Prediction Of Mortality Risk In Patients Research Articles

Does Serum Uric Acid to Creatinine Ratio Predict Mortality Risk in Patients With Heart Failure?

Previous studies have established a positive correlation between serum uric acid to creatinine (SUA/Cr) ratio and cardiovascular disease, but the relationship between SUA/Cr ratio and the prognosis of heart failure (HF) remains unknown. This study investigated the potential of SUA/Cr ratio as a prognostic predictor for patients with HF. This single-center prospective cohort study enrolled 2,122 patients with HF between March 2013 and June 2017. All patients were divided into 3 groups according to SUA/Cr ratio tertiles and were followed up with until December 31, 2022. The association between SUA/Cr ratio and the prognosis of HF was assessed using the Cox proportional hazards model. The mean (SD) age and mean (SD) SUA/Cr ratio of the study cohort (66% male) were 59.3 (14.7) years and 4.71 (2.09), respectively. During a median follow-up period of 15 months (range, 11-26 months), 390 end-point events were observed. Prognosis analysis revealed that a high SUA/Cr ratio was associated with an increased mortality risk of HF (hazard ratio, 1.62 [95% CI, 1.26-2.09]; P < .001) compared with the SUA/Cr ratio in the lowest tertile. After adjusting for covariates, the hazard ratio for mortality risk of HF was 1.71 (95% CI, 1.23-2.37; P = .001). Subgroup analysis showed that mortality risk increased in direct proportion with the SUA/Cr ratio in female patients, patients with a history of hypertension and β-blocker use, and patients with UA levels below 428 μmol/L and creatinine levels less than 97 mg/dL. Stratification by age; by history of diabetes, hyperlipidemia, and smoking; and by level of fasting plasma glucose, however, had no obvious effect on the association between SUA/Cr ratio and HF prognosis. Patients with higher SUA/Cr ratios had reduced left ventricular ejection fraction and increased left ventricular end-diastolic diameter. A high SUA/Cr ratio was an independent risk factor for the mortality risk of HF.

Interpretable machine learning for in-hospital mortality risk prediction in patients with ST-elevation myocardial infarction after percutaneous coronary interventions

Background and objectiveDespite the constant improvement of coronary heart disease (CHD) diagnostics and treatment methods it remains one of the main causes of death in most countries around the world. And myocardial infarction with ST segment elevation on the electrocardiogram (STEMI) still is one of the most dangerous clinical variants of CHD. This study aims to develop an explainable machine learning model for in-hospital mortality (IHM) risk prediction in STEMI patients after myocardial revascularization by percutaneous coronary intervention (PCI). MethodsA single-center observational retrospective study was conducted, enrolling 4677 electronic medical records of patients with STEMI after PCI, which were analyzed using statistical analysis and machine learning methods. A pool of potential IHM predictors was identified, and prognostic models were developed and validated based on multivariate logistic regression, random forest, and stochastic gradient boosting methods at two stages of hospital treatment: during the initial physicians examination in the emergency department and immediately after PCI surgery. To explain the IHM prognosis, threshold values of IHM risk factors were determined using 3 grid search methods for optimal cut-off points, calculating centroids and SHapley Additive exPlanations (SHAP). ResultsIHM prognostic models were developed using clinical and functional status data of STEMI patients during two stages of hospital treatment. The IHM prediction accuracy according to the first scenario was AUC = 0.85, and according to the second - AUC = 0.9. Predictors identified and validated in the models were converted into risk factors. Models whose parameters were risk factors demonstrated high forecast accuracy (AUC = 0.87), with the best model formed using the SHAP method. ConclusionsFor the forecast result interpretation risk factors obtained by categorizing continuous variables can be used by assessing the impact of the latter on the end point using the SHAP method.

Results of 3-year mortality risk prediction in patients with chronic heart failure and reduced left ventricular ejection fraction

