Mortality Risk Score Research Articles

Abstract 4138620: Assessment of the Accuracy of the Perioperative Mortality Risk in Adults with Congenital Heart Disease (PEACH) Score

INTRODUCTION: Previous mortality risk scores for adult cardiac surgery patients did not include adult congenital heart disease (ACHD) patients. Development of ACHD specific mortality risk scores include the procedure-specific ACHS score and recently derived PEACH score. While validated in Europe, further evaluation of the PEACH score in additional settings is necessary. RESEARCH QUESTION: We assessed the efficacy of the PEACH score to predict mortality in ACHD patients undergoing congenital cardiac surgery at our institution. We compared the PEACH score to the ACHS and STS scores and assessed for additional risk factors which may further augment the PEACH score. Methods: A retrospective cohort study was conducted of patients who underwent congenital cardiac surgery at the University of Colorado Hospital and Children’s Hospital Colorado between 2010-2022. Patients were identified by the institutional STS congenital cardiac surgery database and were eligible if 18 years or older and underwent congenital cardiac surgery. Components of the PEACH score and additional postulated risk factors were evaluated by univariate analysis. A receiver operating characteristic (ROC) curve was used to compare the PEACH score to existing risk models. Logistic regression analysis determined additional risk factors to augment the PEACH score. Results: A total of 516 patients were included with a median age of 30 (IQR 23-42) and 51% were male. There were nine deaths overall. The PEACH score was predictive of mortality (OR: 2.09, p<0.001) with a score of 0: 0 deaths/240 patients (0%); 1-2: 6 deaths/229 patients (2.6%); 3+: 3 deaths/41 patients (7.3%). Of additional risk factors tested, platelets less than 184,000 per mcL (OR: 8.36, p=0.01) was significantly associated with mortality. Comparison of the models revealed PEACH score AUC 0.832 (95% CI: 0.740,0.924), ACHS score AUC 0.869 (95% CI: 0.806, 0.932), and STAT AUC 0.769 (95% CI: 0.616, 0.922, Figure 1). The addition of platelet level improved the predictive ability of the PEACH score (AUC 0.881). Conclusion: In this single-center analysis, the PEACH score provided strong predictive ability for peri-operative mortality. The addition of platelet levels may further augment this score.

Proenkephalin improves risk prediction in acute coronary syndromes: derivation and external validation of the KID-ACS risk score

Abstract Background Early assessment of renal and mortality risk in patients with acute coronary syndromes (ACS) is essential to guide treatment decisions according to international guidelines. Circulating proenkephalin (PENK) is a stable opioid metabolite with fast response to changes in kidney function. Purpose This study examined the predictive value of PENK for acute kidney injury (AKI) and mortality in patients presenting with acute coronary syndromes (ACS). Methods Plasma PENK levels were measured using a validated sandwich immunoassay in a prospective multicentre ACS cohort from Switzerland (n = 4 787) and in validation cohorts from the UK (n = 1 141) and Czechia (n = 920). A biomarker-enhanced risk score for simultaneous prediction of both in-hospital acute kidney injury (AKI) and 30-day death (KID-ACS score) with model coefficients adjusted to the outcome was derived and externally validated. In the development of the in-hospital AKI model, patients from one participating study centre were reserved for external validation (Swiss validation cohort). Results Circulating PENK ranked among the top predictors of renal outcomes and mortality in patients with ACS. Accounting for established risk factors and risk models, circulating PENK levels at presentation were independently associated with in-hospital AKI (per log2 increase: adjusted odds ratio [OR] 1.56, 95% CI 1.14 – 2.12, P = 0.005) and with 30-day mortality (per log2 increase: adjusted OR 2.69, 95% CI 1.89 – 3.84, P < 0.001). Dynamic changes in PENK levels within the first 12-24 hours after presentation were strongly and independently associated with in-hospital AKI (increase vs. decrease: adjusted OR 2.83, 95% CI 1.98 – 4.05, P < 0.001). The simple 6-item KID-ACS risk score for in-hospital AKI and 30-day mortality integrates PENK levels at presentation and showed an AUC of 0.72 (95% CI 0.68 – 0.76) for in-hospital AKI, and of 0.91 (95% CI 0.87 – 0.95) for 30-day mortality in the derivation cohort. Upon external validation, KID-ACS had similarly high performance for in-hospital AKI (Swiss validation cohort: AUC 0.73, 95% CI 0.70 – 0.77; Czech validation cohort: 0.74, 95% CI 0.68 – 0.80) and 30-day mortality (UK validation cohort: AUC of 0.87, 95% CI 0.82 – 0.91; Czech validation cohort: 0.91, 95% CI 0.87 – 0.94). The simple 6-item KID-ACS score was non-inferior to established risk models and significantly outperformed the CA-AKI risk score in predicting in-hospital AKI (Zurich cohort: delta AUC 0.11, 95% CI 0.07 – 0.15, P = 0.001) and the GRACE 2.0 in predicting 30-day mortality (UK cohort: delta AUC 0.05, 95% CI 0.01 – 0.09, P = 0.027). Conclusions Circulating PENK was identified as independent predictor of in-hospital AKI and 30-day mortality in patients with ACS. The simple 6-item KID-ACS risk score integrates circulating PENK levels and provides a novel tool for simultaneous assessment of renal and mortality risk in patients presenting with ACS.Independent Predictive Value PENKPerformance and validation of KID-ACS

Long-term outcomes after transcatheter aortic valve implantation: derivation and validation of the long-TAVI prediction model

