scholarly journals 275 Validation of The Radiographic Union Score for Humeral Fractures (RUSHU) For Prediction of Humeral Shaft Nonunion

2021 ◽  
Vol 108 (Supplement_6) ◽  
Author(s):  
N Arora
Keyword(s):  
Scoring System ◽  
Clinical Decision Making ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Roc Curve Analysis ◽  
Exclusion Criteria ◽  
Non Union ◽  
Humeral Shaft Nonunion ◽  
Surgical Fixation ◽  
Radiographic Union

Abstract Aim To validate the use of RUSHu score in prediction of humerus non union. Method All patients having radiographs of humerus performed between Jan 2016 to December 2018 were assessed based on inclusion and exclusion criteria. The RUSHu scoring system as published was used to score each 6-week radiograph, separately by 2 blinded observers. 6 months was used as end point to assess outcome. Cohort of 188 observations were used to assess utility of scoring system to predict non union. Results 94 suitable fractures were identified. Union rate of 72.3% was observed. Mean score in union group was 9.6, 6.4 for non-unions. There was substantial inter-observer reliability with an ICC of 0.73. Rate of union progressively increases with increasing RUSHu scores. ROC curve analysis identifies 8 as most suitable for use as threshold. Area under the curve is high (0.9) Conclusions A low RUSH score at 6 weeks is a reliable predictor of non union down the line. If a score 7 or lower is observed, it should trigger a discussion with the patient and review of correctable factors contributing to development of non union. Consideration of surgical fixation should be made at this stage if instability is felt to be a major contributing cause. A patient with score of 8 or higher is more likely to go on to union. Routine use of RUSHu score can aid in clinical decision making and introduce an element of objectivity in clinical assessment. It has potential to prompt earlier intervention and reduce morbidity duration.

scholarly journals Predicting Risk Score for Mechanical Ventilation in Hospitalized Adult Patients Suffering from COVID-19

2021 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Samira Kafan ◽  
Kiana Tadbir Vajargah ◽  
Mehrdad Sheikhvatan ◽  
Gholamreza Tabrizi ◽  
Ahmad Salimzadeh ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Risk Score ◽  
Scoring System ◽  
Clinical Decision Making ◽  
Wide Spectrum ◽  
Clinical Decision ◽  
Severe Illness ◽  
Mechanical Ventilators ◽  
Roc Curve Analysis ◽  
Logistic Analysis

Background: COVID-19 has become a pandemic since December 2019, causing millions of deaths worldwide. It has a wide spectrum of severity, ranging from mild infection to severe illness requiring mechanical ventilation. In the middle of a pandemic, when medical resources (including mechanical ventilators) are scarce, there should be a scoring system to provide the clinicians with the information needed for clinical decision-making and resource allocation. Objectives: This study aimed to develop a scoring system based on the data obtained on admission, to predict the need for mechanical ventilation in COVID-19 patients. Methods: This study included COVID-19 patients admitted to Sina Hospital, Tehran University of Medical Sciences from February 20 to May 29, 2020. Patients' data on admission were retrospectively recruited from Sina Hospital COVID-19 Registry (SHCo-19R). Multivariable logistic regression and receiver operating characteristic (ROC) curve analysis were performed to identify the predictive factors for mechanical ventilation. Results: A total of 681 patients were included in the study; 74 patients (10.9%) needed mechanical ventilation during hospitalization, while 607 (89.1%) did not. Multivariate logistic analysis revealed that age (OR,1.049; 95% CI:1.008-1.09), history of diabetes mellitus (OR,3.216; 95% CI:1.134-9.120), respiratory rate (OR,1.051; 95% CI:1.005-1.100), oxygen saturation (OR,0.928; 95% CI:0.872-0.989), CRP (OR,1.013; 95% CI:1.001-1.024) and bicarbonate level (OR,0.886; 95% CI:0.790-0.995) were risk factors for mechanical ventilation during hospitalization. Conclusions: A risk score has been developed based on the available data within the first hours of hospital admission to predict the need for mechanical ventilation. This risk score should be further validated to determine its applicability in other populations.

scholarly journals Impact of Grade Groups on Prostate Cancer-Specific and Other-Cause Mortality: Competing Risk Analysis from a Large Single Institution Series

