Mortality Risk Stratification Research Articles

One-year mortality after implantable defibrillator implantation: do risk stratification models help improving clinical practice?

The purpose of this study was to assess the available mortality risk stratification models for implantable cardioverter defibrillator (ICD) and cardiac resynchronization therapy defibrillator (CRT-D) patients. We conducted a review of mortality risk stratification models and tested their ability to improve prediction of 1-year survival after implant in a database of patients who received a remotely controlled ICD/CRT-D device during routine care and included in the independent Home Monitoring Expert Alliance registry. We identified ten predicting models published in peer-reviewed journals between 2000 and 2021 (Parkash, PACE, MADIT, aCCI, CHA2DS2-VASc quartiles, CIDS, FADES, Sjoblom, AAACC, and MADIT-ICD non-arrhythmic mortality score) that could be tested in our database as based on common demographic, clinical, echocardiographic, electrocardiographic, and laboratory variables. Our cohort included 1,911 patients with left ventricular dysfunction (median age 71, 18.3% female) from sites not using any risk stratification score for systematic patient screening. Patients received an ICD (53.8%) or CRT-D (46.2%) between 2011 and 2017, after standard physician evaluation. There were 56 deaths within 1-year post-implant, with an all-cause mortality rate of 2.9% (95% confidence interval [CI], 2.3-3.8%). Four predicting models (Parkash, MADIT, AAACC, and MADIT-ICD non-arrhythmic mortality score) were significantly associated with increased risk of 1-year mortality with hazard ratios ranging from 3.75 (CI, 1.31-10.7) to 6.53 (CI 1.52-28.0, p ≤ 0.014 for all four). Positive predictive values of 1-year mortality were below 25% for all models. In our analysis, the models we tested conferred modest incremental predicting power to ordinary screening methods.

Short- and Long-Term Mortality Prediction in Critically Ill Cancer Patients Admitted to the Intensive Care Unit (CanICU) Using Machine Learning

Background: Although cancer patients are increasingly admitted to the intensive care unit (ICU) for cancer- or treatment-related complications, improved mortality prediction remains a big challenge. Current prognostic models for a general ICU population underestimate the mortality risk in cancer patients. Methods: We developed CanICU, a machine learning-based 28-day and 1-year mortality prediction program in adult cancer patients admitted to ICU from Medical Information Mart for Intensive Care (MIMIC) database in the USA (n=766), Yonsei Cancer Center (YCC, n=3,571), and Samsung Medical Center in Korea (SMC, n=2,563). We constructed the classifier and compared it with a general prognostic model. Findings: A total of 6,900 patients were included, with a 28-day mortality of 10.2%/12.7%/36.6% and 1-year mortality of 30.0%/31.4%/58.5% in the MIMIC, YCC, and SMC cohort, respectively. Nine clinical and laboratory factors were used to construct the classifier using a random forest machine-learning algorithm. CanICU had the area under the receiver operating characteristic (AUROC) of 0.95 (95% CI 0.93-0.97) for 28-day and 0.79 (95% CI 0.76-0.82) for 1-year mortality, showing better performance than a current prognostic model (Sequential Organ Failure Assessment [SOFA], 0.77 [95% CI 0.72-0.81] for 28-day and 0.67 [95% CI 0.64-0.70] for 1-year mortality). Application of CanICU in external data sets across the countries yielded better AUROC for 28-day mortality than SOFA (95% CI, 0.75-0.80 vs. 0.57-0.63 with SOFA in SMC; 95% CI, 0.72-0.79 vs. 0.64-0.72 with SOFA in MIMIC). Interpretation: CanICU offers improved performance for predicting short- and long-term mortality in critically ill cancer patients admitted to ICU. A user-friendly online implementation is available and should be valuable for better mortality risk stratification. This might help physicians to determine to allocate ICU care for patients with cancer. Funding Information: This study was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2019R1C1C1006709, 018R1A5A2025079, 2020M3F7A1094093), a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, the Republic of Korea (No. KHIDIHI19C1015010020), and was also supported by the ational R&D Program for Cancer Control through the National Cancer Center (NCC) funded by the Ministry of Health & Welfare, Republic of Korea (HA21C0065). Declaration of Interests: The authors have no potential conflicts of interest to disclose. Ethics Approval Statement: The institutional review boards of all participating hospitals approved this study and waived the requirement for informed consent because of the observational nature of the research. All patient records and data were anonymized and de-identified before analysis.

