Individual Patient Risk Assessment Research Articles

The application of thromboelastography in risk stratification for selective thromboembolism prophylaxis after total joint arthroplasty in Chinese: a randomized controlled trial.

As Asian populations, Chinese have a lower rate of high-risk gene mutations in venous thrombosis. Therefore, individual patient risk assessment, rather than a "blanket policy", is considered the best thromboembolism prophylaxis for Chinese. The purpose of this study was to evaluate the effectiveness and safety of selective thromboembolism prophylaxis compared with conventional thromboembolism prophylaxis by risk stratification with thromboelastography (TEG) after joint arthroplasty in Chinese. Between August 2016 to August 2017, Chinese patients who underwent hip or knee arthroplasty were randomly divided into a selective anticoagulation group (SAG) and a conventional anticoagulation group (CAG). Participants and outcome assessors were blinded. In the SAG, an anticoagulant was used when TEG indicated hypercoagulability; while in the CAG, an anticoagulant was regularly used until one month after surgery. Outcome evaluation included effectiveness (defined as the incidence of VTE), and safety (defined as the incidence of bleeding events, poor wound healing, blood loss, and infection). A total of 197 patients (79 in the SAG and 118 in the CAG) were included in the study. There was 1 case of deep vein thrombosis (DVT) in the SAG and 2 cases of DVT in the CAG, but there was no significant difference between the two groups. Hidden blood loss in the SAG was 707.4±539.8 mL and hidden blood loss in the CAG was 617±565.0 mL, respectively (P>0.05). No significant difference was observed in perioperative blood loss between the SAG and the CAG (1,024.9±597.9 and 1,139.3±620.9 mL, respectively). Volume of blood transfusion was 92.4±270.2 mL in the SAG and 224.6±416.3 mL in the CAG, respectively, while rate of transfusion was 13.9% in the SAG and 33.9% in the CAG, respectively, which were significantly different between the two groups (P<0.05). In Chinese patients who underwent hip or knee arthroplasty, the efficacy of selective anticoagulation using TEG in risk stratification was comparable to that of conventional anticoagulation. Furthermore, the safety of selective anticoagulant prophylaxis was superior to that of conventional anticoagulant prophylaxis.

Assessment of adherence to cancer-associated venous thromboembolism guideline and pharmacist's impact on anticoagulant therapy.

Cancer-associated venous thromboembolism (VTE) can cause many unfavorable health outcomes. Many institutions have published guidelines, but implementation of these guidelines in cancer clinics is still under investigation. This study aimed to evaluate the guideline adherence and identify potential gaps between the recommendations and their implications in clinics. A prospective study was conducted between September and December 2018 at oncology inpatient and ambulatory settings. The guideline adherence rate was assessed for inpatients during hospital stay by using 8 criteria developed based on the National Comprehensive Cancer Network (NCCN) Guideline on Cancer Associated Venous Thromboembolic Disease Version 1.2018. Guideline-based recommendations were proposed to the consultant physician in case of non-adherence. Khorana risk scores were calculated for each patient at outpatient clinics. In cases where the score was found to be ≥ 3, the consultant physician was informed. A total of 100 inpatients and 200 ambulatory patients were included in the study. The guideline adherence rates ranged between 59 and 100% for 5 out of 8 pre-defined criteria, whereas the rate for others remained at 0-1%. A significant increase was observed in the adherence rates for initiation of prophylaxis at admission and determination of correct dose of an anticoagulant after recommendations being implemented (p < 0.001, McNemar test). Eleven patients were identified as at high-risk of VTE at ambulatory setting; however, an initiation of an anticoagulant was not considered by the consultant physicians. There are potential problems in implementation of guideline recommendations, which leads to low adherence rate. Therefore, liason with pharmacists and consultants for individual risk assessment and monitoring of patients will help to increase guideline adherence rates.