Cancers ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1963
Author(s):  
Daimantas Milonas ◽  
Tomas Ruzgas ◽  
Zilvinas Venclovas ◽  
Mindaugas Jievaltas ◽  
Steven Joniau
Keyword(s):  
Clinical Decision Making ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Competing Risk ◽  
Grade Group ◽  
Tertiary Center ◽  
Stable Performance ◽  
Cancer Specific Mortality ◽  
Risk Of Cancer

Objective: To assess the risk of cancer-specific mortality (CSM) and other-cause mortality (OCM) using post-operative International Society of Urological Pathology Grade Group (GG) model in patients after radical prostatectomy (RP). Patients and Methods: Overall 1921 consecutive men who underwent RP during 2001 to 2017 in a single tertiary center were included in the study. Multivariate competing risk regression analysis was used to identify significant predictors and quantify cumulative incidence of CSM and OCM. Time-depending area under the curve (AUC) depicted the performance of GG model on prediction of CSM. Results: Over a median follow-up of 7.9-year (IQR 4.4-11.7) after RP, 235 (12.2%) deaths were registered, and 52 (2.7%) of them were related to PCa. GG model showed high and stable performance (time-dependent AUC 0.88) on prediction of CSM. Cumulative 10-year CSM in GGs 1 to 5 was 0.9%, 2.3%, 7.6%, 14.7%, and 48.6%, respectively; 10-year OCM in GGs was 15.5%, 16.1%, 12.6%, 17.7% and 6.5%, respectively. The ratio between 10-year CSM/OCM in GGs 1 to 5 was 1:17, 1:7, 1:2, 1:1, and 7:1, respectively. Conclusions: Cancer-specific and other-cause mortality differed widely between GGs. Presented findings could aid in personalized clinical decision making for active treatment.

scholarly journals Identification and Evaluation of Serum Protein Biomarkers Which Differentiate Psoriatic from Rheumatoid Arthritis

2020 ◽  
Author(s):  
Angela Mc Ardle ◽  
Anna Kwasnik ◽  
Agnes Szenpetery ◽  
Melissa Jones ◽  
Belinda Hernandez ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Serum Protein ◽  
Clinical Decision Making ◽  
Multiple Reaction Monitoring ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Additional Patient ◽  
Second Phase ◽  
Protein Biomarkers ◽  
Protein Discovery

AbstractObjectivesTo identify serum protein biomarkers which might separate early inflammatory arthritis (EIA) patients with psoriatic arthritis (PsA) from those with rheumatoid arthritis (RA) to provide an accurate diagnosis and support appropriate early intervention.MethodsIn an initial protein discovery phase, the serum proteome of a cohort of patients with PsA and RA was interrogated using unbiased liquid chromatography mass spectrometry (LC-MS/MS) (n=64 patients), a multiplexed antibody assay (Luminex) for 48 proteins (n=64 patients) and an aptamer-based assay (SOMAscan) targeting 1,129 proteins (n=36 patients). Subsequently, analytically validated targeted multiple reaction monitoring (MRM) assays were developed to further evaluate those proteins identified as discriminatory during the discovery. During an initial verification phase, MRM assays were developed to a panel of 150 proteins (by measuring a total of 233 peptides) and used to re-evaluate the discovery cohort (n=60). During a second verification phase, the panel of proteins was expanded to include an additional 23 proteins identified in other proteomic discovery analyses of arthritis patients. The expanded panel was evaluated using a second, independent cohort of PsA and RA patients (n=167).ResultsMultivariate analysis of the protein discovery data revealed that it was possible to discriminate PsA from RA patients with an area under the curve (AUC) of 0.94 for nLC-MS/MS, 0.69 for Luminex based measurements; 0.73 for SOMAscan analysis. During the initial verification phase, random forest models confirmed that proteins measured by MRM could differentiate PsA and RA patients with an AUC of 0.79 and during the second phase of verification the expanded panel could segregate the two disease groups with an AUC of 0.85.ConclusionWe report a serum protein biomarker panel which can separate EIA patients with PsA from those with RA. We suggest that the routine use of such a panel in EIA patients will improve clinical decision making and with continued evaluation and refinement using additional patient cohorts will support the development of a diagnostic test for patients with PsA.

scholarly journals CT-Based Radiomics Analysis for Preoperative Diagnosis of Pancreatic Mucinous Cystic Neoplasm and Atypical Serous Cystadenomas