The aim – predicting the risk of fatal outcome within 3 years in patients with chronic heart failure (CHF) and reduced left ventricular ejection fraction (LVEF) using appropriate regression logistic models.Materials and methods. The retrospective analysis of 490 medical histories of patients who were hospitalized in the heart failure department between 2011 and 2018 years with CHF II–IV functional class according to NYHA with LVEF ≤ 40 % on the background of coronary heart disease was conducted. There were 373 (76.1 %) men and 117 (23.9 %) women among the subjects. The median age of the patients was 64 years [56.00; 69.00]. Patients with clinical signs of heart failure and II NYHA functional class – 455 (92.8 %) patients and with clinical signs of CHF and III NYHA functional class – 35 (7.2 %) patients. The analysis was conducted for 490 patients: 228 (46.5 %) patients had a fatal event within three years, 262 (53.5 %) patients survived three years. The univariate analysis of the risk of death was performed for 42 risk factors in logistic regression models. To optimize the acceptance/rejection threshold, the receiver operating characteristic curve (ROC) analysis method was used. The logistic regression model was used to analyze the relationship between the risk of fatality and factor characteristics.Results and discussion. The analysis revealed the reliable negative relationship between the risk of a fatal outcome and the parameters of TSAT, blood pressure, LVEF, and GFR, the risk of a fatal event decreases (p&lt;0.05) when these indicators increase. Positive relationship was found between the risk of the fatal outcome and the duration of HF, age, the size of right ventricle and left atrium, LV diastolic volume index, LV systolic volume index, PASP, LV myocardial mass index. The risk of fatal outcome increases when these indicators increase (p&lt;0.05). Such indicators as levels of ferritin, hemoglobin, potassium, sodium, bilirubin, ALT, AST, cholesterol, blood glucose and BMI, gender, presence of COPD, arterial hypertension, diabetes did not demonstrate reliable prognostic information. The use of ACE inhibitors and beta-blockers show the tendency to the better prognosis. Multivariate logistic regression model was used to build a three-year fatality risk prediction model with better prognostic characteristics. Nine factor signs were identified: mineralocorticoid receptor antagonists, ischemic heart disease, body mass index, blood pressure, LV diastolic volume index, LV systolic volume index, PASP, LV myocardial mass index, GFR. This model is adequate (χ2=80.4, p&lt;0.001).Conclusions. In patients with CHF with reduced LVEF, according to the data of a univariate logistic regression model, the predictors of a fatal outcome within 3 years are age, ischemic artery disease, renal dysfunction, atrium fibrillation, duration of HF symptoms, PASP, LV ejection fraction, left atrium size, LV diastolic volume index, LV systolic volume index, right ventricle size, LV myocardial mass index, GFR, TSAT. The risk of a fatal outcome begins to increase with blood pressure ​​≤ 118 mm Hg, LVEF &lt; 30 %, TSAT ≤ 20 %, GFR ≤ 64 ml/min/1, 73 m2, patient’s age &gt; 64 years old, right ventricle’s size &gt; 3.66 cm, left ventricle’s size &gt; 4.94 cm, LV diastolic volume index &gt; 116.9 ml, LV systolic volume index &gt; 87.88 ml, pulmonary artery systolic pressure &gt; 57 mm Hg, LV myocardial mass index &gt; 172.27 g/m2. According to the multifactorial logistic regression model, the predictors of the occurrence of a fatal outcome within 3 years are BMI (OR 1.04, p&lt;0.05), BP (OR 0.97, p&lt;0.004), LV diastolic volume index (OR 0.97, р&lt;0.022), LV systolic volume index (OR 1.05, р&lt;0.004), pulmonary artery systolic pressure (OR 1.02, р&lt;0.024), LV myocardial mass index (OR 1.01, р&lt;0.005), GFR (OR 0.98, p&lt;0.009), MRA in the treatment (OR 0.45, p&lt;0.016). Youden Index (Ycrit=0.5044), the sensitivity of the 9-factor model is 65.4 % (95 % CI 57.6–72.7 %), the specificity of the model is 79.2 % (95 % CI 72.8–84.6 %), predictive significance +PV – 72.1 % (95 % CI 65.8–77.6 %), predictive significance –PV – 73.6 % (95 % CI 69.0–77.7 %).

Association of Urine Albumin to Creatinine Ratio With Cardiovascular Outcomes in Patients With Type 2 Diabetes Mellitus.

The urinary albumin to creatinine ratio (UACR) is a widely used indicator of albuminuria and has predictive value for adverse cardiovascular events. To evaluate the correlation between the UACR and the risk of developing major adverse cardiovascular events (MACEs) and total mortality in patients with type 2 diabetes mellitus (T2DM). This post hoc analysis included 10 171 participants from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study and the ACCORD follow-up study (ACCORDION) with baseline UACR data. The natural logarithm (ln) of each UACR measurement was calculated. Univariate and multivariate Cox proportional hazard regression analyses were conducted to examine the association between the UACR and the risk of MACEs and total mortality. The additional predictive value of UACR was further evaluated. Similar methods were used to analyze the correlation between the UACR and MACEs and total mortality within the normal range. During a median follow-up period of 8.83 years, 1808 (17.78%) participants experienced MACEs, and there were 1934 (19.01%) total deaths. After adjusting for traditional cardiovascular risk factors, the multivariate analysis revealed a significant association between the UACR and the risk of MACEs and total mortality. The inclusion of UACR in the conventional risk model enhanced the predictive efficacy for MACEs and total mortality. An elevated UACR is associated with a higher risk of MACEs and total mortality in patients with T2DM, even when it falls within the normal range. The UACR improves prediction of MACE and total mortality risk in patients with T2DM.