Cysteine-rich angiogenic inducer 61 (CCN1) independently predicts all-cause mortality in patients with dilated cardiomyopathy

Abstract Introduction Cysteine-rich angiogenic inducer 61 (Cyr61, CCN1) is a member of the CCN family of matricellular proteins exerting key functions in inflammation and fibrotic turnover. Recently it has been shown that CCN1 improves risk stratification for all-cause mortality in ACS patients. Purpose Since both inflammation and fibrosis are key processes involved in the pathogenesis of dilated cardiomyopathy (DCM), we aimed to investigate the prognostic value of CCN1 serum levels for survival in a large cohort of DCM patients. Methods The cohort compromised patients with a primary diagnosis of DCM, defined as a reduced left ventricular ejection fraction (LVEF <45%) and an increased left ventricular enddiastolic diameter according to the HENRY score (LVEDD according to HENRY >117%) at the time of diagnosis. Exclusion criteria were primary valvular diseases (≥ second degree), acute myocarditis, active infectious diseases, pulmonary diseases, cancer, chronic alcoholism and heart failure of other origins. CCN1 levels were determined in human serum using an enzyme-linked immunosorbent assay (R&D Systems, USA). Multivariable cox regression models for the association between CCN1 and all-cause mortality were adjusted for age, sex, disease duration, LVEF, estimated glomerular filtration rate (eGFR) measured based on the CKD-EPI formula, high-sensitivity C-reactive protein (hs-CRP) and aminoterminal-proB-type natriuretic peptide (NT-proBNP) levels. Results 306 DCM patients had available biomarker and clinico-demographic data in this single-center cohort (79.3% males) with a mean age of 55.2 years [interquartile range [IQR] 47.9, 64.8]). On average, disease duration was 0.3 years (IQR 0.9, 1.9), LVEF 31% (IQR 25, 37), LVEDD 67.7 mm (IQR 63, 72), and eGFR 91.5 ml/min (IQR 74.1, 102.4). During a median follow-up of 12.5 years (IQR 10.5, 14.1), a total of 114 (37.3%) patients died. Multivariable-adjusted cox regression models revealed an increasing all-cause mortality risk across CCN1 tertiles (p for trend = 0.03), with the highest incidence in the highest tertile (hazard ratio [HR] 1.75; 95%-CI 1.04, 2.94; P=0.034) as compared to the lowest tertile (Figure 1). Conclusion CCN1 predicts long-term survival in DCM patients independent of NT-pro-BNP and other risk determinants. Further research needs to evaluate whether this novel biomarker also plays a causal role in the pathogenesis of DCM. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): Kerckhoff Foundation Figure 1

Estimated pulse wave velocity improves risk stratification for all-cause mortality in patients with COVID-19

Accurate risk stratification in COVID-19 patients consists a major clinical need to guide therapeutic strategies. We sought to evaluate the prognostic role of estimated pulse wave velocity (ePWV), a marker of arterial stiffness which reflects overall arterial integrity and aging, in risk stratification of hospitalized patients with COVID-19. This retrospective, longitudinal cohort study, analyzed a total population of 1671 subjects consisting of 737 hospitalized COVID-19 patients consecutively recruited from two tertiary centers (Newcastle cohort: n = 471 and Pisa cohort: n = 266) and a non-COVID control cohort (n = 934). Arterial stiffness was calculated using validated formulae for ePWV. ePWV progressively increased across the control group, COVID-19 survivors and deceased patients (adjusted mean increase per group 1.89 m/s, P < 0.001). Using a machine learning approach, ePWV provided incremental prognostic value and improved reclassification for mortality over the core model including age, sex and comorbidities [AUC (core model + ePWV vs. core model) = 0.864 vs. 0.755]. ePWV provided similar prognostic value when pulse pressure or hs-Troponin were added to the core model or over its components including age and mean blood pressure (p < 0.05 for all). The optimal prognostic ePWV value was 13.0 m/s. ePWV conferred additive discrimination (AUC: 0.817 versus 0.779, P < 0.001) and reclassification value (NRI = 0.381, P < 0.001) over the 4C Mortality score, a validated score for predicting mortality in COVID-19 and the Charlson comorbidity index. We suggest that calculation of ePWV, a readily applicable estimation of arterial stiffness, may serve as an additional clinical tool to refine risk stratification of hospitalized patients with COVID-19 beyond established risk factors and scores.

