Value In Clinical Decision-making Research Articles

Risk factor analysis and prediction model construction for contralateral central lymph node metastasis in unilateral papillary thyroid carcinoma

ObjectiveTo investigate contralateral central lymph node metastasis (CCLNM) in patients with unilateral papillary thyroid carcinoma (UPTC). To provide a reference for clinical decision-making, a prediction model for the probability of CCLNM was established.MethodThe clinicopathological data of 221 UPTC patients who underwent surgical treatment were retrospectively analyzed. Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors for CCLNM according to clinicopathological characteristics, construct a prediction model to construct a visual nomogram, and evaluate the model.ResultsAccording to univariate and multivariate logistic regression analyses, sex (P = 0.01, OR: 3.790, 95% CI: 1.373–10.465), extrathyroidal tumor extension (ETE) (P = 0.040, OR: 6.364, 95% CI: 1.083–37.381), tumor diameter (P = 0.010, OR: 3.674, 95% CI: 1.372–9.839) and ipsilateral central lymph node metastasis (ICLNM) (P < 0.001, OR: 38.552, 95% CI: 2.675–27.342) were found to be independent risk factors for CCLNM and were used to construct a nomogram for internal verification. The ROC curve had an AUC of 0.852 in the training group and an AUC of 0.848 in the verification group, and the calibration curve indicated that the prediction probability of the model was consistent with the actual probability. Finally, the analysis of the decision curve showed that the model has good application value in clinical decision-making.ConclusionSex, ETE, tumor size, and ICLNM emerged as independent risk factors for CCLNM in UPTC patients. A predictive model was therefore developed, harnessing these variables to enable an objective, personalized estimation of CCLNM risk. This tool offers valuable insights to inform surgical planning and optimize treatment strategies for UPTC management.

Considerations of throwing athletes with upper extremity injury when completing the Single Assessment Numeric Evaluation (SANE).

The Single Assessment Numeric Evaluation (SANE) is a widely used patient-reported outcome (PRO) measure that provides an efficient, but limited view of patient perceptions of health. Knowledge of what throwing athletes with upper extremity injury consider when answering the SANE would inform score interpretation and increase its value for clinical decision-making in this patient population. To investigate the global rating of the SANE and its ability to capture constructs of health reflected in patient-reported outcome measures that are commonly used in throwing athletes with upper extremity sport-related injury. Cross-sectional study. Retrospective database review. De-identified patient records of baseball and softball athletes diagnosed with upper extremity sport-related injury between October 2009 and June 2021 were reviewed. Primary outcomes were scores on the SANE; Functional Arm Scale for Throwers (FAST) total; Disabilities of the Arm, Shoulder and Hand (DASH) total; and Global Rating of Daily Activities (GRODA). The first administration of all PROs that patients completed post-injury were analyzed as potential predictors of SANE scores. The proportion of variance uniquely accounted for in the SANE by each predictor (R2) variable was estimated. Fifty-five patients completed PRO measures. The FAST total uniquely accounted for 32.9% (P <.001), the GRODA uniquely accounted for 11.6% (P <.001), and the DASH total uniquely accounted for 4.6% (P=.036) of the variance in the SANE score. Overall, the predictors accounted for 49.2% of the variance in the SANE score (P <.001). Given that the SANE captures multiple constructs of health, it may be useful in gathering a quick, broad view of throwing athletes' perception of their health. When SANE scores suggest diminished health, then multi-item PROs should be considered to further explore constructs of health most impacted.

D-dimer as a Useful Biomarker in Early Diagnosis of Neonatal Sepsis: A Single-Center Study From Romania.

