Prediction Model For All-cause Mortality Research Articles

A novel risk score incorporating frailty or congestion status predicted mortality in patients with heart failure with preserved ejection fraction.

Abstract Introduction In patients with heart failure (HF), frailty or congestion is strongly associated with clinical outcomes. However, existing risk prediction models for HF prognosis do not incorporate these important clinical factors. The estimated plasma volume status (ePVS) is an easy-to-calculate index to estimate plasma volume. which is associated with the prognosis in HF patients. Purpose This study intends to construct an inclusive risk prediction model for all-cause mortality in patients with HF with preserved ejection fraction (HFpEF), incorporating evaluations of frailty and congestion alongside traditional risk factors. Methods We analyzed data from 1212 patients (81±9 years, 55% female) registered in the PURSUIT-HFpEF (Prospective, Multicenter, Observational Study of Patients with Heart Failure with Preserved Ejection Fraction) study, a multicenter registry of patients hospitalized for acute decompensated HFpEF. We evaluated clinical factors including Clinical Frailty Scale (CFS) and blood test results collected before discharge. We calculated ePVS at discharge using the Duarte formula as ePVS (mL/g)=100×(1-hematocrit)/hemoglobin (g/dL). Patients were followed for median of 713 days (IQR 369-1088 days) to observe all-cause death. Patients were randomly assigned to the derivation and validation cohorts (2:1 ratio). Using the Bayesian Information Criterion (BIC), a stepwise model selection was executed on 51 clinical parameters, including CFS and ePVS, to identify independent predictors of mortality. A risk model for mortality was constructed based on these predictors and their coefficients. The accuracy of the risk score was validated using time-dependent ROC curves both in the derivation cohort (n=801) and in the validation cohort (n=411). Results During the follow-up period, all-cause death was observed in 315 patients (25.9%). Age, sex, CFS, NT-proBNP and ePVS were selected as independent predictors of all-cause death. The score shown in the Table was applied to each factor, and the sum of the scores was calculated as the risk score. The AUC of the risk score for predicting 1-year mortality in the derivation cohort was 0.79 (95% CI 0.74, 0.85). The optimal cut-off value of 6 provided a sensitivity and specificity of 74.9% and 74.4%, respectively. The AUC for 1-year mortality in the validation cohort was 0.70 (95% CI 0.63, 0.77). Conclusions The novel risk prediction model incorporating CFS and ePVS effectively predicted mortality in HFpEF. This model could be a valuable tool for risk stratification.Table

Development and validation of a multifactorial nomogram to predict 48month mortality in decompensated heart failure.

As the incidence of heart failure (HF) increases, the need for practical tools to evaluate the long-term prognosis in these patients remains critical. Our study aimed to develop a 48month prediction model for all-cause mortality in decompensated HF patients using available clinical indicators. HF patients (n=503), 60years or older, were divided into a training cohort (n=402) and a validation cohort (n=101). Data on demographics, comorbidities, laboratory results and medications were gathered. Prediction models were developed using the Prognostic Nutritional Index (PNI), cholinesterase (ChE) and a multifactorial nomogram incorporating clinical variables. These models were constructed using the least absolute shrinkage and selection operator algorithm and multivariate logistic regression analysis. The performance of the model was assessed in terms of calibration, discrimination and clinical utility. The mean age was 77.11±8.85years, and 216 (42.9%) were female. The multifactorial nomogram included variables of ChE, lymphocyte count, albumin, serum creatinine and N-terminal pro-brain natriuretic peptide (all P<0.05). In the training cohort, the nomogram's C-index was 0.926 [95% confidence interval (CI) 0.896-0.950], outperforming the PNI indices at 0.883 and ChE at 0.804 (Z-tests, P<0.05). The C-index in the validation cohort was 0.913 (Z-tests, P<0.05). Calibration and decision curve analysis confirmed model reliability, indicating a more significant net benefit than PNI and ChE alone. Both the ChE- and PNI-based prediction models effectively predict the long-term prognosis in patients over 60years of age with decompensated HF. The multifactorial nomogram model shows superior performance, improving clinical decision-making and patient outcomes.

