External validation of a sunitinib prognostic nomogram in patients (pts) with metastatic renal cell carcinoma (mRCC).

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e15070-e15070
Author(s):  
Changhong Yu ◽  
Michael W. Kattan ◽  
Thomas E. Hutson ◽  
Gary R. Hudes ◽  
Jinyu Yuan ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Utility ◽  
Clinical Decision Making ◽  
External Validation ◽  
Curve Analysis ◽  
Medical Decision ◽  
Phase 2 ◽  
Concordance Index ◽  
Decision Curve Analysis ◽  
Significant Difference

e15070 Background: A nomogram was previously developed from pretreatment clinical features to predict the probability of achieving 12-month progression-free survival (PFS) with sunitinib in treatment (Tx)-naïve mRCC pts from a randomized, phase 3 trial (Cancer 2008;113:1552). Here, validation and update of this nomogram using pts from a phase 2 sunitinib mRCC study (Renal EFFECT Trial) is reported, as is evaluation of its usefulness for clinical decision making. Methods: The Tx-naïve mRCC pts included in the current analysis were randomized 1:1 to sunitinib 50 mg/d on a 4-weeks-on-2-weeks-off schedule (Schedule 4/2; n=146) or 37.5 mg/d on a continuous daily dosing (CDD) schedule (n=146). The variables included in the prior nomogram and used here for validation purposes were corrected serum calcium, number of metastatic sites, hemoglobin, prior nephrectomy, presence of lung and liver metastases, ECOG performance status, thrombocytosis, time from diagnosis to treatment, alkaline phosphatase, and lactate dehydrogenase. The nomogram was updated by removing prior nephrectomy as a variable, including baseline neutrophils and presence of bone metastases, and replacing thrombocytosis with baseline platelets. Validation of the existing and updated nomograms consisted of quantification of the discrimination with the concordance index. A decision curve analysis was used to examine whether this prediction model is useful for medical decision making. Results: With comparable pt characteristics and no significant difference in PFS (8.5 vs. 7.0 months; P=0.070) between the Schedule 4/2 and CDD arms of the phase 2 trial, the combined pt population (N=292) was used to validate the existing nomogram. The overall concordance index was 0.615. Based on the decision curve analysis, the existing nomogram has clinical utility when the probability of 12-month PFS exceeds 60%. Using Schedule 4/2 pts only, the concordance index was 0.594 for the updated nomogram; however, its utility showed more variability. Conclusions: The sunitinib nomogram has been validated in a similar pt cohort; however, its clinical utility may be limited and more research is needed to refine the tool further.

scholarly journals Scoring system development and validation for prediction choledocholithiasis before open cholecystectomy

2015 ◽  
Vol 143 (11-12) ◽  
pp. 681-687 ◽  
Author(s):  
Tomislav Pejovic ◽  
Miroslav Stojadinovic
Keyword(s):  
Bile Duct ◽  
Clinical Utility ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Open Cholecystectomy ◽  
Univariate Analysis ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Scoring Model ◽  
Validation Set

Introduction. Accurate precholecystectomy detection of concurrent asymptomatic common bile duct stones (CBDS) is key in the clinical decision-making process. The standard preoperative methods used to diagnose these patients are often not accurate enough. Objective. The aim of the study was to develop a scoring model that would predict CBDS before open cholecystectomy. Methods. We retrospectively collected preoperative (demographic, biochemical, ultrasonographic) and intraoperative (intraoperative cholangiography) data for 313 patients at the department of General Surgery at Gornji Milanovac from 2004 to 2007. The patients were divided into a derivation (213) and a validation set (100). Univariate and multivariate regression analysis was used to determine independent predictors of CBDS. These predictors were used to develop scoring model. Various measures for the assessment of risk prediction models were determined, such as predictive ability, accuracy, the area under the receiver operating characteristic curve (AUC), calibration and clinical utility using decision curve analysis. Results. In a univariate analysis, seven risk factors displayed significant correlation with CBDS. Total bilirubin, alkaline phosphatase and bile duct dilation were identified as independent predictors of choledocholithiasis. The resultant total possible score in the derivation set ranged from 7.6 to 27.9. Scoring model shows good discriminatory ability in the derivation and validation set (AUC 94.3 and 89.9%, respectively), excellent accuracy (95.5%), satisfactory calibration in the derivation set, similar Brier scores and clinical utility in decision curve analysis. Conclusion. Developed scoring model might successfully estimate the presence of choledocholithiasis in patients planned for elective open cholecystectomy.

