Mortality Risk Stratification Research Articles

MO-0884 Machine learning on clinical data for mortality risk-stratification after radiotherapy for NSCLC

MO-0884 Machine learning on clinical data for mortality risk-stratification after radiotherapy for NSCLC

A novel biomarker panel index improves risk stratification after ischemic stroke.

We investigated 92 blood biomarkers implicated in the pathophysiological pathways of ischemic injury, inflammation, hemostasis, and regulation of vascular resistance to predict post-stroke mortality. Based on the most promising markers, we aimed to create a novel Biomarker Panel Index (BPI) for risk stratification. In this prospective study, we measured 92 biomarkers in 320 stroke patients. The primary outcome measure was mortality within 90 days. We estimated the association of each biomarker using logistic regression adjusting for multiple testing. The most significant 16 biomarkers were used to create the BPI. We fitted regression models to estimate the association and the discriminatory accuracy of the BPI with mortality and stroke etiology. Adjusted for demographic and vascular covariates, the BPI remained independently associated with mortality (odds ratio (OR) 1.68, 95% confidence interval (CI): 1.29-2.18) and cardioembolic stroke etiology (OR 1.38, 95% CI: 1.10-1.74), and improved the discriminatory accuracy to predict mortality (area under the receiver operating characteristic curve (AUC) 0.93, 95% CI: 0.89-0.96) and cardioembolic stroke etiology (AUC 0.70, 95% CI: 0.64-0.77) as compared to the best clinical prediction models alone (AUC 0.89, 95% CI: 0.84-0.94 and AUC 0.66, 95% CI: 0.60-0.73, respectively). We identified a novel BPI improving risk stratification for mortality after ischemic stroke beyond established demographic and vascular risk factors. Furthermore, the BPI is associated with underlying cardioembolic stroke etiology. These results need external validation.

Long-Term Outcomes and Risk Stratification of Patients With Heart Failure With Recovered Ejection Fraction

This study aimed to understand the long-term outcomes of patients with heart failure with recovered ejection fraction, identify predictors of adverse events, and develop a risk stratification model. From an academic healthcare system, we retrospectively identified 133 patients (median age 66, 38% female, 30% ischemic etiology) who had an improvement in left ventricular ejection fraction (LVEF) from <40% to ≥53%. Significant predictors of all-cause mortality, hospitalization, and future reduction in LVEF were identified through Cox regression analysis. Kaplan-Meier survival was 70% at 5 years. Freedom from hospitalization was 58% at 1 year, and the risk of future LVEF reduction to <40% was 28% at 3 years. Diuretic dose and B-type natriuretic peptide (BNP) at the time of LVEF recovery were the strongest predictors of mortality and hospitalization in multivariate-adjusted analysis (BNP hazard ratio 1.13 per 100 pg/ml increase [p <0.01]; furosemide-equivalent dose hazard ratio 1.19 per 40 mg increase [p=0.02]). An all-cause mortality Cox proportional hazard risk model incorporating New York Heart Association functional class, BNP and diuretic dose at the time of recovery showed excellent risk discrimination (c-statistic 0.79) and calibration. In conclusion, patients with heart failure with recovered ejection fraction have heterogenous clinical outcomes and are not "cured." A risk model using New York Heart Association functional class, BNP, and diuretic dose can accurately stratify mortality risk.

Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk.

