Mortality Risk Stratification Research Articles

Prognostic nomogram for risk of mortality after hip fracture surgery in geriatrics

PurposeHip fracture is a significant public health problem, with associated high morbidity and mortality. Orthopedic surgeons are concerned to improve prognosis and stratify mortality risk after hip fracture surgery. This study established a nomogram that combines the Charlson Comorbidity Index (CCI) with specific laboratory parameters to predict mortality risk after hip fracture surgery in geriatrics. MethodsThe records of consecutive patients who underwent hip fracture surgery from January 2015 through May 2020 at one medical center were reviewed for perioperative factors and mortality. Patients with age ≥ 70 years who were diagnosed with intertrochanteric or femoral neck fractures were included. Patients who were diagnosed with pathological fracture, received only conservative treatment or lost to follow-up were excluded. A multivariate Cox proportional hazards regression model was used to identify risk factors. A nomogram was established with R software and evaluated using concordance (C)-index, area under receiver operating characteristic (AUC), calibration curves, and decision curve analysis (DCA). ResultsIn total, 454 patients were included with a mean age of 81.6 years. The mean follow-up and one-year mortality rate were 37.2 months and 10.4%, respectively. Five identified risk variables for mortality after hip fracture surgery in geriatrics comprised age (HR 1.05, 95% CI 1.01-1.08; P = 0.003), CCI (HR 1.38, 95% CI 1.24-1.54; P = 0.000), albumin (HR 1.78, 95% CI 1.31-2.43; P = 0.000), sodium (HR 1.59, 95% CI 1.18-2.15; P = 0.002) and hemoglobin (HR 1.46, 95% CI 1.07-2.00; P = 0.02). A nomogram was proposed and evaluated, showing a C-index of 0.76 ± 0.02. The AUCs for 6-month, 1-year, and 3-year mortality predictions were 0.83, 0.79, and 0.77, respectively. The calibration curve and DCA showed good discrimination and clinical usefulness. ConclusionThis novel nomogram for stratifying the mortality risk after hip fracture surgery in geriatrics incorporated age, CCI, serum albumin, sodium, and hemoglobin. Internal validation indicated that the model has good accuracy and usefulness. This nomogram had improved convenience and precision compared with other models. External validation is warranted to confirm its performance.

Are outcomes in congenital cardiac surgery better than ever?

Congenital heart disease is the most common congenital defect among infants born in the United States. Within the first year of life, 1 in 4 of these infants will need surgery. Only one generation removed from an overall mortality of 14%, many changes have been introduced into the field. Have these changes measurably improved outcomes? The literature search was conducted through PubMed MEDLINE and Google Scholar from inception to October 31, 2021. Ultimately, 78 publications were chosen for inclusion. The outcome of overall mortality has experienced continuous improvements in the modern era of the specialty despite the performance of more technically demanding surgeries on patients with complex comorbidities. This modality does not account for case-mix, however. In turn, clinical outcomes have not been consistent from center to center. Furthermore, variation in practice between institutions has also been documented. A recurring theme in the literature is a movement toward standardization and universalization. Examples include mortality risk-stratification that has allowed direct comparison of outcomes between programs and improved definitions of morbidities which provide an enhanced framework for diagnosis and management. Overall mortality is now below 3%, which suggests that more patients are surviving their interventions than in any previous era in congenital cardiac surgery. Focus has transitioned from survival to improving the quality of life in the survivors by decreasing the incidence of morbidity and associated long-term effects. With the transformation toward standardization and interinstitutional collaboration, future advancements are expected.

Assessment of National Early Warning Score 2 as a Tool to Predict the Outcome of COVID-19 Patients on Admission.