scholarly journals Dynamic nomograms combining N classification with ratio-based nodal classifications to predict long-term survival for patients with lung adenocarcinoma after surgery: a SEER population-based study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Suyu Wang ◽  
Yue Yu ◽  
Wenting Xu ◽  
Xin Lv ◽  
Yufeng Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
External Validation ◽  
Predictive Performance ◽  
Information Criterion ◽  
Curve Analysis ◽  
Tnm Stage ◽  
Concordance Index ◽  
Cancer Specific Survival ◽  
Decision Curve Analysis

Abstract Background The prognostic roles of three lymph node classifications, number of positive lymph nodes (NPLN), log odds of positive lymph nodes (LODDS), and lymph node ratio (LNR) in lung adenocarcinoma are unclear. We aim to find the classification with the strongest predictive power and combine it with the American Joint Committee on Cancer (AJCC) 8th TNM stage to establish an optimal prognostic nomogram. Methods 25,005 patients with T1-4N0–2M0 lung adenocarcinoma after surgery between 2004 to 2016 from the Surveillance, Epidemiology, and End Results database were included. The study cohort was divided into training cohort (13,551 patients) and external validation cohort (11,454 patients) according to different geographic region. Univariate and multivariate Cox regression analyses were performed on the training cohort to evaluate the predictive performance of NPLN (Model 1), LODDS (Model 2), LNR (Model 3) or LODDS+LNR (Model 4) respectively for cancer-specific survival and overall survival. Likelihood-ratio χ2 test, Akaike Information Criterion, Harrell concordance index, integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to evaluate the predictive performance of the models. Nomograms were established according to the optimal models. They’re put into internal validation using bootstrapping technique and external validation using calibration curves. Nomograms were compared with AJCC 8th TNM stage using decision curve analysis. Results NPLN, LODDS and LNR were independent prognostic factors for cancer-specific survival and overall survival. LODDS+LNR (Model 4) demonstrated the highest Likelihood-ratio χ2 test, highest Harrell concordance index, and lowest Akaike Information Criterion, and IDI and NRI values suggested Model 4 had better prediction accuracy than other models. Internal and external validations showed that the nomograms combining TNM stage with LODDS+LNR were convincingly precise. Decision curve analysis suggested the nomograms performed better than AJCC 8th TNM stage in clinical practicability. Conclusions We constructed online nomograms for cancer-specific survival and overall survival of lung adenocarcinoma patients after surgery, which may facilitate doctors to provide highly individualized therapy.

scholarly journals Clinical Utility of a Nomogram for Predicting 30-Days Poor Outcome in Hospitalized Patients With COVID-19: Multicenter External Validation and Decision Curve Analysis

2020 ◽  
Vol 7 ◽  
Author(s):  
Bin Zhang ◽  
Qin Liu ◽  
Xiao Zhang ◽  
Shuyi Liu ◽  
Weiqi Chen ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Clinical Utility ◽  
Predictive Accuracy ◽  
Fasting Blood Glucose ◽  
External Validation ◽  
Predictive Performance ◽  
Curve Analysis ◽  
Discriminative Ability ◽  
Decision Curve Analysis ◽  
Poor Outcome

Aim: Early detection of coronavirus disease 2019 (COVID-19) patients who are likely to develop worse outcomes is of great importance, which may help select patients at risk of rapid deterioration who should require high-level monitoring and more aggressive treatment. We aimed to develop and validate a nomogram for predicting 30-days poor outcome of patients with COVID-19.Methods: The prediction model was developed in a primary cohort consisting of 233 patients with laboratory-confirmed COVID-19, and data were collected from January 3 to March 20, 2020. We identified and integrated significant prognostic factors for 30-days poor outcome to construct a nomogram. The model was subjected to internal validation and to external validation with two separate cohorts of 110 and 118 cases, respectively. The performance of the nomogram was assessed with respect to its predictive accuracy, discriminative ability, and clinical usefulness.Results: In the primary cohort, the mean age of patients was 55.4 years and 129 (55.4%) were male. Prognostic factors contained in the clinical nomogram were age, lactic dehydrogenase, aspartate aminotransferase, prothrombin time, serum creatinine, serum sodium, fasting blood glucose, and D-dimer. The model was externally validated in two cohorts achieving an AUC of 0.946 and 0.878, sensitivity of 100 and 79%, and specificity of 76.5 and 83.8%, respectively. Although adding CT score to the clinical nomogram (clinical-CT nomogram) did not yield better predictive performance, decision curve analysis showed that the clinical-CT nomogram provided better clinical utility than the clinical nomogram.Conclusions: We established and validated a nomogram that can provide an individual prediction of 30-days poor outcome for COVID-19 patients. This practical prognostic model may help clinicians in decision making and reduce mortality.