Biomarker-Based Guideline-Directed Medical Therapy of Heart Failure: The Gap Between Guidelines and Clinical Practice

Current clinical recommendations provided by the 2016 European Society of Cardiology (ESC) and 2017 American College of Cardiology (ACC)/American Heart Association (AHA)/Heart Failure Society of America (HFSA) are substantially distinguished in the use of circulating biomarkers in the management of heart failure (HF). To date, natriuretic peptides continue being the universal biomarkers used in diagnosis, risk stratification, and prediction of cardiovascular death, all-cause mortality, and HF-related outcomes for patients with both phenotypes of HF. However, biomarkers of fibrosis and inflammation, including soluble suppressor of tumourgenicity 2 and galectin-3, were able to increase predictive ability of natriuretic peptides in HF patients regardless of cardiovascular risk-factor presentation and HF phenotypes. Therefore, there are many various biomarkers describing several pathophysiological processes such as fibrosis, inflammation, oxidative stress, neurohumoral activation, extracellular matrix turnover, and vascular reparation, that play a pivotal role in the natural evolution of HF. This review discusses whether multiple biomarker models are more effective than a single biomarker in improving risk stratification strategies in patients with HF. It emphasises how in routine clinical practice, the multiple biomarker approach to elicit response to therapy of HF and predict clinical outcomes is rare, probably because of the relatively high cost, low affordability, lack of clear recommendations for clinical implementation, and significant disagreements in the interpretation of the data obtained.

Biomarker-Based Guideline-Directed Medical Therapy of Heart Failure: The Gap Between Guidelines and Clinical Practice

Current clinical recommendations provided by the 2016 European Society of Cardiology (ESC) and 2017 American College of Cardiology (ACC)/American Heart Association (AHA)/Heart Failure Society of America (HFSA) are substantially distinguished in the use of circulating biomarkers in the management of heart failure (HF). To date, natriuretic peptides continue being the universal biomarkers used in diagnosis, risk stratification, and prediction of cardiovascular death, all-cause mortality, and HF-related outcomes for patients with both phenotypes of HF. However, biomarkers of fibrosis and inflammation, including soluble suppressor of tumourgenicity 2 and galectin-3, were able to increase predictive ability of natriuretic peptides in HF patients regardless of cardiovascular risk-factor presentation and HF phenotypes. Therefore, there are many various biomarkers describing several pathophysiological processes such as fibrosis, inflammation, oxidative stress, neurohumoral activation, extracellular matrix turnover, and vascular reparation, that play a pivotal role in the natural evolution of HF. This review discusses whether multiple biomarker models are more effective than a single biomarker in improving risk stratification strategies in patients with HF. It emphasises how in routine clinical practice, the multiple biomarker approach to elicit response to therapy of HF and predict clinical outcomes is rare, probably because of the relatively high cost, low affordability, lack of clear recommendations for clinical implementation, and significant disagreements in the interpretation of the data obtained.

Multiple Cardiac Biomarker Testing Among Patients With Acute Dyspnea From the ICON-RELOADED Study

BackgroundAmong patients with acute dyspnea, concentrations of N-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T, and insulin-like growth factor binding protein-7 predict cardiovascular outcomes and death. Understanding the optimal means to interpret these elevated biomarkers in patients presenting with acute dyspnea remains unknown. Methods and ResultsConcentrations of NT-proBNP, high-sensitivity cardiac troponin T, and insulin-like growth factor binding protein-7 were analyzed in 1448 patients presenting with acute dyspnea from the prospective, multicenter International Collaborative of NT-proBNP-Re-evaluation of Acute Diagnostic Cut-Offs in the Emergency Department (ICON-RELOADED) Study. Eight biogroups were derived based upon patterns in biomarker elevation at presentation and compared for differences in baseline characteristics. Of 441 patients with elevations in all 3 biomarkers, 218 (49.4%) were diagnosed with acute heart failure (HF). The frequency of acute HF diagnosis in this biogroup was higher than those with elevations in 2 biomarkers (18.8%, 44 of 234), 1 biomarker (3.8%, 10 of 260), or no elevated biomarkers (0.4%, 2 of 513). The absolute number of elevated biomarkers on admission was prognostic of the composite end point of mortality and HF rehospitalization. In adjusted models, patients with one, 2, and 3 elevated biomarkers had 3.74 (95% confidence interval [CI], 1.26–11.1, P = .017), 12.3 (95% CI, 4.60–32.9, P < .001), and 12.6 (95% CI, 4.54–35.0, P < .001) fold increased risk of 180-day mortality or HF rehospitalization. ConclusionsA multimarker panel of NT-proBNP, hsTnT, and IGBFP7 provides unique clinical, diagnostic, and prognostic information in patients presenting with acute dyspnea. Differences in the number of elevated biomarkers at presentation may allow for more efficient clinical risk stratification of short-term mortality and HF rehospitalization.