This study evaluates the role of D-dimer in identifying neonatal sepsis and their potential value in clinical decision-making due to challenges in early detection. A case-control study was conducted on 102 neonates at the Children's Clinical Hospital "Louis Turcanu" in Timisoara, Romania, from October 2018 to July 2023. The participants were divided into two groups: those with neonatal sepsis and those without sepsis. The study found that neonates with sepsis were more likely to be delivered by cesarean section and had higher rates of premature ruptured membranes compared to those without sepsis. The D-dimer biomarker's predictive value for sepsis was assessed using a receiver operating characteristic (ROC) curve, with an area under the curve (AUC) exceeding 0.982 and an optimum cutoff value of 342 ng/mL. An increase in neonatal D-dimer significantly increases the likelihood of sepsis by 2.7% per unit increase. A value above 250 ng/mL indicates a 127-fold increased likelihood of sepsis. The D-dimer's ability to predict mortality innewborns with sepsis is unsatisfactory, with an AUC of 0.528. D-dimer, a potential biomarker of neonatal sepsis, warrants further clinical investigation to enhance diagnostic sensitivity and specificity, demonstrating its potential in conjunction with other sepsis markers.

Management of procalcitonin test overuse in an emergency department through a computer algorithm.

To show the procalcitonin (PCT) test demand from an emergency department (ED) over several years, to decrease PCT measurement via a computerized algorithm based on C-reactive protein (CRP) value, and to evaluate the subsequent economic savings. A cross-sectional study was performed from January 1, 2018, to May 31, 2019, to evaluate an intervention to avoid PCT measurement in the ED of Hospital Universitario San Juan in Alicante in Spain, when CRP values are low. A PCT result of at least 1.5 ng/mL was agreed upon with ED providers in our study as the value for clinical decision-making, with values less than 1.5 ng/mL considered negative. We retrospectively reviewed all PCT and CRP values for ED patients and calculated the diagnostic indicators for PCT at 4 different CRP cutoffs using the PCT quantification as the gold standard. From July 1, 2019, to April 30, 2021, the agreed-upon strategy was implemented, and we counted the PCT tests avoided and calculated the savings. PCT was not measured when CRP values were less than the selected CRP cutoff of 0.8 mg/dL, at which false-negative results were 1% and the 99th percentile of PCT was 1.5 ng/mL. In the postintervention period, 1091 PCT values were not measured and $11,553.69 was saved. An intervention to decrease PCT measurement in the ED designed by the clinical laboratory staff in consensus with requesting clinicians and based on CRP values decreased PCT testing and generated significant economic savings.

A Simple Frailty Score Predicts Survival and Early Mortality in Systemic AL Amyloidosis.

Systemic AL amyloidosis is a challenging disease for which many patients are considered frail in daily clinical practice. However, no study has so far addressed frailty and its impact on the outcome of these patients. We built a simple score to predict mortality based on three frailty-associated variables: age, ECOG performance status (<2 vs. ≥2) and NT-proBNP (<8500 vs. ≥8500 ng/L). Four-hundred and sixteen consecutive newly diagnosed patients diagnosed at ten sites from the Spanish Myeloma Group were eligible for the study. The score was developed in a derivation cohort from a referral center, and it was externally validated in a multicenter cohort. Multivariate analysis showed that the three variables were independent predictors of survival. The score was able to discriminate four groups of patients in terms of overall survival and early mortality in both cohorts. Comorbidity was also analyzed with the Charlson comorbidity index, but it did not reach statistical significance in the model. A nomogram was created to easily estimate the mortality risk of each patient at each time point. This score is a simple, robust, and efficient approach to dynamically assess frailty-dependent mortality both at diagnosis and throughout follow-up. The optimal treatment for frail AL amyloidosis patients remains to be determined but we suggest that the estimation of frailty-associated risk could complement current staging systems, adding value in clinical decision-making in this complex scenario.

