Risk Stratification Scores For Patients Research Articles

Performance Characteristics and Limitations of the Available Assays for the Detection and Quantitation of Monoclonal Free Light Chains and New Emerging Methodologies.

Light chain measurements form an essential component of the testing strategy for the detection and monitoring of patients with suspected and/or proven plasma cell disorders. Urine-based electrophoretic assays remain at the centre of the international guidelines for response assessment but the supplementary role of serum-free light chain (FLC) assays in response assessment and the detection of disease progression due to their increased sensitivity has been increasingly recognised since their introduction in 2001. Serum FLC assays have also been shown to be prognostic across the spectrum of plasma cell disorders and are now incorporated into risk stratification scores for patients with monoclonal gammopathy of undetermined significance (MGUS), smouldering multiple myeloma, and light chain amyloidosis (AL amyloidosis), as well as being incorporated into the criteria for defining symptomatic multiple myeloma. There are now multiple different commercially available serum FLC assays available with differing performance characteristics, which are discussed in this review, along with the implications of these for patient monitoring. Finally, newer methodologies for the identification and characterisation of monoclonal FLC, including modifications to electrophoretic techniques, mass spectrometry-based assays and Amylite, are also described along with the relevant published data available regarding the performance of each assay.

Evaluation of Pulmonary Embolism Risk Stratification Scores in Patients Admitted to the Internal Medicine Clinic

Evaluation of Pulmonary Embolism Risk Stratification Scores in Patients Admitted to the Internal Medicine Clinic

Plasma matrix metalloproteinase 2 is associated with severity and mortality in pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) is a life‐threatening disease characterized by vasoconstriction and remodeling of the pulmonary vessels. Risk stratification in PAH could potentially be improved by including novel biomarkers related to PAH pathobiology. We aimed to investigate the relationship between extracellular matrix (ECM)‐related proteins, survival, and European Society of Cardiology and European Respiratory Society (ESC/ERS) risk stratification scores in patients with PAH. Plasma samples and hemodynamics were collected from PAH patients during right heart catheterizations at diagnosis (n = 48) and early follow‐up, after treatment initiation (n = 33). Plasma levels of 14 ECM‐related proteins, with altered levels in PAH compared to healthy controls, were analyzed with proximity extension assays, and related to hemodynamics, transplant‐free survival time, and ESC/ERS risk score. Glypican‐1 levels were higher before versus after treatment initiation (p = 0.048). PAH patients with high plasma levels of matrix metalloproteinase (MMP) ‐2, MMP‐7, MMP‐9, MMP‐12, perlecan, and tissue inhibitor of metalloproteinase 4 (TIMP‐4) at baseline, had worse transplant‐free survival (p < 0.03) than patients with low levels. Hazard ratio (95% confidence interval) was for MMP‐2 1.126 (1.011–1.255), perlecan 1.0099 (1.0004–1.0196), and TIMP‐4 1.037 (1.003–1.071) in age and sex‐adjusted Cox‐regression model. MMP‐2 correlated with ESC/ERS risk scores (r s = 0.34, p = 0.019), mean right atrial pressure (r s = 0.44, p = 0.002), NT‐proBNP (r s = 0.49, p ≤ 0.001), and six‐minute walking distance (r s = −0.34, p = 0.02). The present study indicates that high levels of MMP‐2, perlecan, and TIMP‐4 are associated with poor survival in PAH. High plasma MMP‐2, correlated with poor prognosis in PAH. Further validation in larger studies is needed to better determine this association.

Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules.

Guidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time within electronic health records (EHRs) would overcome a major impediment. This requires an automated mechanism to accurately identify ("phenotype") patients with GIB at the time of presentation. The goal is to identify patients with acute GIB by developing and evaluating EHR-based phenotyping algorithms for emergency department (ED) patients. We specified criteria using structured data elements to create rules for identifying patients and also developed multiple natural language processing (NLP)-based approaches for automated phenotyping of patients, tested them with tenfold cross-validation for 10 iterations (n=7144) and external validation (n=2988) and compared them with a standard method to identify patient conditions, the Systematized Nomenclature of Medicine. The gold standard for GIB diagnosis was the independent dual manual review of medical records. The primary outcome was the positive predictive value. A decision rule using GIB-specific terms from ED triage and ED review-of-systems assessment performed better than the Systematized Nomenclature of Medicine on internal validation and external validation (positive predictive value=85% confidence interval:83%-87% vs 69% confidence interval:66%-72%; P<0.001). The syntax-based NLP algorithm and Bidirectional Encoder Representation from Transformers neural network-based NLP algorithm had similar performance to the structured-data fields decision rule. An automated decision rule employing GIB-specific triage and review-of-systems terms can be used to trigger EHR-based deployment of risk stratification models to guide clinical decision making in real time for patients with acute GIB presenting to the ED.

Comparative Analysis of Risk Stratification Scores in Atrial Fibrillation.