Modification of the GRACE Risk Score for Risk Prediction in Patients With Acute Coronary Syndromes

The Global Registry of Acute Coronary Events (GRACE) risk score, a guideline-recommended risk stratification tool for patients presenting with acute coronary syndromes (ACS), does not consider the extent of myocardial injury. To assess the incremental predictive value of a modified GRACE score incorporating high-sensitivity cardiac troponin (hs-cTn) T at presentation, a surrogate of the extent of myocardial injury. This retrospectively designed longitudinal cohort study examined 3 independent cohorts of 9803 patients with ACS enrolled from September 2009 to December 2017; 2 ACS derivation cohorts (Heidelberg ACS cohort and Newcastle STEMI cohort) and an ACS validation cohort (SPUM-ACS study). The Heidelberg ACS cohort included 2535 and the SPUM-ACS study 4288 consecutive patients presenting with a working diagnosis of ACS. The Newcastle STEMI cohort included 2980 consecutive patients with ST-elevation myocardial infarction treated with primary percutaneous coronary intervention. Data were analyzed from March to June 2023. In-hospital, 30-day, and 1-year mortality risk estimates derived from an updated risk score that incorporates continuous hs-cTn T at presentation (modified GRACE). The predictive value of continuous hs-cTn T and modified GRACE risk score compared with the original GRACE risk score. Study end points were all-cause mortality during hospitalization and at 30 days and 1 year after the index event. Of 9450 included patients, 7313 (77.4%) were male, and the mean (SD) age at presentation was 64.2 (12.6) years. Using continuous rather than binary hs-cTn T conferred improved discrimination and reclassification compared with the original GRACE score (in-hospital mortality: area under the receiver operating characteristic curve [AUC], 0.835 vs 0.741; continuous net reclassification improvement [NRI], 0.208; 30-day mortality: AUC, 0.828 vs 0.740; NRI, 0.312; 1-year mortality: AUC, 0.785 vs 0.778; NRI, 0.078) in the derivation cohort. These findings were confirmed in the validation cohort. In the pooled population of 9450 patients, modified GRACE risk score showed superior performance compared with the original GRACE risk score in terms of reclassification and discrimination for in-hospital mortality end point (AUC, 0.878 vs 0.780; NRI, 0.097), 30-day mortality end point (AUC, 0.858 vs 0.771; NRI, 0.08), and 1-year mortality end point (AUC, 0.813 vs 0.797; NRI, 0.056). In this study, using continuous rather than binary hs-cTn T at presentation, a proxy of the extent of myocardial injury, in the GRACE risk score improved the mortality risk prediction in patients with ACS.

Adding value of the Charlson comorbidity index to the Grace score for mortality risk prediction in patients with acute coronary syndrome

Abstract Funding Acknowledgements Type of funding sources: None. Background The Global Registry of Acute Coronary Events (GRACE) risk score is widely recommended for risk assessment in patients with acute coronary syndrome (ACS). The Charlson Comorbidity Index (CCI) is a recognized measure of comorbid burden, and a useful tool for estimating prognosis in patients with multiple coexisting illnesses. Despite it has been validated in the context of ACS-related mortality, scarce and conflicting data still exist about the role of the CCI when added to the traditional GRACE risk score for outcome prediction in patients with acute coronary syndrome (ACS). Purpose The assessment of the potential predictive value of the CCI both for short, and long term all-cause mortality, in a contemporary ACS population, and its potential adding value beyond the GRACE risk score. Methods All consecutive admission due to ST-elevation myocardial infarction-STEMI, non ST elevated myocardial infarction-NSTEMI, and unstable angina-UA, from 01/01/2018 to 31/12/2020 were retrospectively reviewed from an internal database of a tertiary cardiac center in Italy. Logistic and Cox proportional regression analysis were performed in order to assess the contribution of the CCI on short and long term mortality. In order to specify the CCI adding value to the GRACE score, several measures of improvement in discrimination (increase in the area under the receiver-operating characteristic curve-AUC, the integrated discrimination improvement-IDI, and the continuous net reclassification improvement-cNRI&amp;gt;0) , were implemented. Robustness of the results were assessed through an internal validation procedure with bootstrapping. Results A total of 1310 patients (646 STEMI, 588 NSTEMI, and 76 UA) were identified. Almost 28% were female, and the median age was 68 (58-78). 97 (7,4%) and 136 (11,2%) deaths occurred, respectively, in hospital and during long term follow-up (median follow-up time: 12 months; IQR: 8-23). After multivariate logistic regression analysis the CCI was not associated with in-hospital death, while it was significantly and independently associated with the long-term mortality along with GRACE score (HR: 1,33, 95% CI: 1,22-1,43; p&amp;lt;0,001, Fig.1). An additive effect of CCI with the GRACE risk score was observed in predicting long-term mortality with an AUC from 0,762 to 0.821 (p &amp;lt; 0,001). The incremental predictive value of combining CCI and the GRACE risk score for the long term mortality was significantly improved, as shown by the net reclassification improvement (cNRI&amp;gt;0: 0.678, p &amp;lt; 0.001, Table 1) and integrated discrimination improvement (IDI: 0.08, p &amp;lt; 0.001). Conclusion The CCI is a predictor of long-term mortality and improves risk stratification of patients with ACS over the GRACE risk score.