Optimized derivative fast Fourier transform with high resolution and low noise from encoded time signals: Ovarian NMR spectroscopy

The unfiltered derivative fast Fourier transform (dFFT) of degrees higher than two fails flagrantly for encoded time signals. These data are always dominated by noise at larger times of encodings. Such a breakdown is due to processing the unweighted product of the time signal and the time power function. The latter is generated by the frequency derivative operator applied to the fast Fourier transform (FFT). As a result, the unfiltered dFFT cannot separate the overlapped resonances and it dramatically decreases signal-to-noise ratio (SNR) relative to the FFT. This problem is solved by a derivative-adapted optimization with the properly attenuated filters. The ensuing optimized dFFT achieves the long sought simultaneous enhancement of both resolution and SNR. It uncovers the genuine resonances hidden within overlapping peaks to enable quantitative interpretations. It does not impose any model on the input time signals nor on the output lineshape in the spectra. It is computationally expedient as it uses the Cooley-Tukey fast algorithm. The present applications deal with time signals encoded by in vitro NMR spectroscopy from human malignant ovarian cyst fluid. A remarkably successful performance of the optimized dFFT is demonstrated for reconstructed spectra of potentially added value in clinical decision-making.

Analysis of Related Risk Factors and Prognostic Factors of Gastric Cancer with Liver Metastasis: A SEER and External Validation Based Study.

Gastric cancer (GC) has a poor prognosis, particularly in patients with liver metastasis (LM). This study aims to identify relevant factors associated with the occurrence of LM in GC patients and factors influencing the prognosis of gastric cancer with liver metastasis (GCLM) patients, in addition to developing diagnostic and prognostic nomograms specifically. Overall, 6184 training data were from the Surveillance, Epidemiology, and End Results (SEER) database from 2011 to 2015. 1527 validation data were from our hospital between January 2018 and December 2022. Logistic regression was used to identify the risk factors associated with the occurrence of LM in GC patients, Cox regression was used to confirm the prognostic factors of GCLM patients. Two nomogram models were established to predict the risk and overall survival (OS) of patients with GCLM. The performance of the two models was evaluated using the area under the curve (AUC), concordance index (C-index), and calibration curves. A nomogram included five independent factors from multivariate logistic regression: sex, lymph node removal, chemotherapy, T stage and N stage were constructed to calculate the possibility of LM. Internal and external verifications of AUC were 0.786 and 0.885, respectively. The other nomogram included four independent factors from multivariate Cox regression: surgery at primary site, surgery at other site, chemotherapy, and N stage were constructed to predict OS. C-index for internal and external validations were 0.714 and 0.702, respectively, and the calibration curves demonstrated the robust discriminative ability of the models. Based on the SEER database and validation data, we defined effective nomogram models to predict risk and OS in patients with GCLM. They have important value in clinical decision-making and personalized treatment.

Association of multimorbidity with mortality after stroke stratified by age, severity, etiology, and prior disability.

Multimorbidity is common in patients with stroke and is associated with increased medium- to long-term mortality, but its value for clinical decision-making and case-mix adjustment will depend on other factors, such as age, stroke severity, etiological subtype, prior disability, and vascular risk factors. In the absence of previous studies, we related multimorbidity to long-term post-stroke mortality with stratification by these factors. In patients ascertained in a population-based stroke incidence study (Oxford Vascular Study; 2002-2017), we related pre-stroke multimorbidity (weighted/unweighted Charlson comorbidity index (CCI)) to all-cause/vascular/non-vascular mortality (1/5/10 years) using regression models adjusted/stratified by age, sex, predicted early outcome (THRIVE score), stroke severity (NIH stroke scale (NIHSS)), etiology (Trial of Org 10172 in Acute Stroke Treatment (TOAST)), premorbid disability (modified Rankin Scale (mRS)), and non-CCI risk factors (hypertension, hyperlipidemia, atrial fibrillation, smoking, deprivation, anxiety/depression). Among 2454 stroke patients (M/SD age: 74.1/13.9 years; 48.9% male; M/SD NIHSS: 5.7/7.0), 1375/56.0% had ⩾ 1 CCI comorbidity and 685/27.9% had ⩾ 2. After age/sex adjustment, multimorbidity (unweighted CCI ⩾ 2 vs 0) predicted (all ps < 0.001) mortality at 1 year (aHR = 1.57, 95% CI = 1.38-1.78), 5 years (aHR = 1.73, 95% CI = 1.53-1.96), and 10 years (aHR = 1.79, 95% CI = 1.58-2.03). Although multimorbidity was independently associated with premorbid disability (mRS > 2: aOR = 2.76, 2.13-3.60) and non-CCI risk factors (hypertension: 1.56, 1.25-1.95; hyperlipidemia: 2.58, 2.03-3.28; atrial fibrillation: 2.31; 1.78-2.98; smoking: 1.37, 1.01-1.86), it predicted death after adjustment for all measured confounders (10-year-aHR = 1.56, 1.37-1.78, p < 0.001), driven mainly by non-vascular death (aHR = 1.89, 1.55-2.29). Predictive value for 10-year all-cause death was greatest in patients with lower expected early mortality: lower THRIVE score (pint < 0.001), age < 75 years (aHR = 2.27, 1.71-3.00), NIHSS < 5 (1.84, 1.53-2.21), and lacunar stroke (3.56, 2.14-5.91). Results were similar using the weighted CCI. Pre-stroke multimorbidity is highly prevalent and is an independent predictor of death after stroke, supporting its inclusion in case-mix adjustment models and in informing decision-making by patients, families, and carers. Prediction in younger patients and after minor stroke, particularly for non-vascular death, suggests potential clinical utility in targeting interventions that require survival for 5-10 years to achieve a favorable risk/benefit ratio. Data requests will be considered by the Oxford Vascular Study (OXVASC) Study Director (P.M.R.-peter.rothwell@ndcn.ox.ac.uk).