Development and validation of a preoperative prediction model for all-cause mortality in patients following abdominal aortic aneurysm repair.

Development and validation of a preoperative prediction model for all-cause mortality in patients following abdominal aortic aneurysm repair.

Development and validation of a prediction model for all-cause mortality in maintenance dialysis patients: a multicenter retrospective cohort study

Background The mortality risk varies considerably among individual dialysis patients. This study aimed to develop a user-friendly predictive model for predicting all-cause mortality among dialysis patients. Methods Retrospective data regarding dialysis patients were obtained from two hospitals. Patients in training cohort (N = 1421) were recruited from the Fifth Affiliated Hospital of Sun Yat-sen University, and patients in external validation cohort (N = 429) were recruited from the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine. The follow-up endpoint event was all-cause death. Variables were selected by LASSO-Cox regression, and the model was constructed by Cox regression, which was presented in the form of nomogram and web-based tool. The discrimination and accuracy of the prediction model were assessed using C-indexes and calibration curves, while the clinical value was assessed by decision curve analysis (DCA). Results The best predictors of 1-, 3-, and 5-year all-cause mortality contained nine independent factors, including age, body mass index (BMI), diabetes mellitus (DM), cardiovascular disease (CVD), cancer, urine volume, hemoglobin (HGB), albumin (ALB), and pleural effusion (PE). The 1-, 3-, and 5-year C-indexes in the training set (0.840, 0.866, and 0.846, respectively) and validation set (0.746, 0.783, and 0.741, respectively) were consistent with comparable performance. According to the calibration curve, the nomogram predicted survival accurately matched the actual survival rate. The DCA showed the nomogram got more clinical net benefit in both the training and validation sets. Conclusions The effective and convenient nomogram may help clinicians quantify the risk of mortality in maintenance dialysis patients.

Combination of the glycated hemoglobin levels and prognostic nutritional index as a prognostic marker in patients with acute coronary syndrome and type 2 diabetes mellitus

BackgroundIncreased susceptibility to malnutrition and inadequate glycemic control are frequently observed in diabetic patients with coronary artery disease. The assessment of malnutrition is performed using the prognosis nutritional index (PNI). The inadequate glycemic control is measured using glycated hemoglobin (HbA1c). However, the combined effect of PNI and HbA1c on the prognosis in diabetic patients with coronary artery disease remains unknown.MethodsA study was conducted at Beijing Anzhen Hospital and included 2,005 patients diagnosed with type 2 diabetes mellitus (T2DM) accompanied by acute coronary syndrome (ACS) who underwent percutaneous coronary intervention (PCI) from September 2021 to January 2022. Based on the median PNI and HbA1c, we categorized the patients into four groups including high (H)-PNI/low (L)-HbA1c, H-PNI/H-HbA1c, L-PNI/L-HbA1c, and L-PNI/H-HbA1c. Major adverse cardiac and cerebrovascular events (MACCE) were the primary outcome, including all-cause mortality, nonfatal myocardial infarction (MI), and nonfatal strokes.ResultsThroughout a median follow-up of 16.3 months, 73 patients had MACCE, which comprised 36 cases of all-cause mortality. In comparison to the H-PNI, the L-PNI showed an obvious rise in MACCE and all-cause mortality (log-rank P = 0.048 and 0.021, respectively) among the H-HbA1c group. Compared to the other groups, the L-PNI/H-HbA1c group exhibited the greatest risk of MACCE (adjusted hazard ratio [aHR]: 2.50, 95% confidence interval [CI] 1.20–5.23, P = 0.014) and all-cause mortality (HR: 3.20, 95% CI 1.04–9.82, P = 0.042). With the addition of PNI, MACCE and all-cause mortality prediction models performed significantly better in patients with ACS and T2DM after PCI, particularly in those with H-HbA1c levels.ConclusionsThe combination of L-PNI and H-HbA1c is a prognostic marker for MACCE and all-cause mortality in patients diagnosed with ACS and T2DM who underwent PCI. The PNI can serve as an assessment tool of malnutrition in patients with ACS and T2DM accompanied by H-HbA1c who underwent PCI. Therefore, monitoring the long-term change of the PNI deserves attention in clinical practice.