Immune landscape and a novel immunotherapy-related gene signature associated with clinical outcome in early-stage lung adenocarcinoma

Patients with early-stage lung adenocarcinoma (LUAD) exhibit different overall survival (OS) rates and immunotherapy responses. Understanding the immune landscape facilitates the personalized treatment of LUAD. The immune cell populations in tumour tissues were quantified to depict the immune landscape in early-stage LUAD patients in The Cancer Genome Atlas (TCGA). Early-stage LUAD patients in three immune clusters identified by the immune landscape exhibited different survival potentials. A prognostic immune-related gene signature was built to predict the survival of early-stage LUAD patients. Several machine learning methods (support vector machine, naive Bayes, random forest, and neural network-based deep learning) were applied to train the classifiers to identify the immune clusters in early-stage LUAD based on the gene signature. The four classifiers exhibited a robust effect in identifying the immune clusters. A random forest regression model identified that TP53 was the most important gene mutation associated with the immune-related signature. Furthermore, a decision tree and a nomogram were constructed based on the immune-related gene signature and clinicopathological traits to improve risk stratification and quantify risk assessment for individual patients. Five external test cohorts were applied to validate the accuracy of the immune-related signature. Our study might contribute to the development of immunotherapy and the personalized treatment of early-stage LUAD.Key messagesImmune landscape correlates with the clinical outcome of early-stage adenocarcinoma (LUAD).Machine learning methods identifies a prognostic gene signature to predict the survival and prognosis of early-stage LUAD.TP53 gene mutation status correlates with the immune landscape in early-stage LUAD.

Development and validation of a hypoxia-related gene signature to predict overall survival in early-stage lung adenocarcinoma patients.

Background:Patients with early-stage lung adenocarcinoma (LUAD) exhibit significant heterogeneity in overall survival. The current tumour-node-metastasis staging system is insufficient to provide precise prediction for prognosis.Methods:We quantified the levels of various hallmarks of cancer and identified hypoxia as the primary risk factor for overall survival in early-stage LUAD. Different bioinformatic and statistical methods were combined to construct a robust hypoxia-related gene signature for prognosis. Furthermore, a decision tree and a nomogram were constructed based on the gene signature and clinicopathological features to improve risk stratification and quantify risk assessment for individual patients.Results:The hypoxia-related gene signature discriminated high-risk patients at an early stage in our investigated cohorts. Survival analyses demonstrated that our gene signature served as an independent risk factor for overall survival. The decision tree identified risk subgroups powerfully, and the nomogram exhibited high accuracy.Conclusions:Our study might contribute to the optimization of risk stratification for survival and personalized management of early-stage LUAD.

Large platelet size is associated with poor outcome in patients with metastatic pancreatic cancer

Background Platelets are a major cellular component of blood and their interaction with cancer cells is well-established to influence cancer progression and metastases. The physical size of platelets may have a critical impact on the interaction with cancer cells. In this study, we explored the potential prognostic role of platelet size measured by the determination of the mean platetlet volume (MPV) in patients with pancreatic ductal adenocarcinoma (PDAC). Methods Data from 527 patients with PDAC diagnosed and treated between 2004 and 2015 at a single center were evaluated retrospectively. Associations between MPV and baseline covariates were assessed with Wilcoxon's rank-sum tests, χ2-tests, and Fisher's exact tests. Median follow-up was estimated with a reverse Kaplan-Meier estimator according to Schemper and Smith. Analysis of time-to-death was performed with Kaplan-Meier estimators, log-rank tests and uni- and multivariable Cox proportional hazards models. Results The median MPV was 10.5 femto liter (fL) [9.8-11.3], ranged from 5.9 to 17.7 fL. A large platelet volume was associated with high-grade G3/4 tumors (p=0.004) and worse overall survival (OS) in patients with metastatic disease in univariable analysis (hazard ratio [HR] per fL increase in MPV=1.13 [95% CI: 1.04-1.23, p=0.005]). In multivariable analysis of metatatic PDAC patients, the adverse association between large platelets and a higher risk-of-death prevailed (adjusted HR per doubling of MPV=2.00; 95% CI: 1.10-3.62, p=0.02). Conclusions Large platelets represent a negative prognostic factor and add an independent prognostic information to well-established factors in PDAC patients. MPV should be considered for future individual risk assessment in patients with stage IV PDAC.