Identification of disulfidptosis-related subtypes and development of a prognosis model based on stacking framework in renal clear cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor with an unsatisfactory prognosis. This study aims to identify the expression patterns of disulfidptosis-related genes (DRGs), develop a prognostic model, and predict immunological profiles. First, we identified differentially expressed DRGs in TCGA-KIRC cohort and analyzed their mutational profiles, methylation levels, and interaction networks. Subsequently, we identified disulfidptosis-associated molecular subtypes and investigated their prognostic and immunological characteristics. Simultaneously, a disulfidptosis-related prognostic signature (DRPS) was developed using a two-stage stacking framework consisting of 5 machine learning models. The effect of DRPS on immune cell infiltration levels was explored using seven different algorithms, and the status and function of T cells for distinct risk-score groups were evaluated based on T cell exhaustion and dysfunction scores. Additionally, the study also examined differences in clinical characteristics and therapy efficacy between high- and low-risk groups. We found two disulfidptosis-associated clusters, one of which had a poor prognosis and was linked to high immune cell infiltration but impaired T cell function. DRPS showed excellent predictive performance in all four cohorts and could accurately identified disulfidptosis-related molecular subtypes. The DRPS-based risk score was positively associated with poor prognosis, malignant pathological features, high immune cell infiltration levels, and T cell exhaustion or dysfunction, and better respond to immunotherapy and targeted therapy.Additionally, we have identified a close association between ISG20 and disulfidptosis as well as tumor immunity. Our study identified distinct disulfidptosis-related subtypes in ccRCC patients, and constructed the highly accurate and robust DRPS based on an ensemble learning framework, which has critical reference value in clinical decision-making and individualized treatment.And this workalso revealedISG20 exhibits promising potential as a therapeutic target for ccRCC.

A prediction model for prognosis of gastric adenocarcinoma based on six metabolism-related genes