Machine learning for longitudinal mortality risk prediction in patients with malignant neoplasm in São Paulo, Brazil

Artificial intelligence is becoming an important diagnostic and prognostic tool in recent years, as machine learning algorithms have been shown to improve clinical decision-making. These algorithms will have some of their most important applications in developing regions with restricted data collection, but their performance under this condition is still widely unknown. We analyzed longitudinal data from São Paulo, Brazil, to develop machine learning algorithms to predict the risk of death in patients with cancer. We tested different algorithms using nine separate model structures. Considering the area under the ROC curve (AUC-ROC), we obtained values of 0.946 for the general model, 0.945 for the model with the five main cancers, 0.899 for bronchial and lung cancer, 0.947 for breast cancer, 0.866 for stomach cancer, 0.872 for colon cancer, 0.923 for rectum cancer, 0.955 for prostate cancer, and 0.917 for uterine cervix cancer. Our results indicate the potential of building models for predicting mortality risk in cancer patients in developing regions using only routinely-collected data.

Utility of Measuring Circulating Bio-Adrenomedullin and Proenkephalin for 30-Day Mortality Risk Prediction in Patients with COVID-19 and Non-COVID-19 Interstitial Pneumonia in the Emergency Department.

Background and Objectives: In order to accelerate the risk stratification of patients referred to the Emergency Department (ED) with interstitial pneumonia, it could be useful to provide new and effective laboratory tests for use. The aim of our study was to evaluate the prognostic role of two biomarkers, bio-adrenomedullin (Bio-ADM) and proenkephalin (penKid), in patients with interstitial pneumonia (IP) at ED admission. Materials and Methods: In 153 consecutive patients with IP, both from COVID-19 or non-COVID-19 etiology, we measured, in a prospective observational manner, penKid and Bio-ADM at ED admission and after 24 h. In order to evaluate patient outcomes, 30-day follow-ups were also performed. The endpoints were 24 h, 10-day, and 30-day mortality. Results: Both biomarkers were shown to be good predictors of adverse events at 30 days, with Bio-ADM outperforming penKid. Bio-ADM was linked with 24 h and 10-day patient mortality. Moreover, PenKid was related to parameters defining worsening kidney function. Conclusions: Both in patients with COVID-19 or non-COVID-19 interstitial pneumonia at ED admission, Bio-ADM and penKid were good predictors of patient mortality. To evaluate these two biomarkers could be considered to be useful during the first evaluation in the ED when integrated with clinical scores.

MiR-223 and MiR-186 Are Associated with Long-Term Mortality after Myocardial Infarction.

Background—The identification and stratification of patients at risk of fatal outcomes after myocardial infarction (MI) is of considerable interest to guide secondary prevention therapies. Currently, no accurate biomarkers are available to identify subjects who are at risk of suffering acute manifestations of coronary heart disease as well as to predict adverse events after MI. Non-coding circulating microRNAs (miRNAs) have been proposed as novel diagnostic and prognostic biomarkers in cardiovascular diseases. The aims of the study were to investigate the clinical value of a panel of circulating miRNAs as accurate biomarkers associated with MI and mortality risk prediction in patients with documented MI. Methods and Results—seven circulating plasma miRNAs were analyzed in 67 MI patients and 80 control subjects at a high cardiovascular risk but without known coronary diseases. Multivariate logistic regression analyses demonstrated that six miRNAs were independently associated with MI occurrence. Among them, miR-223 and miR-186 reliably predicted long-term mortality in MI patients, in particular miR-223 (HR 1.57 per one-unit increase, p = 0.02), after left ventricular ejection fraction (LVEF) adjustment. Kaplan–Meier survival analyses provided a predictive threshold value of miR-223 expression (p = 0.028) for long-term mortality. Conclusions—Circulating miR-223 and miR-186 are promising predictive biomarkers for long-term mortality after MI.

