scholarly journals An easy-to-operate web-based calculator for predicting the progression of chronic kidney disease

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Qian Xu ◽  
Yunyun Wang ◽  
Yiqun Fang ◽  
Shanshan Feng ◽  
Cuiyun Chen ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Clinical Decision Making ◽  
Area Under The Curve ◽  
Progression Free Survival ◽  
Clinical Decision ◽  
Training Set ◽  
Web Based ◽  
Free Survival ◽  
Validation Set

Abstract Background This study aimed to establish and validate an easy-to-operate novel scoring system based on simple and readily available clinical indices for predicting the progression of chronic kidney disease (CKD). Methods We retrospectively evaluated 1045 eligible CKD patients from a publicly available database. Factors included in the model were determined by univariate and multiple Cox proportional hazard analyses based on the training set. Results Independent prognostic factors including etiology, hemoglobin level, creatinine level, proteinuria, and urinary protein/creatinine ratio were determined and contained in the model. The model showed good calibration and discrimination. The area under the curve (AUC) values generated to predict 1-, 2-, and 3-year progression-free survival in the training set were 0.947, 0.931, and 0.939, respectively. In the validation set, the model still revealed excellent calibration and discrimination, and the AUC values generated to predict 1-, 2-, and 3-year progression-free survival were 0.948, 0.933, and 0.915, respectively. In addition, decision curve analysis demonstrated that the model was clinically beneficial. Moreover, to visualize the prediction results, we established a web-based calculator (https://ncutool.shinyapps.io/CKDprogression/). Conclusion An easy-to-operate model based on five relevant factors was developed and validated as a conventional tool to assist doctors with clinical decision-making and personalized treatment.

scholarly journals A CT-based radiomics nomogram for predicting prognosis of coronavirus disease 2019 (COVID-19) radiomics nomogram predicting COVID-19

2021 ◽  
Vol 94 (1117) ◽  
pp. 20200634
Author(s):  
Hang Chen ◽  
Ming Zeng ◽  
Xinglan Wang ◽  
Liping Su ◽  
Yuwei Xia ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Predictive Performance ◽  
Clinical Decision ◽  
Ct Images ◽  
Prediction Performance ◽  
Clinical Factors ◽  
Training Set ◽  
Clinical Model ◽  
Potential Biomarker ◽  
Validation Set

Objectives: To identify the value of radiomics method derived from CT images to predict prognosis in patients with COVID-19. Methods: A total of 40 patients with COVID-19 were enrolled in the study. Baseline clinical data, CT images, and laboratory testing results were collected from all patients. We defined that ROIs in the absorption group decreased in the density and scope in GGO, and ROIs in the progress group progressed to consolidation. A total of 180 ROIs from absorption group (n = 118) and consolidation group (n = 62) were randomly divided into a training set (n = 145) and a validation set (n = 35) (8:2). Radiomics features were extracted from CT images, and the radiomics-based models were built with three classifiers. A radiomics score (Rad-score) was calculated by a linear combination of selected features. The Rad-score and clinical factors were incorporated into the radiomics nomogram construction. The prediction performance of the clinical factors model and the radiomics nomogram for prognosis was estimated. Results: A total of 15 radiomics features with respective coefficients were calculated. The AUC values of radiomics models (kNN, SVM, and LR) were 0.88, 0.88, and 0.84, respectively, showing a good performance. The C-index of the clinical factors model was 0.82 [95% CI (0.75–0.88)] in the training set and 0.77 [95% CI (0.59–0.90)] in the validation set. The radiomics nomogram showed optimal prediction performance. In the training set, the C-index was 0.91 [95% CI (0.85–0.95)], and in the validation set, the C-index was 0.85 [95% CI (0.69–0.95)]. For the training set, the C-index of the radiomics nomogram was significantly higher than the clinical factors model (p = 0.0021). Decision curve analysis showed that radiomics nomogram outperformed the clinical model in terms of clinical usefulness. Conclusions: The radiomics nomogram based on CT images showed favorable prediction performance in the prognosis of COVID-19. The radiomics nomogram could be used as a potential biomarker for more accurate categorization of patients into different stages for clinical decision-making process. Advances in knowledge: Radiomics features based on chest CT images help clinicians to categorize the patients of COVID-19 into different stages. Radiomics nomogram based on CT images has favorable predictive performance in the prognosis of COVID-19. Radiomics act as a potential modality to supplement conventional medical examinations.