Application of Peak Glucose Range and Diabetes Status in Mortality Risk Stratification in Critically Ill Patients with Sepsis

The effects of diabetes and glucose on the outcomes of patients with sepsis are somewhat conflicting. This retrospective study enrolled 1214 consecutive patients with sepsis, including a subpopulation of 148 patients with immune profiles. The septic patients were stratified according to their Diabetes mellitus (DM) status or peak glucose level (three-group tool; P1: ≤140 mg/dL, P2: 141–220 mg/dL, P3: >220 mg/dL) on day 1. Although the DM group had a lower hazard ratio (HR) for 90-day mortality compared to non-DM patients, the adjusted HRs were insignificant. The modified sequential organ failure assessment-glucose (mSOFA-g) score can predict 90-day survival in patients with and without diabetes (β = 1.098, p < 0.001; β = 1.202, p < 0.001). The goodness of fit of the mSOFA-g score was 5% higher than the SOFA score of the subgroup without diabetes. The SOFA score and human leukocyte antigen-D-related (HLA-DR) expression were comparable between the groups. The P3 group had lower HLA-DR expression on days 1 and 3 and a higher 90-day mortality. The three-group tool was useful for predicting 90-day mortality in patients with separate Kaplan-Meier survival curves and mortality HRs in the construction and validation cohorts. The peak glucose level, instead of diabetes status, can be used as an easy adjunctive tool for mortality risk stratification in critically ill septic patients.

Performance of acute respiratory distress syndrome definitions in a high acuity paediatric intensive care unit

BackgroundFor years, paediatric critical care practitioners used the adult American European Consensus Conference (AECC) and revised Berlin Definition (BD) for acute respiratory distress syndrome (ARDS) to study the epidemiology of paediatric ARDS (PARDS). In 2015, the paediatric specific definition, Paediatric Acute Lung Injury Consensus Conference (PALICC) was developed. The use of non-invasive metrics of oxygenation to stratify disease severity were introduced in this definition, although this potentially may lead to a confounding effect of disease severity since it is more common to place indwelling arterial lines in sicker patients. We tested the hypothesis that PALICC outperforms AECC/BD in our high acuity PICU, which employs a liberal use of indwelling arterial lines and high-frequency oscillatory ventilation (HFOV).MethodsWe retrospectively collected data from children < 18 years mechanically ventilated for at least 24 h in our tertiary care, university-affiliated paediatric intensive care unit. The primary endpoint was the difference in the number of PARDS cases between AECC/BD and PALICC. Secondary endpoints included mortality and ventilator free days. Performance was assessed by the area under the receiver operating characteristics curve (AUC-ROC).ResultsData from 909 out of 2433 patients was eligible for analysis. AECC/BD identified 35 (1.4%) patients (mortality 25.7%), whereas PALICC identified 135 (5.5%) patients (mortality 14.1%). All but two patients meeting AECC/Berlin criteria were also identified by PALICC. Almost half of the cohort (45.2%) had mild, 33.3% moderate and 21.5% severe PALICC PARDS at onset. Highest mortality rates were seen in patients with AECC acute lung injury (ALI)/mild Berlin and severe PALICC PARDS. The AUC-ROC for Berlin was the highest 24 h (0.392 [0.124–0.659]) after onset. PALICC showed the highest AUC-ROC at the same moment however higher than Berlin (0.531 [0.345–0.716]). Mortality rates were significantly increased in patients with bilateral consolidations (9.3% unilateral vs 26.3% bilateral, p = 0.025).ConclusionsPALICC identified more new cases PARDS than the AECC/Berlin definition. However, both PALICC and Berlin performed poorly in terms of mortality risk stratification. The presence of bilateral consolidations was associated with a higher mortality rate. Our findings may be considered in future modifications of the PALICC criteria.