One and Five-Year Mortality Risk Prediction in Patients with Moderate and Severe Aortic Stenosis.

(1) Background: Our goal was to develop a risk prediction model for mortality in patients with moderate and severe aortic stenosis (AS). (2) Methods: All patients aged 40–95 years, with echocardiographic evidence of moderate and severe AS at a single institution, were studied over a median of 2.8 (1.5–4.8) years, between 2013–2018. Patient characteristics and mortality were compared using Chi-squares, t-tests, and Kaplan–Meier (KM) curves, as appropriate. The risk calculation for mortality was derived using the Cox proportional hazards model. A risk score was calculated for each parameter, and the total sum of scores predicted the individualized risks of 1-and 5-year mortality. (3) Results: A total of 1991 patients with severe and 2212 with moderate AS were included. Severe AS patients were older, had a lower ejection fraction %, were more likely to be Caucasian, and had lower rates of obesity and smoking, but had higher rates of cardiac comorbidities and AVR (49.3% vs. 2.8%, p < 0.0001). The unadjusted overall mortality was 41.7% vs. 41%, p = 0.6530, and was not different using KM curves (log rank, p = 0.0853). The models included only patients with complete follow-up (3966 in the 1-year, and 816 in the 5-year model) and included 13 variables related to patient characteristics, degree of AS, and AVR. The C-statistic was 0.75 and 0.72 for the 1-year and the 5-year models, respectively. (4) Conclusions: Patients with moderate and severe AS experience high morbidity and mortality. The usage of a risk prediction model may provide guidance for clinical decision making in complex patients.

A Computer-Assisted System for Early Mortality Risk Prediction in Patients with Traumatic Brain Injury Using Artificial Intelligence Algorithms in Emergency Room Triage.

Traumatic brain injury (TBI) remains a critical public health challenge. Although studies have found several prognostic factors for TBI, a useful early predictive tool for mortality has yet to be developed in the triage of the emergency room. This study aimed to use machine learning algorithms of artificial intelligence (AI) to develop predictive models for TBI patients in the emergency room triage. We retrospectively enrolled 18,249 adult TBI patients in the electronic medical records of three hospitals of Chi Mei Medical Group from January 2010 to December 2019, and undertook the 12 potentially predictive feature variables for predicting mortality during hospitalization. Six machine learning algorithms including logistical regression (LR) random forest (RF), support vector machines (SVM), LightGBM, XGBoost, and multilayer perceptron (MLP) were used to build the predictive model. The results showed that all six predictive models had high AUC from 0.851 to 0.925. Among these models, the LR-based model was the best model for mortality risk prediction with the highest AUC of 0.925; thus, we integrated the best model into the existed hospital information system for assisting clinical decision-making. These results revealed that the LR-based model was the best model to predict the mortality risk in patients with TBI in the emergency room. Since the developed prediction system can easily obtain the 12 feature variables during the initial triage, it can provide quick and early mortality prediction to clinicians for guiding deciding further treatment as well as helping explain the patient’s condition to family members.

Predicting All-Cause Mortality Risk in Atrial Fibrillation Patients: A Novel LASSO-Cox Model Generated From a Prospective Dataset.

Background: Although mortality remains high in patients with atrial fibrillation (AF), there have been limited studies exploring machine learning (ML) models on mortality risk prediction in patients with AF.Objectives: This study sought to develop an ML model that captures important variables in order to predict all-cause mortality in AF patients.Methods: In this single center prospective study, an ML-based mortality prediction model was developed and validated using a dataset of 2,012 patients who experienced AF from November 2018 to February 2020 at the First Affiliated Hospital of Shantou University Medical College. The dataset was randomly divided into a training set (70%, n = 1,223) and a validation set (30%, n = 552). A total of 122 features were collected for variable selection. Least absolute shrinkage and selection operator (LASSO) and random forest (RF) algorithms were used for variable selection. Ten ML models were developed using variables selected by LASSO or RF. The best model was selected and compared with conventional risk scores. A nomogram and user-friendly online tool were developed to facilitate the mortality predictions and management recommendations.Results: Thirteen features were selected by the LASSO regression algorithm. The LASSO-Cox model achieved an area under the curve (AUC) of 0.842 in the training dataset, and 0.854 in the validation dataset. A nomogram based on eight independent features was developed for the prediction of survival at 30, 180, and 365 days following discharge. Both the time dependent receiver operating characteristic (ROC) and decision curve analysis (DCA) showed better performances of the nomogram compared to the CHA2DS2-VASc and HAS-BLED models.Conclusions: The LASSO-Cox mortality predictive model shows potential benefits in death risk evaluation for AF patients over the 365-day period following discharge. This novel ML approach may also provide physicians with personalized management recommendations.