scholarly journals Prediction and clinical utility of a contralateral breast cancer risk model

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Daniele Giardiello ◽  
Ewout W. Steyerberg ◽  
Michael Hauptmann ◽  
Muriel A. Adank ◽  
Delal Akdeniz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Prediction Model ◽  
Germline Mutation ◽  
Clinical Utility ◽  
Contralateral Breast Cancer ◽  
Contralateral Breast ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Mutation Status

Abstract Background Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making. Methods We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. Results In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52–0.74; at 10 years, 0.53–0.72). Calibration-in-the-large was -0.13 (95% PI: -1.62–1.37), and the calibration slope was 0.90 (95% PI: 0.73–1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52–0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4–10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. Conclusions We developed a reasonably calibrated model to predict the risk of CBC in women of European-descent; however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision-making regarding contralateral preventive mastectomy, especially in the general breast cancer population where limited information of the mutation status in BRCA1/2 is available, remains challenging.

scholarly journals 1157. Determining the Clinical Utility of 16S rRNA in the Management of Pediatric Infections

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S670-S670
Author(s):  
Peter Paul Lim ◽  
Ankita P Desai ◽  
Sree Sarah Cherian ◽  
Sindhoosha Malay
Keyword(s):  
Decision Making ◽  
16S Rrna ◽  
Clinical Utility ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Joint Fluid ◽  
Significant Difference ◽  
Pediatric Infections ◽  
Tissue Specimens ◽  
Antimicrobial Regimen

Abstract Background Conventional culture remains the gold standard to facilitate a targeted antimicrobial regimen in the treatment of bacterial infections. However, certain pediatric infections are caused by fastidious organisms and treatment with antibiotics prior to specimen collection may hamper growth of pathogens in routine culture. The use of 16S rRNA in culture negative infections has improved identification of bacterial pathogens in select scenarios. However, the specific impact of 16S rRNA on clinical decision making, especially in pediatric infections, is not well-defined. This study aims to elucidate the utility of 16S rRNA on clinical management of pediatric infections. Methods A retrospective analysis was done on different clinical specimens which had 16S rRNA performed from August 2016 – March 2020 in our institution. Detailed chart review was performed to determine how the 16S rRNA result impacted clinical decision making. Clinical utility was defined as change in patient’s overall antimicrobial regimen, pathogen confirmation, and treatment duration. Results Seventy-four samples from 71 pediatric patients were included in the analysis: 32 (43%) were fluid specimens and 42 (57%) were tissue specimens. Significant clinical utility was identified in 30 (40.5%) of 74 clinical samples (p < 0.0001). Of all specimens, pulmonary samples yielded the most clinical utility (n=9, 30%) followed equally by joint fluid (n=6, 20%) and bone (n=6, 20%). There was no significant difference in clinical utility between fluid and tissue specimens (p= 0.346). In 64 patients whose antimicrobial spectrum coverage was analyzed, patients with broad spectrum coverage was decreased from 48 to 21 and narrow spectrum coverage increased from 16 to 43 using 16S rRNA result, though not significant (p= 0.4111). Of all patients included in the analysis, the median number of antibiotics used before 16S rRNA result, 2, was significantly decreased to 1 (p < 0.0001). Conclusion 16S rRNA has a significant impact in terms of decreasing number of antibiotics used in treatment of pediatric infections. Pulmonary specimens have the highest clinical utility among all samples. Additional cost benefit analysis needs to be completed to further determine clinical benefit. Disclosures All Authors: No reported disclosures

scholarly journals Scoring system development for prediction of extravesical bladder cancer

2014 ◽  
Vol 71 (9) ◽  
pp. 851-857 ◽  
Author(s):  
Rade Prelevic ◽  
Miroslav Stojadinovic ◽  
Dejan Simic ◽  
Aleksandar Spasic ◽  
Nikola Petrovic
Keyword(s):  
Bladder Cancer ◽  
Scoring System ◽  
Clinical Decision Making ◽  
External Validation ◽  
Multivariable Analysis ◽  
Curve Analysis ◽  
Clinical Usefulness ◽  
Clinical Staging ◽  
Surgical Specimen ◽  
Decision Curve Analysis

