P1125IS THE SURPRISE QUESTION USEFUL AS A PREDICTOR OF MORTALITY IN HEMODIALYSIS PATIENTS?

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Jorge Estifan Kasabji ◽  
Cristina Lucas ◽  
Arancha Sastre ◽  
Benjamin De León ◽  
Carmen Barnes ◽  
...  
Keyword(s):  
Predictive Value ◽  
Normal Activity ◽  
Early Mortality ◽  
Medical Attention ◽  
Karnofsky Performance Scale ◽  
Term Survival ◽  
Hemodialysis Unit ◽  
High Negative Predictive Value ◽  
Surprise Question ◽  
Karnofsky Index

Abstract Background and Aims among the predictors of hemodialysis mortality, the “surprise” question (SQ) (Would you be surprised if this patient died within the next 6 or 12 months?) is a subjective variable, based on the patient's medical history, experience and knowledge. Recognized as a useful tool to identify a patient with a high risk of early mortality in Hemodialysis. Objective to assess a prognostic model of early mortality, based on clinical - biochemical parameters and the prediction of the clinician attending the patient. Method SQ is performed on 4 nurses and 4 nephrologists of the hospital hemodialysis unit, the Karnofsky Performance Scale Index of the patients is collected (KPSI 0: normal activity, KPSI 1: Unable to work, frequent medical attention, KPSI 2 : Unable to self-care, requires special care), and prospectively analyzed mortality at 6 and 12 months. Results The prevalent population studied in Hemodialysis is 180 patients, average age 69 years ± 14.1 (R 27-94), According to sex (Male 69%-Female 31%), the follow-up of the study was 1 year, we had 11 deaths 6 months and 17 deaths at 12 months, total 28 patients (15.7%). The distribution of patients according to nurses and nephrologists staff (table 1) and patients characteristics (Table 2) Conclusion: T he surprise question is a specific and sensitive instrument to predict sort-term survival in dialysis population especially in those with older age, more comorbid illnesses, lower functional status and hypoalbuminemia. of the analyzed factors; Karnofsky Index, age, surprise question and albumin have significant predictive value for mortality at 6 and 12 months. We observed that the surprise question for nephrologists Staff is closer to prediction than nursing, and with a high negative predictive value for the “group of NO surprise”

FDG-PET in Hodgkin lymphoma

2008 ◽  
Vol 47 (06) ◽  
pp. 235-238 ◽  
Author(s):  
M. Dietlein ◽  
C. Mauz-Körholz ◽  
A. Engert ◽  
P. Borchmann ◽  
O. Sabri ◽  
...  
Keyword(s):  
Hodgkin Lymphoma ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Fdg Pet ◽  
Study Groups ◽  
Therapy Control ◽  
German Study ◽  
High Negative Predictive Value ◽  
Consolidation Radiotherapy

SummaryThe high negative predictive value of FDG-PET in therapy control of Hodgkin lymphoma is proven by the data acquired up to now. Thus, the analysis of the HD15 trial has shown that consolidation radiotherapy might be omitted in PET negative patients after effective chemotherapy. Further response adapted therapy guided by PET seems to be a promising approach in reducing the toxicity for patients undergoing chemotherapy. The criteria used for the PET interpretation have been standardized by the German study groups for Hodgkin lymphoma patients and will be reevaluated in the current studies.

Predictive value of gene expression profiling for long-term survival after heart transplantation

2017 ◽  
Vol 41 ◽  
pp. 27-31 ◽  
Author(s):  
Buntaro Fujita ◽  
Emir Prashovikj ◽  
Uwe Schulz ◽  
Jochen Börgermann ◽  
Jakub Sunavsky ◽  
...  
Keyword(s):  
Gene Expression ◽  
Heart Transplantation ◽  
Gene Expression Profiling ◽  
Predictive Value ◽  
Expression Profiling ◽  
Term Survival ◽  
Long Term Survival

Predictive Value of Serum Carbohydrate Antigen 19-9 (CA19-9) for Early Mortality in Advanced Pancreatic Cancer

2017 ◽  
Vol 49 (4) ◽  
pp. 481-486 ◽  
Author(s):  
Pedro L. S. Usón Junior ◽  
Donato Callegaro-Filho ◽  
Diogo D. G. Bugano ◽  
Fernando Moura ◽  
Fernando C. Maluf
Keyword(s):  
Pancreatic Cancer ◽  
Predictive Value ◽  
Advanced Pancreatic Cancer ◽  
Carbohydrate Antigen ◽  
Early Mortality ◽  
Serum Carbohydrate Antigen

scholarly journals Association between clinical risk scores and mortality in atrial fibrillation: Systematic review and network meta-regression of 669,000 patients

2018 ◽  
Vol 27 (6) ◽  
pp. 633-644 ◽  
Author(s):  
Marco Proietti ◽  
Alessio Farcomeni ◽  
Giulio Francesco Romiti ◽  
Arianna Di Rocco ◽  
Filippo Placentino ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Predictive Ability ◽  
Risk Scores ◽  
Clinical Risk ◽  
Increased Risk ◽  
High Negative Predictive Value ◽  
Meta Regression