Introduction: The ongoing pandemic due to coronavirus disease 2019 (COVID-19) has put tremendous strain on the healthcare system around the world. There is a paucity of data describing the role of National Early Warning Score 2 (NEWS2) in the assessment of COVID-19 cases. This study aimed at identifying NEWS2 calculated on admission as a valuable tool for risk stratification and prediction of in-hospital mortality in COVID-19 patients.Materials and method: This prospective, observational study included 814 confirmed COVID-19 cases and was conducted over a period of three months. Vital parameters were assessed and NEWS2 was calculated on admission. Data were entered in excel format and statistical analysis was done in Python 3.8 statistical software (Wilmington, DE: Python Software Foundation). Pearson's chi-squared test was used following which a significant NEWS2 cut-off score to predict in-hospital mortality was determined by means of receiver operating characteristic (ROC) curve.Results: Mortality of 9.09% was noted and correlations were made with age, comorbidity, and NEWS2 score. For in-hospital deaths, comorbidities were present in 66.21% of patients, the mean age was 60.14 years, and average NEWS2 score was 9. For discharged patients only 21.89% had comorbidities, mean age was 42.96 years, and average NEWS2 score was 1.17. NEWS2 score of ≥ 6 had a sensitivity of 93.24% and specificity of 98.91%, and hence was a statistically significant cut-off value for predicting mortality on admission.Conclusion:Age, presence of comorbidities, and NEWS2 have a positive correlation with mortality in COVID-19 patients. NEWS2 score being easy, reliable, and quick to calculate, should be used to triage these patients on admission. Scores ≥ 6 should be considered to have a higher risk of adverse outcomes and hence should be managed prudently along with clinical judgment.

The Timing of Death in Acute Pulmonary Embolism Patients Regarding the Mortality Risk Stratification at Admission to the Hospital

The Timing of Death in Acute Pulmonary Embolism Patients Regarding the Mortality Risk Stratification at Admission to the Hospital

Novel Hamburg spondylodiscitis assessment score (HSAS) for immediate mortality risk stratification on hospital admission

Novel Hamburg spondylodiscitis assessment score (HSAS) for immediate mortality risk stratification on hospital admission

Shock Severity Assessment in Cardiac Intensive Care Unit Patients With Sepsis and Mixed Septic-Cardiogenic Shock.

We sought to validate the Society for Cardiovascular Angiography and Interventions (SCAI) cardiogenic shock classification for mortality risk stratification in patients with sepsis and concomitant cardiovascular disease or mixed septic-cardiogenic shock. We conducted a single-center retropective cohort study of cardiac intensive care unit patients with an admission diagnosis of sepsis. We used clinical, vital sign, and laboratory data during the first 24 hours after admission to assign SCAI shock stage. We included 605 patients with a median age of 69.4 years (interquartile range, 57.9 to 79.8 years), 222 of whom (36.7%) were female. Acute coronary syndrome or heart failure was present in 480 patients (79.3%), and cardiogenic shock or cardiac arrest was present in 271 patients (44.8%). The median day 1 Sequential Organ Failure Assessment (SOFA) cardiovascular subscore was 1.5 (interquartile range, 1 to 4), and the admission SCAI shock stage distribution was stage B, 40.7% (246); stage C, 19.3% (117); stage D, 32.9% (199); and stage E, 7.1% (43). In-hospital mortality occurred in 177 of the 605 patients (29.3%) and increased incrementally with higher SCAI shock stage. After multivariable adjustment, admission SCAI shock stage was associated with in-hospital mortality (adjusted odds ratio per stage, 1.46; 95% CI, 1.14 to 1.88; P=.003). Admission SCAI shock stage had higher discrimination for in-hospital mortality than the day 1 SOFA cardiovascular subscore (area under the receiver operating characteristic curve, 0.68 vs 0.64; P=.04 by the DeLong test). Admission SCAI shock stage was associated with 1-year mortality (adjusted hazard ratio per stage, 1.19; 95% CI, 1.03 to 1.37; P=.02). The SCAI shock classification provides improved mortality risk stratification over the day 1 SOFA cardiovascular subscore in cardiac intensive care unit patients with sepsis and concomitant cardiovascular disease or mixed septic-cardiogenic shock.

The Dynamic Changes of AFP From Baseline to Recurrence as an Excellent Prognostic Factor of Hepatocellular Carcinoma After Locoregional Therapy: A 5-Year Prospective Cohort Study.