Atrial Fibrillation (AF) has become a major global health concern and is associated with an increased risk of poor outcomes. Identifying risk factors in patients with AF can be challenging, given the high burden of comorbidities in these patients. Risk stratification schemes appear to facilitate accurate prediction of outcomes and assist therapeutic management decisions. To summarize current evidence on risk stratification scores for patients with AF. Traditional risk models rely heavily on demographics and comorbidities, while newer tools have been gradually focusing on novel biomarkers and diagnostic imaging to facilitate more personalized risk assessment. Several studies have been conducted to compare existing risk schemes and identify specific patient populations in which the prognostic ability of each scheme excels. However, current guidelines do not appear to encourage the implementation of risk models in clinical practice, as they have not incorporated new ones in their recommendations for the management of patients with AF for almost a decade. Further work is warranted to analyze new reliable risk stratification schemes and optimally implement them into routine clinical life.

Risk stratification scores for patients with acute heart failure in the Emergency Department: A systematic review.

This study aimed to systematically identify and summarise all risk scores evaluated in the emergency department setting to stratify acute heart failure patients. A systematic review of PubMed and Web of Science was conducted including all multicentre studies reporting the use of risk predictive models in emergency department acute heart failure patients. Exclusion criteria were: (a) non-original articles; (b) prognostic models without predictive purposes; and (c) risk models without consecutive patient inclusion or exclusively tested in patients admitted to a hospital ward. We identified 28 studies reporting findings on 19 scores: 13 were originally derived in the emergency department (eight exclusively using acute heart failure patients), and six in emergency department and hospitalised patients. The outcome most frequently predicted was 30-day mortality. The performance of the scores tended to be higher for outcomes occurring closer to the index acute heart failure event. The eight scores developed using acute heart failure patients only in the emergency department contained between 4-13 predictors (age, oxygen saturation and creatinine/urea included in six scores). Five scores (Emergency Heart Failure Mortality Risk Grade, Emergency Heart Failure Mortality Risk Grade 30 Day mortality ST depression, Epidemiology of Acute Heart Failure in Emergency department 3 Day, Acute Heart Failure Risk Score, and Multiple Estimation of risk based on Emergency department Spanish Score In patients with Acute Heart Failure) have been externally validated in the same country, and two (Emergency Heart Failure Mortality Risk Grade and Multiple Estimation of risk based on Emergency department Spanish Score In patients with Acute Heart Failure) further internationally validated. The c-statistic for Emergency Heart Failure Mortality Risk Grade to predict seven-day mortality was between 0.74-0.81 and for Multiple Estimation of risk based on Emergency department Spanish Score In patients with Acute Heart Failure to predict 30-day mortality was 0.80-0.84. There are several scales for risk stratification of emergency department acute heart failure patients. Two of them are accurate, have been adequately validated and may be useful in clinical decision-making in the emergency department i.e. about whether to admit or discharge.

ProACS risk score: An early and simple score for risk stratification of patients with acute coronary syndromes

IntroductionThere are barriers to proper implementation of risk stratification scores in patients with acute coronary syndromes (ACS), including their complexity. Our objective was to develop a simple score for risk stratification of all-cause in-hospital mortality in a population of patients with ACS. MethodsThe score was developed from a nationwide ACS registry. The development and internal validation cohorts were obtained from the first 31829 patients, randomly separated (60% and 40%, respectively). The external validation cohort consisted of the last 8586 patients included in the registry. This cohort is significantly different from the other cohorts in terms of baseline characteristics, treatment and mortality. Multivariate logistic regression analysis was used to select four variables with the highest predictive potential. A score was allocated to each parameter based on the regression coefficient of each variable in the logistic regression model: 1 point for systolic blood pressure ≤116 mmHg, Killip class 2 or 3, and ST-segment elevation; 2 points for age ≥72 years; and 3 points for Killip class 4. ResultsThe new score had good discriminative ability in the development cohort (area under the curve [AUC] 0.796), and it was similar in the internal validation cohort (AUC 0.785, p=0.333). In the external validation cohort, there was also excellent discriminative ability (AUC 0.815), with an adequate fit. ConclusionsThe ProACS risk score enables easy and simple risk stratification of patients with ACS for in-hospital mortality that can be used at the first medical contact, with excellent predictive ability in a contemporary population.