Background/Aim. Staging of bladder cancer is crucial for optimal management of the disease. However, clinical staging is not perfectly accurate. The aim of this study was to derive a simple scoring system in prediction of pathological advanced muscle-invasive bladder cancer (MIBC). Methods. Logistic regression and bootstrap methods were used to create an integer score for estimating the risk in prediction of pathological advanced MIBC using precystectomy clinicopathological data: demographic, initial transurethral resection (TUR) [grade, stage, multiplicity of tumors, lymphovascular invasion (LVI)], hydronephrosis, abdominal and pelvic CT radiography (size of the tumor, tumor base width), and pathological stage after radical cystectomy (RC). Advanced MIBC in surgical specimen was defined as pT3-4 tumor. Receiving operating characteristic (ROC) curve quantified the area under curve (AUC) as predictive accuracy. Clinical usefulness was assessed by using decision curve analysis. Results. This single-center retrospective study included 233 adult patients with BC undergoing RC at the Military Medical Academy, Belgrade. Organ confined disease was observed in 101 (43.3%) patients, and 132 (56.7%) had advanced MIBC. In multivariable analysis, 3 risk factors most strongly associated with advanced MIBC: grade of initial TUR [odds ratio (OR) = 4.7], LVI (OR = 2), and hydronephrosis (OR = 3.9). The resultant total possible score ranged from 0 to 15, with the cut-off value of > 8 points, the AUC was 0.795, showing good discriminatory ability. The model showed excellent calibration. Decision curve analysis showed a net benefit across all threshold probabilities and clinical usefulness of the model. Conclusion. We developed a unique scoring system which could assist in predicting advanced MIBC in patients before RC. The scoring system showed good performance characteristics and introducing of such a tool into daily clinical decision-making may lead to more appropriate integration of perioperative chemotherapy. Clinical value of this model needs to be further assessed in external validation cohorts.

scholarly journals Predicting mortality in adults with suspected infection in a Rwandan hospital: an evaluation of the adapted MEWS, qSOFA and UVA scores

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e040361
Author(s):  
Amanda Klinger ◽  
Ariel Mueller ◽  
Tori Sutherland ◽  
Christophe Mpirimbanyi ◽  
Elie Nziyomaze ◽  
...  
Keyword(s):  
Decision Making ◽  
Quality Improvement ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Tertiary Hospital ◽  
Early Warning Score ◽  
Receiver Operating Curve ◽  
Predictive Capacity ◽  
Suspected Infection ◽  
Significant Difference

RationaleMortality prediction scores are increasingly being evaluated in low and middle income countries (LMICs) for research comparisons, quality improvement and clinical decision-making. The modified early warning score (MEWS), quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA), and Universal Vital Assessment (UVA) score use variables that are feasible to obtain, and have demonstrated potential to predict mortality in LMIC cohorts.ObjectiveTo determine the predictive capacity of adapted MEWS, qSOFA and UVA in a Rwandan hospital.Design, setting, participants and outcome measuresWe prospectively collected data on all adult patients admitted to a tertiary hospital in Rwanda with suspected infection over 7 months. We calculated an adapted MEWS, qSOFA and UVA score for each participant. The predictive capacity of each score was assessed including sensitivity, specificity, positive and negative predictive value, OR, area under the receiver operating curve (AUROC) and performance by underlying risk quartile.ResultsWe screened 19 178 patient days, and enrolled 647 unique patients. Median age was 35 years, and in-hospital mortality was 18.1%. The proportion of data missing for each variable ranged from 0% to 11.7%. The sensitivities and specificities of the scores were: adapted MEWS >4, 50.4% and 74.9%, respectively; qSOFA >2, 24.8% and 90.4%, respectively; and UVA >4, 28.2% and 91.1%, respectively. The scores as continuous variables demonstrated the following AUROCs: adapted MEWS 0.69 (95% CI 0.64 to 0.74), qSOFA 0.65 (95% CI 0.60 to 0.70), and UVA 0.71 (95% CI 0.66 to 0.76); there was no statistically significant difference between the discriminative capacities of the scores.ConclusionThree scores demonstrated a modest ability to predict mortality in a prospective study of inpatients with suspected infection at a Rwandan tertiary hospital. Careful consideration must be given to their adequacy before using them in research comparisons, quality improvement or clinical decision-making.