Local Ablative Therapies in Oligometastatic NSCLC: New Data and New Directions

2020 ◽  
Vol 41 (03) ◽  
pp. 369-376
Author(s):  
Pencilla Lang ◽  
Daniel R. Gomez ◽  
David A. Palma
Keyword(s):  
Clinical Decision Making ◽  
Treatment Phase ◽  
Progression Free Survival ◽  
Clinical Decision ◽  
Stereotactic Ablative Radiotherapy ◽  
Free Survival ◽  
Fractionated Radiotherapy ◽  
Disease States ◽  
Ablative Therapies ◽  
Clinical Scenarios

AbstractThe oligometastatic and oligoprogressive disease states have been recently recognized as common clinical scenarios in the management of non-small cell lung cancer (NSCLC). As a result, there has been increasing interest in treating these patients with locally ablative therapies including surgery, conventionally fractionated radiotherapy, stereotactic ablative radiotherapy, and radiofrequency ablation. This article provides an overview of oligometastatic and oligoprogressive disease in the setting of NSCLC and reviews the evidence supporting ablative treatment. Phase II randomized controlled trials and retrospective series suggest that ablative treatment of oligometastases may substantially improve progression-free survival and overall survival, and additional large randomized studies testing this hypothesis in a definitive context are ongoing. However, several challenges remain, including quantifying the possible benefits of ablative therapies for oligoprogressive disease and developing prognostic and predictive models to assist in clinical decision making.

Prognostic Factors in Patients With Metastatic Germ Cell Tumors Who Experienced Treatment Failure With Cisplatin-Based First-Line Chemotherapy

2010 ◽  
Vol 28 (33) ◽  
pp. 4906-4911 ◽  
Author(s):  
Keyword(s):  
Treatment Failure ◽  
Prognostic Model ◽  
Survival Rates ◽  
Germ Cell Tumors ◽  
Progression Free Survival ◽  
Training Set ◽  
First Line ◽  
Free Survival ◽  
Validation Set ◽  
Line Chemotherapy

Purpose To develop a prognostic model in patients with germ cell tumors (GCT) who experience treatment failure with cisplatin-based first-line chemotherapy. Patients and Methods Data from 1,984 patients with GCT who progressed after at least three cisplatin-based cycles and were treated with cisplatin-based conventional-dose or carboplatin-based high-dose salvage chemotherapy was retrospectively collected from 38 centers/groups worldwide. One thousand five hundred ninety-four (80%) of 1,984 eligible patients were randomly divided into a training set of 1,067 patients (67%) and a validation set of 527 patients (33%). Seminomas were set aside for posthoc analyses. Primary end point was the 2-year progression-free survival after salvage treatment. Results Overall, 990 patients (62%) relapsed and 604 patients (38%) remained relapse free. Histology, primary tumor location, response, and progression-free interval after first-line treatment, as well as levels of alpha fetoprotein, human chorionic gonadotrophin, and the presence of liver, bone, or brain metastases at salvage were identified as independent prognostic variables and used to build a prognostic model in the training set. Survival rates in the training and validation set were very similar. The estimated 2-year progression-free survival rates in patients not included in the training set was 75% in very low risk, 51% in low risk, 40% in intermediate risk, 26% in high risk, and only 6% in very high-risk patients. Due to missing values in individual variables, 69 patients could not reliably be classified into one of these categories. Conclusion Prognostic variables are important in patients with GCT who experienced treatment failure with cisplatin-based first-line chemotherapy and can be used to construct a prognostic model to guide salvage strategies.

scholarly journals Cronic Kidney Disease, Life Quality Assessment Of Patients

2016 ◽  
Vol 12 (6) ◽  
pp. 103
Author(s):  
Marsida Duli ◽  
Qamil Dika ◽  
Matilda Bushati
Keyword(s):  
Quality Of Life ◽  
Chronic Kidney Disease ◽  
Decision Making ◽  
Kidney Disease ◽  
Clinical Decision Making ◽  
Well Being ◽  
Clinical Decision ◽  
Healthy Population ◽  
Important Indicator