BackgroundAlthough many studies have confirmed the prognostic value of preoperative alpha-fetoprotein (AFP) in patients with hepatocellular carcinoma (HCC), the association between AFP at baseline (b-AFP), subsequent AFP at relapse (r-AFP), and AFP alteration and overall survival in HCC patients receiving locoregional therapy has rarely been systematically elucidated.Patients and MethodsA total of 583 subjects with newly diagnosis of virus-related HCC who were admitted to Beijing You ‘an Hospital, Capital Medical University from January 1, 2012 to December 31, 2016 were prospectively enrolled. The influence of b-AFP, subsequent r-AFP, and AFP alteration on relapse and post-recurrence survival were analyzed.ResultsBy the end of follow-up, a total of 431 (73.9%) patients relapsed and 200 (34.3%) died. Patients with positive b-AFP had a 24% increased risk of recurrence compared with those who were negative. Patients with positive r-AFP had a 68% increased risk of death after relapse compared with those who were negative. The cumulative recurrence-death survival (RDS) rates for 1, 3, 5 years in patients with negative r-AFP were 85.6% (184/215), 70.2%(151/215), and 67.4%(145/215), while the corresponding rates were 75.1% (154/205), 51.2% (105/205), and 48.8% (100/205) in those with positive AFP (P<0.001). 35 (21.6%) of the 162 patients with negative b-AFP turned positive at the time of recurrence, and of this subset, only 12 (34.3%) survived. Of the 255 patients with positive b-AFP, 86 (33.7%) turned negative at the time of relapse, and of this subset, only 30 (34.9%) died. The 1-, 3-, and 5-year cumulative RDS rates were also compared among groups stratified by AFP at baseline and relapse. The present study found that patients with positive AFP at baseline and relapse, as well as those who were negative turned positive, had the shortest RDS and OS.ConclusionsNot only AFP at baseline but also subsequent AFP at relapse can be used to predict a post-recurrence survival, which can help evaluate mortality risk stratification of patients after relapse.

Assessing the relationship between systemic immune-inflammation index and mortality in patients with hypertrophic cardiomyopathy.

BackgroundThis study investigates the predictive value of the systemic immune-inflammation index (SII), which was calculated as platelet × neutrophil/lymphocyte ratio, for all-cause mortality in patients with hypertrophic cardiomyopathy (HCM).MethodsA total of 360 HCM patients were enrolled. They were divided into three groups based on the tertiles of baseline SII. The association between SII and all-cause mortality was analyzed.ResultsThere were 53 HCM patients who died during a mean follow-up time of 4.8 years (min: 6 days and max: 10.8 years), and the mortality rate was 3.0 per 100 person years. The cumulative mortality rate was significantly different among the three tertiles of SII (P = 0.004), and the mortality rate in tertile 3 was much higher than that in the first two tertiles. In reference to tertile 1, the fully adjusted hazard ratios of all-cause mortality were 1.02 for the tertile 2 (95% confidence interval [CI]: 0.45–2.31, P = 0.966) and 2.31 for tertile 3 (95% CI: 1.10–4.87, P = 0.027). No significant interactions between SII and other variables were observed during subgroup analysis. The discriminative power was better for mid-term outcome than that for short-term or long-term outcomes. Sensitivity analyses including patients with normal platelet and white blood cell count have revealed similar results.ConclusionSII was a significant risk factor for all-cause mortality in HCM patients. However, the discriminative power was poor to moderate. It could be used in combination with other risk factors in mortality risk stratification in HCM.

Towards risk stratification and prediction of disease severity and mortality in COVID-19: Next generation metabolomics for the measurement of host response to COVID-19 infection.

This study investigated the association between COVID-19 infection and host metabolic signatures as prognostic markers for disease severity and mortality. We enrolled 82 patients with RT-PCR confirmed COVID-19 infection who were classified as mild, moderate, or severe/critical based upon their WHO clinical severity score and compared their results with 31 healthy volunteers. Data on demographics, comorbidities and clinical/laboratory characteristics were obtained from medical records. Peripheral blood samples were collected at the time of clinical evaluation or admission and tested by quantitative mass spectrometry to characterize metabolic profiles using selected metabolites. The findings in COVID-19 (+) patients reveal changes in the concentrations of glutamate, valeryl-carnitine, and the ratios of Kynurenine/Tryptophan (Kyn/Trp) to Citrulline/Ornithine (Cit/Orn). The observed changes may serve as predictors of disease severity with a (Kyn/Trp)/(Cit/Orn) Receiver Operator Curve (ROC) AUC = 0.95. Additional metabolite measures further characterized those likely to develop severe complications of their disease, suggesting that underlying immune signatures (Kyn/Trp), glutaminolysis (Glutamate), urea cycle abnormalities (Cit/Orn) and alterations in organic acid metabolism (C5) can be applied to identify individuals at the highest risk of morbidity and mortality from COVID-19 infection. We conclude that host metabolic factors, measured by plasma based biochemical signatures, could prove to be important determinants of Covid-19 severity with implications for prognosis, risk stratification and clinical management.