A prediction model of surgical site infection after instrumented thoracolumbar spine surgery in adults

PurposeThe aim of this study was to develop and internally validate a multivariable model for accurate prediction of surgical site infection (SSI) after instrumented spine surgery using a large cohort of a Western European academic center.MethodData of potential predictor variables were collected in 898 adult patients who underwent instrumented posterior fusion of the thoracolumbar spine. We used logistic regression analysis to develop the prediction model for SSI. The ability to discriminate between those who developed SSI and those who did not was quantified as the area under the receiver operating characteristic curve (AUC). Model calibration was evaluated by visual inspection of the calibration plot and by computing the Hosmer and Lemeshow goodness-of-fit test.ResultsSixty patients (6.7%) were diagnosed with an SSI. After backward stepwise elimination of predictor variables, we formulated a model in which an individual’s risk of an SSI can be computed. Age, body mass index, ASA score, degenerative or revision surgery and NSAID use appeared to be independent predictor variables for the risk of SSI. The AUC was 0.72 (95% CI 0.65–0.79), indicating reasonable discriminative ability.ConclusionsWe developed and internally validated a prediction model for SSI after instrumented thoracolumbar spine surgery using predictor variables of standard clinical practice that showed reasonable discriminative ability and calibration. Identification of patients at risk for SSI allows for individualized patient risk assessment with better patient-specific counseling and may accelerate the implementation of multi-disciplinary strategies for reduction of SSI.Graphical abstractThese slides can be retrieved under Electronic Supplementary Material.

An Integrated Score and Nomogram Combining Clinical and Immunohistochemistry Factors to Predict High ISUP Grade Clear Cell Renal Cell Carcinoma.

Objective: The International Society of Urological Pathology (ISUP) has proposed a grading system to classify renal cell carcinoma (RCC). However, classification using biopsy specimens remains problematic and, consequently, the accuracy of a biopsy-based diagnosis is relatively poor. This study aims to combine clinical and immunohistochemical (IHC) factors for the prediction of high ISUP grade clear cell RCC (ccRCC) in an attempt to complement and improve the accuracy of a biopsy-based diagnosis.Methods: A total of 362 ccRCC patients were enrolled in this study and used for the training set. We performed IHC analysis of 18 protein markers on standard tissue sections using an automated stainer. Multivariate logistic regression models were developed to evaluate independent predictors for high ISUP grade. We evaluated different prediction models using receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) analysis. A nomogram for the derivation of an integrated score for predicting high ISUP grade ccRCC and a calibration curve were also plotted. Finally, an internal validation cohort was examined to evaluate the performance of our integrated scoring system and nomogram.Results: Multivariate logistic analyses revealed seven credible candidates for predicting high grade ISUP. These were age, tumor diameter, surgery, and CK7, Ki-67, PTEN, and MTOR protein expression. The ROC curves for the clinical, IHC and integrated models were compared in the training set, and the AUC for each was 0.731, 0.744, and 0.801, respectively. DeLong's test showed that the integrated model was significantly better at predicting high ISUP grade, when compared with the other models. Internal validation confirmed the good performance of the integrated score in predicting ISUP grade.Conclusion: We have developed a nomogram integrating clinical and immunohistochemical parameters to predict high ISUP grade for M0 ccRCC patients. This nomogram may offer potentially useful information during preoperative individualized patient risk assessment, and consequently may help urologists when planning personalized management regimens.

Development and internal validation of a nine-lncRNA prognostic signature for prediction of overall survival in colorectal cancer patients.

BackgroundColorectal cancer remains a serious public health problem due to the poor prognosis. In the present study, we attempted to develop and validate a prognostic signature to predict the individual mortality risk in colorectal cancer patients.Materials and MethodsThe original study datasets were downloaded from The Cancer Genome Atlas database. The present study finally included 424 colorectal cancer patients with wholly gene expression information and overall survival information.ResultsA nine-lncRNA prognostic signature was built through univariate and multivariate Cox proportional regression model. Time-dependent receiver operating characteristic curves in model cohort demonstrated that the Harrell’s concordance indexes of nine-lncRNA prognostic signature were 0.768 (95% CI [0.717–0.819]), 0.778 (95% CI [0.727–0.829]) and 0.870 (95% CI [0.819–0.921]) for 1-year, 3-year and 5-year overall survival respectively. In validation cohort, the Harrell’s concordance indexes of nine-lncRNA prognostic signature were 0.761 (95% CI [0.710–0.812]), 0.801 (95% CI [0.750–0.852]) and 0.883 (95% CI [0.832–0.934]) for 1-year, 3-year and 5-year overall survival respectively. According to the median of nine-lncRNA prognostic signature score in model cohort, 424 CRC patients could be stratified into high risk group (n = 212) and low risk group (n = 212). Kaplan–Meier survival curves showed that the overall survival rate of high risk group was significantly lower than that of low risk group (P < 0.001).DiscussionThe present study developed and validated a nine-lncRNA prognostic signature for individual mortality risk assessment in colorectal cancer patients. This nine-lncRNA prognostic signature is helpful to evaluate the individual mortality risk and to improve the decision making of individualized treatments in colorectal cancer patients.