Aims Many clinical scores for risk stratification in patients with atrial fibrillation have been proposed, and some have been useful in predicting all-cause mortality. We aim to analyse the relationship between clinical risk score and all-cause death occurrence in atrial fibrillation patients. Methods We performed a systematic search in PubMed and Scopus from inception to 22 July 2017. We considered the following scores: ATRIA-Stroke, ATRIA-Bleeding, CHADS2, CHA2DS2-VASc, HAS-BLED, HATCH and ORBIT. Papers reporting data about scores and all-cause death rates were considered. Results Fifty studies and 71 scores groups were included in the analysis, with 669,217 patients. Data on ATRIA-Bleeding, CHADS2, CHA2DS2-VASc and HAS-BLED were available. All the scores were significantly associated with an increased risk for all-cause death. All the scores showed modest predictive ability at five years (c-indexes (95% confidence interval) CHADS2: 0.64 (0.63–0.65), CHA2DS2-VASc: 0.62 (0.61–0.64), HAS-BLED: 0.62 (0.58–0.66)). Network meta-regression found no significant differences in predictive ability. CHA2DS2-VASc score had consistently high negative predictive value (≥94%) at one, three and five years of follow-up; conversely it showed the highest probability of being the best performing score (63% at one year, 60% at three years, 68% at five years). Conclusion In atrial fibrillation patients, contemporary clinical risk scores are associated with an increased risk of all-cause death. Use of these scores for death prediction in atrial fibrillation patients could be considered as part of holistic clinical assessment. The CHA2DS2-VASc score had consistently high negative predictive value during follow-up and the highest probability of being the best performing clinical score.

scholarly journals Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label

2020 ◽  
Author(s):  
Chuansheng Zheng ◽  
Xianbo Deng ◽  
Qiang Fu ◽  
Qiang Zhou ◽  
Jiapei Feng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Predictive Value ◽  
Learning Algorithm ◽  
Chest Ct ◽  
Lung Region ◽  
Deep Learning Algorithm ◽  
High Negative Predictive Value ◽  
Probability Threshold ◽  
Weakly Supervised ◽  
Learning Software

AbstractAccurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learning-based model for automatic COVID-19 detection on chest CT is helpful to counter the outbreak of SARS-CoV-2. A weakly-supervised deep learning-based software system was developed using 3D CT volumes to detect COVID-19. For each patient, the lung region was segmented using a pre-trained UNet; then the segmented 3D lung region was fed into a 3D deep neural network to predict the probability of COVID-19 infectious. 499 CT volumes collected from Dec. 13, 2019, to Jan. 23, 2020, were used for training and 131 CT volumes collected from Jan 24, 2020, to Feb 6, 2020, were used for testing. The deep learning algorithm obtained 0.959 ROC AUC and 0.976 PR AUC. There was an operating point with 0.907 sensitivity and 0.911 specificity in the ROC curve. When using a probability threshold of 0.5 to classify COVID-positive and COVID-negative, the algorithm obtained an accuracy of 0.901, a positive predictive value of 0.840 and a very high negative predictive value of 0.982. The algorithm took only 1.93 seconds to process a single patient’s CT volume using a dedicated GPU. Our weakly-supervised deep learning model can accurately predict the COVID-19 infectious probability in chest CT volumes without the need for annotating the lesions for training. The easily-trained and highperformance deep learning algorithm provides a fast way to identify COVID-19 patients, which is beneficial to control the outbreak of SARS-CoV-2. The developed deep learning software is available at https://github.com/sydney0zq/covid-19-detection.

scholarly journals Diagnosis and Treatment of Pediatric Brain Tumors

2021 ◽  
Vol 49 (1, 2, 3) ◽  
pp. 37
Author(s):  
Mirsad Hodžić ◽  
Zlatko Ercegović ◽  
Dželil Korkut ◽  
Mirza Moranjkić ◽  
Harun Brkić ◽  
...  
Keyword(s):  
Brain Tumors ◽  
Neurological Status ◽  
Pediatric Brain Tumors ◽  
Clinicopathological Characteristics ◽  
Cancer Prognosis ◽  
Diagnosis And Treatment ◽  
Medical Attention ◽  
Mri Findings ◽  
Term Survival ◽  
Average Patient

<p><strong>Objective</strong>. Tumors of the brain and spine make up about 20% of all childhood cancers; they are the second most common form of childhood cancer after leukemia. Brain tumors are the most common solid tumor in children. Symptoms depend on a variety of factors, including location of the tumor, age of child, and rate of tumor growth. The aim of study was to present our experience with the diagnosis and treatment of brain tumors in children.</p><p><strong>Patients and Methods</strong>. The aim of this study is to analyze clinicopathological characteristics, treatments, complications, and outcomes in children with brain tumors. This study is a retrospective analysis of 27 consecutive patients younger than 16 years and hospitalized for surgical treatment of brain tumors. Intracranial hypertension, neurological status, radiological computerized tomography (CT) or magnetic resonance imaging (MRI) findings, tumor localization, type of resection, hydrocephalus treatment, histopathology, complications, and outcome were analyzed.</p><p><strong>Results</strong>. Twenty-seven surgeries were performed in patients for brain tumors. There were 9 females and 18 males. The average patient age was 7.8 years. There were 11 (40%) children with astrocytoma; of these, there were 9 (82%) pilocytic astrocytomas and 2 (18%) ordinary histopathological subtypes of high-grade tumors.</p><p><strong>Conclusion</strong>. As with any cancer, prognosis and long-term survival vary greatly from child to child. Prompt medical attention and aggressive therapy are important for the best prognosis. Continuous follow-up care is essential for a child diagnosed with a brain tumor.</p>

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