Backgroundageing is an important risk factor for a variety of human pathologies. Biological age (BA) may better capture ageing-related physiological changes compared with chronological age (CA).Objectivewe developed a deep learning (DL) algorithm to predict BA based on retinal photographs and evaluated the performance of our new ageing marker in the risk stratification of mortality and major morbidity in general populations.Methodswe first trained a DL algorithm using 129,236 retinal photographs from 40,480 participants in the Korean Health Screening study to predict the probability of age being ≥65 years (‘RetiAGE’) and then evaluated the ability of RetiAGE to stratify the risk of mortality and major morbidity among 56,301 participants in the UK Biobank. Cox proportional hazards model was used to estimate the hazard ratios (HRs).Resultsin the UK Biobank, over a 10-year follow up, 2,236 (4.0%) died; of them, 636 (28.4%) were due to cardiovascular diseases (CVDs) and 1,276 (57.1%) due to cancers. Compared with the participants in the RetiAGE first quartile, those in the RetiAGE fourth quartile had a 67% higher risk of 10-year all-cause mortality (HR = 1.67 [1.42–1.95]), a 142% higher risk of CVD mortality (HR = 2.42 [1.69–3.48]) and a 60% higher risk of cancer mortality (HR = 1.60 [1.31–1.96]), independent of CA and established ageing phenotypic biomarkers. Likewise, compared with the first quartile group, the risk of CVD and cancer events in the fourth quartile group increased by 39% (HR = 1.39 [1.14–1.69]) and 18% (HR = 1.18 [1.10–1.26]), respectively. The best discrimination ability for RetiAGE alone was found for CVD mortality (c-index = 0.70, sensitivity = 0.76, specificity = 0.55). Furthermore, adding RetiAGE increased the discrimination ability of the model beyond CA and phenotypic biomarkers (increment in c-index between 1 and 2%).Conclusionsthe DL-derived RetiAGE provides a novel, alternative approach to measure ageing.

Identifying modifiable risk factors for mortality in children aged 1–59 months admitted with WHO-defined severe pneumonia: a single-centre observational cohort study from rural Malawi

ObjectiveAlthough HIV infection, severe malnutrition and hypoxaemia are associated with high mortality in children with WHO-defined severe pneumonia in sub-Saharan Africa, many do not have these conditions and yet mortality...

Indexed Donor Cardiac Output for Improved Size Matching in Heart Transplantation

<h3>Purpose</h3> Implantation of an appropriately sized donor heart is critical for optimal outcomes after heart transplantation. While predicted heart mass (PHM) has recently gained consideration, there remains a need for improved granularity in size matching, particularly among small donor hearts. We hypothesized that indexed donor cardiac output (CO) would be a sensitive metric to assess adequacy of a donor heart for a given recipient. <h3>Methods</h3> Isolated heart transplant recipients from the UNOS database were included if donor CO was reported (<i>N</i>=3,619). Donor CO was divided by recipient BSA to compute cardiac index (dCI) and PHM ratio (PHMr) was computed as donor/recipient PHM. The primary outcome was mortality <1 year after transplant, adjusting for age, gender, donor inotropes, and recipient hospitalization status. <h3>Results</h3> Among transplanted recipients, donor CO was 7.3 (5.8-9.0) LPM and dCI was 3.7 (3.0-4.6) LPM/m². PHMr was 1.01 (0.91-1.13). dCI moderately correlated with recipient weight (<i>r_s</i>=-0.18, <i>P</i><0.001), BSA (<i>r_s</i>=-0.18, <i>P</i><0.001), and PHMr (<i>r_s</i>=0.29, <i>P</i><0.001). In univariate analyses, higher dCI was associated with lower 1 year mortality risk (OR=0.89, <i>P</i>=0.042), but not strongly associated with stroke (<i>P</i>=0.176) or dialysis (<i>P</i>=0.161). Those in the highest dCI quartile (>4.4 LPM/m²) had a lower risk of death compared to those in the lowest quartile (<3.0 LPM/m²; <i>P</i>=0.027). After multivariable adjustment, higher dCI continued to be associated with lower 1 year mortality risk (OR=0.89, <i>P</i>=0.042; Figure). Recipients with PHMr<0.80 (<i>n</i>=227) had lower median dCI than those with PHMr≥0.80 (3.3 vs 3.7, <i>P</i><0.001). Among those with PHMr<0.80, higher dCI decreased the odds of 1 year mortality (OR=0.63, <i>P</i>=0.083). <h3>Conclusion</h3> Higher dCI was associated with a lower probability of 1 year mortality among patients undergoing heart transplantation and served to stratify mortality risk among those with a PHMr<0.80. dCI appears to be an effective tool for size matching and may serve as an adjunctive strategy among small donor hearts with a low PHMr.