Cardiorespiratory Fitness and Mortality in Healthy Men and Women

BackgroundThere is a well-established inverse relationship between cardiorespiratory fitness (CRF) and mortality. However, this relationship has almost exclusively been studied using estimated CRF. ObjectivesThis study aimed to assess the association of directly measured CRF, obtained using cardiopulmonary exercise (CPX) testing with all-cause, cardiovascular disease (CVD), and cancer mortality in apparently healthy men and women. MethodsParticipants included 4,137 self-referred apparently healthy adults (2,326 men, 1,811 women; mean age: 42.8 ± 12.2 years) who underwent CPX testing to determine baseline CRF. Participants were followed for 24.2 ± 11.7 years (1.1 to 49.3 years) for mortality. Cox-proportional hazard models were performed to determine the relationship of CRF (ml·kg-1·min-1) and CRF level (low, moderate, and high) with mortality outcomes. ResultsDuring follow-up, 727 participants died (524 men, 203 women). CPX-derived CRF was inversely related to all-cause, CVD, and cancer mortality. Low CRF was associated with higher risk for all-cause (hazard ratio [HR]: 1.73; 95% confidence interval [CI]: 1.20 to 3.50), CVD (HR: 2.27; 95% CI: 1.20 to 3.49), and cancer (HR: 2.07; 95% CI: 1.18 to 3.36) mortality compared with high CRF. Further, each metabolic equivalent increment increase in CRF was associated with a 11.6%, 16.1%, and 14.0% reductions in all-cause, CVD, and cancer mortality, respectively. ConclusionsGiven the prognostic ability of CPX-derived CRF for all-cause and disease-specific mortality outcomes, its use should be highly considered for apparently healthy populations as it may help to improve the efficacy of the individualized patient risk assessment and guide clinical decisions.

Machine Meets Biology: a Primer on Artificial Intelligence in Cardiology and Cardiac Imaging.

An understanding of the basics concepts of deep learning can be helpful in not only understanding the potential applications of this technique but also in critically reviewing literature in which neural networks are utilized for analysis and modeling. The term "deep learning" has been applied to a subset of machine learning that utilizes a "neural network" and is often used interchangeably with "artificial intelligence." It has been increasingly utilized in healthcare for computational "learning", especially for pattern recognition for diagnostic imaging. Another promising application is the potential for these neural networks to improve the accuracy in the identification of patients who are at risk for cardiovascular events and could benefit most from preventive treatment in comparison with more conventional statistical techniques. The importance of such tailored cardiovascular risk assessment and disease management in individual patients is far reaching given that cardiovascular disease is the leading cause of morbidity and mortality in the world. Nearly half of myocardial infarctions and strokes occur in patients who are not predicted to be at risk for cardiovascular events by current guideline-based approaches. Equally important are individuals who are not at risk for cardiovascular events and yet are given expensive and unnecessary preventive treatment with potential untoward side effects. The application of powerful artificial intelligence/deep learning tools in medicine is likely to result in more effective and efficient health care delivery with the potential for significant cost savings by shifting preventative treatment from inappropriate to appropriate patient subgroups.

Novel biomarkers for patient stratification in colorectal cancer: A review of definitions, emerging concepts, and data.