End-to-End Non-Small-Cell Lung Cancer Prognostication Using Deep Learning Applied to Pretreatment Computed Tomography.

Clinical TNM staging is a key prognostic factor for patients with lung cancer and is used to inform treatment and monitoring. Computed tomography (CT) plays a central role in defining the stage of disease. Deep learning applied to pretreatment CTs may offer additional, individualized prognostic information to facilitate more precise mortality risk prediction and stratification. We developed a fully automated imaging-based prognostication technique (IPRO) using deep learning to predict 1-year, 2-year, and 5-year mortality from pretreatment CTs of patients with stage I-IV lung cancer. Using six publicly available data sets from The Cancer Imaging Archive, we performed a retrospective five-fold cross-validation using pretreatment CTs of 1,689 patients, of whom 1,110 were diagnosed with non-small-cell lung cancer and had available TNM staging information. We compared the association of IPRO and TNM staging with patients' survival status and assessed an Ensemble risk score that combines IPRO and TNM staging. Finally, we evaluated IPRO's ability to stratify patients within TNM stages using hazard ratios (HRs) and Kaplan-Meier curves. IPRO showed similar prognostic power (concordance index [C-index] 1-year: 0.72, 2-year: 0.70, 5-year: 0.68) compared with that of TNM staging (C-index 1-year: 0.71, 2-year: 0.71, 5-year: 0.70) in predicting 1-year, 2-year, and 5-year mortality. The Ensemble risk score yielded superior performance across all time points (C-index 1-year: 0.77, 2-year: 0.77, 5-year: 0.76). IPRO stratified patients within TNM stages, discriminating between highest- and lowest-risk quintiles in stages I (HR: 8.60), II (HR: 5.03), III (HR: 3.18), and IV (HR: 1.91). Deep learning applied to pretreatment CT combined with TNM staging enhances prognostication and risk stratification in patients with lung cancer.

Machine Learning Approach Using Routine Immediate Postoperative Laboratory Values for Predicting Postoperative Mortality.

Background: Several prediction models have been proposed for preoperative risk stratification for mortality. However, few studies have investigated postoperative risk factors, which have a significant influence on survival after surgery. This study aimed to develop prediction models using routine immediate postoperative laboratory values for predicting postoperative mortality. Methods: Two tertiary hospital databases were used in this research: one for model development and another for external validation of the resulting models. The following algorithms were utilized for model development: LASSO logistic regression, random forest, deep neural network, and XGBoost. We built the models on the lab values from immediate postoperative blood tests and compared them with the SASA scoring system to demonstrate their efficacy. Results: There were 3817 patients who had immediate postoperative blood test values. All models trained on immediate postoperative lab values outperformed the SASA model. Furthermore, the developed random forest model had the best AUROC of 0.82 and AUPRC of 0.13, and the phosphorus level contributed the most to the random forest model. Conclusions: Machine learning models trained on routine immediate postoperative laboratory values outperformed previously published approaches in predicting 30-day postoperative mortality, indicating that they may be beneficial in identifying patients at increased risk of postoperative death.

Development and validation of a prognostic scoring model for mortality risk stratification in patients with recurrent or metastatic gastric carcinoma