Abstract Background Identification of high-risk individuals after transcatheter aortic valve implantation (TAVI) is essential to personalized care. Established risk models require a plethora of variables, fail to account for biomarker dynamics and imaging parameters, mostly focus on the short term, and offer limited predictive performance. Purpose We aimed to develop an easily applicable prediction model for long-term mortality or heart failure (HF) admission following TAVI. Methods Patients undergoing TAVI at a high-volume university centre in the United Kingdom (n = 990) were recruited into a prospective cohort study. Enrolled patients were divided into a development cohort (07/2007 – 12/2016) and a validation cohort (01/2017 – 11/2018) and followed for up to 15.1 (median 5.3, interquartile range [IQR] 2.8 – 7.4) years. Patient characteristics along with pre- and post-procedural imaging results and brain natriuretic peptide (BNP) levels before the procedure and at 3 months were recorded. A clinical risk score for long-term mortality and HF admission (i.e., Long-TAVI risk score) was derived using Cox regression and evaluated using time-dependent area under the receiver operating characteristics curve (AUC). Results Age at presentation was 79.8 (standard deviation [SD] 8.9) years, 961 (99.0%) patients had signs of heart failure, left ventricular ejection fraction was 52 (SD 11.2), and pre-TAVI BNP levels were 298.0 (interquartile range [IQR] 159.0 – 571.5) pg/mL. High BNP levels at presentation (higher vs. lower than 400 pg/mL: HR 1.50, 95% CI 1.17 – 1.92, P < 0.001) and dynamic changes in BNP with the first 3 months (low/high vs. low/low: 2.61, 95% CI 1.17 – 5.86, P = 0.020; high/high vs. low/low: 2.41, 95% CI 1.26 – 4.64, P = 0.008) were independent predictors mortality or HF admission. The simple 8-item Long-TAVI score accounts for periprocedural BNP dynamics and shows good discrimination (AUC 0.76, 95% CI 0.72 – 0.79) and calibration in the development cohort performing similarly well in the external validation cohort (AUC 0.70, 95% CI 0.63 – 0.77). Long-TAVI significantly outperformed the logistic EURO score (AUC 0.56, 95% CI 0.52 – 0.59, P < 0.001). Conclusion Both pre-TAVI BNP levels and periprocedural BNP trajectories are independently associated with long-term outcomes after TAVI. Accounting for clinical characteristics, imaging parameters, and circulating BNP levels, the newly developed Long-TAVI risk score achieves high performance and provides the first prediction model for long-term mortality or HF admission. Long-TAVI represents a novel tool for personalized risk assessment in patients undergoing TAVI.Correlation at baseline A) and FUP B)Score performance A) and risk groups B)

Association of TRI-SCORE with clinical outcomes after percutaneous tricuspid annuloplasty

Abstract Background Significant tricuspid regurgitation (TR) has been associated with excess mortality and heart failure. Functional TR is associated to progressive right ventricular remodeling and dysfunction, leading to comorbidities which elevate the perioperative mortality of isolated tricuspid regurgitation surgery, with an in-hospital mortality between 6-10% according to series. Transcatheter tricuspid annuloplasty is a promising therapeutic alternative in patients with high surgical risk. Appropriate risk stratification and patient selection is essential to ensure benefits of this intervention. European System for Cardiac Operative Risk Evaluation (EUROSCORE) II and the Society of Thoracic Surgeons (STS) score are widely spread in-hospital mortality risk scores in valvular surgery. TRI-SCORE is focused on isolated TR surgery; nevertheless, application of these scores to transcatheter annuloplasty has not been specifically studied yet. Purpose To analyze the utility of surgical scores (EUROSCORE-II, STS and TRISCORE) to predict all-cause mortality in patients undergoing transcatheter tricuspid annuloplasty. Methods A cohort of 49 patients with significant TR was followed up prospectively after the transcatheter tricuspid annuloplasty with Cardioband between 2019 and January 2024 (mean follow-up 18,43 ± 10,95 months). Every score was evaluated in all patients. According to the TRI-SCORE –– 6 points was the 75-percentile and taken as cutoff value –– we divided the cohort in two groups: a low score group (≤6 points) and a high score group (>6 points). Survival was analyzed in the whole cohort and in both subgroups. The curves are depicted using Kaplan-Meier method and were compared using log rank test. Results The study included 49 patients, with mean age of 77,6 ± 7,67 years, 77,6% being women. All the patients had a secondary etiology of TR and 46,9% massive or torrential TR before the intervention. 49% had previous left heart surgery. The median surgical risk values are represented in Table 1. All-cause mortality during follow-up occurred in 9 patients (18,3%). Survival analysis based on EUROSCORE II and STS did not find statically significant association (Table 1). TRI-SCORE had a hazard ratio (HR) of 1,59, also without statically significant association (CI 0.93 – 2.71; p 0.089). Survival analysis in high score group found an increased risk of all-cause mortality (HR 8.5; p 0.025). According to our findings TRI-SCORE > 6 before the procedure confers significant risk of all-cause death (log rank p = 0.009). Figure 1. Conclusions Transcatheter tricuspid annuloplasty is a promising therapy for patients with significant TR regurgitation who are at high surgical risk. According to our findings, TRI-SCORE is an appropriate scoring model to predict all-cause mortality after percutaneous annuloplasty with band. It is not so for other surgical risk scores. Extended follow-up and broader experienced are needed to corroborate these results.Surgical scores: median and mortalitySurvival curves TRI-SCORE