Colorectal cancer (CRC) treatment has become more personalised, incorporating a combination of the individual patient risk assessment, gene testing, and chemotherapy with surgery for optimal care. The improvement of staging with high-resolution imaging has allowed more selective treatments, optimising survival outcomes. The next step is to identify biomarkers that can inform clinicians of expected prognosis and offer the most beneficial treatment, while reducing unnecessary morbidity for the patient. The search for biomarkers in CRC has been of significant interest, with questions remaining on their impact and applicability. The study of biomarkers can be broadly divided into metabolic, molecular, microRNA, epithelial-to-mesenchymal-transition (EMT), and imaging classes. Although numerous molecules have claimed to impact prognosis and treatment, their clinical application has been limited. Furthermore, routine testing of prognostic markers with no demonstrable influence on response to treatment is a questionable practice, as it increases cost and can adversely affect expectations of treatment. In this review we focus on recent developments and emerging biomarkers with potential utility for clinical translation in CRC. We examine and critically appraise novel imaging and molecular-based approaches; evaluate the promising array of microRNAs, analyze metabolic profiles, and highlight key findings for biomarker potential in the EMT pathway.

Management of cardiogenic shock complicating myocardial infarction.

Up to 10% of acute coronary syndromes are complicated by cardiogenic shock (CS) with contemporary mortality rates of 40-50%. The extent of ischemic myocardium has a profound impact on the initial, in-hospital, and post-discharge management and prognosis in this patient population. Individualized patient risk assessment plays an important role in determining appropriate revascularization, drug treatment with inotropes and vasopressors, mechanical circulatory support, intensive care support of other organ systems, hospital level of care triage, and allocation of clinical resources. This review will outline the underlying causes and diagnostic criteria, pathophysiology, and treatment of CS complicating acute coronary syndromes with a focus on (a) potential therapeutic issues from the perspective an interventional cardiologist, an emergency physician, and an intensive care physician, (b) the type of revascularization, and (c) new therapeutic advancements in pharmacologic and mechanical percutaneous circulatory support.

Prognosis for patients with amyotrophic lateral sclerosis: development and validation of a personalised prediction model

Amyotrophic lateral sclerosis (ALS) is a relentlessly progressive, fatal motor neuron disease with a variable natural history. There are no accurate models that predict the disease course and outcomes, which complicates risk assessment and counselling for individual patients, stratification of patients for trials, and timing of interventions. We therefore aimed to develop and validate a model for predicting a composite survival endpoint for individual patients with ALS. We obtained data for patients from 14 specialised ALS centres (each one designated as a cohort) in Belgium, France, the Netherlands, Germany, Ireland, Italy, Portugal, Switzerland, and the UK. All patients were diagnosed in the centres after excluding other diagnoses and classified according to revised El Escorial criteria. We assessed 16 patient characteristics as potential predictors of a composite survival outcome (time between onset of symptoms and non-invasive ventilation for more than 23 h per day, tracheostomy, or death) and applied backward elimination with bootstrapping in the largest population-based dataset for predictor selection. Data were gathered on the day of diagnosis or as soon as possible thereafter. Predictors that were selected in more than 70% of the bootstrap resamples were used to develop a multivariable Royston-Parmar model for predicting the composite survival outcome in individual patients. We assessed the generalisability of the model by estimating heterogeneity of predictive accuracy across external populations (ie, populations not used to develop the model) using internal-external cross-validation, and quantified the discrimination using the concordance (c) statistic (area under the receiver operator characteristic curve) and calibration using a calibration slope. Data were collected between Jan 1, 1992, and Sept 22, 2016 (the largest data-set included data from 1936 patients). The median follow-up time was 97·5 months (IQR 52·9-168·5). Eight candidate predictors entered the prediction model: bulbar versus non-bulbar onset (univariable hazard ratio [HR] 1·71, 95% CI 1·63-1·79), age at onset (1·03, 1·03-1·03), definite versus probable or possible ALS (1·47, 1·39-1·55), diagnostic delay (0·52, 0·51-0·53), forced vital capacity (HR 0·99, 0·99-0·99), progression rate (6·33, 5·92-6·76), frontotemporal dementia (1·34, 1·20-1·50), and presence of a C9orf72 repeat expansion (1·45, 1·31-1·61), all p<0·0001. The c statistic for external predictive accuracy of the model was 0·78 (95% CI 0·77-0·80; 95% prediction interval [PI] 0·74-0·82) and the calibration slope was 1·01 (95% CI 0·95-1·07; 95% PI 0·83-1·18). The model was used to define five groups with distinct median predicted (SE) and observed (SE) times in months from symptom onset to the composite survival outcome: very short 17·7 (0·20), 16·5 (0·23); short 25·3 (0·06), 25·2 (0·35); intermediate 32·2 (0·09), 32·8 (0·46); long 43·7 (0·21), 44·6 (0·74); and very long 91·0 (1·84), 85·6 (1·96). We have developed an externally validated model to predict survival without tracheostomy and non-invasive ventilation for more than 23 h per day in European patients with ALS. This model could be applied to individualised patient management, counselling, and future trial design, but to maximise the benefit and prevent harm it is intended to be used by medical doctors only. Netherlands ALS Foundation.