Ground-glass opacity score predicts the prognosis of anti-MDA5 positive dermatomyositis: a single-centre cohort study

ObjectiveDermatomyositis (DM) positive with anti-melanoma differentiation-associated gene 5 (anti-MDA5-DM) is a systemic autoimmune disease with high mortality. This study aimed to explore the risk factors of death in anti-MDA5-DM and validate a prediction model for all-cause mortality in anti-MDA5-DM.MethodWe conducted a retrospective study using a single-centre cohort of patients with newly onset anti-MDA5-DM from June 1, 2018 to August 31, 2021. Patients were divided into four groups according to baseline ground-glass opacity (GGO) score: Group A, GGO ≤ 1; Group B, 1 < GGO ≤ 2; Group C, 2 < GGO ≤ 3; Group D, GGO > 3. The primary outcome was death during the follow-up. Secondary outcomes included death within 3, 6, 12 months, severe infection, and remission during the first 12 months.ResultsA total of 200 patients were included in the study. Based on multivariable Cox regression, the prognostic factors at baseline were identified as CRP > 5 mg/L, serum ferritin (SF) > 600ng/ml, positive anti-Ro52 antibody, prophylactic use of compound sulfamethoxazole (SMZ Co), four-category GGO score: GGO ≤ 1, 1 < GGO ≤ 2, 2 < GGO ≤ 3, GGO > 3. The final mortality of four groups was 16.4, 22.2, 48.5, 92.0%, respectively. Compared with Group A, the Hazards Ratio (HR) of Group B was 1.408, (p = 0.408), HR of Group C was 3.433 (p = 0.005), HR of Group D was 4.376 (p = 0.001).ConclusionsGGO score is a reliable predictor for risk stratification in anti-MDA5-DM and may provide guidance for individualized managements of patients.

Framingham risk score conventional risk factors are potent to predict all-cause mortality using machine learning algorithms: a population-based prospective cohort study over 40 years in China.

Predicting all-cause mortality using available or conveniently modifiable risk factors is potentially crucial in reducing deaths precisely and efficiently. Framingham risk score (FRS) is widely used in predicting cardiovascular diseases, and its conventional risk factors are closely pertinent to deaths. Machine learning is increasingly considered to improve the predicting performances by developing predictive models. We aimed to develop the all-cause mortality predictive models using five machine learning (ML) algorithms (decision trees, random forest, support vector machine (SVM), XgBoost, and logistic regression) and determine whether FRS conventional risk factors are sufficient for predicting all-cause mortality in individuals over 40 years. Our data were obtained from a 10-year population-based prospective cohort study in China, including 9143 individuals over 40 years in 2011, and 6879 individuals followed-up in 2021. The all-cause mortality prediction models were developed using five ML algorithms by introducing all features available (182 items) or FRS conventional risk factors. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the predictive models. The AUC and 95% confidence interval of the all-cause mortality prediction models developed by FRS conventional risk factors using five ML algorithms were 0.75 (0.726-0.772), 0.78 (0.755-0.799), 0.75 (0.731-0.777), 0.77 (0.747-0.792), and 0.78 (0.754-0.798), respectively, which is close to the AUC values of models established by all features (0.79 (0.769-0.812), 0.83 (0.807-0.848), 0.78 (0.753-0.798), 0.82 (0.796-0.838), and 0.85 (0.826-0.866), respectively). Therefore, we tentatively put forward that FRS conventional risk factors were potent to predict all-cause mortality using machine learning algorithms in the population over 40 years.

Development and external validation of a clinical prediction model for survival in patients with IDH wild-type glioblastoma.