BackgroundSurvival times differ among patients with advanced gastric carcinoma. A precise and universal prognostic evaluation strategy has not yet been established. The current study aimed to construct a prognostic scoring model for mortality risk stratification in patients with advanced gastric carcinoma.MethodsPatients with advanced gastric carcinoma from two hospitals (development and validation cohort) were included. Cox proportional hazards regression analysis was conducted to identify independent risk factors for survival. A prognostic nomogram model was developed using R statistics and validated both in bootstrap and external cohort. The concordance index and calibration curves were plotted to determine the discrimination and calibration of the model, respectively. The nomogram score and a simplified scoring system were developed to stratify patients in the two cohorts.ResultsDevelopment and validation cohort was comprised of 401 and 214 gastric cancer patients, respectively. Mucinous or non-mucinous histology, ECOG score, bone metastasis, ascites, hemoglobin concentration, serum albumin level, lactate dehydrogenase level, carcinoembryonic antigen level, and chemotherapy were finally incorporated into prognostic nomogram. The concordance indices were 0.689 (95% CI: 0.664 ~ 0.714) and 0.673 (95% CI: 0.632 ~ 0.714) for bootstrap and external validation. 100 and 200 were set as the cut-off values of nomogram score, patients in development cohort were stratified into low-, intermediate- and high-risk groups with median overall survival time 15.8 (95% CI: 12.2 ~ 19.5), 8.4 (95% CI: 6.7 ~ 10.2), and 3.9 (95% CI: 2.7 ~ 5.2) months, respectively; the cut-off values also worked well in validation cohort with different survival time in subgroups. A simplified model was also established and showed good consistency with the nomogram scoring model in both of development and validation cohorts.ConclusionThe prognostic scoring model and its simplified surrogate can be used as tools for mortality risk stratification in patients with advanced gastric carcinoma.

Mortality risk stratification in isolated severe traumatic brain injury using the revised cardiac risk index

PurposeTraumatic brain injury (TBI) continues to be a significant cause of mortality and morbidity worldwide. As cardiovascular events are among the most common extracranial causes of death after a severe TBI, the Revised Cardiac Risk Index (RCRI) could potentially aid in the risk stratification of this patient population. This investigation aimed to determine the association between the RCRI and in-hospital deaths among isolated severe TBI patients.MethodsAll adult patients registered in the TQIP database between 2013 and 2017 who suffered an isolated severe TBI, defined as a head AIS ≥ 3 with an AIS ≤ 1 in all other body regions, were included. Patients were excluded if they had a head AIS of 6. The association between different RCRI scores (0, 1, 2, 3, ≥ 4) and in-hospital mortality was analyzed using a Poisson regression model with robust standard errors while adjusting for potential confounders, with RCRI 0 as the reference.Results259,399 patients met the study’s inclusion criteria. RCRI 2 was associated with a 6% increase in mortality risk [adjusted IRR (95% CI) 1.06 (1.01–1.12), p = 0.027], RCRI 3 was associated with a 17% increased risk of mortality [adjusted IRR (95% CI) 1.17 (1.05–1.31), p = 0.004], and RCRI ≥ 4 was associated with a 46% increased risk of in-hospital mortality [adjusted IRR(95% CI) 1.46 (1.11–1.90), p = 0.006], compared to RCRI 0.ConclusionAn elevated RCRI ≥ 2 is significantly associated with an increased risk of in-hospital mortality among patients with an isolated severe traumatic brain injury. The simplicity and bedside applicability of the index makes it an attractive choice for risk stratification in this patient population.

Abstract 9703: Prevalence and Outcomes of Low Ankle Brachial Index in ASCVD Risk Categories- from the National Health and Nutrition Examination Survey

Introduction: The utility of Ankle Brachial Index(ABI) as a risk-enhancer to the pooled cohort equations(PCE) in assessing cardiovascular risk for primary prevention of atherosclerotic cardiovascular disease(ASCVD) is uncertain. We aim to assess the prevalence and all-cause mortality of people with low ABI across ASCVD risk categories. Hypothesis: Would the addition of ABI to standard ASCVD risk assessment improve all cause mortality risk stratification? Methods: Participants were recruited as part of the National Health and Nutrition Examination Survey from 1999-2004 and mortality assessed on 12/31/2015. We included participants aged 40-75years with ABI values≤1.3 and data for ASCVD computation using the PCE. Persons with ABI&lt;0.9 and ABI 0.9-1.3 were classified as having low ABI and normal ABI, respectively. Results: The mean follow-up time was 156months(95%CI 154-158). Persons with low ABI were older(61 Vs 53years) and had higher mean systolic blood pressure(135 Vs 126mmHg). There was no difference in mean total cholesterol(mg/dl)[212(95%CI 210-214) vs. 208( 202-215)], LDL(mg/dl) [128 (95%CI 125-130) vs. 121(112-129)] and HDL(mg/dl) [54 (95%CI 53-54) vs. 52(50-55)] between the two ABI categories. The all-cause mortality was higher among persons with low ABI(43.1%) compared to those with normal ABI(12.2%), p&lt;0.001. The all-cause mortality in the normal ABI and low ABI categories was 4.8% and 8.8% in the low risk ASCVD group and 7.8% and 41.6% in the borderline/intermediate ASCVD risk group, respectively. Conclusions: Approximately 67% of participants with low ABI were classified as low, borderline or intermediate risk by PCE. Low ABI was associated with increased all-cause mortality in the intermediate/borderline and high ASCVD risk categories but not in the low ASCVD risk category. Our study supports the use of ABI in risk stratification for primary prevention of ASCVD in individuals with borderline or intermediate risk.