Characterising the relationships between physiological indicators and all-cause mortality (NHANES): a population-based cohort study

SummaryBackgroundMortality risk stratification based on dichotomising a physiological indicator with a cutoff point might not adequately capture increased mortality risk and might not account for non-linear associations. We aimed to characterise the linear and non-linear relationships of 27 physiological indicators with all-cause mortality to evaluate whether the current clinical thresholds are suitable in distinguishing patients at high risk for mortality from those at low risk.MethodsFor this observational cohort study of the US non-institutionalised population, we used data from adults (≥18 years) included in the 1999–2014 National Health and Nutrition Examination Survey (NHANES) linked with National Death Index mortality data collected from Jan 1, 1999, up until Dec 31, 2015. We used Cox proportional hazards regression models adjusted for age, sex, and race or ethnicity to assess associations of physiological indicators with all-cause mortality. We assessed non-linear associations by discretising the physiological indicator into nine quantiles (termed novemtiles) and by using a weighted sum of cubic polynomials (spline). We used ten-fold cross validation to select the most appropriate model using the concordance index, Nagelkerke R2, and Akaike Information Criterion. We identified the level of each physiological indicator that led to a 10% increase in mortality risk to define our cutoffs used to compare with the current clinical thresholds.FindingsWe included 47 266 adults of 82 091 assessed for eligibility. 25 (93%) of 27 indicators showed non-linear associations with substantial increases compared with linear models in mortality risk (1·5–2·5-times increase). Height and 60 s pulse were the only physiological indicators to show linear associations. For example, participants with an estimated glomerular filtration rate (GFR) of less than 65 mL/min per 1·73 m2 or between 90–116 mL/min per 1·73 m2 are at moderate (hazard ratio 1–2) mortality risk. Those with a GFR greater than 117 mL/min per 1·73 m2 show substantial (hazard ratio ≥2) mortality risk. Both lower and higher values of cholesterol are associated with increased mortality risk. The current clinical thresholds do not align with our mortality-based cutoffs for fat deposition indices, 60 s pulse, triglycerides, cholesterol-related indicators, alkaline phosphatase, glycohaemoglobin, homoeostatic model assessment of insulin resistance, and GFR. For these indicators, the misalignment suggests the need to consider an additional bound when only one is provided.InterpretationMost clinical indicators were shown to have non-linear associations with all-cause mortality. Furthermore, considering these non-linear associations can help derive reliable cutoffs to complement risk stratification and help inform clinical care delivery. Given the poor alignment with our proposed cutoffs, the current clinical thresholds might not adequately capture mortality risk.

Application of a 72 h National Early Warning Score and Incorporation with Sequential Organ Failure Assessment for Predicting Sepsis Outcomes and Risk Stratification in an Intensive Care Unit: A Derivation and Validation Cohort Study.

We investigated the best timing for using the National Early Warning Score 2 (NEWS2) for predicting sepsis outcomes and whether combining the NEWS2 and the Sequential Organ Failure Assessment (SOFA) was applicable for mortality risk stratification in intensive care unit (ICU) patients with severe sepsis. All adult patients who met the Third International Consensus Definitions for Sepsis and Septic Shock criteria between August 2013 and January 2017 with complete clinical parameters and laboratory data were enrolled as a derivation cohort. The primary outcomes were the 7-, 14-, 21-, and 28-day mortalities. Furthermore, another group of patients under the same setting between January 2020 and March 2020 were also enrolled as a validation cohort. In the derivation cohort, we included 699 consecutive adult patients. The 72 h NEWS2 had good discrimination for predicting 7-, 14-, 21-, and 28-day mortalities (AUC: 0.780, 0.724, 0.700, and 0.667, respectively) and was not inferior to the SOFA (AUC: 0.740, 0.680, 0.684, and 0.677, respectively). With the new combined NESO tool, the hazard ratio was 1.854 (1.203–2.950) for the intermediate-risk group and 6.810 (3.927–11.811) for the high-risk group relative to the low-risk group. This finding was confirmed in the validation cohort using a separated survival curve for 28-day mortality. The 72 h NEWS2 alone was non-inferior to the admission SOFA or day 3 SOFA for predicting sepsis outcomes. The NESO tool was found to be useful for 7-, 14-, 21-, and 28-day mortality risk stratification in patients with severe sepsis.

Combined evaluation of aminotransferases improves risk stratification for overall and cause-specific mortality in older patients.