2021 ◽  
Vol 11 ◽  
Author(s):  
Tiansong Xie ◽  
Xuanyi Wang ◽  
Zehua Zhang ◽  
Zhengrong Zhou
Keyword(s):  
Predictive Value ◽  
Cross Validation ◽  
Clinical Decision Making ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Cystic Neoplasm ◽  
Cystic Neoplasms ◽  
Serous Cystadenomas ◽  
Radiological Model ◽  
Fold Cross Validation

ObjectivesTo investigate the value of CT-based radiomics analysis in preoperatively discriminating pancreatic mucinous cystic neoplasms (MCN) and atypical serous cystadenomas (ASCN).MethodsA total of 103 MCN and 113 ASCN patients who underwent surgery were retrospectively enrolled. A total of 764 radiomics features were extracted from preoperative CT images. The optimal features were selected by Mann-Whitney U test and minimum redundancy and maximum relevance method. The radiomics score (Rad-score) was then built using random forest algorithm. Radiological/clinical features were also assessed for each patient. Multivariable logistic regression was used to construct a radiological model. The performance of the Rad-score and the radiological model was evaluated using 10-fold cross-validation for area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy.ResultsTen screened optimal features were identified and the Rad-score was then built based on them. The radiological model was built based on four radiological/clinical factors. In the 10-fold cross-validation, the Rad-score was proved to be robust and reliable (average AUC: 0.784, sensitivity: 0.847, specificity: 0.745, PPV: 0.767, NPV: 0.849, accuracy: 0.793). The radiological model performed slightly less well in classification (average AUC: average AUC: 0.734 sensitivity: 0.748, specificity: 0.705, PPV: 0.732, NPV: 0.798, accuracy: 0.728.ConclusionsThe CT-based radiomics analysis provided promising performance for preoperatively discriminating MCN from ASCN and showed good potential in improving diagnostic power, which may serve as a novel tool for guiding clinical decision-making for these patients.

scholarly journals A novel severity score to predict inpatient mortality in COVID-19 patients

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
David J. Altschul ◽  
Santiago R. Unda ◽  
Joshua Benton ◽  
Rafael de la Garza Ramos ◽  
Phillip Cezayirli ◽  
...  
Keyword(s):  
Severity Score ◽  
Clinical Decision Making ◽  
Risk Model ◽  
Validation Cohort ◽  
Clinical Decision ◽  
International Normalized Ratio ◽  
Severe Illness ◽  
Roc Curve Analysis ◽  
Derivation Cohort ◽  
Mortality Incidence

Abstract COVID-19 is commonly mild and self-limiting, but in a considerable portion of patients the disease is severe and fatal. Determining which patients are at high risk of severe illness or mortality is essential for appropriate clinical decision making. We propose a novel severity score specifically for COVID-19 to help predict disease severity and mortality. 4711 patients with confirmed SARS-CoV-2 infection were included. We derived a risk model using the first half of the cohort (n = 2355 patients) by logistic regression and bootstrapping methods. The discriminative power of the risk model was assessed by calculating the area under the receiver operating characteristic curves (AUC). The severity score was validated in a second half of 2356 patients. Mortality incidence was 26.4% in the derivation cohort and 22.4% in the validation cohort. A COVID-19 severity score ranging from 0 to 10, consisting of age, oxygen saturation, mean arterial pressure, blood urea nitrogen, C-Reactive protein, and the international normalized ratio was developed. A ROC curve analysis was performed in the derivation cohort achieved an AUC of 0.824 (95% CI 0.814–0.851) and an AUC of 0.798 (95% CI 0.789–0.818) in the validation cohort. Furthermore, based on the risk categorization the probability of mortality was 11.8%, 39% and 78% for patient with low (0–3), moderate (4–6) and high (7–10) COVID-19 severity score. This developed and validated novel COVID-19 severity score will aid physicians in predicting mortality during surge periods.

scholarly journals INNV-34. PATTERNS OF FAILURE AND DEVELOPMENT OF A NOVEL PROGNOSTIC SCORING SYSTEM IN ELDERLY PATIENTS WITH GLIOBLASTOMA – FOLLOW UP ON 10 YEAR ANALYSIS OF THE BC CANCER AGENCY POPULATION

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi137-vi137
Author(s):  
Jonathan Zeng ◽  
Kimberly DeVries ◽  
Andra Krauze
Keyword(s):  
Decision Making ◽  
Scoring System ◽  
Clinical Decision Making ◽  
Radiographic Progression ◽  
Clinical Decision ◽  
Patient Stratification ◽  
Patterns Of Failure ◽  
Resection Status ◽  
Cancer Agency