Sarcopenia on preoperative chest computed tomography predicts cancer-specific and all-cause mortality following pneumonectomy for lung cancer: A multicenter analysis.

BackgroundMortality risk prediction in patients undergoing pneumonectomy for non‐small cell lung cancer (NSCLC) remains imperfect. Here, we aimed to assess whether sarcopenia on routine chest computed tomography (CT) independently predicts worse cancer‐specific (CSS) and overall survival (OS) following pneumonectomy for NSCLC.MethodsWe included consecutive adults undergoing standard or carinal pneumonectomy for NSCLC at Massachusetts General Hospital and Heidelberg University from 2010 to 2018. We measured muscle cross‐sectional area (CSA) on CT at thoracic vertebral levels T8, T10, and T12 within 90 days prior to surgery. Sarcopenia was defined as T10 muscle CSA less than two standard deviations below the mean in healthy controls. We adjusted time‐to‐event analyses for age, body mass index, Charlson Comorbidity Index, forced expiratory volume in 1 second in % predicted, induction therapy, sex, smoking status, tumor stage, side of pneumonectomy, and institution.ResultsThree hundred and sixty‐seven patients (67.4% male, median age 62 years, 16.9% early‐stage) underwent predominantly standard pneumonectomy (89.6%) for stage IIIA NSCLC (45.5%) and squamous cell histology (58%). Sarcopenia was present in 104 of 367 patients (28.3%). Ninety‐day all‐cause mortality was 7.1% (26/367). After a median follow‐up of 20.5 months (IQR, 9.2–46.9), 183 of 367 patients (49.9%) had died. One hundred and thirty‐three (72.7%) of these deaths were due to lung cancer. Sarcopenia was associated with shorter CSS (HR 1.7, p = 0.008) and OS (HR 1.7, p = 0.003).ConclusionsThis transatlantic multicenter study confirms that sarcopenia on preoperative chest CT is an independent risk factor for CSS and OS following pneumonectomy for NSCLC.

Application of dynamic pulse pressure and vasopressor tools for predicting outcomes in patients with sepsis in intensive care units

PurposeWe aimed to determine whether the combination of dynamic pulse pressure and vasopressor (DPV) use is applicable for mortality risk stratification in patients with severe sepsis. We proposed the use of the DPV tool and compared it with traditional sepsis severity indices. Materials and methodsAll adult patients who met the sepsis criteria of the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) between August 2013 and January 2017 were eligible for the study. Patients who expired within 3 days of admission to the intensive care unit (ICU) were excluded. The primary outcomes were 7-day and 28-day mortality. ResultsThe study participants included 757 consecutive adult patients. A subpopulation of 155 patients underwent immune profiling assays on days 1, 3, and 7 of ICU admission. The DPV tool had a better performance for predicting 7-day mortality (area under curve, AUC: 0.70), followed by the Sequential Organ Failure Assessment (SOFA) (AUC: 0.64), the plus pulse pressure (AUC: 0.64). For predicting 28-day mortality, the DPV tool was not inferior to the SOFA (AUC: 0.61), DPV tool (AUC: 0.59). ConclusionsThe DPV tool can be applied for 7-day and 28-day mortality risk prediction in patients with sepsis.

Value of Adding the CHA2DS2-VASc Score to the GRACE Score for Mortality Risk Prediction in Patients With Acute Coronary Syndrome