Development and validation of a gene expression-based signature to predict distant metastasis in locoregionally advanced nasopharyngeal carcinoma: a retrospective, multicentre, cohort study

Gene expression patterns can be used as prognostic biomarkers in various types of cancers. We aimed to identify a gene expression pattern for individual distant metastatic risk assessment in patients with locoregionally advanced nasopharyngeal carcinoma. In this multicentre, retrospective, cohort analysis, we included 937 patients with locoregionally advanced nasopharyngeal carcinoma from three Chinese hospitals: the Sun Yat-sen University Cancer Center (Guangzhou, China), the Affiliated Hospital of Guilin Medical University (Guilin, China), and the First People's Hospital of Foshan (Foshan, China). Using microarray analysis, we profiled mRNA gene expression between 24 paired locoregionally advanced nasopharyngeal carcinoma tumours from patients at Sun Yat-sen University Cancer Center with or without distant metastasis after radical treatment. Differentially expressed genes were examined using digital expression profiling in a training cohort (Guangzhou training cohort; n=410) to build a gene classifier using a penalised regression model. We validated the prognostic accuracy of this gene classifier in an internal validation cohort (Guangzhou internal validation cohort, n=204) and two external independent cohorts (Guilin cohort, n=165; Foshan cohort, n=158). The primary endpoint was distant metastasis-free survival. Secondary endpoints were disease-free survival and overall survival. We identified 137 differentially expressed genes between metastatic and non-metastatic locoregionally advanced nasopharyngeal carcinoma tissues. A distant metastasis gene signature for locoregionally advanced nasopharyngeal carcinoma (DMGN) that consisted of 13 genes was generated to classify patients into high-risk and low-risk groups in the training cohort. Patients with high-risk scores in the training cohort had shorter distant metastasis-free survival (hazard ratio [HR] 4·93, 95% CI 2·99-8·16; p<0·0001), disease-free survival (HR 3·51, 2·43-5·07; p<0·0001), and overall survival (HR 3·22, 2·18-4·76; p<0·0001) than patients with low-risk scores. The prognostic accuracy of DMGN was validated in the internal and external cohorts. Furthermore, among patients with low-risk scores in the combined training and internal cohorts, concurrent chemotherapy improved distant metastasis-free survival compared with those patients who did not receive concurrent chemotherapy (HR 0·40, 95% CI 0·19-0·83; p=0·011), whereas patients with high-risk scores did not benefit from concurrent chemotherapy (HR 1·03, 0·71-1·50; p=0·876). This was also validated in the two external cohorts combined. We developed a nomogram based on the DMGN and other variables that predicted an individual's risk of distant metastasis, which was strengthened by adding Epstein-Barr virus DNA status. The DMGN is a reliable prognostic tool for distant metastasis in patients with locoregionally advanced nasopharyngeal carcinoma and might be able to predict which patients benefit from concurrent chemotherapy. It has the potential to guide treatment decisions for patients at different risk of distant metastasis. The National Natural Science Foundation of China, the National Science & Technology Pillar Program during the Twelfth Five-year Plan Period, the Natural Science Foundation of Guang Dong Province, the National Key Research and Development Program of China, the Innovation Team Development Plan of the Ministry of Education, the Health & Medical Collaborative Innovation Project of Guangzhou City, China, and the Program of Introducing Talents of Discipline to Universities.

A Patient Risk Model of Chemotherapy-Induced Febrile Neutropenia: Lessons Learned From the ANC Study Group.