Validation of the MIDA mortality risk score in a surgical cohort of patients with primary mitral regurgitation

Abstract Background and aim The MIDA scale is a prognostic tool for stratification of short- and long-term mortality risk in patients with primary mitral regurgitation (PMR). The aim of this study is to validate the MIDA (1) score in our population of PMR treated with surgery. Methods Retrospective analysis of a prospective cohort of patients with PMR who underwent mitral valve surgery in a tertiary care center from 2014 to 2022. The necessary parameters for calculating the MIDA Score (age, symptoms, atrial fibrillation, left atrial diameter, right ventricular systolic pressure (RVSP), left ventricular end-systolic diameter (LVESD), and LV ejection fraction (LVEF)) were collected, and the MIDA score for each patient was calculated. Thereafter, patients were classified into seven different risk groups. One year and long-term mortality during follow-up were recorded. A logistic regression analysis was performed to evaluate the association of each parameter with outcomes. Finally, curves for the cumulative incidence of all-cause mortality according to the MIDA score were generated with the Kaplan–Meier method. Results A total of 349 patients were included. Mean age was 68.5 [12.4] years, 55.8% were male, and the median LVEF was 60% (55-65). Mean follow up was 3.28 (2) years. One year mortality occurred in 29 (8.3%) patients, and long-term mortality in 59 (16.9%). According to the MIDA score, the distribution of patients in each subgroup was as follows: score 0 (n=28), score 1-2 (n=35), score 3-4 (n=60), score 5-6 (n=99), score 7-8 (n=81), score 9-10 (n=35), score > 11 (n=11). Most of our cohort was classified as intermediate-risk patients, with a median score of 6 (3-8). In the univariable logistic regression analysis, age, symptoms before surgery and RVSP were the only MIDA parameters significantly associated with mortality (Table-Figure 1). In our population, the discriminatory capacity of the MIDA score, was moderate, with an area under the ROC curve of 0.676 CI (0.624-0.725) for one year mortality, and 0.702 (0.651-0.749) for long term mortality. The best cut-off point for predicting one-year and long-term mortality was a MIDA score of 7. Kaplan-Meier curves for mortality during follow-up are shown in Figure 2. Conclusions The MIDA score showed a fair discriminatory capacity in our surgical cohort of patients with PMR. Variables included in the score that had a significant association with mortality were age, presence of symptoms and elevated RVSP. LV parameters included in the MIDA, which are the current class I indication for surgery, did not show a significant association with outcomes. Different echocardiographic variables to evaluate cardiac damage in PMR are needed to achieve better stratification.Table-Figure 1Figure 2

Effect of air pollution, genetic susceptibility on the risk of all-cause mortality and cardiovascular outcomes among atrial fibrillation patients

Objective: To analyze the association between air pollution, genetic susceptibility, and the risk of all-cause mortality and cardiovascular outcomes in patients with atrial fibrillation (AF). Methods: AF patients aged between 40-69 years old registered in the United Kingdom Biobank from 2006 to 2010 were included. After excluding those lost to follow-up or with incomplete data during follow-up, 5 814 subjects were analyzed. Long-term exposure to air pollution was estimated at the geocoded residential address of each participant. Genetic risk scores for all-cause mortality, cardiovascular disease, heart failure, myocardial infarction, and stroke were constructed separately for each object to assess the corresponding genetic susceptibility. The Cox proportional hazards model was used to analyze the association between air pollution, genetic susceptibility, and the risk of all-cause mortality and cardiovascular outcomes in AF patients. Results: During a median follow-up of 12.4 years, there were 929 of all-cause mortality (15.98%) and 1 772 of cardiovascular events (30.48%). Multivariable-adjusted analyses revealed that higher exposure to PM2.5, PM10, NOx, and NO2 was associated with an increased risk of cardiovascular disease mortality, heart failure, myocardial infarction, and stroke, with hazard ratios (HRs) ranging from 1.26 to 1.48. Specifically, for each interquartile range (IQR) increase in PM2.5 exposure, the HRs for the outcomes mentioned above were 1.33 (95%CI: 1.14-1.54), 1.42 (95%CI: 1.31-1.54), 1.46 (95%CI: 1.30-1.64), and 1.43 (95%CI: 1.27-1.61), respectively. Both NOx and NO2 exposures were associated with a 9% increased risk of all-cause mortality per IQR increment, with corresponding HRs of 1.09 (95%CI: 1.02-1.17) and 1.09 (95%CI: 1.01-1.17), respectively. Individuals with high genetic susceptibility to AF had a higher risk of myocardial infarction and stroke compared to those with low genetic susceptibility, with corresponding HRs of 1.39 (95%CI: 1.04-1.87) and 1.46 (95%CI: 1.09-1.95), respectively. Compared to AF patients with low air pollution exposure, those with high air pollution exposure have adjusted population attributable fractions of up to 33.57% (95%CI: 17.87%-46.26%) for cardiovascular mortality, 28.61% (95%CI: 20.67%-35.75%) for heart failure, 33.35% (95%CI: 20.97%-43.79%) for myocardial infarction, and 42.29% (95%CI: 30.05%-52.71%) for stroke. Furthermore, there was an additive interaction between PM2.5, NOx, and NO2 exposure and high genetic susceptibility on the incidence of myocardial infarction. An additive interaction was also observed between NOx, NO2 exposure, and high genetic susceptibility on the incidence of heart failure (all P<0.05). Conclusions: Both air pollution and genetic susceptibility increase the risk of all-cause mortality and cardiovascular outcomes in AF patients.