BackgroundThe study of tumor metabolism is of great value to elucidate the mechanism of tumorigenesis and predict the prognosis of patients. However, the prognostic role of metabolism-related genes (MRGs) in gastric adenocarcinoma (GAD) remains poorly understood. MethodsWe downloaded the gene chip dataset GSE79973 (n = 20) of GAD from the Gene Expression Omnibus (GEO) database to compare differentially expressed genes (DEGs) between normal and tumor tissues. We then extracted MRGs from these DEGs and systematically investigated the prognostic value of these differential MRGs for predicting patients' overall survival by univariable and multivariable Cox regression analysis. Six metabolic genes (ACOX3, APOE, DIO2, HSD17B4, NUAK1, and WHSC1L1) were identified as prognosis-associated hub genes, which were used to build a prognostic model in the training dataset GSE15459 (n = 200), and then validated in the dataset GSE62254 (n = 300). ResultsPatients were divided into high-risk and low-risk subgroups based on the model's risk score, and it was found that patients in the high-risk subgroup had shorter overall survival than those in the low-risk subgroup, both in the training and testing datasets. In addition, for the training and testing cohorts, the area under the ROC curve of the prognostic model for one-year survival prediction was 0.723 and 0.667, respectively, indicating that the model has good predictive performance. Furthermore, we established a nomogram based on tumor stage and risk score to effectively predict the overall survival (OS) of GAD patients. The expression of 6 MRGs at the protein level was confirmed by immunohistochemistry (IHC). Kaplan-Meier survival analysis further confirmed that their expression influenced OS in GAD patients. ConclusionCollectively, the 6 MRGs signature might be a reliable tool for assessing OS in GAD patients, with potential application value in clinical decision-making and individualized therapy.

Molecular Biomarkers of Malignant Transformation in Head and Neck Dysplasia.

Head and neck squamous cell carcinoma (HNSCC) and its treatments are associated with substantial morbidity, often resulting in cosmetic deformity and loss of physiologic functions including speech and swallowing. Despite advancements in treatment, 5-year survival rates for mucosal malignancies remain below 70%. Effective prevention of HNSCC demands an understanding of the molecular pathways of carcinogenesis. Specifically, defining features of pre-cancerous dysplastic lesions that indicate a better or worse prognosis is necessary to help identify patients who are likely to develop a carcinoma and allow a more aggressive approach to management. There remains a need for identification of biomarkers that can provide both early prognostic and predictive value in clinical decision-making by serving as both therapeutic targets as well as predictors of therapy response. Here, we comprehensively review the most frequently altered molecular biomarkers of malignant transformation in head and neck dysplasia. These markers are involved in a wide range of cellular processes in head and neck carcinogenesis, including extracellular matrix degradation, cell motility and invasion, cell-cell adhesion, solute transport, immortalization, metabolism, the cell cycle and apoptosis, transcription, and cell signaling.

Role of Chromogranin A in the Diagnosis and Follow-up of Neuroendocrine Tumors

The aim of this study was to assess the utility of serum chromogranin A (CgA) along the clinical pathway of patients with neuroendocrine tumors (NETs). A retrospective review of medical records was conducted of patients with NET who had at least 1 measurement of CgA between January 2015 and April 2021 at a large metropolitan Australian hospital. Chromogranin A was classified as increased or decreased if there was at least a 25% change in sequential levels and was compared with disease response by anatomical or functional imaging if within 6 weeks (considered concurrent). Of 102 patients with NETs, 67 had at least 1 serum CgA level: 50 had been ordered during diagnostic workup, of which 33 were elevated (sensitivity: 66%; 95% confidence interval, 51%-79%). Of 129 CgA results concurrent with imaging, the sensitivity for detecting progressive disease was 28% (95% confidence interval, 15%-44%). Our findings support previous concerns that CgA adds little value in clinical decision-making.

A Machine Learning Algorithm for Predicting the Risk of Developing to M1b Stage of Patients With Germ Cell Testicular Cancer.