Neutropenia and its complications, including febrile neutropenia (FN), represent major toxicities associated with cancer chemotherapy, resulting in considerable morbidity, mortality, and costs. The myeloid growth factors such as granulocyte colony-stimulating factor (G-CSF) have been shown to reduce the risk of neutropenia complications while enabling safe and effective chemotherapy dose intensity. Concerns about the high costs of these agents along with limited physician adherence to clinical practice guidelines, resulting in both overuse and underuse, has stimulated interest in models for individual patient risk assessment to guide appropriate use of G-CSF. In a model developed and validated by the ANC Study Group, half of patients were classified as high risk and half as low risk based on patient-, disease-, and treatment-related factors. This model has been further validated in an independent patient population. Physician-assessed risk of FN, as well as the decision to use prophylactic CSF, has been shown to correlate poorly with the FN risk estimated by the model. Additional modeling efforts in both adults and children receiving cancer treatment have been reported. Identification of patients at a high individual risk for FN and its consequences may offer the potential for optimal chemotherapy delivery and patient outcomes. Likewise, identification of patients at low risk for neutropenic events may reduce costs when such supportive care is not warranted. This article reviews and summarizes FN modeling studies and the opportunities for personalizing supportive care in patients receiving chemotherapy.

Establishment and Validation of a Prediction Equation to Estimate Risk of Intraoperative Hypothermia in Patients Receiving General Anesthesia

Inadvertent intraoperative hypothermia (core temperature <36 °C) is a frequent but preventable complication of general anesthesia. Accurate risk assessment of individual patients may help physicians identify patients at risk for hypothermia and apply preventive approaches, which include active intraoperative warming. This study aimed to develop and validate a risk-prediction model for intraoperative hypothermia. Two independent observational studies in China, the Beijing Regional Survey and the China National Survey, were conducted in 2013 and 2014, respectively, to determine the incidence of hypothermia and its underlying risk factors. In this study, using data from these two studies, we first derived a risk calculation equation, estimating the predictive risk of hypothermia using National Survey data (3132 patients), then validated the equation using the Beijing Regional Survey data (830 patients). Measures of accuracy, discrimination and calibration were calculated in the validation data set. Through validation, this model, named Predictors Score, had sound overall accuracy (Brier Score = 0.211), good discrimination (C-Statistic = 0.759) and excellent calibration (Hosmer-Lemeshow, P = 0.5611). We conclude that the Predictors Score is a valid predictor of the risk of operative hypothermia and can be used in deciding whether intraoperative warming is a cost-effective measure in preventing the hypothermia.

Algoritm pentru evaluarea riscului de carie dentară

Introduction. Caries risk assessment in individual patients is mandatory as it becomes a starting point for developing patient-centered preventive strategies. Any method designed for caries risk assessment should be based on the risk and protection factors involved. Material and method. The first part of this study consists of a fundamental research based on a meta-analysis style study of a number of 128 articles present in the main online databases. The other part of the study is dedicated to the development of a mathematical algorithm of dental caries risk. Results. The caries risk is based on the algorithm presented in this study which takes into account 8 risk or protection factors, their frequency and coefficients being found in the main publications flow and assigned according to an evaluation scale. Conclusions. Although at first glance it seems to be a simple and easy to apply mathematical formula, this algorithm must be validated in a future and specially designed study before it could be used on a large scale.

Associations between clinical outcome and navigated transcranial magnetic stimulation characteristics in patients with motor-eloquent brain lesions: a combined navigated transcranial magnetic stimulation-diffusion tensor imaging fiber tracking approach.