CHA2DS2-VASc score stratifies mortality risk in heart failure patients aged 75 years and older with and without atrial fibrillation.

BackgroundDuring the last decade, the CHA2DS2-VASc score has been associated with adverse clinical outcomes in several cardiovascular (CV) and non-cardiovascular diseases beyond atrial fibrillation (AF). Whether the CHA2DS2-VASc score stratifies mortality risk in elderly patients with AF and without AF is not well established.MethodsAll consecutive patients aged ≥ 75 yrs hospitalized due to heart failure (HF), between January 2020 and November 2020, were retrospectively enrolled. All patients underwent physical examination, blood tests, electrocardiography and conventional transthoracic echocardiography. Primary endpoint was all-cause mortality, while secondary endpoint was the composite of all-cause mortality + rehospitalizations for all causes over mid-term follow-up.ResultsThe study included 261 HF patients (86.3 ± 6.4 years, 60.5% females). 85 AF and 176 non-AF patients were separately analyzed. Compared to non-AF patients, those with AF had significantly higher CHA2DS2-VASc score (5.6 ± 1.4 vs 5.1 ± 1.4, p = 0.007) and lower ejection fraction (47.4 ± 16.5 vs 56.7 ± 15.1%, p < 0.001). Mean follow-up was 1.7 ± 0.5 yrs. During follow-up, 96 patients died (58.3% due to CV causes) and 79 were rehospitalized (58.2% due to CV causes). CHA2DS2-VASc score was independently associated with all-cause mortality in whole study population (HR 1.61, 95% CI 1.36–1.92) and in both AF (HR 1.41, 95% CI 1.09–1.82) and non-AF patients (HR 1.84, 95% CI 1.40–2.40). CHA2DS2-VASc score also predicted the secondary endpoint in the same study groups. CHA2DS2-VASc score ≥ 5 was the best cut-off value for predicting both outcomes.ConclusionAt mid-term follow-up, a CHA2DS2-VASc score ≥ 5 predicts increased risk of all-cause mortality and re-hospitalizations for all causes in elderly HF patients, regardless of AF.

Echocardiographic left ventricular stroke work index: An integrated noninvasive measure of shock severity.

Echocardiographic findings vary with shock severity, as defined by the Society for Cardiovascular Angiography and Intervention (SCAI) shock stage. Left ventricular stroke work index (LVSWI) measured by transthoracic echocardiography (TTE) can predict mortality in the cardiac intensive care unit (CICU). We sought to determine whether LVSWI could refine mortality risk stratification by the SCAI shock classification in the CICU. We included consecutive CICU patients from 2007 to 2015 with TTE data available to calculate the LVSWI, specifically the mean arterial pressure, stroke volume index and medial mitral E/e' ratio. In-hospital mortality as a function of LVSWI was evaluated across the SCAI shock stages using logistic regression, before and after multivariable adjustment. We included 3635 unique CICU patients, with a mean age of 68.1 ± 14.5 years (36.5% females); 61.1% of patients had an acute coronary syndrome. The LVSWI progressively decreased with increasing shock severity, as defined by increasing SCAI shock stage. A total of 203 (5.6%) patients died during hospitalization, with higher in-hospital mortality among patients with lower LVSWI (adjusted OR 0.66 per 10 J/m2 higher) or higher SCAI shock stage (adjusted OR 1.24 per each higher stage). A LVSWI <33 J/m2 was associated with higher adjusted in-hospital mortality, particularly among patients with shock (SCAI stages C, D and E). The LVSWI by TTE noninvasively characterizes the severity of shock, including both systolic and diastolic parameters, and can identify low-risk and high-risk patients at each level of clinical shock severity.

Postoperative mortality risk assessment in colorectal cancer: development and validation of a clinical prediction model using data from the Dutch ColoRectal Audit.