Objective:Distant metastasis other than non-regional lymph nodes and lung (i.e., M1b stage) significantly contributes to the poor survival prognosis of patients with germ cell testicular cancer (GCTC). The aim of this study was to develop a machine learning (ML) algorithm model to predict the risk of patients with GCTC developing the M1b stage, which can be used to assist in early intervention of patients.MethodsThe clinical and pathological data of patients with GCTC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Combing the patient's characteristic variables, we applied six machine learning (ML) algorithms to develop the predictive models, including logistic regression(LR), eXtreme Gradient Boosting (XGBoost), light Gradient Boosting Machine (lightGBM), random forest (RF), multilayer perceptron (MLP), and k-nearest neighbor (kNN). Model performances were evaluated by 10-fold cross-receiver operating characteristic (ROC) curves, which calculated the area under the curve (AUC) of models for predictive accuracy. A total of 54 patients from our own center (October 2006 to June 2021) were collected as the external validation cohort.ResultsA total of 4,323 patients eligible for inclusion were screened for enrollment from the SEER database, of which 178 (4.12%) developing M1b stage. Multivariate logistic regression showed that lymph node dissection (LND), T stage, N stage, lung metastases, and distant lymph node metastases were the independent predictors of developing M1b stage risk. The models based on both the XGBoost and RF algorithms showed stable and efficient prediction performance in the training and external validation groups.ConclusionS-stage is not an independent factor for predicting the risk of developing the M1b stage of patients with GCTC. The ML models based on both XGBoost and RF algorithms have high predictive effectiveness and may be used to predict the risk of developing the M1b stage of patients with GCTC, which is of promising value in clinical decision-making. Models still need to be tested with a larger sample of real-world data.

‘Is this something I should be worried about?’: A study of nurses' recontextualisation work when making clinical decisions based on patient reported outcome data

As clinical care practices are becoming more digitalised, information about patients is increasingly being encoded as quantified data, and the processes of sorting data are often supported by algorithmic computations. One such practice becoming more prevalent across Western countries is the clinical use of Patient Reported Outcome (PRO) data. Drawing on ethnographic fieldwork carried out in a Danish setting among nurses managing PRO-based breast cancer follow-up, we examine how clinical decisions are made on the basis of PRO-data and what this requires from the nurses. By applying the concept of recontextualisation work as an analytical perspective, we shed light on the efforts of nurses when mobilising complementary information about patients in order to recontextualise the otherwise decontextualised data, thereby giving data practical value in clinical decision-making. Recontextualisation work, we show, is shaped by organisational structure, available resources, and nurses' professional capacity. Drawing analytical attention to the work of recontextualisation allows for a nuanced understanding of the efforts required to make data workable and hence what it takes to carry out clinical decisions in today's datafied healthcare system.

Development and validation of a RNA binding protein-associated prognostic model for head and neck squamous cell carcinoma.

Evidence shows that defects in RNA-binding proteins (RBPs) are closely related to the occurrence and development of HNSCC. We obtained 502 tumors and 44 normal samples from the TCGA database, among which 190 differentially expressed RBPs were screened. Finally, a prognostic model containing nine RBPs (CELF2, CPEB1, DDX39B, EIF3L, EZH2, KHDRBS3, RNASE10, RNASE3 and SIDT1) was produced. Further analysis showed that the overall survival rate in the high-risk group was lower than that in the low-risk group. The area under the ROC curve (AUC) in the training and testing groups was significant (3-year AUC, 0.735 vs 0.796; 5-year AUC, 0.821 vs 0.804). In addition, a comprehensive analysis of nine identified RBPs showed that most of them were related to the OS of HNSCC patients, and three of them (CELF2, EZH2, and SIDT1) were differentially expressed in HNSCC and control tissues at the protein level. In addition, our data revealed that the identified RBPs are highly interconnected, with high frequency copy number changes in HNSCC samples. GSEA indicated that the abnormal biological processes related to RNA and the activation of some classical tumor signaling pathways were important driving forces for the development of HNSCC. Our results provide novel insights into the pathogenesis of HNSCC, among which nine RBP markers have potential application value in clinical decision-making and individualized treatment of HNSCC.

Development and validation of an immune-related gene prognostic model for stomach adenocarcinoma.