Prognostication of glioblastoma survival has become more refined due to the molecular reclassification of these tumors into isocitrate dehydrogenase (IDH) wild-type and IDH mutant. Since this molecular stratification, however, robust clinical prediction models relevant to the entire IDH wild-type glioblastoma patient population are lacking. This study aimed to provide an updated model that predicts individual survival prognosis in patients with IDH wild-type glioblastoma. Databases from Germany and the Netherlands provided data on 1036 newly diagnosed glioblastoma patients treated between 2012 and 2018. A clinical prediction model for all-cause mortality was developed with Cox proportional hazards regression. This model included recent glioblastoma-associated molecular markers in addition to well-known classic prognostic variables, which were updated and refined with additional categories. Model performance was evaluated according to calibration (using calibration plots and calibration slope) and discrimination (using a C-statistic) in a cross-validation procedure by country to assess external validity. The German and Dutch patient cohorts consisted of 710 and 326 patients, respectively, of whom 511 (72%) and 308 (95%) had died. Three models were developed, each with increasing complexity. The final model considering age, sex, preoperative Karnofsky Performance Status, extent of resection, O6-methylguanine DNA methyltransferase (MGMT) promoter methylation status, and adjuvant therapeutic regimen showed an optimism-corrected C-statistic of 0.73 (95% confidence interval 0.71-0.75). Cross-validation between the national cohorts yielded comparable results. This prediction model reliably predicts individual survival prognosis in patients with newly diagnosed IDH wild-type glioblastoma, although additional validation, especially for long-term survival, may be desired. The nomogram and web application of this model may support shared decision-making if used properly.

Construction of a Prediction Model for the Mortality of Elderly Patients with Diabetic Nephropathy.

To construct a prediction model for all-cause mortality in elderly diabetic nephropathy (DN) patients, in this cohort study, the data of 511 DN patients aged ≥65 years were collected and the participants were divided into the training set (n = 358) and the testing set (n = 153). The median survival time of all participants was 2 years. The data in the training set were grouped into the survival group (n = 203) or the death group (n = 155). Variables with P ≤ 0.1 between the two groups were selected as preliminary predictors and involved into the multivariable logistic regression model and the covariables were gradually adjusted. The receiver operator characteristic (ROC), Kolmogorov-Smirnov (KS), and calibration curves were plotted for evaluating the predictive performance of the model. Internal validation of the performance of the model was verified in the testing set. The predictive values of the model were also conducted in terms of people with different genders and ages or accompanied with chronic kidney disease (CKD) or cardiovascular diseases (CVD), respectively. In total, 216 (42.27%) elderly DN patients were dead within 2 years. The prediction model for the 2-year mortality of elderly patients with DN was established based on length of stay (LOS), temperature, heart rate, peripheral oxygen saturation (SpO2), serum creatinine (Scr), red cell distribution width (RDW), the simplified acute physiology score-II (SAPS-II), hyperlipidemia, and the Chronic Kidney Disease Epidemiology Collaboration equation for estimated glomerular filtration rate (eGFR-CKD-EPI). The AUC of the model was 0.78 (95% CI: 0.73–0.83) in the training set and 0.72 (95% CI: 0.63–0.80) in the testing set. The AUC of the model was 0.78 (95% CI: 0.65–0.91) in females and 0.78 (95%CI: 0.68–0.88) in patients ≤75 years. The AUC of the model was 0.74 (95% CI: 0.64–0.84) in patients accompanied with CKD. The model had good predictive value for the mortality of elderly patients with DN within 2 years. In addition, the model showed good predictive values for female DN patients, DN patients ≤75 years, and DN patients accompanied with CKD.

Circulating Metabolites Associate With and Improve the Prediction of All-Cause Mortality in Type 2 Diabetes.

Death rate is increased in type 2 diabetes. Unraveling biomarkers of novel pathogenic pathways capable to identify high-risk patients is instrumental to tackle this burden. We investigated the association between serum metabolites and all-cause mortality in type 2 diabetes and then whether the associated metabolites mediate the effect of inflammation on mortality risk and improve ENFORCE (EstimatioN oF mORtality risk in type2 diabetic patiEnts) and RECODe (Risk Equation for Complications Of type 2 Diabetes), two well-established all-cause mortality prediction models in diabetes. Two cohorts comprising 856 individuals (279 all-cause deaths) were analyzed. Serum metabolites (n = 188) and pro- and anti-inflammatory cytokines (n = 7) were measured. In the pooled analysis, hexanoylcarnitine, kynurenine, and tryptophan were significantly and independently associated with mortality (hazard ratio [HR] 1.60 [95% CI 1.43-1.80]; 1.53 [1.37-1.71]; and 0.71 [0.62-0.80] per 1 SD). The kynurenine-to-tryptophan ratio (KTR), a proxy of indoleamine-2,3-dioxygenase, which degrades tryptophan to kynurenine and contributes to a proinflammatory status, mediated 42% of the significant association between the antiatherogenic interleukin (IL) 13 and mortality. Adding the three metabolites improved discrimination and reclassification (all P < 0.01) of both mortality prediction models. In type 2 diabetes, hexanoylcarnitine, tryptophan, and kynurenine are associated to and improve the prediction of all-cause mortality. Further studies are needed to investigate whether interventions aimed at reducing KTR also reduce the risk of death, especially in patients with low IL-13.