BackgroundAs the outcome of modern colorectal cancer (CRC) surgery has significantly improved over the years, however, renewed and adequate risk stratification for mortality is important to identify high-risk patients. This population-based study was conducted to analyse postoperative outcomes in patients with CRC and to create a risk model for 30-day mortality.MethodsData from the Dutch Colorectal Audit were used to assess differences in postoperative outcomes (30-day mortality, hospital stay, blood transfusion, postoperative complications) in patients with CRC treated from 2009 to 2017. Time trends were analysed. Clinical variables were retrieved (including stage, age, sex, BMI, ASA grade, tumour location, timing, surgical approach) and a prediction model with multivariable regression was computed for 30-day mortality using data from 2009 to 2014. The predictive performance of the model was tested among a validation cohort of patients treated between 2015 and 2017.ResultsThe prediction model was obtained using data from 51 484 patients and the validation cohort consisted of 32 926 patients. Trends of decreased length of postoperative hospital stay and blood transfusions were found over the years. In stage I–III, postoperative complications declined from 34.3 per cent to 29.0 per cent (P < 0.001) over time, whereas in stage IV complications increased from 35.6 per cent to 39.5 per cent (P = 0.010). Mortality decreased in stage I–III from 3.0 per cent to 1.4 per cent (P < 0.001) and in stage IV from 7.6 per cent to 2.9 per cent (P < 0.001). Eight factors, including stage, age, sex, BMI, ASA grade, tumour location, timing, and surgical approach were included in a 30-day mortality prediction model. The results on the validation cohort documented a concordance C statistic of 0.82 (95 per cent c.i. 0.80 to 0.83) for the prediction model, indicating good discriminative ability.ConclusionPostoperative outcome improved in all stages of CRC surgery in the Netherlands. The developed model accurately predicts postoperative mortality risk and is clinically valuable for decision-making.

COVID-19 mortality risk assessments for individuals with and without diabetes mellitus: Machine learning models integrated with interpretation framework

This research develops machine learning models equipped with interpretation modules for mortality risk prediction and stratification in cohorts of hospitalised coronavirus disease-2019 (COVID-19) patients with and without diabetes mellitus (DM). To this end, routinely collected clinical data from 156 COVID-19 patients with DM and 349 COVID-19 patients without DM were scrutinised. First, a random forest classifier forecasted in-hospital COVID-19 fatality utilising admission data for each cohort. For the DM cohort, the model predicted mortality risk with the accuracy of 82%, area under the receiver operating characteristic curve (AUC) of 80%, sensitivity of 80%, and specificity of 56%. For the non-DM cohort, the achieved accuracy, AUC, sensitivity, and specificity were 80%, 84%, 91%, and 56%, respectively. The models were then interpreted using SHapley Additive exPlanations (SHAP), which explained predictors’ global and local influences on model outputs. Finally, the k-means algorithm was applied to cluster patients on their SHAP values. The algorithm demarcated patients into three clusters. Average mortality rates within the generated clusters were 8%, 20%, and 76% for the DM cohort, 2.7%, 28%, and 41.9% for the non-DM cohort, providing a functional method of risk stratification.

Abstract EP57: A Non-exercise Prediction Of Cardiorespiratory Fitness For Patients With Cardiovascular Disease: Data From The Fitness Registry And The Importance Of Exercise International Database (FRIEND)