Exploring the Utility of the Modified Hospitalized-Patient One-Year Mortality Risk Score to Trigger Referrals to Palliative Care for Inpatients With Cancer.

Estimating prognosis can be a barrier to timely palliative care involvement. The modified Hospitalized-patient One-year Mortality Risk (mHOMR) score uses hospital admission data to calculate the risk of death within 12 months and may be a useful tool to trigger a referral to palliative care. The mHOMR tool was retrospectively applied to consecutive acute admissions to a quaternary cancer center in Toronto, Canada from March 1 to May 31, 2018. The study aimed to investigate the association between dichotomized mHOMR scores (the cohort median score of 0.27 and the developer-recommended score of 0.21) and the risk of death, and whether these could be used to identify patients who may benefit from timely palliative care involvement. Of 269 inpatients, 87 were elective admissions and excluded from further analyses. At the median mHOMR score of 0.27, 91/182 patients (50%) were categorized as high-risk of death within 12 months (mHOMR+), 53 (58%) were referred to palliative care. At the lower cut-off of 0.21, 103 patients were mHOMR+, of whom 57 (55.3%) were referred to palliative care. The higher mHOMR was significantly associated with mortality (29.7% mHOMR- vs. 39.8% mHOMR+ at 12 months, log-rank p < 0.05). The association between the developer-recommended mHOMR cut-off (≥ 0.21) and mortality was not significant (p = 0.15). A higher mHOMR score was significantly associated with the risk of mortality in patients with advanced cancer. However, the developer-recommended mHOMR cut-off of 0.21 failed to identify a statistically significant difference between patients with advanced cancer at low versus high scores. While mHOMR may be a useful tool to augment clinical judgment and identify inpatients with advanced cancer at high risk of death, who in turn may benefit from referral to palliative care, the optimal mHOMR cutoff may warrant adjustment for this population.

Analysis of angiotensin II type 1 receptor and osteoprotegerin gene polymorphism on the risk of cardiovascular mortality risk and progressivity of chronic kidney disease.

Chronic kidney disease (CKD) is a significant global public health issue with increased risk of atherosclerotic cardiovascular disease (ASCVD) and cardiovascular mortality. Single nucleotide polymorphisms (SNPs) on angiotensin II type 1 receptor (AT1R) A1166C and osteoprotegerin (OPG) C950T gene have received significant attention as a genetic risk factor for cardiovascular disease and CKD. This was a cross-sectional study involving 75 adults with CKD recruited from Nephrology Outpatient Clinics of Universitas Airlangga Hospital, Surabaya, Indonesia. Demographic data was obtained from interviews and medical records. The "CKD Patch" application was used to asses ASCVD and cardiovascular mortality risk scores. Statistical analysis was performed by using SPSS version 26. We detected four different AT1R gene polymorphisms (A1166C, A1160C, G1170T, and G1181C) and two OPG gene polymorphisms (T950C and G1181C) in Indonesian CKD patients. A1160C and G1181C polymorphisms were novel SNPs, newly discovered in this research. No significant association was found between AT1R SNPs and kidney prognostic markers or ASCVD risk/mortality risk scores. However, for OPG C950T we found that TT genotype had a significantly higher ACR than TC or CC genotype (P=0.032). As for OPG G1181C, we found that GG genotype had a higher serum creatinine and albumin to creatinine ratio compared to GC and CC genotypes (P=0.004 and 0.029, respectively). Genotype GC for OPG G1181C was also shown to be protective for having better kidney markers and lowest cardiovascular mortality risk compared to GG and CC genotypes (P=0.018 and 0.032, respectively). Increased ASCVD risk and mortality risk score was not found on individuals with AT1R gene SNPs. However, for OPG gene polymorphism, C950T and G1181C were associated with kidney progression and cardiovascular mortality.

Risk factors associated with escalation of care in a quaternary academic hospital at home program.

Hospital-at-home has become a more recognized way to care for patients requiring inpatient hospitalization. At times, these patients may require escalation of care (transfer from home back to the brick-and-mortar (BAM) hospital for ongoing hospitalization care needs), a process that has not been extensively studied. To evaluate what patient factors contribute to escalations of care in the hospital-at-home delivery model. We conducted a retrospective review of all patients admitted to Mayo Clinic's Advanced Care at Home (ACH) program from January 1, 2022 to December 31, 2022. None. Patient information was collected via electronic health record including demographic, socioeconomic, and clinical status. The primary outcome was the of occurrence of an escalation. A total of 904 patients were included, of whom 80 (8.8%) required an escalation of care. In multivariable analysis, risk of an escalation was significantly higher for patients who were married or had a life partner (HR: 1.82, 95% CI: 1.05-3.23, p = .033) for patients admitted with procedure-related disorders (HR: 2.61, 95% CI: 1.35-5.05, p = .005) and patients with an increased mortality risk score (HR [per each 1-category increase] = 1.86, 95% CI: 1.39-2.50, p < .001).