Developing a Prediction Model for 7-Year and 10-Year All-Cause Mortality Risk in Type 2 Diabetes Using a Hospital-Based Prospective Cohort Study.

Leveraging easily accessible data from hospitals to identify high-risk mortality rates for clinical diabetes care adjustment is a convenient method for the future of precision healthcare. We aimed to develop risk prediction models for all-cause mortality based on 7-year and 10-year follow-ups for type 2 diabetes. A total of Taiwanese subjects aged ≥18 with outpatient data were ascertained during 2007–2013 and followed up to the end of 2016 using a hospital-based prospective cohort. Both traditional model selection with stepwise approach and LASSO method were conducted for parsimonious models’ selection and comparison. Multivariable Cox regression was performed for selected variables, and a time-dependent ROC curve with an integrated AUC and cumulative mortality by risk score levels was employed to evaluate the time-related predictive performance. The prediction model, which was composed of eight influential variables (age, sex, history of cancers, history of hypertension, antihyperlipidemic drug use, HbA1c level, creatinine level, and the LDL /HDL ratio), was the same for the 7-year and 10-year models. Harrell’s C-statistic was 0.7955 and 0.7775, and the integrated AUCs were 0.8136 and 0.8045 for the 7-year and 10-year models, respectively. The predictive performance of the AUCs was consistent with time. Our study developed and validated all-cause mortality prediction models with 7-year and 10-year follow-ups that were composed of the same contributing factors, though the model with 10-year follow-up had slightly greater risk coefficients. Both prediction models were consistent with time.

All-cause mortality prediction models in type 2 diabetes: applicability in the early stage of disease.

AimsThe rate of all-cause mortality is twofold higher in type 2 diabetes than in the general population. Being able to identify patients with the highest risk from the very beginning of the disease would help tackle this burden.MethodsWe tested whether ENFORCE, an established prediction model of all-cause mortality in type 2 diabetes, performs well also in two independent samples of patients with early-stage disease prospectively followed up.ResultsENFORCE’s survival C-statistic was 0.81 (95%CI: 0.72–0.89) and 0.78 (95%CI: 0.68–0.87) in both samples. Calibration was also good. Very similar results were obtained with RECODe, an alternative prediction model of all-cause mortality in type 2 diabetes.ConclusionsIn conclusion, our data show that two well-established prediction models of all-cause mortality in type 2 diabetes can also be successfully applied in the early stage of the disease, thus becoming powerful tools for educated and timely prevention strategies for high-risk patients.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00592-021-01746-2.

Derivation and validation of a simple nomogram prediction model for all-cause mortality among middle-aged and elderly general population.