Standardized Synoptic Cancer Pathology Reports — So What and Who Cares?: A Population-Based Satisfaction Survey of 970 Pathologists, Surgeons, and Oncologists

2013 ◽  
Vol 137 (11) ◽  
pp. 1599-1602 ◽  
Author(s):  
Sara Lankshear ◽  
John Srigley ◽  
Thomas McGowan ◽  
Marta Yurcan ◽  
Carol Sawka
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Physician Satisfaction ◽  
Cross Sectional ◽  
Cancer Pathology ◽  
Report Turnaround Time ◽  
Significant Difference ◽  
Pathology Reporting ◽  
The Impact

Context.—Cancer Care Ontario implemented synoptic pathology reporting across Ontario, impacting the practice of pathologists, surgeons, and medical and radiation oncologists. The benefits of standardized synoptic pathology reporting include enhanced completeness and improved consistency in comparison with narrative reports, with reported challenges including increased workload and report turnaround time. Objective.—To determine the impact of synoptic pathology reporting on physician satisfaction specific to practice and process. Design.—A descriptive, cross-sectional design was utilized involving 970 clinicians across 27 hospitals. An 11-item survey was developed to obtain information regarding timeliness, completeness, clarity, and usability. Open-ended questions were also employed to obtain qualitative comments. Results.—A 51% response rate was obtained, with descriptive statistics reporting that physicians perceive synoptic reports as significantly better than narrative reports. Correlation analysis revealed a moderately strong, positive relationship between respondents' perceptions of overall satisfaction with the level of information provided and perceptions of completeness for clinical decision making (r = 0.750, P < .001) and ease of finding information for clinical decision making (r = 0.663, P < .001). Dependent t tests showed a statistically significant difference in the satisfaction scores of pathologists and oncologists (t169 = 3.044, P = .003). Qualitative comments revealed technology-related issues as the most frequently cited factor impacting timeliness of report completion. Conclusion.—This study provides evidence of strong physician satisfaction with synoptic cancer pathology reporting as a clinical decision support tool in the diagnosis, prognosis, and treatment of cancer patients.

scholarly journals Creating Significant Learning Experiences for Clinical Reasoning by Physical Therapist Students

2019 ◽  
Author(s):  
Lois Stickley
Keyword(s):  
Decision Making ◽  
Clinical Reasoning ◽  
Curriculum Design ◽  
Clinical Decision Making ◽  
Physical Therapist ◽  
Learning Experiences ◽  
First Year ◽  
Clinical Decisions ◽  
First Year Students ◽  
Significant Difference

Background: Clinical reasoning skills are embedded in all aspects of practice. There is a lack of consensus and standards for curriculum design and teaching methods of clinical reasoning in entry-level education of health professionals. Purpose: The purpose was to describe a process of designing one comprehensive, planned sequence of four courses to create significant learning experiences for clinical reasoning for Doctor of Physical Therapy students. Method: Fink’s design process was used to develop four clinical decision-making courses to ensure a close alignment of learning goals, feedback and assessment, and learning activities to engage students in practicing components of clinical reasoning. Student outcomes were measured by self-efficacy ratings for clinical reasoning in a practical exam for first-year students and by ratings of performance by clinical instructors for third-year students. Results: 41 first-year students ranked their confidence in making clinical decisions both before and after a midterm practical. A paired t-test found a significant difference (.05t40 = -6.66, ρ=0.00) in the mean ratings of students from the pre-practical assessment to the post-practical assessment about confidence in making clinical decisions. Third-year students received ratings that met or exceeded expectations on five audited skills from the Physical Therapist manual for the Assessment of Clinical Skills (PT MACS), both at midterm and at the final assessment. No significant differences between midterm and final ratings on any of the selected skills were found using a Chi-Square Test of Independence (α=.05). Conclusion: The four-course sequence was designed using four themes: patient-centered care, models of practice, and evidence-based practice, and ethics/legal issues. This paper offers specific details about how one method of teaching clinical reasoning meets the current trends in education and health care for accountability and meaningful outcomes. Students gained practical knowledge and skills in the components of clinical reasoning and decision-making by participating in active and engaging significant learning experiences.

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