Global Registry of Acute Coronary Events (GRACE) risk score has been routinely used for risk stratification of acute coronary syndrome (ACS) patients. We aimed to examine whether the addition of the CHA2DS2-VASc score to the GRACE score improves risk stratification. Included were patients with ACS who were divided into high (>140), intermediate (110< GRACE score ≤140) and low (<110) GRACE score. Each group was further divided into 3 subgroups categorized according to their CHA2DS2-VASc score: 0-1, 2-3, and ≥4. Management and Outcomes were compared for each GRACE score group and CHA2DS2-VASc score subgroups. Included 6,854 ACS patients, of them 3596 (52.5%) were classified as low risk, 1,937 (28.3%) were at intermediate risk and 1,321 (19.3%) were high-risk patients. In the intermediate risk group, patients with a higher CHA2DS2-VASc score more frequently underwent percutaneous coronary revascularization. For low risk patients, 30-day mortality rates were 0.8%, 1.5%, and 1.3% (p = 0.02), and 1-year all-cause mortality rates were 1.3%, 3%, and 2.6% (p = 0.002) for CHA2DS2-VASc score 0-1, 2-3, ≥4, respectively. For intermediate risk patients, 30-day mortality rates were 2.9%, 3.4%, and 3.8% (p = 0.8), and 1-year all-cause mortality rates were 6.4%, 7.8%, and 11.2% (p = 0.01) for CHA2DS2-VASc score 0-1, 2-3, ≥4, respectively. Among patients with a GRACE score <140, each 1 point increase in the CHA2DS2-VASc score was associated with a 57% increase in 1-year mortality rates. In conclusion, the addition of the CHA2DS2-VASc score to the GRACE risk score in ACS patients improves risk stratification of patients with low and intermediate risk.

Additive value of the CRUSADE score to the GRACE score for mortality risk prediction in patients with acute coronary syndromes

IntroductionAcute coronary syndrome (ACS) treatments increase bleeding complications that also impair prognosis. Bleeding risk scores reclassification of actual mortality risk estimated by the GRACE score might improve overall estimation. MethodsObservational and prospective study of all ACS patients admitted in two hospitals. Mortality risk was assessed by the GRACE score and bleeding risk by the CRUSADE score. We analyzed the net reclassification improvement (NRI) of adding the CRUSADE score to the GRACE score. ResultsWe included 6997 patients, mean age 67.4 (12.9), 38.0% ST-elevation ACS, mean GRACE score 145.2 (39.9). The percentage of patients with CRUSADE score >20 or >50 increased as the GRACE score was higher. Hospital mortality was 5.3% and the addition of the CRUSADE score reclassified a relevant percentage of patients with GRACE score >109; NRI was 3.80% (1.10–6.10). During follow-up, (median 53.0months) mortality rate was 22.6% and patients with CRUSADE score >50 had significantly higher mortality rates in all GRACE score categories; NRI was high (46.6%, 95% CI 41.0–53.1). The multivariate analysis outlined the independent predictive value of CRUSADE score >20 or >50 as well as GRACE scores 109–139 and >140. ConclusionsThe addition of the CRUSADE score to the GRACE score improved mortality risk estimation. A CRUSADE score >50 identified patients with higher post-discharge mortality and higher hospital mortality if GRACE score was >109. The CRUSADE score improved hospital and long-term mortality prediction in patients with GRACE score >140. Individual mortality risk estimation should integrate the CRUSADE and GRACE scores.

Evaluation of the obesity surgery mortality risk score for the prediction of postoperative complications after primary and revisional laparoscopic Roux-en-Y gastric bypass

BackgroundThe Obesity Surgery Mortality Risk Score (OS-MRS) is a validated instrument for mortality risk prediction in patients undergoing laparoscopic Roux-en-Y gastric bypass (LRYGB) procedures classifying patients into low risk (class A), intermediate risk (class B), and high risk (class C). ObjectivesThe primary aim of this study was to evaluate the accuracy of the OS-MRS in predicting postoperative complications after LRYGB. Secondarily, the postoperative complication rate between primary and revisional LRYGB was systematically analyzed. SettingThe Obesity Center Amsterdam, located in a large teaching hospital, in Amsterdam, The Netherlands. MethodsThe OS-MRS was applied to a consecutive database of patients who underwent LRYGB from November 2007 onwards. Postoperative complications were scored according to the Clavien-Dindo classification. Revisional LRYGB was separately analyzed. ResultsLRYGB was performed in 1667 patients either as a primary (81.5%) or revisional (18.5%) procedure. The majority (n = 1371, 82.2%) were female, mean age 44.6 (standard deviation 14.4) years and mean body mass index 44.2 (6.5) kg/m2. Nine hundred and four (54.2%) were OS-MRS class A, 642 class B (38.5%), and 121 (7.3%) class C. Complications occurred in 143 (10.5%) and 44 (14.2%) patients after primary and revisional surgery, respectively. In both primary and revisional LRYGB, there was no association between complications and the OS-MRS classification. Subanalysis comparing primary with revisional LRYGB found a significant association between revisional surgery and the development of severe complications (Clavien-Dindo≥3) (P = .003) and mortality (P = .017). ConclusionThe OS-MRS was not an accurate predictor for postoperative complications in patients who underwent primary or revisional LRYGB. As in other studies, revisional surgery is an independent risk factor for the development of severe complications.