A simple clinical model that can predict all-cause mortality in the middle-aged and older adults in general population based on demographics and physical measurement indicators. The aim of this study was to develop a simple nomogram prediction model for all-cause mortality in middle-aged and elderly general population based on demographics and physical measurement indicators. This was a prospective cohort study. We used data from the 1999-2006 National Health and Nutrition Examination Survey (NHANES), which included adults aged ≥40 years with mortality status updated through 31 December 2015. Cox proportional hazards regression, nomogram and least absolute shrinkage and selection operator (LASSO) binomial regression model were performed to evaluate the prediction model in the derivation and validation cohort. A total of 13,026 participants (6,414 men, mean age was 61.59±13.80 years) were included, of which 6,671 (3,263 men) and 6,355 (3,151 men) were included in the derivation cohort and validation cohort, respectively. During an average follow-up period of 129.23±9.62 months, 4,321 died. We developed a 9-item nomogram mode included age, gender, smoking, alcohol intake, diabetes, hypertension, marriage status, education and poverty to income ratio (PIR). The area under the curve (AUC) was 0.842 and had good calibration. Internal validation showed good discrimination of the nomogram model with AUC of 0.849 and good calibration. Application of the LASSO regression model in the validation cohort also revealed good discrimination (AUC =0.854) and good calibration. A time-dependent and optimism-corrected AUC value for the model showed no significant relationship with the change of follow-up time. A simple nomogram model, including age, gender, smoking, alcohol intake, diabetes, hypertension, marriage, education and PIR, could predict all-cause mortality well in middle-aged and elderly general population.

Quantifying the Varying Predictive Value of Physical Activity Measures Obtained from Wearable Accelerometers on All-Cause Mortality over Short to Medium Time Horizons in NHANES 2003-2006.

Physical activity measures derived from wearable accelerometers have been shown to be highly predictive of all-cause mortality. Prediction models based on traditional risk factors and accelerometry-derived physical activity measures are developed for five time horizons. The data set contains 2978 study participants between 50 and 85 years old with an average of 13.08 years of follow-up in the NHANES 2003–2004 and 2005–2006. Univariate and multivariate logistic regression models were fit separately for five datasets for one- to five-year all-cause mortality as outcome (number of events 46, 94, 155, 218, and 297, respectively). In univariate models the total activity count (TAC) was ranked first in all five horizons (AUC between 0.831 and 0.774) while the active to sedentary transition probability (ASTP) was ranked second for one- to four-year mortality models and fourth for the five-year all-cause mortality model (AUC between 0.825 and 0.735). In multivariate models age and ASTP were significant in all one- to five-year all-cause mortality prediction models. Physical activity measures are consistently among the top predictors, even after adjusting for demographic and lifestyle variables. Physical activity measures are strong stand-alone predictors and substantially improve the prediction performance of models based on traditional risk factors.

The Synergic Association of hs-CRP and Serum Amyloid P Component in Predicting All-Cause Mortality in Patients With Type 2 Diabetes.

Type 2 diabetes is characterized by increased death rate. In order to tackle this dramatic event, it becomes essential to discover novel biomarkers capable of identifying high-risk patients to be exposed to more aggressive preventive and treatment strategies. hs-CRP and serum amyloid P component (SAP) are two acute-phase inflammation proteins, which interact physically and share structural and functional features. We investigated their combined role in associating with and improving prediction of mortality in type 2 diabetes. Four cohorts comprising 2,499 patients with diabetes (643 all-cause deaths) were analyzed. The improvement of mortality prediction was addressed using two well-established prediction models, namely, EstimatioN oF mORtality risk in type 2 diabetiC patiEnts (ENFORCE) and Risk Equations for Complications of Type 2 Diabetes (RECODe). Both hs-CRP and SAP were independently associated with all-cause mortality (hazard ratios [HRs] [95% CIs]: 1.46 [1.34-1.58] [P < 0.001] and 0.82 [0.76-0.89] [P < 0.001], respectively). Patients with SAP ≤33 mg/L were at increased risk of death versus those with SAP >33 mg/L only if hs-CRP was relatively high (>2 mg/L) (HR 1.96 [95% CI 1.52-2.54] [P < 0.001] and 1.20 [0.91-1.57] [P = 0.20] in hs-CRP >2 and ≤2 mg/L subgroups, respectively; hs-CRP-by-SAP strata interaction P < 0.001). The addition of hs-CRP and SAP significantly (all P < 0.05) improved several discrimination and reclassification measures of both ENFORCE and RECODe all-cause mortality prediction models. In type 2 diabetes, hs-CRP and SAP show opposite and synergic associations with all-cause mortality. The use of both markers, possibly in combination with others yet to be unraveled, might improve the ability to predict the risk of death in the real-life setting.

Mortality risk prediction in lupus patients complicated with invasive infection in the emergency department: LUPHAS score.