Purpose: Stomach adenocarcinoma (STAD) is one of the most common malignant tumors, and its occurrence and prognosis are closely related to inflammation. The aim of the present study was to identify gene signatures and construct an immune-related gene (IRG) prognostic model in STAD using bioinformatics analysis.Methods: RNA sequencing data from healthy samples and samples with STAD, IRGs, and transcription factors were analyzed. The hub IRGs were identified using univariate and multivariate Cox regression analyses. Using the hub IRGs, we constructed an IRG prognostic model. The relationships between IRG prognostic models and clinical data were tested.Results: A total of 289 differentially expressed IRGs and 20 prognostic IRGs were screened with a threshold of P<0.05. Through multivariate stepwise Cox regression analysis, we developed a prognostic model based on seven IRGs. The prognostic model was validated using a GEO dataset (GSE 84437). The IRGs were significantly correlated with the clinical outcomes (age, histological grade, N, and M stage) of STAD patients. The infiltration abundances of dendritic cells and macrophages were higher in the high-risk group than in the low-risk group.Conclusions: Our results provide novel insights into the pathogenesis of STAD. An IRG prognostic model based on seven IRGs exhibited the predictive value, and have potential application value in clinical decision-making and individualized treatment.

Large scale, robust, and accurate whole transcriptome profiling from clinical formalin-fixed paraffin-embedded samples

Transcriptome profiling can provide information of great value in clinical decision-making, yet RNA from readily available formalin-fixed paraffin-embedded (FFPE) tissue is often too degraded for quality sequencing. To assess the clinical utility of FFPE-derived RNA, we performed ribo-deplete RNA extractions on > 3200 FFPE slide samples; 25 of these had direct FFPE vs. fresh frozen (FF) replicates, 57 were sequenced in 2 different labs, 87 underwent multiple library analyses, and 16 had direct microdissected vs. macrodissected replicates. Poly-A versus ribo-depletion RNA extraction methods were compared using transcriptomes of TCGA cohort and 3116 FFPE samples. Compared to FF, FFPE transcripts coding for nuclear/cytoplasmic proteins involved in DNA packaging, replication, and protein synthesis were detected at lower rates and zinc finger family transcripts were of poorer quality. The greatest difference in extraction methods was in histone transcripts which typically lack poly-A tails. Encouragingly, the overall sequencing success rate was 81%. Exome coverage was highly concordant in direct FFPE and FF replicates, with 98% agreement in coding exon coverage and a median correlation of whole transcriptome profiles of 0.95. We provide strong rationale for clinical use of FFPE-derived RNA based on the robustness, reproducibility, and consistency of whole transcriptome profiling.

THE UTILITY OF MRI IN CLINICAL DECISION-MAKING FOR PEDIATRIC ATHLETES WITH SYMPTOMATIC SUBFIBULAR OSSICLES

Background:Pediatric athletes commonly sustain inversion-type ankle fractures.1,2 Approximately 1% will form post-traumatic subfibular ossicles (SO), especially after tip avulsion fractures.3,4 Athletes with SOs can have pain and recurrent sprains.5,6,7 Here we show the utility of magnetic resonance imaging (MRI) in clinical decision-making for athletes with ankle symptoms and the presence of a SO. Our hypothesis is that MRI can predict which athletes have a stable SO and will respond to non-operative management, and which athletes have an unstable SO and will need surgery.Methods:We performed an IRB approved retrospective cohort study. Children were eligible from our practices if (1) they had radiographic evidence of a SO, (2) they had symptoms of pain and recurrent sprains, and (3) they had undergone MRI during their clinical evaluation. We identified 19 eligible children (20 ankles;) eight girls and eleven boys, ages 5–19 years. Nine involved the left ankle; 11 the right ankle. Most frequent sport was soccer (12/16) followed by basketball (3/14.) From the radiograph we determined ossicle size and location. MRI images were considered positive if fluid-sensitive sequences showed a high-intensity signal between the SO and the fibular epiphysis. Main outcome was treatment (non-surgical or surgical) relative to the MRI findings.Results:Size shape and location:Size and shape were variable. Width ranged from 2 – 10.4 mm and length from 4 – 13.5 mm. Concerning location all were in the distal 1/3 pf the epiphysis. Six were anterior and 14 were anterior-inferior to the fibular tip.MRI findings:Sixteen of the 20 ankles (80%) had positive MRI findings (figure 1), and 4 had negative findings (figure 2). The ATFL attached directly to the fragment in 11 of the 16 MRI positive ankles.Clinical decision making:All athletes with negative MRI findings responded to non-operatively management. Ten of the 16 ankles with positive MRIs have had surgery. Eight had excision of the ossicle and two had internal fixation based on the size of the ossicle. Surgical findings confirmed attachment of the ATFL to the fragment (figure 3.) Six athletes with positive MRIs continue to be under observation.ConclusionsThe results support our hypothesis that MRI has predictive value in clinical decision-making for symptomatic athletes with a SO. When fluid-sensitive MRI sequences show high signal intensity between the ossicle and the fibular epiphysis, and when the ATFL attaches to the ossicle, the athlete has a poor prognosis for non-operative management.Figure 1.Seventeen-year-old with positive MRI who had internal fixation of the fragmentFigure 2.Note direct attachment of the ATFL to the mobile SO.