Abstract 12636: ECG-Based Risk Stratification of Long-Term Mortality in Suspected Acute Coronary Syndrome

Introduction: Non-invasive risk stratification can play a significant role in the initial triage of patients with suspected acute coronary syndrome. Numerous ECG features have been previously linked with mortality in both the general and cardiac populations. We sought to build an ECG-based model that can predict time-to-death in high-risk chest pain patients transported by Emergency Medical Services. Methods: This was a prospective observational cohort study of patients evaluated for suspected acute coronary syndrome. The study recruited consecutive patients evaluated at the emergency department of three UPMC-affiliated tertiary care hospitals. We included consecutive patients in whom a prehospital echocardiogram was performed during indexed encounter. Baseline ECGs were preprocessed using manufacturer specific algorithms. We evaluated 554 temporal-spatial features of the 12-lead ECG in multivariate proportional hazards Cox regression model for predicting all-cause death during long term follow up. Death was ascertained using phenotyping UPMC electronic hospital records and through the CDC National Death Index database. Model fit was evaluated using C-statistic and AIC. We set alpha at 0.01 for two-tailed hypothesis testing. Results: Our sample included 2311 patients (aged 59 ± 16.6, 47% females, 41% Black). Over a median follow up period of 1868 days (IQR = 688; [Q1, Q3] = [1511, 2199]), there was 622 (26.9%) adjudicated deaths. During univariate analysis, 288 features were significantly associated with time-to-death. In the final multivariate model (c-statistic = 0.79, AIC = 9066.05), controlling for demographic and clinical data, 15 ECG features remained predictive of the study outcome, with Selvester score being the strongest predictor (Hazard Ratio = 1.09, 95% CI = 1.03 - 1.16). Other clinically important ECG features included horizontal-plane QRS axis, QRS-T angle, QTc interval, QTpeak, T peak-end, S wave morphology the inferior leads, QRS morphology in lead aVR, and T wave morphology in the anterior leads. Conclusions: The presenting 12-lead ECG can serve as a powerful risk stratification tool of long-term mortality in patients with chest pain. Selvester score can serve as a plausible and easy to use clinical decision tool.

Abstract 11860: Long Term Outcomes and Risk Stratification in Patients With Heart Failure With Recovered Ejection Fraction

Introduction: Data are lacking on the natural history of heart failure with recovered ejection fraction (HFrecEF) with no available risk model for future heart failure events. This study aimed to understand the long-term outcomes of patients with HFrecEF, identify predictors of adverse events, and develop a risk stratification model. Methods: From an academic healthcare system we retrospectively identified patients who had an improvement in left ventricular ejection fraction (LVEF) from &lt;40% to ≥53%. Significant predictors of all-cause mortality, hospitalization and recurrent reduction in LVEF were identified via Cox regression analysis. A Cox proportional hazard risk model was developed for all-cause mortality. Results: 133 patients were included (median age 67, 39% female, 30% ischemic etiology). Kaplan-Meier survival was 70% at 5 years. Freedom from hospitalization was 58% at 1 year, and the risk of recurrent LVEF reduction to &lt;40% was 28% at 3 years. Diuretic dose and BNP at the time of LVEF recovery were the strongest predictors of mortality and hospitalization in multivariate adjusted analysis (BNP HR per 100pg/mL increase 1.13 [p&lt;0.01]; furosemide equivalent dose HR per 40mg increase 1.19 [p=0.02]). An all-cause mortality risk model incorporating NYHA functional class, BNP and diuretic dose at the time of recovery showed excellent risk discrimination (c-statistic 0.79) and calibration. Conclusions: Patients with HFrecEF have significant heterogeneity in clinical outcomes ranging from persistent recovery to requiring advanced heart failure therapies or death. A risk model utilizing NYHA functional class, BNP and diuretic dose can accurately stratify mortality risk.