Assessing quality of life in patients with varying degrees of chronic kidney disease is an important issue because of its impact on clinical decision-making as increasing the efficiency of resources in the health system. Through this survey provided an attempt to assess the quality of life of patients with chronic kidney disease undergoing dialysis. Commitment to maximize their functioning and well-being constitutes the essence of the purpose of health care. In recent decades elaborate SF 36 is cut by a gauge derive so simple and basic that helps to evaluate the function of the target of researchers, a certain age group, a disease or a treatment group. Short questionnaire forms SF36 instrument gauge is used to determine the level of quality of life in patients with chronic renal failure under the different stages of treatment with dialysis. The study involved 206 people, 112 from patients to Tirana and Shkodra and 94 healthy persons, who collaborated consensually for completing the questionnaires. Based on the results, the quality of life of dialysis patients is significantly worse than that of the healthy population and patients with other injuries less severe of renal function. Survey indicates that a more holistic approach to be used in the treatment of patients with chronic kidney disease including clinical decision making and patient perception. Precisely for this it is recommended to enter the practice of clinical interest that a set of questionnaires that provide information on patients' perception of health as an important indicator that facilitates the physician-patient collaboration in order to better treatment of the disease and increase the quality the life of the patient.

scholarly journals Prospective validation of a molecular prognostication panel for clival chordoma

2019 ◽  
Vol 130 (5) ◽  
pp. 1528-1537 ◽  
Author(s):  
Georgios A. Zenonos ◽  
Juan C. Fernandez-Miranda ◽  
Debraj Mukherjee ◽  
Yue-Fang Chang ◽  
Klea Panayidou ◽  
...  
Keyword(s):  
Tumor Cells ◽  
Clinical Decision Making ◽  
Cox Model ◽  
Wide Spectrum ◽  
Progression Free Survival ◽  
Clinical Decision ◽  
Homozygous Deletion ◽  
Ki 67 ◽  
Free Survival ◽  
Clival Chordoma

OBJECTIVEThere are currently no reliable means to predict the wide variability in behavior of clival chordoma so as to guide clinical decision-making and patient education. Furthermore, there is no method of predicting a tumor’s response to radiation therapy.METHODSA molecular prognostication panel, consisting of fluorescence in situ hybridization (FISH) of the chromosomal loci 1p36 and 9p21, as well as immunohistochemistry for Ki-67, was prospectively evaluated in 105 clival chordoma samples from November 2007 to April 2016. The results were correlated with overall progression-free survival after surgery (PFSS), as well as progression-free survival after radiotherapy (PFSR).RESULTSAlthough Ki-67 and the percentages of tumor cells with 1q25 hyperploidy, 1p36 deletions, and homozygous 9p21 deletions were all found to be predictive of PFSS and PFSR in univariate analyses, only 1p36 deletions and homozygous 9p21 deletions were shown to be independently predictive in a multivariate analysis. Using a prognostication calculator formulated by a separate multivariate Cox model, two 1p36 deletion strata (0%–15% and > 15% deleted tumor cells) and three 9p21 homozygous deletion strata (0%–3%, 4%–24%, and ≥ 25% deleted tumor cells) accounted for a range of cumulative hazard ratios of 1 to 56.1 for PFSS and 1 to 75.6 for PFSR.CONCLUSIONSHomozygous 9p21 deletions and 1p36 deletions are independent prognostic factors in clival chordoma and can account for a wide spectrum of overall PFSS and PFSR. This panel can be used to guide management after resection of clival chordomas.

Prediction of benefit from treatment beyond the first line in patients with metastatic breast cancer (MBC).

