Nomogram Predicting Overall Survival After Surgical Resection For Retroperitoneal Leiomyosarcoma Patients

Aim: To develop a survival nomogram for patients with retroperitoneal leiomyosarcoma (RLMS) after surgery.Methods: 118 patients with RLMS after surgical resection at the General Surgery Department, Shanghai Public Health Clinical Center, Fudan University were retrospectively analyzed. The nomogram was constructed based on COX regression model and discrimination was assessed using the concordance index (c-index). The predicted and actual survival was evaluated through calibration plots.Results: The c-index of the nomogram was 0.779 (95% CI, 0.659-0.898). The predicted and actual survival probabilities are in good agreement in all calibration curve.Conclusion: This study built the first survival nomogram for patients with surgical resected RLMS.

How well do neurosurgeons predict survival in patients with high-grade glioma?

Due to the lack of reliable prognostic tools, prognostication and surgical decisions largely rely on the neurosurgeons’ clinical prediction skills. The aim of this study was to assess the accuracy of neurosurgeons’ prediction of survival in patients with high-grade glioma and explore factors possibly associated with accurate predictions. In a prospective single-center study, 199 patients who underwent surgery for high-grade glioma were included. After surgery, the operating surgeon predicted the patient’s survival using an ordinal prediction scale. A survival curve was used to visualize actual survival in groups based on this scale, and the accuracy of clinical prediction was assessed by comparing predicted and actual survival. To investigate factors possibly associated with accurate estimation, a binary logistic regression analysis was performed. The surgeons were able to differentiate between patients with different lengths of survival, and median survival fell within the predicted range in all groups with predicted survival < 24 months. In the group with predicted survival > 24 months, median survival was shorter than predicted. The overall accuracy of surgeons’ survival estimates was 41%, and over- and underestimations were done in 34% and 26%, respectively. Consultants were 3.4 times more likely to accurately predict survival compared to residents (p = 0.006). Our findings demonstrate that although especially experienced neurosurgeons have rather good predictive abilities when estimating survival in patients with high-grade glioma on the group level, they often miss on the individual level. Future prognostic tools should aim to beat the presented clinical prediction skills.

Nomograms for predicting the overall survival of patients with cerebellar glioma: An analysis of the SEER (Surveillance Epidemiology and End Results) database

Purpose: At present, our understanding of cerebellar glioma is still not enough. This study collected information on patients in the seer database to determine the predictive factors patients with cerebellar glioma. Patients and Methods: The data of patients with cerebellar glioma diagnosed from 1975 to 2018 were retrieved from the Surveillance Epidemiology and End Results Database. We randomly divide the data into a training group and a validation group, establish a nomogram based on the training group, and use the validation group data to verify the clinical value of the model. Results: A total of 508 patients were included in this study. Multivariate analysis was performed based on the data before randomization, and the results showed that the patient's age, the tumor's WHO grade, histological type, and extension are significantly correlated with the survival rate. The c index of OS nomograms of the training cohort was 0.909 (95% CI,(0.880-0.938）) and 0.932(95% CI, (0.889-0.975)) in validation group. The calibration curve of OS for 3 and 5 years show that there was a good consistency between the actual survival probability and the predicted survival probability.Conclusion: For patients with cerebellar glioma, the age of the patient at the time of diagnosis, WHO grade of the glioma, histological type, and extension are the four most prominent factors that affect the overall survival outcomes. Furthermore, our model may be a useful tool for predicting OS in these patients.

Construction and validation of prognostic nomogram for metaplastic breast cancer

In this study we aimed to develop nomogram models for predicting the overall survival (OS) and cancer-specific survival (CSS) of patients with metaplastic breast cancer (MBC). Data of patients diagnosed with MBC from 1973 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox analyses were performed to identify independent prognostic factors for OS and CSS of MBC patients. The obtained prognostic variables were combined to construct nomogram models for predicting OS and CSS in patients with MBC. Model performance was evaluated using concordance index (C-index) and calibration plots. Data from 1125 patients were collected and divided into a training (750) and a validation (375) cohort. The multivariate Cox model identified age, TNM stage, tumor size, and radiotherapy as independent covariates associated with OS and CSS. The nomogram constructed based on these covariates demonstrated excellent accuracy in estimating 3-, and 5-year OS and CSS, with a C-index of 0.769 (95% CI, 0.731-0.808) for OS and 0.761 (95% CI, 0.713-0.809) for CSS in the training cohort. In the validation cohort, the nomogram-predicted C-index was 0.738 (95%CI, 0.676-0.800) for OS and 0.747 (95%CI, 0.667-0.827) for CSS. All calibration curves exhibited good consistency between predicted and actual survival. The nomogram models established in this study may enhance the accuracy of prognosis prediction and therefore may improve individualized assessment of survival risks and enable constructive therapeutic suggestions.