HEART RATE TURBULENCE AS A MORTALITY PREDICTOR IN LONG-TERM STUDY IN PATIENTS WITH CORONARY HEART DISEASE

Pathological heart rate turbulence (HRT) after premature ventricular complexes (PVCs) in patients with coronary heart disease (CHD) and myocardial infarction (MI) may predict higher mortality rate. Aim. To estimate the predictive power HRT in 5 year observational study in patients with CHD. Material and methods. 173 patients with CHD and in whom HRT was possible to record, were analyzed from 2010-2011 until 2015 with survival rate and turbulence slope (TS) and turbulence onset (TO) estimation. Results. Pathological TO showed no correlation with survival rate (p>0,05) but pathological TS in 5 years period (p=0,00026) correlated with survival rate with post-MI patients. Moreover, it had a predictive power also with non-MI patients (p=0,0032). The survival (Kaplan-Mayer) curves between normal and pathological TS started to divide from the 36 months of observation. Presence of nTS in post MI patients increased mortality rate in 5,14 times (p=0,00002) and in non-MI — in 4,99 times (p=0,00002). Conclusions. HRT slope parameter showed to be highly effective in mortality risk prediction in patients with CHD.

Validation of Seattle Heart Failure Model for mortality risk prediction in patients treated with cardiac resynchronization therapy

Survival prediction by the Seattle Heart Failure Model (SHFM) of patients treated with cardiac resynchronization therapy (CRT) remains ill defined. The performance of the SHFM in this clinical setting was therefore evaluated. Data from 1309 consecutive CRT patients (five centres) were collected retrospectively; 1139 of these patients were considered for analysis. Three-hundred and seven deaths occurred over 40.1 months (interquartile range 25.2-60.0 months; mean event rate 9.7%/year; survival of 89, 81, and 64% at 1, 2, and 5 years). Kaplan-Meier event-free survival analysis stratified according to tertile of SHFM score was significant (log rank test P < 0.001). High-risk tertile (T1) survival was 82, 67, and 46% at 1, 2, and 5 years, respectively. Observed compared with SHFM-predicted survival was 0.11 vs. 0.08, 0.19 vs. 0.16, and 0.36 vs. 0.36, at 1, 2, and 5 years. Model discrimination by c-statistic was 0.64; the logistic models' area under the receiver operating characteristic curve (AUC-ROC) of risk tertiles was 0.66, 0.68, and 0.67, at 1, 2, and 5 years. Compared with the other two groups, T1 was globally more compromised. Within the T1 group, independent predictors of death were male gender, ischaemic heart failure aetiology, lower body weight, and CRT pacemaker. SHFM performance was found to be modest, tending to overestimate survival. However, SHFM identified a high-risk, globally more compromised patient subgroup, hence supporting a comprehensive approach, which should include nutritional, metabolic, and immunological aspects, as well as defibrillator back-up.

Usefulness of the Addition of Beta-2-Microglobulin, Cystatin C and C-Reactive Protein to an Established Risk Factors Model to Improve Mortality Risk Prediction in Patients Undergoing Coronary Angiography

Evidence-based therapies are available to reduce the risk for death from cardiovascular disease, yet many patients go untreated. Novel methods are needed to identify those at highest risk for cardiovascular death. In this study, the biomarkers β2-microglobulin, cystatin C, and C-reactive protein were measured at baseline in a cohort of participants who underwent coronary angiography. Adjusted Cox proportional-hazards models were used to determine whether the biomarkers predicted all-cause and cardiovascular mortality. Additionally, improvements in risk reclassification and discrimination were evaluated by calculating the net reclassification improvement, C-index, and integrated discrimination improvement with the addition of the biomarkers to a baseline model of risk factors for cardiovascular disease and death. During a median follow-up period of 5.6 years, there were 78 deaths among 470 participants. All biomarkers independently predicted future all-cause and cardiovascular mortality. A significant improvement in risk reclassification was observed for all-cause (net reclassification improvement 35.8%, p= 0.004) and cardiovascular (net reclassification improvement 61.9%, p= 0.008) mortality compared to the baseline risk factors model. Additionally, there was significantly increased risk discrimination with C-indexes of 0.777 (change in C-index 0.057, 95% confidence interval 0.016 to 0.097) and 0.826 (change in C-index 0.071, 95% confidence interval 0.010 to 0.133) for all-cause and cardiovascular mortality, respectively. Improvements in risk discrimination were further supported using the integrated discrimination improvement index. In conclusion, this study provides evidence that β2-microglobulin, cystatin C, and C-reactive protein predict mortality and improve risk reclassification and discrimination for a high-risk cohort of patients who undergo coronary angiography.