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Nomogram for predicting severe morbidity after pheochromocytoma surgery.

ABSTRACT: PURPOSE:Although resection is the primary treatment strategy for pheochromocytoma, surgery is associated with a high risk of morbidity. At present, there is no nomogram for prediction of severe morbidity after pheochromocytoma surgery, thus the aim of the present study was to develop and validate a nomogram for prediction of severe morbidity after pheochromocytoma surgery. METHODS:The development cohort consisted of 262 patients who underwent unilateral laparoscopic or open pheochromocytoma surgery at our center between January 1, 2007 and December 31, 2016. The patients' clinicopathological characters were recorded. The least absolute shrinkage and selection operator (LASSO) binary logistic regression model was used for data dimension reduction and feature selection, then multivariable logistic regression analysis was used to develop the predictive model. An independent validation cohort consisted of 128 consecutive patients from January 1, 2017 and December 31, 2018. The performance of the predictive model was assessed in regards to discrimination, calibration, and clinical usefulness. RESULTS:Predictors of this model included sex, body mass index, coronary heart disease, arrhythmia, tumor size, intraoperative hemodynamic instability, and surgical duration. For the validation cohort, the model showed good discrimination with an AUROC of 0.818 (95% CI, 0.745, 0.891) and good calibration (Unreliability test, p=0.440). Decision curve analysis demonstrated that the model was also clinically useful. CONCLUSIONS:A nomogram was developed to facilitate the individualized prediction of severe morbidity after pheochromocytoma surgery and may help to improve the perioperative strategy and treatment outcome.

SUBMITTER: Wang H

PROVIDER: S-EPMC7159264 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Nomogram for predicting severe morbidity after pheochromocytoma surgery.

Wang Hongyan H Wu Bin B Yao Zichuan Z Zhu Xianqing X Jiang Yunzhong Y Bai Song S

Endocrine connections 20200401 4

<h4>Purpose</h4>Although resection is the primary treatment strategy for pheochromocytoma, surgery is associated with a high risk of morbidity. At present, there is no nomogram for prediction of severe morbidity after pheochromocytoma surgery, thus the aim of the present study was to develop and validate a nomogram for prediction of severe morbidity after pheochromocytoma surgery.<h4>Methods</h4>The development cohort consisted of 262 patients who underwent unilateral laparoscopic or open pheoch ...[more]

PMID: 32182582

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Project description:PurposeSurgery is the major treatment option for pheochromocytoma but carries potential risks, including hemorrhage and hemodynamic instability. Even with laparoscopic adrenalectomy, intraoperative blood transfusion happens from time to time, but few studies have investigated risk factors. For the first time we develop and validate a nomogram for prediction of red blood cell transfusion in pheochromocytoma surgery.MethodsThere were 246 patients in our center and 56 patients in Peking Union Medical College Hospital, who underwent pheochromocytoma surgery, enrolled in the study. We incorporated clinical and radiological risk factors, and presented this with a nomogram. Lasso regression model was used for feature selection. Logistic regression analysis was performed to identify the odd ratios. The performance of the nomogram was assessed with respect to its discrimination, calibration and clinical usefulness.ResultsThirty-two features were reduced to five, which were phenoxybenzamine use, phenoxybenzamine treatment duration, preinduction heart rate, tumor diameter and surgical procedure. The model showed good discrimination (C-index, 0.857; 95% CI, 0.781-0.836) and application in the validation sets also gave good discrimination (internal validation: C-index, 0.831; 95% CI, 0.750-0.822; external validation: C-index, 0.924; 95% CI, 0.766-1.000). Calibration tested with the Hosmer-Lemeshow test yielded a good agreement between prediction and observation (training P=0.358; internal validation P=0.205; external validation P=0.395). Odd ratios of phenoxybenzamine use, phenoxybenzamine treatment duration, preinduction HR, tumor diameter and open surgery were 13.32 (95% CI, 1.48-197.38; P = 0.034), 1.04 (95% CI, 0.99-1.08; P = 0.092), 1.04 (95% CI, 1.01-1.08; P=0.006), 1.03 (95% CI, 1.02-1.06; P<0.001), 17.13 (95% CI, 5.18-78.79; P<0.001), respectively. Decision curve analysis demonstrated the clinical usefulness of the nomogram.ConclusionsThis study presents a nomogram that may be used to facilitate the prediction of red blood cell transfusion in pheochromocytoma surgery and help to do the preoperative management more efficiently.