Is the Median Hourly Ambulatory Heart Rate Range Helpful in Stratifying Mortality Risk among Newly Diagnosed Atrial Fibrillation Patients?

Background: The application of heart rate variability is problematic in patients with atrial fibrillation (AF). This study aims to explore the associations between all-cause mortality and the median hourly ambulatory heart rate range () compared with other parameters obtained from the Holter monitor in patients with newly diagnosed AF. Material and Methods: A total of 30 parameters obtained from 521 persistent AF patients’ Holter monitor were analyzed retrospectively from 1 January 2010 to 31 July 2014. Every patient was followed up to the occurrence of death or the end of 30 June 2017. Results: was the most feasible Holter parameter. Lower was associated with increased risk of all-cause mortality (adjusted hazard ratio [aHR] for every 10-bpm reduction: 2.70, 95% confidence interval [CI]: 1.75–4.17, p < 0.001). The C-statistic of alone was 0.707 (95% CI: 0.658–0.756), and 0.697 (95% CI: 0.650–0.744) for the CHA2DS2-VASc score alone. By combining with the CHA2DS2-VASc score, the C-statistic could improve to 0.764 (95% CI: 0.722–0.806). While using 20 bpm as the cut-off value, the aHR was 3.66 (95% CI: 2.05–6.52) for patients with < 20 bpm in contrast to patients with ≥ 20 bpm. Conclusions: could be helpful for risk stratification for AF in addition to the CHA2DS2-VASc score.

A Simplified Novel Prognostic Score to Predict Early Mortality and Disease Progression in Patients with Aggressive Adult T-Cell Leukemia/Lymphoma

▪Background: Adult T-cell leukemia/lymphoma (ATLL) is an aggressive mature peripheral T-cell neoplasm caused by HTLV-1 infection. Despite aggressive therapy, ATLL carries a very poor prognosis. Current prognostic models (i.e., the International Prognostic Index [IPI] and the Prognostic Index for T-cell lymphoma [PIT]) have shown to not completely predict survival in ATLL (Phillips, Cancer, 2010). Moreover, these models have not been validated in Latin American ATLL patients. Therefore, we aim to validate current prognostic systems (IPI, PIT) and to develop a novel model to predict early mortality in patients with ATLL.Methods: We conducted a retrospective cohort study by analyzing 12 demographic, clinical, and laboratory variables of 169 Latin American ATLL patients with acute and lymphomatous subtypes recruited by the “Grupo de Estudio Latinoamericano de Linfoproliferativos” (GELL). All patients received cancer-directed treatment. Conditional inference trees were used to construct a new prognostic model, the ATLL-GELL, by including serum lactate dehydrogenase (LDH) and the Eastern Cooperative Oncology Group (ECOG) performance status. Our primary outcome was overall survival (OS) at 15 months, while our secondary outcome was progression-free survival (PFS). The model identified a low-risk (LDH ≤2 times upper limit value [ULV] + ECOG score 0-1), and a high-risk (LDH >2 times ULV + ECOG score ≥2; or LDH >2 times ULV) group (Figure 1). The model was validated in an independent cohort of 95 North American ATLL patients with acute and lymphomatous subtypes from Miami, FL. We used the Kaplan-Meier method and the log-rank test to estimate the survival probabilities for the score. Cox regression was used to evaluate the risk of mortality and disease progression for each score. We used the C-index to determine the discriminatory power of each score.Results: Table 1 summarizes the demographic and clinical characteristics. The mean age was 57 (± 15.1) years with a male predominance (53%). Lymphomatous ATLL was the most common subtype (66%) in the training cohort, while acute ATLL (59%) in the testing cohort. Most patients had an Ann Arbor stage III-IV (88%) and received multiagent chemotherapy (76%) as first-line treatment (Table 1). With a median follow-up of 21 months, the 15-month OS and PFS rates of the training cohort were 29% and 27%, respectively. Patients with a high-risk ATLL-GELL score had a worse OS (16% vs. 61%, p<0.0001) and PFS (14% vs. 60%, p<0.0001) (Figure 2). Table 2 shows that the ATLL-GELL model had comparable prognostic discrimination than the IPI and PIT scores, according to the C-index. Similarly, the Cox regression analysis identified a better risk stratification of the new model for OS and PFS in the training and testing cohorts. In contrast, poor risk stratification for mortality and disease progression was found with the IPI and PIT scores in the training cohort. Table 2 shows that the risk for mortality and disease progression was higher in the high-intermediate (HR 7.91 and 8.07, respectively) than the high-risk (HR 7.12, and 7.85, respectively) group according to the IPI score. Moreover, the PIT score could not properly stratify patients' OS and PFS risk (Table 2). Figures 3 and 4 illustrate narrow and crossing survival curves between risk subgroups in both scores for OS and PFS estimates, respectively, suggesting poor visual discrimination of the IPI and PIT scores.Conclusion: ATLL carries a dismal prognosis, therefore we hypothesized that current prognostic systems might not yield an appropriate risk stratification since almost universally aggressive ATLL patients are at high risk for rapid disease progression and death. After considering different clinical-epidemiological and laboratory variables, our simplified prognostic model was able to predict early mortality and disease progression in patients with aggressive ATLL. Poor performance status, thus unfit for aggressive therapy, along with a high disease burden seems to reliably predict mortality in these patients. The ATLL-GELL model also showed better risk stratification and a more clear distinction of low- versus high-risk groups for aggressive ATLL than traditional models. We are currently validating our findings in a prospective cohort of Latin American ATLL patients and to improve clinical decision-making in those deemed at high risk for early mortality. [Display omitted] DisclosuresCastillo: Abbvie: Consultancy, Research Funding; BeiGene: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; Janssen: Consultancy; Roche: Consultancy; TG Therapeutics: Research Funding.