Recent studies identified low levels of alanine aminotransferase (ALT) as strong predictors of mortality in older people. Here we verified if the combined evaluation of aminotransferases may improve risk stratification for adverse outcomes in older patients. Data are from 761 participants aged more than 65years from a prospective population-based database (InCHIANTI study), without known baseline chronic liver disease or malignancies. Associations between aminotransferase levels and the risk of all-cause, cardiovascular- and cancer-death were assessed by Cox-models with time-dependent covariates. The association of ALT and aspartate aminotransferase (AST) with mortality was non-linear, mirroring a J- and a U-shaped curve, respectively. Based on quintiles of transaminase activities and on their association with overall mortality, low, intermediate (reference group) and high levels were defined. Having at least one transaminase in the low range [aHR 1.76 (1.31-2.36), p < 0.001], mainly if both [(aHR 2.39 (1.81-3.15), p < 0.001], increased the risk of overall mortality, as well as having both enzymes in the high range [aHR 2.14 (1.46-3.15), p < 0.001]. While similar trends were confirmed with respect to cardiovascular mortality, subjects with the highest risk of cancer mortality were those with both enzymes in the high range [aHR 3.48 (1.43-8.44), p = 0.006]. Low levels of transaminases were associated with frailty, sarcopenia and disability, while high levels did not capture any known proxy of adverse outcome. Conclusions and discussion The prognostic information is maximized by the combination of the 2 liver enzymes. While both aminotransferases in low range are characteristically found in the most fragile phenotype, both enzymes in high range are more likely to identify new-onset vascular/infiltrative diseases with adverse outcome.

Development and Validation of a Modified Pathologic Nodal Classification System for Cutaneous Melanoma

Given the evolving patterns of lymph node evaluation for cutaneous melanoma, it is unclear whether the current nodal classification system will continue to accurately reflect prognosis in the modern era. Existing nodal staging for cutaneous melanoma was developed primarily for patients undergoing completion lymph node dissection (CLND) for node-positive disease and does not produce groups with continuously increasing mortality. To develop and validate a modified nodal classification system for cutaneous melanoma. This retrospective cohort analysis included 105 785 patients with cutaneous melanoma undergoing surgery and nodal evaluation from January 1, 2004, to December 31, 2015, in the National Cancer Database. Extent of lymph node dissection was available for patients diagnosed in 2012 and onward. Multivariable models were generated with number of positive lymph nodes modeled using restricted cubic splines. A modified nodal classification system was derived using recursive partitioning analysis (RPA). The proposed lymph node classification system was validated in 85 499 patients from the Surveillance, Epidemiology, and End Results (SEER-18) database. Data were analyzed from April 9, 2020, to May 28, 2021. Overall survival. Among the 105 785 patients included in the analysis (62 496 men [59.1%]; mean [SD] age, 59.9 [15.5] years), number of positive lymph nodes (hazard ratio [HR] per lymph node for 0 to 2 positive lymph nodes, 2.48 [95% CI, 2.37-2-61; P < .001]; HR per lymph node for ≥3 positive lymph nodes, 1.10 [95% CI 1.07-1.13; P < .001]), clinically detected metastases (HR, 1.35; 95% CI, 1.27-1.42; P < .001), and in-transit metastases (HR, 1.48; 95% CI, 1.34-1.65; P < .001) were independently associated with mortality. An RPA-derived system using these variables demonstrated continuously increasing mortality for each proposed lymph node classification group, with HRs of 1.83 (95% CI, 1.76-1.91) for N1a, 2.72 (95% CI, 2.58-2.86) for N1b, 3.79 (95% CI, 3.51-4.08) for N2a, 4.56 (95% CI, 4.22-4.92) for N2b, 6.15 (95% CI, 5.59-6.76) for N3a, and 8.25 (95% CI, 7.64-8.91) for N3b in the proposed system (P < .001). By contrast, the current American Joint Committee on Cancer (AJCC) nodal classification system produced a more haphazard mortality profile, with HRs of 1.83 (95% CI, 1.76-1.91) for N1a, 3.81 (95% CI, 3.53-4.12) for N1b, 2.59 (95% CI, 2.30-2.93) for N1c, 2.71 (95% CI, 2.56-2.87) for N2a, 4.51 (95% CI, 4.17-4.87) for N2b, 3.44 (95% CI, 2.60-4.55) for N2c, 6.06 (95% CI, 5.51-6.67) for N3a, 8.15 (95% CI, 7.54-8.81) for N3b, and 6.90 (95% CI, 5.60-8.49) for N3c. As a sensitivity analysis, the proposed system continued to accurately stratify patients when excluding those undergoing CLND for microscopic lymph node metastases. This system was validated for overall survival and cause-specific mortality in SEER-18. Last, a new overall staging system for node-positive patients was developed by RPA and demonstrated improved concordance vs the AJCC, 8th edition system (C statistic, 0.690 [95% CI, 0.689-0.691] vs 0.666 [95% CI, 0.666-0.668]). The findings of this cohort study suggest that a modified nodal classification system can accurately stratify mortality risk in cutaneous melanoma in an era of increasing use of sentinel lymph node biopsy without CLND and should be considered for future staging systems.