Project description:ObjectiveTo develop a practicable nomogram aimed at predicting the risk of severe exacerbations in COPD patients at three and five years.MethodsCOPD patients with prospective follow-up data were extracted from Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) obtained from National Heart, Lung and Blood Institute (NHLBI) Biologic Specimen and Data Repository Information Coordinating Center. We comprehensively considered the demographic characteristics, clinical data and inflammation marker of disease severity. Cox proportional hazard regression was performed to identify the best combination of predictors on the basis of the smallest Akaike Information Criterion. A nomogram was developed and evaluated on discrimination, calibration, and clinical efficacy by the concordance index (C-index), calibration plot and decision curve analysis, respectively. Internal validation of the nomogram was assessed by the calibration plot with 1000 bootstrapped resamples.ResultsAmong 1711 COPD patients, 523 (30.6%) suffered from at least one severe exacerbation during follow-up. After stepwise regression analysis, six variables were determined including BMI, severe exacerbations in the prior year, comorbidity index, post-bronchodilator FEV1% predicted, and white blood cells. Nomogram to estimate patients' likelihood of severe exacerbations at three and five years was established. The C-index of the nomogram was 0.74 (95%CI: 0.71-0.76), outperforming ADO, BODE and DOSE risk score. Besides, the calibration plot of three and five years showed great agreement between nomogram predicted possibility and actual risk. Decision curve analysis indicated that implementation of the nomogram in clinical practice would be beneficial and better than aforementioned risk scores.ConclusionOur new nomogram was a useful tool to assess the probability of severe exacerbations at three and five years for COPD patients and could facilitate clinicians in stratifying patients and providing optimal therapies.

Project description:ObjectiveTo develop and validate an expanded obstetric comorbidity score for predicting severe maternal morbidity that can be applied consistently across contemporary U.S. patient discharge data sets.MethodsDischarge data from birth hospitalizations in California during 2016-2017 were used to develop the score. The outcomes were severe maternal morbidity, defined using the Centers for Disease Control and Prevention index, and nontransfusion severe maternal morbidity (excluding cases where transfusion was the only indicator of severe maternal morbidity). We selected 27 potential patient-level risk factors for severe maternal morbidity, identified using International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis codes. We used a targeted causal inference approach integrated with machine learning to rank the risk factors based on adjusted risk ratios (aRRs). We used these results to assign scores to each comorbidity, which sum to a single numeric score. We validated the score in California and national data sets and compared the performance to that of a previously developed obstetric comorbidity index.ResultsAmong 919,546 births, the rates of severe maternal morbidity and nontransfusion severe maternal morbidity were 168 and 74 per 10,000 births, respectively. The highest risk comorbidity was placenta accreta spectrum (aRR of 30.5 for severe maternal morbidity and 54.7 for nontransfusion severe maternal morbidity) and the lowest was gestational diabetes mellitus (aRR of 1.06 for severe maternal morbidity and 1.12 for nontransfusion severe maternal morbidity). Normalized scores based on the aRR were developed for each comorbidity, which ranged from 1 to 59 points for severe maternal morbidity and from 1 to 36 points for nontransfusion severe maternal morbidity. The overall performance of the expanded comorbidity scores was good (C-statistics were 0.78 for severe maternal morbidity and 0.84 for nontransfusion severe maternal morbidity in California data and 0.82 and 0.87, respectively, in national data) and improved on prior comorbidity indices developed for obstetric populations. Calibration plots showed good concordance between predicted and actual risks of the outcomes.ConclusionWe developed and validated an expanded obstetric comorbidity score to improve comparisons of severe maternal morbidity rates across patient populations with different comorbidity case mixes.

Project description:ObjectiveThe aim of this study was to establish a nomogram model for predicting the risk of short-term recurrence in glioma patients.MethodsThe clinical data of recurrent glioma patients were summarized and analyzed in this study. Univariate and multivariate logistic regression analyses were performed to analyze the correlation between clinical data and the risk of short-term recurrence after operation. A nomogram was established based on the multivariate logistic regression model results.ResultsA total of 175 patients with recurrent glioma were enrolled, with 53 patients in the short-term recurrence (STR) group (recurrent time ≤6 months) and 122 patients in the long-term recurrence (LTR) group (recurrent time ≥36 months). Univariate analysis revealed that age at diagnosis, Karnofsky performance scores (KPSs), tumor location, glioma grade, glioma type, extent of resection (EOR), adjuvant chemotherapy (ad-CT), concurrent chemotherapy (co-CT), and isocitrate dehydrogenase (IDH) status were significantly associated with the short-term glioma recurrence. Multivariate analyses revealed that age at diagnosis, KPS, glioma grade, EOR, and IDH were independent risk factors for short-term glioma recurrence. A risk nomogram for the short-term recurrence of glioma was established, with the concordance index (C-index) of 0.971. The findings of calibration and receiver operating characteristic (ROC) curves showed that our nomogram model had good performance and discrimination to estimate short-term recurrence probability.ConclusionThis nomogram model provides reliable information about the risk of short-term glioma recurrence for oncologists and neurosurgeons. This model can predict the short-term recurrence probability and give assistance to decide the interval of follow-up or formulate individualized treatment strategies based on the predicted results. A free online prediction risk tool for this nomogram is provided: https://rj2021.shinyapps.io/Nomogram_ recurrence-risk/.

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Nomogram for predicting severe morbidity after pheochromocytoma surgery.

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Nomogram for predicting severe morbidity after pheochromocytoma surgery.

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