The incessant search for renal biomarkers: is it really justified?

This review summarizes the most recent and relevant findings in the search for novel biomarkers for the most common renal diseases. Unprecedented, fast-paced technical advances in biomedical research have offered an opportunity to identify novel and more specific renal biomarkers in several clinical settings. However, despite the huge efforts made, the molecules identified so far have generally failed to provide relevant information beyond what has already been generated by established biomarkers, such as serum creatinine and proteinuria, whereas the complexity and costs of these technology platforms hamper their widespread implementation. No novel renal biomarkers have added clear-cut additional value in clinical decision-making. The only exception is anti-phospholipase A2 receptor antibodies, which have been implemented successfully as a diagnostic and prognostic biomarker of membranous nephropathy. This achievement, along with the large number of ongoing collaborative projects worldwide, should lead the renal community to be quite confident regarding the successful qualification of novel and effective diagnostic, prognostic and therapeutic response biomarkers for kidney diseases, hopefully in the next few years.

Validation of high-sensitivity performance for a United States Food and Drug Administration cleared cardiac troponin I assay

BackgroundHigh-Sensitivity (hs) cardiac troponin (cTn) assays are categorized by two criteria: (i) cTn values above the limit of detection (LoD) for >50% of male and female healthy cohorts of ≥300 individuals; (ii) imprecision ≤10% total %CV for sex-specific 99th-percentile clinical decision values (CDVs). No documented hs-Tn assay has yet been FDA-cleared. MethodsThe PATHFAST cTnI-II assay's LoD was 2.3 ng/L using CLSI EP-17. The AACC Universal Sample Bank of 847 healthy men (50.6%) and women (49.4%) was used to determine 99th-percentile CDVs with Nonparametric, Harrell-Davis and Robust modeling. Health/Medication questionnaires and Amino-terminal proBNP, Hemoglobin A1c and estimated Glomerular Filtration Rate surrogates excluded underlying health conditions. ResultsThe cTnI-II test's total CV was 6.1% at 29 ng/L and 7.1% at 22 ng/L; the LoD was 2.3 ng/L. Of the full 847-member healthy cohort, 113 (13.3%) were excluded by abnormal surrogate biomarkers. The final 734-member healthy population had the following (% > LoD): overall, 487 (66.3%); women, 186 (52.8%); and men, 301 (78.8%). 99th-percentile CDVs by Nonparametric modeling were: 28 ng/L (90% CI: 20–30), overall final 732-member healthy population; 20 ng/L (90% CI: 13–30), 352 women; and 30 ng/L (90% CI: 21–37), 382 men. Differences between sex-specific CDVs were not significantly different (p > .05) with Nonparametric or Harrell-Davis modeling; however, Robust Modeling did show significance (<0.05), with lower CDVs at 11 ng/L (90% CI: 9–12) and 16 ng/L (90% CI: 15–18) for the female and male cohorts, respectively. ConclusionscTnI-II is the only FDA-cleared assay that has demonstrated high-sensitivity cTn assay. Use of recommended modeling in >300 healthy subjects for determining sex-specific 99th-percentile CDVs did not show statistically significant differences except with the Robust modeling.