Development of a CVD mortality risk score using nutritional predictors: A risk prediction model in the Golestan Cohort Study

Background and AimWe aimed to develop a dietary score using prediction model method for evaluating the risk of cardiovascular disease (CVD) mortality and suggesting a simple and practical scoring system within the healthcare context. Method and ResultsA total of 43878 adult participants (aged 37-80 years) from the Golestan Cohort Study (GCS) were included in analysis. A random split of the subjects into the derivation (n=28930) and the validation sets (n=14948) was done. The Cox proportional hazard model was used to develop prediction model for the 8-year risk of CVD mortality. The model’s discrimination and calibration were assessed by C-statistic and calibration plot, respectively. To enhance clinical utility, we devised a point-based scoring system derived from our model. This prediction model was developed by nine predictors including age, physical activity level (MET minutes/week), waist-to-hip ratio, tea intake (cup/day), vegetable intake (gr/1000 kcal/day), white meat intake (gr/1000 kcal/day), salt intake (gr/1000 kcal/day), dairy intake (Cup/1000 kcal/day), and percentage of protein intake. The model had an acceptable discrimination in both derivation (C-statistic: 0.76, p<0.001) and validation (C- statistic: 0.77, p<0.001) samples. Also, the calibration of model in both derivation and validation datasets was 0.81. ConclusionThis is the first attempt to develop a risk prediction model of CVD mortality and the risk scoring system by the majority of nutritional predictors in a large cohort study. This nutritional risk assessment tool is suitable for motivating at-risk individuals to make lifestyle and dietary pattern changes to reduce future risk to prevent health problems.

WP5.13 - National Emergency Laparotomy (NELA) critical care admissions: A cohort study

Abstract Aims NELA is committed to enhancing quality of peri-operative care of patients undergoing emergency laparotomy. NELA emphasizes importance of identifying high risk patients, who are associated with significant morbidity &amp; mortality. Critical care allows close monitoring, organ (basic/advanced) support. Aim of study is to review the NELA critical care patients. Methods Cohort study of NELA patients that received post-operative ITU care (direct/indirect admission) at single centre. Descriptive demography, and outcomes were evaluated. NELA parsimonious risk &amp; CCMDS ITU scores used. NELA patient that did not require ITU care were controls. Results NELA critical care (n = 400) NELA non critical care (n = 466) Mann Whitney U (p value) Deaths (n) - (n) Age(yrs) 71.8 64.0 &amp;lt; 0.0001 Sex(M:F) 173 : 227 220 : 246 NS ASA 3 2 &amp;lt; 0.0001 BMI(kg/m2) 26.3 25.0 NS Lap(n) 67 168 Open(n) 333 293 Op time(mins) 131.0 109.4 &amp;lt; 0.0001 LOS(days) 18 9.5 &amp;lt; 0.0001 Deaths 30 day 50 25 60 day 66 37 90 day 73 40 Lactate(mmol/L) 2.4 1.8 &amp;lt;0.01 WCC(x109/L) 13.0 11.8 &amp;lt;0.05 CRP(mg/L) 90.1 56.6 &amp;lt;0.0001 Physiological-score 25.6 19.8 &amp;lt;0.0001 Operative-severity-score 17.9 15.9 &amp;lt;0.0001 Prepossum-score 21.6 7.5 &amp;lt;0.0001 PPmortality-score 11.9 4.9 &amp;lt;0.0001 PPmorbidity-score 77.3 55.7 &amp;lt;0.0001 Survival Rate (%) 1styear 75.4 86.2 5thyear 53.8 70.8 Logrank 10thyear 41.1 58.7 p&amp;lt;0.0001 Conclusion NELA critical care patients had poorer survival rates in our study. Theatre return or indirect critical care transfers can adversely affect survival. These patients were older, frail, multiple co-morbid status. NELA parsimonious mortality risk score &amp;gt;5% should encourage consultant level supervision, mortality risk score &amp;gt;10% should encourage direct critical care involvement.

ThP7.18 - Post-operative management and outcomes in emergency laparotomy - an audit of the updated 2023 NELA Standards in a busy district general hospital

Abstract Background The updated NELA Standards, encourage pre-operative decision-making, were published and utilised from April 2023. Our busy district general hospital has not been audited against these updated standards. Methods Single-centre, retrospective audit of emergency laparotomies between April 2023 and December 2023. Patients were included if the NELA risk score was ≥5% (‘high-risk’) or if they were deemed ‘high-risk’ based on clinical judgement. Demographic, management, and outcome data were collected from the local NELA database and EPR. The primary outcome was the proportion of ‘high-risk’ patients directly admitted to critical care. The secondary outcome was to investigate differences between high-risk patients admitted to critical care and those admitted to ward care using length of stay (LOS) and 30-day observed/expected (O/E) mortality ratio. Results 112 laparotomies were performed and 43 (38%) were deemed ‘high-risk’. Of ‘high-risk’ patients, the median age was 73 (44 - 95), 56% were male, and the median mortality risk was 10.5% (3.1% – 62.7%). 38 ‘high-risk’ patients were directly admitted to critical care. They had a median LOS of 14 days (3 – 72) and 7 died within 30 days with an O/E mortality ratio of 1.12. None of the 5 patients admitted directly to the ward died and they had a median LOS of 8 days (3 – 39). Conclusion The mortality risk score is a tool for post-operative management in emergency laparotomy but appropriate clinical judgement can prevent excessive care and preserve critical care beds.