Abstract PURPOSE Glioblastomas (GBM) are the most common primary brain tumour recurring in most patients despite maximal management. Patient selection for appropriate treatment modality remains challenging resulting in heterogeneity in management. We examined the patterns of failure and developed a scoring system for patient stratification to optimise clinical decision making. METHODS 822 adults (BC Cancer Agency registry) diagnosed 2005–2015 age ≥60 with histologically confirmed GBM ICD-O-3 codes (9440/3, 9441/3, 9442/3) were reviewed. Univariate and Kaplan-Meier analysis were performed. Performance status (PS), age and resection status were assigned a score, cummulative maximal (favorable) score of 10 and minimum (unfavorable) score of 3. Patterns of failure were further analysed in the subset of patients with radiographic follow-up. RESULTS PS score of 3(KPS >80, ECOG 0/1), 2 (KPS 60–70, ECOG 2), 1 (KPS < 60, ECOG 3/4) (median OS 11, 6, 3 months respectively), age score and resection status were prognostic for OS with PS resulting in the most significant curve separation (p< 0.0001). Biopsy as compared to STR/GTR resulted in poorer OS in patients over 70 (age score 1/2) but had less impact in patients younger than 70 (age scores 3/4). The median OS for cumulative scores of 9/10 (123 patients), 7/8 (286 patients), 5/6 (313 patients), and 3/4 (55 patients) were 14, 8, 4 and 2 months respectively (p< 0.0001) allowing for stratification into 4 prognostic groups. 133 patients had >3 MRIs following diagnosis allowing for clinical and radiographic analysis of progression. Clinical/radiographic progression occurred within 3 months (29%/45%), 6 months (50%/66%), 9 months (70%/81%). Progression type (radiographic, clinical, both was not associated with OS. CONCLUSION Our novel prognostic scoring system is effective in achieving patient stratification and may guide clinical decision making. Early radiographic progression appears to precede clinical deterioration and may represent true progression in the elderly.

scholarly journals A new “ASAP Scoring System and Risk Table” in patients with Atypical Small Acinar Proliferation (ASAP) to predict the second prostate biopsy outcomes

2021 ◽  
Author(s):  
Caner Ediz ◽  
Serkan Akan ◽  
Neslihan Kaya Terzi ◽  
Aysenur Ihvan
Keyword(s):  
Prostate Biopsy ◽  
Roc Curve ◽  
Scoring System ◽  
Risk Group ◽  
Area Under The Curve ◽  
Threshold Value ◽  
Roc Curve Analysis ◽  
Atypical Small Acinar Proliferation ◽  
The Mean ◽  
Density Assessment

Background: To discuss the necessity of the second prostate biopsy in the patients with atypical small acinar proliferation (ASAP) and to develop a scoring system and risk table as a new re-biopsy criteria. Methods: 2845 patients who were performed transrectal ultrasonography-guided prostate biopsy between January 2008 and May 2019 were evaluated. 128 patients, whose data were reached, were enrolled into the study. Before the first and the second biopsy, tPSA, fPSA, f/tPSA rate and PSA-Density assessment and changes in these parameters between the two biopsies were recorded. “ASAP Scoring System and risk table” (ASS-RT) was evaluated before the second biopsy. Results: The mean age of 128 patients with ASAP was 62.9±7.8 years. The ASS-RT scores of the patients with PCa were statistically significantly higher than the patients with non-PCa (p: 0.001). In the ROC curve analysis of ASS-RT, area under the curve was 0.804 and the standard error was 0.04. The area under the ROC curve was significantly higher than 0.5 (p:0.001). The cut-off point of ASS-RT score in diagnosis of malignancy was ≥ 7. The sensitivity of this value was found to be 60.8% and its specificity as 80.5%. Conclusions: The threshold value for the ASS-RT score may be used as 7 and the second biopsy may be performed immediately to patients over this value. We think that there may be no need for a second biopsy if the ASS-RT score under the 7 (especially low-risk group) before the second biopsy.

scholarly journals A Nomogram-Based Model to Predict Neoplastic Risk For Patients With Gallbladder Polyps

2021 ◽  
Author(s):  
Xudong Zhang ◽  
Jin-Cheng Wang ◽  
Baoqiang Wu ◽  
Tao Li ◽  
Lei Jin ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Early Stage ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Training Group ◽  
Clinical Decision ◽  
Polyp Size ◽  
Training Cohort ◽  
Discrimination Ability ◽  
Gallbladder Polyps