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e11565-e11565
Author(s):  
Marta Bonotto ◽  
Lorenzo Gerratana ◽  
Alessandro Minisini ◽  
Elena Poletto ◽  
Stefania Russo ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Statistical Significance ◽  
Metastatic Breast ◽  
Progression Free Survival ◽  
Clinical Decision ◽  
Consecutive Series ◽  
First Line ◽  
Her2 Positive ◽  
Free Survival ◽  
The Impact

e11565 Background: Despite the availability of several therapeutic options for MBC, palliative treatments beyond 1st line lack of predictive factors that could help clinical decision making. We aimed to determine which is the impact of benefit at 1stline into the benefit from subsequent therapeutic lines. Methods: We analyzed a consecutive series of 472 MBC patients treated with chemotherapy (CT) and/or endocrine therapy (ET) at the Department of Oncology of Udine, Italy, between 2004 and 2012. We evaluated Progression Free Survival at 1st (PFS1), 2nd (PFS2), 3rd (PFS3) and 4th (PFS4) line of treatment. Three distinct analyses were conducted: the first for the lines of CT, the second for the lines of ET and the third by considering both CT and ET as a line of treatment. A PFS longer than 6 months was defined as “6-month benefit". Results: Median Overall Survival was 34.5 mo (25th – 75th percentile: 14.5 – 58.8), median overall PFS1 and PFS2 was 8.9 mo and 4.3 mo respectively. Median PFS1 and PFS2 in CT lines only was 7 mo and 3.7 mo, respectively. Median PFS1 and PFS2 in ET lines only was 9.4 mo and 4.6 mo respectively. Overall, 289 patients (63.5%) presented 6-month benefit at 1st line, 128 (40.5%) at 2nd, 76 (33.8%) at 3rd and 34 (23.3%) at 4th. Not having a 6-month benefit in overall PFS1 was associated with a lack of benefit both at 2nd line (OR=0.48; p=0.0026) and at any line beyond the 1st (OR=0.39; p< 0.0001). Taking into consideration CT lines only, not having a 6-month benefit in CT PFS1 was associated with a lack of benefit both at 2nd line (OR=0.45; p=0.0072) and at any line beyond the 1st (OR=0.43; p=0.0026). A lack of benefit at the 1st ET line was not associated with further ET outcome neither in 2nd line nor in any line beyond the 1st. Stratification according to immunophenotype highlighted a statistical significance only among HER2 positive tumors (OR=0.2; p=0.0152 in 2nd line and OR=0.14; p=0.0036 beyond 1st line). Conclusions: Our results suggest that the absence of a “6-month benefit” in PFS1 predicts a lack of benefit in subsequent therapy lines, especially in HER2 positive disease. However, a lack of benefit at first line ET appears not to be detrimental to further anti-hormonal lines.

scholarly journals Preliminary demonstration of benchtop NMR metabolic profiling of feline urine: chronic kidney disease as a case study

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Natalie Finch ◽  
Benita Percival ◽  
Elena Hunter ◽  
Robin J. Blagg ◽  
Emily Blackwell ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Metabolic Profiling ◽  
Clinical Decision Making ◽  
Decision Support Tool ◽  
Urinary Metabolites ◽  
Clinical Decision ◽  
Support Tool ◽  
Disease Case ◽  
Metabolic Conditions

Abstract Objective The use of benchtop metabolic profiling technology based on nuclear magnetic resonance (NMR) was evaluated in a small cohort of cats with a view to applying this as a viable and rapid metabolic tool to support clinical decision making. Results Urinary metabolites were analysed from four subjects consisting of two healthy controls and two chronic kidney disease (CKD) IRIS stage 2 cases. The study identified 15 metabolites in cats with CKD that were different from the controls. Among them were acetate, creatinine, citrate, taurine, glycine, serine and threonine. Benchtop NMR technology is capable of distinguishing between chronic kidney disease case and control samples in a pilot feline cohort based on metabolic profile. We offer perspectives on the further development of this pilot work and the potential of the technology, when combined with sample databases and computational intelligence techniques to offer a clinical decision support tool not only for cases of renal disease but other metabolic conditions in the future.