Heart rate n-variability (HRnV) measures for prediction of mortality in sepsis patients presenting at the emergency department

Sepsis is a potentially life-threatening condition that requires prompt recognition and treatment. Recently, heart rate variability (HRV), a measure of the cardiac autonomic regulation derived from short electrocardiogram tracings, has been found to correlate with sepsis mortality. This paper presents using novel heart rate n-variability (HRnV) measures for sepsis mortality risk prediction and comparing against current mortality prediction scores. This study was a retrospective cohort study on patients presenting to the emergency department of a tertiary hospital in Singapore between September 2014 to April 2017. Patients were included if they were above 21 years old and were suspected of having sepsis by their attending physician. The primary outcome was 30-day in-hospital mortality. Stepwise multivariable logistic regression model was built to predict the outcome, and the results based on 10-fold cross-validation were presented using receiver operating curve analysis. The final predictive model comprised 21 variables, including four vital signs, two HRV parameters, and 15 HRnV parameters. The area under the curve of the model was 0.77 (95% confidence interval 0.70–0.84), outperforming several established clinical scores. The HRnV measures may have the potential to allow for a rapid, objective, and accurate means of patient risk stratification for sepsis severity and mortality. Our exploration of the use of wealthy inherent information obtained from novel HRnV measures could also create a new perspective for data scientists to develop innovative approaches for ECG analysis and risk monitoring.

Circulating MR-proADM levels, as an indicator of endothelial dysfunction, for early risk stratification of mid-term mortality in COVID-19 patients

ObjectivesThromboinflammation, resulting from a complex interaction between thrombocytopathy, coagulopathy, and endotheliopathy, contributes to increased mortality in COVID-19 patients. MR-proADM, as a surrogate of adrenomedullin system disruption, leading to endothelial damage, has been reported as a promising biomarker for short-term prognosis. We evaluated the role of MR-proADM in the mid-term mortality in COVID-19 patients.MethodsA prospective, observational study enrolling COVID-19 patients from August to October 2020. A blood sample for laboratory test analysis was drawn on arrival in the emergency department. The primary endpoint was 90-day mortality. The area under the curve (AUC) and Cox regression analyses were used to assess discriminatory ability and association with the endpoint.ResultsA total of 359 patients were enrolled, and the 90-day mortality rate was 8.9%. ROC AUC for MR-proADM predicting 90-day mortality was 0.832. An optimal cutoff of 0.80 nmol/L showed a sensitivity of 96.9% and a specificity of 58.4%, with a negative predictive value of 99.5%. Circulating MR-proADM levels (inverse transformed), after adjusting by a propensity score including eleven potential confounders, were an independent predictor of 90-day mortality (HR: 0.162 [95% CI: 0.043-0.480])ConclusionsOur data confirm that MR-proADM has a role in the mid-term prognosis of COVID-19 patients and might assist physicians with risk stratification.

Fitness and Mortality Among Persons 70 Years and Older Across the Spectrum of Cardiovascular Disease Risk Factor Burden: The FIT Project

ObjectiveTo determine whether fitness could improve mortality risk stratification among older adults compared with cardiovascular disease (CVD) risk factors. MethodsWe examined 6509 patients 70 years of age and older without CVD from the Henry Ford ExercIse Testing Project (FIT Project) cohort. Patients performed a physician-referred treadmill stress test between 1991 and 2009. Traditional categorical CVD risk factors (hypertension, hyperlipidemia, diabetes, and smoking) were summed from 0 to 3 or more. Fitness was grouped as low, moderate, and high (<6, 6 to 9.9, and ≥10 metabolic equivalents of task). All-cause mortality was ascertained through US Social Security Death Master files. We calculated age-adjusted mortality rates, multivariable adjusted Cox proportional hazards, and Kaplan-Meier survival models. ResultsPatients had a mean age of 75±4 years, and 3385 (52%) were women; during a mean follow-up of 9.4 years, there were 2526 deaths. A higher fitness level (P<.001), not lower CVD risk factor burden (P=.31), was associated with longer survival. The age-adjusted mortality rate per 1000 person-years was 56.7 for patients with low fitness and 0 risk factors compared with 24.9 for high fitness and 3 or more risk factors. Among patients with 3 or more risk factors, the adjusted mortality hazard was 0.68 (95% CI, 0.61 to 0.76) for moderate and 0.51 (95% CI, 0.44 to 0.60) for high fitness compared with the least fit. ConclusionAmong persons aged 70 years and older, there was no significant difference in survival of patients with 0 vs 3 or more risk factors, but a higher fitness level identified older persons with good long-term survival regardless of CVD risk factor burden.