The prognostic value of respiratory symptoms and performance status in ambulatory cancer patients and unsuspected pulmonary embolism; analysis of an international, prospective, observational cohort study

BackgroundOptimal risk stratification of unsuspected pulmonary embolism (UPE) in ambulatory cancer patients (ACPs) remains unclear. Existing clinical predictive rules (CPRs) are derived from retrospective databases and have limitations. The UPE registry is a prospective international registry with pre‐specified characteristics of ACPs with a recent UPE. The aim of this study was to assess the utility of risk factors captured in the UPE registry in predicting proximate (30‐, 90‐ and 180‐day) mortality and how they performed when applied to an existing CPR. ObjectivesTo evaluate risk factors for proximate mortality, overall survival, recurrent venous thromboembolism and major bleeding, in the patients enrolled in the UPE registry cohort. MethodsData from the 695 ACPs in this registry were subjected to multivariate logistic regression analyses to identify predictors independently associated with proximate mortality and overall survival. The most consistent predictors were applied to the Hull CPR, an existing 5‐point prediction rule. ResultsThe most consistent predictors of mortality were patient‐reported respiratory symptoms within 14 days before, and ECOG performance status at the time of UPE. These predictors applied to the Hull‐CPR produced a consistent correlation with proximate mortality and overall survival (area under the curve [AUC] = 0.70 [95% CI 0.63, 077], AUC = 0.65 [95% CI 0.60, 070], AUC = 0.64 [95% CI 0.59, 068], and AUC = 0.61, 95% CI 0.57, 0.65, respectively). ConclusionIn ACPs with UPE, ECOG performance status logged contemporaneously to the UPE diagnosis and respiratory symptoms prior to UPE diagnosis can stratify mortality risk. When applied to the HULL‐CPR these risk predictors confirmed the risk stratification clusters of low‐intermediate and high‐risk for proximate mortality as seen in the original derivation cohort.

CHA2DS2-VASc score stratifies mortality risk in patients with and without atrial fibrillation

ObjectivesThe CHA2DS2-VASc score is the preferred risk model for anticoagulation decision-making in atrial fibrillation (AF) patients. Recent studies have found this score to have prognostic value in other cardiovascular diseases....