A protein risk score for all-cause and respiratory-specific mortality in non-Hispanic white and African American individuals who smoke

Protein biomarkers are associated with mortality in cardiovascular disease, but their effect on predicting respiratory and all-cause mortality is not clear. We tested whether a protein risk score (protRS) can improve prediction of all-cause mortality over clinical risk factors in smokers. We utilized smoking-enriched (COPDGene, LSC, SPIROMICS) and general population-based (MESA) cohorts with SomaScan proteomic and mortality data. We split COPDGene into training and testing sets (50:50) and developed a protRS based on respiratory mortality effect size and parsimony. We tested multivariable associations of the protRS with all-cause, respiratory, and cardiovascular mortality, and performed meta-analysis, area-under-the-curve (AUC), and network analyses. We included 2232 participants. In COPDGene, a penalized regression-based protRS was most highly associated with respiratory mortality (OR 9.2) and parsimonious (15 proteins). This protRS was associated with all-cause mortality (random effects HR 1.79 [95% CI 1.31–2.43]). Adding the protRS to clinical covariates improved all-cause mortality prediction in COPDGene (AUC 0.87 vs 0.82) and SPIROMICS (0.74 vs 0.6), but not in LSC and MESA. Protein–protein interaction network analyses implicate cytokine signaling, innate immune responses, and extracellular matrix turnover. A blood-based protein risk score predicts all-cause and respiratory mortality, identifies potential drivers of mortality, and demonstrates heterogeneity in effects amongst cohorts.

What value do teaching hospitals provide commercial beneficiaries when in an ACO?

The number of commercial beneficiaries cared for by accountable care organizations (ACOs) is growing, but the literature examining their trends is nascent. We examined commercial claims data from 2019 to 2021 to compare beneficiaries attributed to participants in Medicare Shared Savings Program ACOs with and without a major teaching hospital. We calculated mortality and spending by setting for each ACO type by year. Compared with per-beneficiary rates at nonteaching ACOs, major teaching ACOs have lower mortality rates by up to 2.2 percentage points depending on the patient age group, $283 lower inpatient spending, and lower emergency department utilization in inpatient (-0.008) and outpatient (-0.013) settings, as well as $146 higher overall outpatient spending. Upward trends in mortality and beneficiary risk scores across both ACO types show disruption to health outcomes during COVID-19. These results provide evidence that ACOs with major teaching hospitals may be more likely to achieve the value-based goals of ACOs. Means to accomplish those goals may include avoiding higher-intensity care and supporting access to lower-cost alternatives where clinically appropriate, such as reducing inpatient and emergency department stays by delivering timely, high-quality outpatient care.

COVID-19 increases mortality in hemodialysis patients: exploring links with inflammation and telomere attrition.

An increased risk of mortality and hospitalization was consistently demonstrated in hemodialysis (HD) patients affected by pandemic coronavirus infection (COVID-19). In this study, we analyzed parameters that may impact mortality in COVID-19 HD patients, including neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase (LDH), C-reactive protein (CRP), COVID-19 disease status and telomere length in peripheral blood cells (TL). A total of 130 chronic hemodialysis patients were enrolled and followed up for 18 months. Patients were categorized into groups based on their COVID-19 disease history and subsequent data about their survival status at the end of the study. Routine laboratory parameters were assessed using standard automated methods and TL was determined using the modified Cawthon method. Survival predictors were analyzed using Kaplan-Meier analysis. Deceased patients (30%) were older with higher body mass index (BMI), higher levels of LDH, NLR index, CRP and lower TL and lymphocytes count compared to survivors. Kaplan-Meier survival analysis showed six parameters were significant mortality predictors in the following order of significance: COVID-19 history, 2-years cardiovascular mortality risk score, NLR, TL, CRP, LDH. Using binary logistic regression analysis Summary risk score, a combination of these six parameters revealed as the best predictor of patient's survival in this group of parameters (log rank 25.4, p < 0.001). Compared to the general population, the mortality rate among HD patients persists at a higher level despite advancements in HD technology and patient care. The situation has been exacerbated by COVID-19, by significant increase in mortality rate among these patients.

Development and validation of a mortality risk prediction model for chronic obstructive pulmonary disease: a cross-sectional study using probabilistic graphical modelling