Abstract Background: Gallbladder polyps (GBPs) assessment seeks to identify early-stage gallbladder carcinoma (GBC). Many studies have analyzed the risk factors for malignant GBPs, and we try to establish a more accurate predictive model for potential neoplastic polyps in patients with GBPs.Methods: This retrospective study developed a nomogram-based model in a training cohort of 233 GBP patients. Clinical information, ultrasonographic findings, and blood tests were retrospectively analyzed. Spearman correlation and logistic regression analysis were used to identify independent predictors and establish a nomogram model. An internal validation was conducted in 225 consecutive patients. Performance of models was evaluated through the receiver operating characteristic curve (ROC) and decision curve analysis (DCA). Results: Age, cholelithiasis, CEA, polyp size and sessile were confirmed as independent predictors for neoplastic potential of GBPs in the training group. Compared with other proposed prediction methods, the established nomogram model presented good discrimination ability in the training cohort (area under the curve [AUC]: 0.845) and the validation cohort (AUC: 0.836). DCA demonstrated the most clinical benefits can be provided by the nomogram. Conclusions: Our developed preoperative nomogram model can successfully evaluate the neoplastic potential of GBPs based on simple clinical variables, that maybe useful for clinical decision-making.

scholarly journals Predicting Cardiovascular Risk in Athletes: Resampling Improves Classification Performance

2020 ◽  
Vol 17 (21) ◽  
pp. 7923
Author(s):  
Davide Barbieri ◽  
Nitesh Chawla ◽  
Luciana Zaccagni ◽  
Tonći Grgurinović ◽  
Jelena Šarac ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Cardiovascular Risk ◽  
Clinical Decision Making ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Classification Performance ◽  
Biomedical Data ◽  
Sport Practice

Cardiovascular diseases are the main cause of death worldwide. The aim of the present study is to verify the performances of a data mining methodology in the evaluation of cardiovascular risk in athletes, and whether the results may be used to support clinical decision making. Anthropometric (height and weight), demographic (age and sex) and biomedical (blood pressure and pulse rate) data of 26,002 athletes were collected in 2012 during routine sport medical examinations, which included electrocardiography at rest. Subjects were involved in competitive sport practice, for which medical clearance was needed. Outcomes were negative for the largest majority, as expected in an active population. Resampling was applied to balance positive/negative class ratio. A decision tree and logistic regression were used to classify individuals as either at risk or not. The receiver operating characteristic curve was used to assess classification performances. Data mining and resampling improved cardiovascular risk assessment in terms of increased area under the curve. The proposed methodology can be effectively applied to biomedical data in order to optimize clinical decision making, and—at the same time—minimize the amount of unnecessary examinations.

scholarly journals Development, Implementation, and Evaluation of an In-Hospital Optimized Early Warning Score for Patient Deterioration

2020 ◽  
Vol 5 (1) ◽  
pp. 238146831989966 ◽  
Author(s):  
Cara O’Brien ◽  
Benjamin A. Goldstein ◽  
Yueqi Shen ◽  
Matthew Phelan ◽  
Curtis Lambert ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Early Warning ◽  
Clinical Decision Making ◽  
Risk Model ◽  
Clinical Status ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Early Warning Score ◽  
Support Tool ◽  
National Early Warning Score

Background. Identification of patients at risk of deteriorating during their hospitalization is an important concern. However, many off-shelf scores have poor in-center performance. In this article, we report our experience developing, implementing, and evaluating an in-hospital score for deterioration. Methods. We abstracted 3 years of data (2014–2016) and identified patients on medical wards that died or were transferred to the intensive care unit. We developed a time-varying risk model and then implemented the model over a 10-week period to assess prospective predictive performance. We compared performance to our currently used tool, National Early Warning Score. In order to aid clinical decision making, we transformed the quantitative score into a three-level clinical decision support tool. Results. The developed risk score had an average area under the curve of 0.814 (95% confidence interval = 0.79–0.83) versus 0.740 (95% confidence interval = 0.72–0.76) for the National Early Warning Score. We found the proposed score was able to respond to acute clinical changes in patients’ clinical status. Upon implementing the score, we were able to achieve the desired positive predictive value but needed to retune the thresholds to get the desired sensitivity. Discussion. This work illustrates the potential for academic medical centers to build, refine, and implement risk models that are targeted to their patient population and work flow.

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