ProACS risk score: An early and simple score for risk stratification of patients with acute coronary syndromes

IntroductionThere are barriers to proper implementation of risk stratification scores in patients with acute coronary syndromes (ACS), including their complexity. Our objective was to develop a simple score for risk stratification of all-cause in-hospital mortality in a population of patients with ACS. MethodsThe score was developed from a nationwide ACS registry. The development and internal validation cohorts were obtained from the first 31829 patients, randomly separated (60% and 40%, respectively). The external validation cohort consisted of the last 8586 patients included in the registry. This cohort is significantly different from the other cohorts in terms of baseline characteristics, treatment and mortality. Multivariate logistic regression analysis was used to select four variables with the highest predictive potential. A score was allocated to each parameter based on the regression coefficient of each variable in the logistic regression model: 1 point for systolic blood pressure ≤116 mmHg, Killip class 2 or 3, and ST-segment elevation; 2 points for age ≥72 years; and 3 points for Killip class 4. ResultsThe new score had good discriminative ability in the development cohort (area under the curve [AUC] 0.796), and it was similar in the internal validation cohort (AUC 0.785, p=0.333). In the external validation cohort, there was also excellent discriminative ability (AUC 0.815), with an adequate fit. ConclusionsThe ProACS risk score enables easy and simple risk stratification of patients with ACS for in-hospital mortality that can be used at the first medical contact, with excellent predictive ability in a contemporary population.

Does von Willebrand factor improve the predictive ability of current risk stratification scores in patients with atrial fibrillation?

Von Willebrand factor (vWF) is a biomarker of endothelial dysfunction. We investigated its role on prognosis in anticoagulated atrial fibrillation (AF) patients and determined whether its addition to clinical risk stratification schemes improved event-risk prediction. Consecutive outpatients with non-valvular AF were recruited and rates of thrombotic/cardiovascular events, major bleeding and mortality were recorded. The effect of vWF on prognosis was calculated using a Cox regression model. Improvements in predictive accuracy over current scores were determined by calculating the integrated discrimination improvement (IDI), net reclassification improvement (NRI), comparison of receiver-operator characteristic (ROC) curves and Decision Curve Analysis (DCA). 1215 patients (49% males, age 76 (71–81) years) were included. Follow-up was almost 7 years. Significant associations were found between vWF and cardiovascular events, stroke, mortality and bleeding. Based on IDI and NRI, addition of vWF to CHA2DS2-VASc statistically improved its predictive value, but c-indexes were not significantly different. For major bleeding, the addition of vWF to HAS-BLED improved the c-index but not IDI or NRI. DCA showed minimal net benefit. vWF acts as a simple prognostic biomarker in AF and, whilst its addition to current scores statistically improves prediction for some endpoints, absolute changes and impact on clinical decision-making are marginal.

Is it possible to simplify risk stratification scores for patients with ST-segment elevation myocardial infarction undergoing primary angioplasty?

IntroductionThere are several risk scores for stratification of patients with ST-segment elevation myocardial infarction (STEMI), the most widely used of which are the TIMI and GRACE scores. However, these are complex and require several variables. The aim of this study was to obtain a reduced model with fewer variables and similar predictive and discriminative ability. MethodsWe studied 607 patients (age 62 years, SD=13; 76% male) who were admitted with STEMI and underwent successful primary angioplasty. Our endpoints were all-cause in-hospital and 30-day mortality. Considering all variables from the TIMI and GRACE risk scores, multivariate logistic regression models were fitted to the data to identify the variables that best predicted death. ResultsCompared to the TIMI score, the GRACE score had better predictive and discriminative performance for in-hospital mortality, with similar results for 30-day mortality. After data modeling, the variables with highest predictive ability were age, serum creatinine, heart failure and the occurrence of cardiac arrest. The new predictive model was compared with the GRACE risk score, after internal validation using 10-fold cross validation. A similar discriminative performance was obtained and some improvement was achieved in estimates of probabilities of death (increased for patients who died and decreased for those who did not). ConclusionIt is possible to simplify risk stratification scores for STEMI and primary angioplasty using only four variables (age, serum creatinine, heart failure and cardiac arrest). This simplified model maintained a good predictive and discriminative performance for short-term mortality.

Is it possible to simplify risk stratification scores for patients with ST-segment elevation myocardial infarction undergoing primary angioplasty?

IntroductionThere are several risk scores for stratification of patients with ST-segment elevation myocardial infarction (STEMI), the most widely used of which are the TIMI and GRACE scores. However, these are complex and require several variables. The aim of this study was to obtain a reduced model with fewer variables and similar predictive and discriminative ability. MethodsWe studied 607 patients (age 62 years, SD=13; 76% male) who were admitted with STEMI and underwent successful primary angioplasty. Our endpoints were all-cause in-hospital and 30-day mortality. Considering all variables from the TIMI and GRACE risk scores, multivariate logistic regression models were fitted to the data to identify the variables that best predicted death. ResultsCompared to the TIMI score, the GRACE score had better predictive and discriminative performance for in-hospital mortality, with similar results for 30-day mortality. After data modeling, the variables with highest predictive ability were age, serum creatinine, heart failure and the occurrence of cardiac arrest. The new predictive model was compared with the GRACE risk score, after internal validation using 10-fold cross validation. A similar discriminative performance was obtained and some improvement was achieved in estimates of probabilities of death (increased for patients who died and decreased for those who did not). ConclusionIt is possible to simplify risk stratification scores for STEMI and primary angioplasty using only four variables (age, serum creatinine, heart failure and cardiac arrest). This simplified model maintained a good predictive and discriminative performance for short-term mortality.