsequent optimization of such models and the development of a strong, clinically practical, and universal risk-screening tool for postoperative complications of CRC.
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ObjectiveTo establish and validate a clinical prediction model for 1-year major adverse cardiovascular events（MACEs）risk after percutaneous coronary intervention （PCI） in coronary heart disease （CHD） patients with blood stasis syndrome. MethodThe consecutive CHD patients diagnosed with blood stasis syndrome in the Department of Integrative Cardiology at China-Japan Friendship Hospital from September 1， 2019 to March 31， 2021 were selected for a retrospective study， and basic clinical features and relevant indicators were collected. Eligible patients were classified into a derivation set and a validation set at a ratio of 7∶3， and each set was further divided into a MACEs group and a non-MACEs group. The factors affecting the outcomes were screened out by least absolute shrinkage and selection operator （Lasso） and used to establish a logistic regression model and identify independent prediction variables. The goodness-of-fit of the model was evaluated by the Hosmer-Lemeshow test， and the area under curve （AUC） of the receiver operating characteristic （ROC） curve， calibration curve， decision curve analysis （DCA）， and clinical impact curve （CIC） were employed to evaluate the discrimination， calibration， and clinical impact of the model. ResultA total of 731 consecutive patients were assessed and 404 eligible patients were enrolled， including 283 patients in the derivation set and 121 patients in the validation set. Lasso identified ten variables influencing outcomes， which included age， sex， fasting plasma glucose （FPG）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C）， homocysteine （Hcy）， brachial-ankle pulse wave velocity （baPWV）， flow-mediated dilatation （FMD）， left ventricular ejection fraction （LVEF）， and Gensini score. The multivariate Logistic regression preliminarily identified age， FPG， TG， Hcy， LDL-C， LVEF， and Gensini score as the independent variables that influenced the outcomes. Of these variables， male， high FMD and high LVEF were protective factors， and the rest were risk factors. The prediction model for 1-year MACEs risk after PCI in CHD patients with blood stasis syndrome showed χ2=12.371 （P=0.14） in Hosmer-Lemeshow test and the AUC of 0.90. With the threshold probability > 10%， the model showed better prediction performance for 1-year MACEs risk after PCI in CHD patients with blood stasis syndrome than for that in all the patients. With the threshold probability > 60%， the estimated value was much closer to the real number of patients. ConclusionThe established clinical prediction model facilitates the early prediction of 1-year MACEs risk after PCI in CHD patients with blood stasis syndrome， which can provide ideas for the precise treatment of CHD patients after PCI and has guiding significance for improving the prognosis of the patients. Meanwhile， multi-center studies with larger sample sizes are expected to further validate， improve， and update the model.
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ObjectiveTo establish a diagnostic prediction model for non-suicidal self-injury （NSSI） behaviors in adolescents with depressive disorder， in order to provide references for early identification of NSSI behaviors in them. MethodsRetrospective analysis was performed on the clinical data of adolescents with depressive disorder （n=366） who were admitted to the Pediatric Department of Shenzhen Kangning Hospital from January 1 to December 31， 2021. According to the Diagnostic criteria of Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5） diagnostic criteria for NSSI， the patients were divided into comorbid NSSI group （n=289） and non-NSSI group （n=77）. The selected adolescents were randomly divided into a training set （n=258） and a verification set （n=108） in a 7∶3 ratio. Logistic regression analysis was used to screen the independent risk factors for NSSI behaviors in adolescents with depressive disorder， which served as the basis for prediction model. Finally， the receiver operating characteristic （ROC） curve was established and the area under curve （AUC） was calculated to evaluate the discrimination in the training set and validation set. Calibration curve was applied to evaluate the calibration degree of the model. The Homser-Lemeshow （HL） test was conducted to evaluate the goodness of fit of the model. And decision curve analysis （DCA） was performed to evaluate the clinical benefit of the model. ResultsGender （β=1.734， OR=5.561， 95% CI： 2.678~11.964）， education level （β=0.864， OR=2.737， 95% CI： 1.174~4.795）， history of suicide attempts （β=0.932， OR=2.539， 95% CI： 1.253~5.144）， being an only child （β=0.745， OR=2.106， 95% CI： 1.029~4.311） and depression severity （β=0.056， OR=1.058， 95% CI： 1.025~1.092） were independent risk factors related to NSSI behaviors in adolescents with depressive disorder （P<0.05 or 0.01）. The AUC was 0.808 （95% CI： 0.746~0.870） in the training set， and was 0.722 （95% CI： 0.581~0.864） in the validation set. The prediction model showed good calibration with the HL test （P=0.561）. ConclusionGender， education level， suicide attempt history， being an only child and depression severity are independent risk factors for NSSI behaviors in adolescents with depressive disorder， furthermore， the diagnostic clinical prediction model constructed using above factors for NSSI behaviors in adolescents with depressive disorder has displayed good sensitivity and specificity.
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OBJECTIVE@#To develop the clinical prediction model of therapeutic effect in treatment with acupuncture and moxibustion for the patients with stroke at recovery stage under different conditions so as to provide a tool for predicting the therapeutic effect of acupuncture and moxibustion.@*METHODS@#A total of 1410 patients with stroke at recovery stage were collected from the Third Affiliated Hospital of Zhejiang Chinese Medical University from 2012 to 2019. The relevant data were extracted, i.e. sex, age, time of onset, neurological functional deficit score (NFDS) and acupuncture and moxibustion therapy. The difference of NFDS before and after treatment was adopted to evaluate the therapeutic effect in the patients. Using SPSS26.0 software and CART decision tree analysis, the clinical prediction model was developed.@*RESULTS@#The key variables in the prediction model of therapeutic effect in the patients with stroke at recovery stage under different conditions included age, time of onset, hypertension, cardiac disease, diabetes, TCM diagnosis, hemoglobin (HB), serum homocysteine (HCY) and acupuncture and moxibustion therapy. There were 12 main rules generated by the decision tree model, including 8 rules for predicting the improvements of therapeutic effect and 4 rules for predicting the absence of improvements (i.e. no change and deterioration). The accuracy rates of the model training set and test set were 80.0% and 72.8% respectively, the area under curve (AUC) of ROC was 0.797 and the model identification and classification results were satisfactory.@*CONCLUSION@#The clinical prediction model developed by CART decision tree analysis is high in accuracy for the prediction of the therapeutic effect in the patients with stroke at recovery stage under different conditions. Based on the therapeutic effect predicted in the hospital visit, the physicians may adopt the corresponding regimens of acupuncture and moxibustion therapy in patients.
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Humans , Acupuncture Therapy , Models, Statistical , Moxibustion , Prognosis , Stroke/therapyABSTRACT
A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymptomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education. Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statistical analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model development and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for developing and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection; handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods for developing clinical prediction models with comparable examples from real practice. After model development and vigorous validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading to active applications in real clinical practice.