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of mortality. Predicting mortality risk in patients with COPD can be important for disease management strategies. Although all-cause mortality predictors have been developed previously, limited research exists on factors directly affecting COPD-specific mortality. In a retrospective study, we used probabilistic graphs to analyse clinical cross-sectional data (COPDGene cohort), including demographics, spirometry, quantitative chest imaging, and symptom features, as well as gene expression data. COPDGene recruited current and former smokers, aged 45-80 years with >10 pack-years smoking history, from across the USA (Phase 1, 11/2007-4/2011) and invited them for a follow-up visit (Phase 2, 7/2013-7/2017). ECLIPSE cohort recruited current and former smokers (COPD patients and controls from USA and Europe), aged 45-80 with smoking history >10 pack-years (12/2005-11/2007). We applied graphical models on multi-modal data COPDGene Phase 1 participants to identify factors directly affecting all-cause and COPD-specific mortality (primary outcomes); and on Phase 2 follow-up cohort to identify additional molecular and social factors affecting mortality. We used penalized Cox regression with features selected by the causal graph to build VAPORED, a mortality risk prediction model. VAPORED was compared to existing scores (BODE: BMI, airflow obstruction, dyspnoea, exercise capacity; ADO: age, dyspnoea, airflow obstruction) on the ability to rank individuals by mortality risk, using four evaluation metrics (concordance, concordance probability estimate (CPE), cumulative/dynamic (C/D) area under the receiver operating characteristic curve (AUC), and integrated C/D AUC). The results were validated in ECLIPSE. Graphical models, applied on the COPDGene Phase 1 samples (n=8610), identified 11 and 7 variables directly linked to all-cause and COPD-specific mortality, respectively. Although many appear in both models, non-lung comorbidities appear only in the all-cause model, while forced vital capacity (FVC %predicted) appears in COPD-specific mortality model only. Additionally, the graph model of Phase 2 data (n=3182) identified internet access, CD4 T cells and platelets to be linked to lower mortality risk. Furthermore, using the 7 variables linked to COPD-specific mortality (forced expiratory volume in 1s/forced vital capacity (FEV1/FVC) ration, FVC %predicted, age, history of pneumonia, oxygen saturation, 6-min walk distance, dyspnoea) we developed VAPORED mortality risk score, which we validated on the ECLIPSE cohort (3-yr all-cause mortality data, n=2312). VAPORED performed significantly better than ADO, BODE, and updated BODE indices in predicting all-cause mortality in ECLIPSE in terms of concordance (VAPORED [0.719] vs ADO [0.693; FDR p-value 0.014], BODE [0.695; FDR p-value 0.020], and updated BODE [0.694; FDR p-value 0.021]); CPE (VAPORED [0.714] vs ADO [0.673; FDR p-value <0.0001], BODE [0.662; FDR p-value <0.0001], and updated BODE [0.646; FDR p-value <0.0001]); 3-year C/D AUC (VAPORED [0.728] vs ADO [0.702; FDR p-value 0.017], BODE [0.704; FDR p-value 0.021], and updated BODE [0.703; FDR p-value 0.024]); integrated C/D AUC (VAPORED [0.723] vs ADO [0.698; FDR p-value 0.047], BODE [0.695; FDR p-value 0.024], and updated BODE [0.690; FDR p-value 0.021]). Finally, we developed a web tool to help clinicians calculate VAPORED mortality risk and compare it to ADO and BODE predictions. Our work is an important step towards improving our identification of high-risk patients and generating hypotheses of potential biological mechanisms and social factors driving mortality in patients with COPD at the population level. The main limitation of our study is the fact that the analysed datasets consist of older people with extensive smoking history and limited racial diversity. Thus, the results are relevant to high-risk individuals or those diagnosed with COPD and the VAPORED score is validated for them. This research was supported by NIH [NHLBI, NLM]. The COPDGene study is supported by the COPD Foundation, through grants from AstraZeneca, Bayer Pharmaceuticals, Boehringer Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer and Sunovion.

Higher Circulating Neutrophil Counts Is Associated with Increased Risk of All-Cause Mortality and Cardiovascular Disease in Patients with Diabetic Kidney Disease.

Accumulating evidence has suggested the pathogenic roles of chronic inflammation and neutrophils in diabetic kidney disease (DKD). This study investigated the relationship between neutrophils, all-cause, and cardiovascular disease (CVD) mortality in type 2 diabetes mellitus (T2DM) patients with DKD. We used data from the National Health and Nutrition Examination Surveys (NHANES) from 2005 to 2020 to investigate the relationship between circulating neutrophils counts, kidney function indices, all-cause, and CVD mortality in adult T2DM patients with DKD. Clinical predictive models and risk scores for long-term mortality were constructed. 44,332 patients [8034 with T2DM and 36,323 without T2DM] were included. Two thousand two hundred twenty patients had DKD, and 775 died (31.5% related to CVD) during a follow-up of 6.18 (range: 5.94-6.42) years. Higher neutrophil counts (Quartile 4, Q4) were associated with increased all-cause and CVD mortality [HR 1.73 (95% CI 1.34-2.25) and 1.81 (95% CI 1.14-2.89), respectively, p < 0.0001 and 0.01]. Neutrophil counts in Q4 showed a positive correlation with urine albumin-creatinine ratio (UACR) but a negative association with eGFR (p < 0.01 for all). Clinical predictive models incorporating neutrophil counts showed satisfactory performance in forecasting 5-year and 10-year CVD mortality-free survival (ROC AUC 0.824 and 0.842, respectively), and the nomogram-predicted survival demonstrated good concordance with observed survival. Higher levels of circulating neutrophil counts show a significant correlation with renal abnormalities and higher all-cause and CVD mortality in T2DM patients with DKD. The novel clinical predictive models and risk scores incorporating neutrophil counts may facilitate stratification and, hence, risk factor management in DKD patients.

Unsupervised machine learning to identify subphenotypes among cardiac intensive care unit patients with heart failure.