Background:Infection remains a major cause of morbidity and mortality in patients with systemic lupus erythematosus (SLE). This study aimed to establish a clinical prediction model for the 3-month all-cause mortality of invasive infection events in patients with SLE in the emergency department.Methods:SLE patients complicated with invasive infection admitted into the emergency department were included in this study. Patient’s demographic, clinical, and laboratory characteristics on admission were retrospectively collected as baseline data and compared between the deceased and the survivors. Independent predictors were identified by multivariable logistic regression analysis. A prediction model for all-cause mortality was established and evaluated by receiver operating characteristic (ROC) curve analysis.Results:A total of 130 eligible patients were collected with a cumulative 38.5% 3-month mortality. Lymphocyte count <800/ul, urea >7.6mmol/l, maximum prednisone dose in the past ⩾60 mg/d, quick Sequential Organ Failure Assessment (qSOFA) score, and age at baseline were independent predictors for all-cause mortality (LUPHAS). In contrast, a history of hydroxychloroquine use was protective. In a combined, odds ratio-weighted LUPHAS scoring system (score 3–22), patients were categorized to three groups: low-risk (score 3–9), medium-risk (score 10–15), and high-risk (score 16–22), with mortalities of 4.9% (2/41), 45.9% (28/61), and 78.3% (18/23) respectively. ROC curve analysis indicated that a LUPHAS score could effectively predict all-cause mortality [area under the curve (AUC) = 0.86, CI 95% 0.79–0.92]. In addition, LUPHAS score performed better than the qSOFA score alone (AUC = 0.69, CI 95% 0.59–0.78), or CURB-65 score (AUC = 0.69, CI 95% 0.59–0.80) in the subgroup of lung infections (n = 108).Conclusions:Based on a large emergency cohort of lupus patients complicated with invasive infection, the LUPHAS score was established to predict the short-term all-cause mortality, which could be a promising applicable tool for risk stratification in clinical practice.

Individualized prediction of mortality using multiple inflammatory markers in patients on dialysis.

This study aimed to evaluate whether the combination of inflammatory markers could provide predictive powers for mortality in individual patients on dialysis and develop a predictive model for mortality according to dialysis modality. Data for inflammatory markers were obtained at the time of enrollment from 3,309 patients on dialysis from a prospective multicenter cohort. Net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated. Cox proportional hazards regression analysis was used to derive a prediction model of mortality and the integrated area under the curve (iAUC) was calculated to compare the predictive accuracy of the models. The incremental additions of albumin, high-sensitive C-reactive protein (hsCRP), white blood count (WBC), and ferritin to the conventional risk factors showed the highest predictive powers for all-cause mortality in the entire population (NRI, 21.0; IDI, 0.045) and patients on peritoneal dialysis (NRI, 25.7; IDI, 0.061). The addition of albumin and hsCRP to the conventional risk factors markedly increased predictive powers for all-cause mortality in HD patients (NRI, 19.0; IDI, 0.035). The prediction model for all-cause mortality using conventional risk factors and combination of inflammatory markers with highest NRI value (iAUC, 0.741; 95% CI, 0.722–0.761) was the most accurate in the entire population compared with a model including conventional risk factors alone (iAUC, 0.719; 95% CI, 0.700–0.738) or model including only significant conventional risk factors and inflammatory markers (iAUC, 0.734; 95% CI, 0.714–0.754). Using multiple inflammatory markers practically available in a clinic can provide higher predictive power for all-cause mortality in patients on dialysis. The predictive model for mortality based on combinations of inflammatory markers enables a stratified risk assessment. However, the optimal combination for the predictive model was different in each dialysis modality.