PREDICTORS OF OVERALL SURVIVAL IN MEN WITH CHRONIC LYMPHOCYTIC LEUKEMIA

The generally accepted staging for chronic lymphocytic leukemia (CLL) suggested by K. Rai and J. Binet allows calculating the median survival depending on the size of the tumor mass. However, in real clinical practice, the overall survival rate may differ significantly from the calculated median. Thus, the search for parameters affecting the overall survival rate of CLL patients is really relevant. The aim of the study was to assess general clinical parameters as predictors of survival in CLL patients. Materials and Methods. The authors examined 60 CLL men (stages A–C according to Binet) with known overall survival rate. Data mining was used to identify significant factors affecting the overall survival in such patients. Patients were divided into two non-overlapping classes: K1 (actual survival was less than the predicted median survival) and K2 (actual survival was more or equal to the predicted median survival). Results. The most significant differences between the classes were obtained for glomerular filtration rate. If the parameter value is more than 76.5 ml/min/1.73 m2, we can say that the patient will overcome the median survival for the corresponding CLL stage according to Binet. Otherwise, the overall survival of a CLL patient will be less than the estimated one. Conclusion. If during diagnosing glomerular filtration rate of a CLL patient is more than 76.5 ml/min/1.73 m2, it can be considered as a predictor of overcoming the median survival according to Binet. The results of the studies obtained are patented. Patent RU 2725877 C1, July 7, 2020. Keywords: overall survival in CLL patients, men, glomerular filtration rate, data mining. Общепризнанные системы стадирования хронического лимфолейкоза (ХЛЛ) К. Rai и J. Binet позволяют рассчитать медиану выживаемости пациента в зависимости от величины опухолевой массы. Однако в реальной клинической практике параметр общей выживаемости пациента может значимо отличаться от расчетной медианы. Ввиду этого поиск параметров, влияющих на показатель общей выживаемости пациентов с ХЛЛ, представляет особую актуальность. Цель исследования – оценить возможность использования общеклинических параметров в качестве предикторов выживаемости больных ХЛЛ. Материалы и методы. Ретроспективно проанализированы данные 60 мужчин с ХЛЛ стадии A–C по Binet c известной общей выживаемостью. Для выявления значимых факторов, влияющих на общую выживаемость пациентов, использовали метод интеллектуального анализа данных. Пациенты были разделены на два непересекающихся класса: K1 (фактическая выживаемость меньше прогнозируемой медианы выживаемости) и K2 (фактическая выживаемость больше или равна прогнозируемой медиане выживаемости). Результаты. Наиболее значимые различия между классами были получены по показателю скорости клубочковой фильтрации. При значении параметра более 76,5 мл/мин/1,73 м2 можно говорить о том, что пациент преодолеет расчетные данные медианы выживаемости для соответствующей стадии ХЛЛ по Binet. В противном случае общая выживаемость пациента ХЛЛ будет меньше расчетной. Выводы. Наличие у пациента с ХЛЛ на момент постановки диагноза скорости клубочковой фильтрации более 76,5 мл/мин/1,73 м2 можно рассматривать в качестве предиктора преодоления расчетного параметра медианы выживаемости по Binet. Результаты полученных исследований запатентованы. Патент RU 2725877 C1 от 7.07.2020. Ключевые слова: общая выживаемость при ХЛЛ, мужчины, скорость клубочковой фильтрации, интеллектуальный анализ данных.

Relationship between corticosteroid effect on dyspnea, prognosis prediction, and actual survival

Background and Aim: The effect of corticosteroids on dyspnea in patients with advanced cancer is expected to be associated with patient prognosis, though details about this are unclear. Thus, we aimed to investigate the relationship between prognostic indicators, actual survival, and the effect of corticosteroid administration on dyspnea in patients with advanced cancer.Method: We predicted the prognosis using the PaP score (palliative prognostic score) at the time of corticosteroid administration in 56 consecutive patients with cancer who started corticosteroid treatment for dyspnea at a single palliative care unit, and compared it with the actual survival. In addition, we investigated the relationship between the effect of corticosteroids on dyspnea, the prognostic score before administration, the actual survival, and other background factors.Results: The effect of corticosteroids strongly correlated with the actual survival, but not the prognostic score before corticosteroid administration, although it was correlated with prognostic score and actual survival. In addition, the effect of corticosteroids on dyspnea was less associated with other factors.Conclusion: The corticosteroid effect strongly associated with actual survival, but not the prognostic score. It was suggested that the effect of corticosteroids in patients could be predicted by using a more accurate prognosis prediction in the future.

Good Leadership Practices in Contexts of Unpredictability

COVID-19 was declared the pandemic on 11 March 2020, and the world is still in the throes of an unprecedented and highly unpredictable public health crisis, with consequences at an individual, group, organisational, and societal level. Under such dire circumstances, leadership is of decisive importance, as the repercussions of the decisions taken may, now more than ever, be crucial. Hence, leadership is currently essential not only for the success, but for the actual survival of organisations. In a scenario of ongoing change with unforeseeable outcomes, the absence of good leadership could mean the demise of an organisation. Grounded on the theory of responsible leadership and the theory of shared leadership, the authors present the good leadership practices that are considered essential during times of major unpredictability such those currently underway.

Validation of Online Prognostication Model that Predicts Survival for Women with Early Breast Cancer in Egypt

Introduction: Breast Cancer is estimated to be the most common female cancer in Egypt. The decision on adjuvant therapy in early breast cancer is complex and requires many considerations. PREDICT is an online prognostication tool for early breast cancer. It was first revealed in 2011 and it has been widely used. This study aimed to illustrate the applicability of this model on Egyptian patients. Methods: This study included 128 female patients treated for early breast cancer with surgery and adjuvant therapy in Alexandria, Egypt in 2005 to validate the efficacy of PREDICT as an online prognostication model to estimate survival in those patients. Patients’ data were obtained from the archives of the Department of Clinical Oncology, Faculty of Medicine, and the Department of Cancer Management and Research, Medical Research Institute, University of Alexandria, Egypt. Patients’ data were manually entered into the prognostication model and the predicted survival results obtained were compared to the actual survival of study patients. Results: The predicted number of survivors after 5 years in the entire cohort was 125 (97.7%) compared to 123 (96.1%) actual survivors. The difference was 1.6% which was not significant (p= 0.625). The predicted number of survivors after 10 years in the entire cohort was 77 (60.2%) compared to 81 (63.3%) actual survivors. The difference was -3.1% which was not significant (p= 0.671). Conclusion: PREDICT accurately predicts 5-year and 10-year OS in the entire study population. This validates the use of this online model to aid in the decision as regard systemic therapy.