Hospitalized patients with heart failure (HF) are a heterogeneous population, with multiple phenotypes proposed. Prior studies have not examined the biological phenotypes of critically ill patients with HF admitted to the contemporary cardiac intensive care unit (CICU). We aimed to leverage unsupervised machine learning to identify previously unknown HF phenotypes in a large and diverse cohort of patients with HF admitted to the CICU. We screened 6008 Mayo Clinic CICU patients with an admission diagnosis of HF from 2007 to 2018 and included those without missing values for common laboratory tests. Consensus k-means clustering was performed based on 10 common admission laboratory values (potassium, chloride, anion gap, blood urea nitrogen, haemoglobin, red blood cell distribution width, mean corpuscular volume, platelet count, white blood cell count and neutrophil-to-lymphocyte ratio). In-hospital mortality was evaluated using logistic regression, and 1year mortality was evaluated using Cox proportional hazard models after multivariable adjustment. Among 4877 CICU patients with HF who had complete admission laboratory data (mean age 69.4years, 38.4% females), we identified five clusters with divergent demographics, comorbidities, laboratory values, admission diagnoses and use of critical care therapies. We labelled these clusters based on the characteristic laboratory profile of each group: uncomplicated (25.7%), iron-deficient (14.5%), cardiorenal (18.4%), inflamed (22.3%) and hypoperfused (19.2%). In-hospital mortality occurred in 10.7% and differed between the phenotypes: uncomplicated, 2.7% (reference); iron-deficient, 8.1% [adjusted odds ratio (OR) 2.18 (1.38-3.48), P<0.001]; cardiorenal, 10.3% [adjusted OR 2.11 (1.37-3.32), P<0.001]; inflamed, 12.5% [adjusted OR 1.79 (1.18-2.76), P=0.007]; and hypoperfused, 21.9% [adjusted OR 4.32 (2.89-6.62), P<0.001]. These differences in mortality between phenotypes were consistent when patients were stratified based on demographics, aetiology, admission diagnoses, mortality risk scores, shock severity and systolic function. One-year mortality occurred in 31.5% and differed between the phenotypes: uncomplicated, 11.9% (reference); inflamed, 26.8% [adjusted hazard ratio (HR) 1.56 (1.27-1.92), P<0.001]; iron-deficient, 33.8% [adjusted HR 2.47 (2.00-3.04), P<0.001]; cardiorenal, 41.2% [adjusted HR 2.41 (1.97-2.95), P<0.001]; and hypoperfused, 52.3% [adjusted HR 3.43 (2.82-4.18), P<0.001]. Similar findings were observed for post-discharge 1year mortality. Unsupervised machine learning clustering can identify multiple distinct clinical HF phenotypes within the CICU population that display differing mortality profiles both in-hospital and at 1year. Mortality was lowest for the uncomplicated HF phenotype and highest for the hypoperfused phenotype. The inflamed phenotype had comparatively higher in-hospital mortality yet lower post-discharge mortality, suggesting divergent short-term and long-term prognosis.

Risk factors and operative risk of large vessel occlusion and stroke during cardiac surgery

ObjectivePerioperative Large Vessel Occlusions (LVOs) occurring during and following surgery are of immense clinical importance. As such, we aim to present risk factors and test if the Society of Thoracic Surgery (STS) mortality and stroke risk scores can be used to assess operative risk. MethodsUsing data containing 7 index cardiac operations at a single tertiary referral center from 2010 to 2022, logistic and multivariate regression analysis was performed to identify factors that correlate to higher operative LVO and stroke rate. Odds ratios and confidence intervals were also obtained to test if the STS-Predicted Risk of Mortality (PROM) and -Predicted Risk of Stroke (PROS) scores were positively correlated to operative LVO and stroke rate. ResultsMultivariate modeling showed primary risk factors for an operative LVO were diabetes (OR: 1.727 [95 % CI: 1.060–2.815]), intracranial or extracranial carotid stenosis (OR: 3.661 [95 % CI: 2.126–6.305]), and heart failure as defined by NYHA class (Class 4, OR: 3.951 [95 % CI: 2.092–7.461]; compared to Class 1). As the STS-PROM increased, the relative rate of LVO occurrence increased (very high risk, OR: 6.576 [95 % CI: 2.92–14.812], high risk, OR: 2.667 [1.125–6.322], medium risk, OR: 2.858 [1.594–5.125]; all compared to low risk). STS-PROS quartiles showed a similar relation with LVO risk (quartile 4, OR: 7.768 [95 % CI: 2.740–22.027], quartile 3, OR: 5.249 [1.800–15.306], quartile 2, OR:2.980 [0.960–9.248]; all compared to quartile 1). ConclusionsPatients with diabetes, carotid disease and heart failure are at high risk for operative LVO. Both STS-PROM and -PROS can be useful metrics for preoperative measuring of LVO risks.

30-Day Risk Score for Mortality and Stroke in Patients with Carotid Artery Stenosis Using Artificial Intelligence Based Carotid Plaque Morphology

The gold standard for determining carotid artery stenosis intervention is based on a combination of percent stenosis and symptomatic status. Few studies have assessed plaque morphology as an additive tool for stroke prediction. Our goal was to create a predictive model and risk score for 30-day stroke and death inclusive of plaque morphology. Patients with a computed tomographic angiography head/neck between 2010 and 2021 at a single institution and a diagnosis of carotid artery stenosis were included in our analysis. Each computed tomography was used to create a three-dimensional image of carotid plaque based off image recognition software. A stepwise backward regression was used to select variables for inclusion in our prediction models. Model discrimination was assessed with area under the receiver operating characteristic curves (AUCs). Additionally, calibration was performed and the model with the least Akaike Information Criterion (AIC) was selected. The risk score was modeled from the Framingham Study. Primary outcome was mortality/stroke. We created 3 models to predict mortality/stroke from 366 patients: model A using only clinical variables, model B using only plaque morphology and model C using both clinical and plaque morphology variables. Model A used age, sex, peripheral arterial disease, hyperlipidemia, body mass index (BMI), chronic obstructive pulmonary disease (COPD), and history of transient ischemia attack (TIA)/stroke and had an AUC of 0.737 and AIC of 285.4. Model B used perivascular adipose tissue (PVAT) volume, lumen area, calcified volume, and target lesion length and had an AUC of 0.644 and AIC of 304.8. Finally, model C combined both clinical and software variables of age, sex, matrix volume, history of TIA/stroke, BMI, PVAT, lipid rich necrotic core, COPD and hyperlipidemia and had an AUC of 0.759 and an AIC of 277.6. Model C was the most predictive because it had the highest AUC and lowest AIC. Our study demonstrates that combining both clinical factors and plaque morphology creates the best predication of a patient's risk for all-cause mortality or stroke from carotid artery stenosis. Additionally, we found that for patients with even 3 points in our risk score model has a 20% chance of stroke/death. Further prospective studies are needed to validate our findings.