scholarly journals Estimation of Kidney Function in Oncology

2019 ◽  
Vol 14 (4) ◽  
pp. 587-595 ◽  
Author(s):  
Morgan A. Casal ◽  
Thomas D. Nolin ◽  
Jan H. Beumer
Keyword(s):  
Chronic Kidney Disease ◽  
Decision Making ◽  
Kidney Disease ◽  
Kidney Function ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Drug Dosing ◽  
Disease Epidemiology ◽  
Patients With Cancer ◽  
Agent Selection

Estimation of kidney function in patients with cancer directly affects drug dosing, agent selection, and eligibility for clinical trials of novel agents. Overestimation of kidney function may lead to overdosing or inappropriate agent selection and corresponding toxicity. Conversely, underestimation of kidney function may lead to underdosing or inappropriate agent exclusion and subsequent therapeutic failure. It would seem obvious that the most accurate estimates of kidney function should be used to reduce variability in decision making and ultimately, the therapeutic outcomes of toxicity and clinical benefit. However, clinical decision making is often more complex. The Cockcroft–Gault formula remains the most universally implemented estimator of kidney function in patients with cancer, despite its relative inaccuracy compared with the Chronic Kidney Disease Epidemiology Collaboration equation. The Chronic Kidney Disease Epidemiology Collaboration equation is a more precise estimator of kidney function; however, many currently used kidney function cutoff values were determined before the development of the Chronic Kidney Disease Epidemiology Collaboration equation and creatinine assay standardization using Cockcroft–Gault estimates. There is a need for additional studies investigating the validity of currently used estimates of kidney function in patients with cancer and the applicability of traditional anticancer dosing and eligibility guidelines to modern and more accurate estimates of kidney function. In this review, we consider contemporary calculation methods used to estimate kidney function in patients with cancer. We discuss the clinical implications of using these various methods, including the potential influence on drug dosing, drug selection, and clinical trial eligibility, using carboplatin and cisplatin as case studies.

scholarly journals Renal diets in cats with chronic kidney disease

2015 ◽  
Vol 176 (14) ◽  
pp. 360-361
Author(s):  
Rachel Dean ◽  
Martin Downes
Keyword(s):  
Chronic Kidney Disease ◽  
Decision Making ◽  
Veterinary Medicine ◽  
Kidney Disease ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Evidence Based ◽  
Veterinary Record ◽  
The University

BestBETs for Vets are generated by the Centre for Evidence-based Veterinary Medicine at the University of Nottingham to help answer specific questions and assist in clinical decision making. Although evidence is often limited, they aim to find, present and draw conclusions from the best available evidence, using a standardised framework. A more detailed description of how BestBETs for Vets are produced is published on pp 354-356 of this issue of Veterinary Record

scholarly journals Towards optimising chronic kidney disease detection and management in primary care: Underlying theory and protocol for technology development using an Integrated Knowledge Translation approach

2021 ◽  
Vol 27 (2) ◽  
pp. 146045822110082
Author(s):  
Jo-Anne Manski-Nankervis ◽  
Karyn Alexander ◽  
Ruby Biezen ◽  
Julia Jones ◽  
Barbara Hunter ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Design Thinking ◽  
Clinical Decision Making ◽  
Technology Development ◽  
Clinical Decision ◽  
Practice Variation ◽  
Service Users ◽  
Future Health ◽  
Technological Platform

Worldwide, Chronic Kidney Disease (CKD), directly or indirectly, causes more than 2.4 million deaths annually with symptoms generally presenting late in the disease course. Clinical guidelines support the early identification and treatment of CKD to delay progression and improve clinical outcomes. This paper reports the protocol for the codesign, implementation and evaluation of a technological platform called Future Health Today (FHT), a software program that aims to optimise early detection and management of CKD in general practice. FHT aims to optimise clinical decision making and reduce practice variation by translating evidence into practice in real time and as a part of quality improvement activities. This protocol describes the co-design and plans for implementation and evaluation of FHT in two general practices invited to test the prototype over 12 months. Service design thinking has informed the design phase and mixed methods will evaluate outcomes following implementation of FHT. Through systematic application of co-design with service users, clinicians and digital technologists, FHT attempts to avoid the pitfalls of past studies that have failed to accommodate the complex requirements and dynamics that can arise between researchers and service users and improve chronic disease management through use of health information technology.

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