Mortality risk stratification helps identify patients with COVID-19 at risk of adverse outcomes.

Mortality risk stratification can identify patients with COVID-19 who are at higher risk of mortality, discharge to skilled nursing facility, and readmission, and may benefit from focused intervention strategies.

Intensive Care Unit Scoring Systems.

Illness severity scoring systems are commonly used in critical care. When applied to the populations for whom they were developed and validated, these tools can facilitate mortality prediction and risk stratification, optimize resource use, and improve patient outcomes. To describe the characteristics and applications of the scoring systems most frequently applied to critically ill patients. A literature search was performed using MEDLINE to identify original articles on intensive care unit scoring systems published in the English language from 1980 to 2020. Search terms associated with critical care scoring systems were used alone or in combination to find relevant publications. Two types of scoring systems are most frequently applied to critically ill patients: those that predict risk of in-hospital mortality at the time of intensive care unit admission (Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score, and Mortality Probability Models) and those that assess and characterize current degree of organ dysfunction (Multiple Organ Dysfunction Score, Sequential Organ Failure Assessment, and Logistic Organ Dysfunction System). This article details these systems' differing features and timing of use, score calculation, patient populations, and comparative performance data. Critical care nurses must be aware of the strengths, limitations, and specific characteristics of severity scoring systems commonly used in intensive care unit patients to effectively employ these tools in clinical practice and critically appraise research findings based on their use.

Derivation and validation of a national multicenter mortality risk stratification model – the ExCare model: a study protocol

IntroductionSurgical care is essential for proper management of various diseases. However, it can result in unfavorable outcomes. In order to identify patients at higher risk of complications, several risk stratification models have been developed. Ideally, these tools should be simple, reproducible, accurate, and externally validated. Unfortunately, none of the best-known risk stratification instruments have been validated in Brazil. In this sense, the Ex-Care model was developed by retrospective data analysis of surgical patients in a major Brazilian university hospital. It consists of four independent predictors easily collected in the preoperative evaluation, showing high accuracy in predicting death within 30 days after surgery. ObjectivesTo update and validate a Brazilian national-based model of postoperative death probability within 30 days based on the Ex-Care model. Also, to develop an application for smartphones that allows preoperative risk stratification by Ex-Care model. MethodsTen participating centers will collect retrospective data from digital databases. Variables age, American Society of Anesthesiologists (ASA) physical status, surgical severity (major or non-major) and nature (elective or urgent) will be evaluated as predictors for in-hospital mortality within 30 postoperative days, considered the primary outcome. Expected resultsWe believe that the Ex-Care model will present discriminative capacity similar to other classically used scores validated for surgical mortality prediction. Furthermore, the mobile application to be developed will provide a practical and easy-to-use tool to the professionals enrolled in perioperative care.

Synergistic Assessment of Mortality Risk According to Body Mass Index and Exercise Ability and Capacity in Patients Referred for Radionuclide Stress Testing

ObjectiveTo determine the interrelationship between body mass index (BMI), mode of stress testing (exercise or pharmacological), exercise capacity, and all-cause mortality in patients referred for stress-rest single photon emission computed tomography myocardial perfusion imaging. Patients and MethodsWe evaluated all-cause mortality in 21,638 patients undergoing stress-rest single photon emission computed tomography myocardial perfusion imaging between January 2, 1991, and December 31, 2012. Patients were divided into exercise and pharmacologically tested groups and 9 BMI categories. The median follow-up was 12.8 years (range, 5.0-26.8 years). ResultsIn exercise patients, mortality was increased with both low and high BMI vs patients with a normal referent BMI of 22.5 to 24.9 kg/m2. In pharmacologically tested patients, only low BMI, but not high BMI, was associated with increased mortality vs normal BMI. When exercise and pharmacologically tested groups were compared directly, pharmacologically tested patients manifested a marked increase in mortality risk vs exercise patients within each BMI category, ranging from an approximately 4-fold increase in mortality in those with normal or high BMI to a 12.3-fold increase in those with low BMI values. Similar findings were observed in a cohort of 4804 exercise and 4804 pharmacologically tested patients matched to have similar age and coronary artery disease risk factor profiles. In exercise patients, further risk stratification was achieved when considering both BMI and metabolic equivalent tasks of achieved exercise. ConclusionThe combined assessment of BMI and exercise ability and capacity provides synergistic and marked risk stratification of future mortality risk in patients referred for radionuclide stress testing, providing considerable insights into the “obesity paradox” that is observed in populations referred for stress testing.