OBJECTIVE Navigated transcranial magnetic stimulation (nTMS) and diffusion tensor imaging fiber tracking (DTI FT) based on nTMS data are increasingly used for preoperative planning and resection guidance in patients suffering from motor-eloquent brain tumors. The present study explores whether nTMS-based DTI FT can also be used for individual preoperative risk assessment regarding surgery-related motor impairment. METHODS Data derived from preoperative nTMS motor mapping and subsequent nTMS-based tractography in 86 patients were analyzed. All patients suffered from high-grade glioma (HGG), low-grade glioma (LGG), or intracranial metastasis (MET). In this context, nTMS-based DTI FT of the corticospinal tract (CST) was performed at a range of fractional anisotropy (FA) levels based on an individualized FA threshold ([FAT]; tracking with 50%, 75%, and 100% FAT), which was defined as the highest FA value allowing for visualization of fibers (100% FAT). Minimum lesion-to-CST distances were measured, and fiber numbers of the reconstructed CST were assessed. These data were then correlated with the preoperative, postoperative, and follow-up status of motor function and the resting motor threshold (rMT). RESULTS At certain FA levels, a statistically significant difference in lesion-to-CST distances was observed between patients with HGG who had no impairment and those who developed surgery-related transient or permanent motor deficits (75% FAT: p = 0.0149; 100% FAT: p = 0.0233). In this context, no patient with a lesion-to-CST distance ≥ 12 mm suffered from any new surgery-related permanent paresis (50% FAT and 75% FAT). Furthermore, comparatively strong negative correlations were observed between the rMT and lesion-to-CST distances of patients with surgery-related transient paresis (Spearman correlation coefficient [rs]; 50% FAT: rs = -0.8660; 75% FAT: rs = -0.8660) or surgery-related permanent paresis (50% FAT: rs = -0.7656; 75% FAT: rs = -0.6763). CONCLUSIONS This is one of the first studies to show a direct correlation between imaging, clinical status, and neurophysiological markers for the integrity of the motor system in patients with brain tumors. The findings suggest that nTMS-based DTI FT might be suitable for individual risk assessment in patients with HGG, in addition to being a surgery-planning tool. Importantly, necessary data for risk assessment were obtained without significant additional efforts, making this approach potentially valuable for direct clinical use.

Peanut Allergen Threshold Study (PATS): Novel single-dose oral food challenge study to validate eliciting doses in children with peanut allergy

Eliciting doses (EDs) of allergenic foods can be defined by the distribution of threshold doses for subjects within a specific population. The ED05 is the dose that elicits a reaction in 5% of allergic subjects. The predicted ED05 for peanut is 1.5mg of peanut protein (6mg of whole peanut). We sought to validate the predicted peanut ED05 (1.5mg) with a novel single-dose challenge. Consecutive eligible children with peanut allergy in 3 centers were prospectively invited to participate, irrespective of previous reaction severity. Predetermined criteria for objective reactions were used to identify ED05 single-dose reactors. Five hundred eighteen children (mean age, 6.8years) were eligible. No significant demographic or clinical differences were identified between 381 (74%) participants and 137 (26%) nonparticipants or between subjects recruited at each center. Three hundred seventy-eight children (206 male) completed the study. Almost half the group reported ignoring precautionary allergen labeling. Two hundred forty-five (65%) children experienced no reaction to the single dose of peanut. Sixty-seven (18%) children reported a subjective reaction without objective findings. Fifty-eight (15%) children experienced signs of a mild and transient nature that did not meet the predetermined criteria. Only 8 (2.1%; 95% CI, 0.6%-3.4%) subjects met the predetermined criteria for an objective and likely related event. No child experienced more than a mild reaction, 4 of the 8 received oral antihistamines only, and none received epinephrine. Food allergy-related quality of life improved from baseline to 1month after challenge regardless of outcome (η2=0.2, P<.0001). Peanut skin prick test responses and peanut- and Ara h 2-specific IgE levels were not associated with objective reactivity to peanut ED05. A single administration of 1.5mg of peanut protein elicited objective reactions in fewer than the predicted 5% of patients with peanut allergy. The novel single-dose oral food challenge appears clinically safe and patient acceptable, regardless of the outcome. It identifies the most highly dose-sensitive population with food allergy not otherwise identifiable by using routinely available peanut skin prick test responses or specific IgE levels, but this single-dose approach has not yet been validated for risk assessment of individual patients.

Appropriateness criteria for the use of cardiovascular imaging in heart valve disease in adults: a European Association of Cardiovascular Imaging report of literature review and current practice.

Heart valve disease is common and a major indication for imaging. Echocardiography is the first-line imaging technique for diagnosis, assessment, and serial surveillance. However, other modalities, notably cardiac magnetic resonance imaging and computerized tomography, are used if echocardiographic imaging is suboptimal or to obtain complementary information, particularly to aid risk assessment in individual patients. This review is a summary of current evidence for state-of-the-art clinical practice to inform appropriateness criteria for heart valve disease. It is divided according to common clinical scenarios: detection of valve disease, assessment of the valve and other cardiac structures, risk assessment, screening, and intervention.