Circulating angiopoietin‐2, its soluble receptor Tie‐2, and mortality in the general population

To assess the association of circulating concentrations of angiopoietin-2 (Ang-2) and its soluble receptor Tie-2 (sTie-2) with all-cause, cardiovascular, and cancer mortality in a population-based sample. Angiopoietin-2 and sTie-2 were measured in 3220 participants (1665 women; mean age 54.4 years) in the Study of Health in Pomerania (SHIP). Multivariable adjusted hazard ratios (HRs) for mortality were estimated using Cox proportional hazard models. During a median follow-up of 6.2 years, 217 participants died. Ang-2 levels were positively associated with all-cause mortality [HR 1.29; 95% confidence interval (CI) 1.19-1.39 per 1 SD increment; P < 0.001] and cardiovascular mortality (HR 1.32; 95% CI 1.18-1.49; P < 0.001), but not with cancer mortality (HR 1.08; 95% CI 0.89-1.32; P = 0.416). Levels of sTie-2 were not significantly related to all-cause mortality (HR 1.12; 95% CI 0.98-1.27; P = 0.102). Adding Ang-2 to a prediction model for all-cause mortality with standard risk factors slightly improved discrimination (Δ Harrell's C, 0.008; P < 0.001) but not risk reclassification (continuous net reclassification improvement, -0.015; P = 0.571). In our community-based sample, higher serum Ang-2 concentrations were associated with greater risk for all-cause and cardiovascular mortality, suggesting that subtle increases in Ang-2 levels might reflect processes such as vascular remodelling that are associated with higher mortality risk. Adding Ang-2 to a mortality prediction model only modestly improved discrimination.

Red cell distribution width is associated with mortality in kidney transplant recipients

Red cell distribution width (RDW), a parameter routinely reported as part of the complete blood count, is associated with increased morbidity and mortality risk in different patient populations. No published data are available about the association between RDW and mortality in kidney transplant recipients. We collected socio-demographic, clinical parameters, medical and transplant history and laboratory data at baseline in 723 prevalent kidney transplant recipients between June and October 2008 [mean age 51 ± 13 (SD) years, 56 % men, 21 % diabetics]. Associations between baseline RDW values and all-cause mortality over 3 years were examined in unadjusted and adjusted models. Increasing RDW was associated with increased mortality in both unadjusted [(HR(1 % increase) = 1.63; 95 % CI 1.41-1.89) and (HR(>median) = 2.74; 95 % CI 1.68-4.48)] and fully adjusted models [(HR(1 % increase) = 1.60; 95 % CI 1.27-1.89) and (HR(>median) = 1.33; 95 % CI 0.76-2.35)]. In reclassification analyses, RDW improved the predictive value of all-cause mortality prediction models [the net reclassification improvement (NRI) was 0.189; p < 0.001]. RDW, a cheap and readily available but largely neglected parameter independently, predicts mortality in prevalent kidney transplant recipients and could potentially been used in everyday risk assessment of kidney transplant recipients.

Comparison of C‐reactive Protein and Albumin Excretion as Prognostic Markers for 10‐Year Mortality After Myocardial Infarction

C-reactive protein (CRP) is an established prognostic marker in the setting of acute coronary syndromes. Recently, albumin excretion rate also has been found to be associated with adverse outcomes in this clinical setting. Our aim was to compare the prognostic power of CRP and albumin excretion rate for long-term mortality following acute myocardial infarction (AMI). To determine whether albumin excretion rate is a better predictor of long-term outcome than CRP in post-AMI patients. We prospectively studied 220 unselected patients with definite AMI (median [interquartile] age 67 [60-74] y, female 26%, heart failure 39%). CRP and albumin-to-creatinine ratio (ACR) were measured on day 1, day 3, and day 7 after admission in 24-hour urine samples. Follow-up duration was 10 years for all patients. At survival analysis, both CRP and ACR were associated with increased risk of 10-year all-cause mortality, also after adjusting for age, hypertension, diabetes mellitus, prehospital time delay, creatine kinase-MB isoenzyme peak, heart failure, and creatinine clearance. CRP and ACR were associated with nonsudden cardiovascular (non-SCV) mortality but not with sudden death (SD) or noncardiovascular (non-CV) death. CRP was not associated with long-term mortality, while ACR was independently associated with outcome both in short- and long-term analyses. At C-statistic analysis, CRP did not improve the baseline prediction model for all-cause mortality, while it did for short-term non-SCV mortality. ACR improved all-cause and non-SCV mortality prediction, both in the short and long term. ACR was a better predictor of long-term mortality after AMI than CRP.