Introduction: The importance of cardiorespiratory fitness (CRF) for stratifying mortality risk and guiding clinical care in patients with cardiovascular disease (CVD) is well-established. An American Heart Association Scientific Statement suggests routine clinical assessment of CRF using non-exercise prediction equations when direct assessment from a cardiopulmonary exercise test is not feasible. However, current prediction equations have been created from cohorts of apparently healthy individuals. Hypothesis: A CVD-specific non-exercise equation would have higher accuracy for predicting CRF compared to an equation developed from a cohort without known CVD. Methods: Participants from the Fitness Registry and Importance of Exercise International Database (FRIEND) with a diagnosis of coronary artery bypass surgery (CABG), myocardial infarction (MI), percutaneous coronary intervention (PCI), or heart failure (HF) who performed a cardiopulmonary exercise test were studied (83% [10,417 of 12,578] male; age 62.7 ± 10.3 years). The cohort (12,578 tests; 49% [6,190] treadmill tests) was split into development (10,062) and validation (2,516) groups. The prediction equation was developed using multiple regression analysis and comparisons were made with a CRF prediction equation developed on an apparently healthy cohort using FRIEND. Results: Age, sex, height, body mass, exercise mode, and CVD diagnosis were all significant predictors of CRF. The regression equation was: CRF (mL/kg/min) = 17.03 – (0.21 * age [years]) + (3.60 * sex [male = 1; female = 0]) + (0.12 * height [cm]) – (0.11 * body mass [kg]) + (3.75 * mode [treadmill = 1; cycle = 0]) – (2.40 * CABG [yes = 1, no = 0]) – (0.29 * MI [yes = 1, no = 0]) + (0.75 * PCI [yes = 1, no = 0]) – (3.90 * HF [yes = 1, no = 0]) (adjusted R 2 = 0.42, SEE = 4.74 mL/kg/min). When compared to measured CRF in the validation group (19.6 ± 6.2 mL/kg/min), predicted CRF was similar for the CVD equation (19.8 ± 4.1 mL/kg/min [101%]) and higher for the healthy cohort equation (28.2 ± 7.0 mL/kg/min [144%]; P &lt;0.05). Significant Pearson correlations were found when using either prediction equation although the correlation when using the CVD equation was higher (r = 0.65) than that for the healthy cohort equation (r = 0.48, P &lt;0.05). Differences between equations were also observed for root mean square error (4.7 and 10.9 mL/kg/min for the CVD and healthy cohort equations, respectively). Conclusions: As hypothesized, the CVD-specific non-exercise equation was a better predictor of CRF in a cohort of individuals with CVD. The new equation for individuals with CVD provided a lower mean error between measured and predicted CRF than an equation developed from an apparently healthy cohort. Thus, population specific equations are needed for predicting CRF; however, the error associated with non-exercise prediction equations suggests CRF should be directly measured whenever feasible.

Uric acid and cardiovascular risk stratification in the acute coronary syndromes: a friend we should mind

Uric acid (UA) is the end product of purine metabolism. Whilst being primarily known as the leading risk factor for the onset of gout [ [1] Dalbeth N. Choi H.K. Joosten L.A.B. Khanna P.P. Matsuo H. Perez-Ruiz F. Stamp L.K. Gout. Nat Rev Dis Primers. 2019; 5 Crossref PubMed Scopus (114) Google Scholar ], UA has also been acknowledged as a significant CV risk factor [ [2] Virdis A. Masi S. Casiglia E. et al. Identification of the uric acid thresholds predicting an increased total and cardiovascular mortality over 20 years. Hypertension. 2020; 75: 302-308 Crossref PubMed Scopus (62) Google Scholar ]. However, international guidelines report as pathological threshold only the saturation point of UA (6.0 mg/dL in women and ≥7.0 mg/dL in men) [ [3] Li Q. Li X. Wang J. et al. Diagnosis and treatment for hyperuricemia and gout: a systematic review of clinical practice guidelines and consensus statements. BMJ Open. 2019; 9 Crossref Scopus (44) Google Scholar ], yet failing to identify a specific CV prevention cut-off [ [4] Visseren F.L.J. Mach F. Smulders Y.M. et al. 2021 ESC guidelines on cardiovascular disease prevention in clinical practice. Europe J Prev Cardiol. 2021; Crossref Google Scholar ]. This somewhat neglects UA predictive role in CV disease [ [5] Kaufman M. Guglin M. Uric acid in heart failure: a biomarker or therapeutic target?. Heart Fail Rev. 2013; 18: 177-186 Crossref PubMed Scopus (31) Google Scholar ]. In a large (n=22,714) Italian-population based observation cohort study, we have described how UA markedly impact all-cause and CV mortality starting from lower serum levels (respectively, 4.7 and 5.6 mg/dL) [ [2] Virdis A. Masi S. Casiglia E. et al. Identification of the uric acid thresholds predicting an increased total and cardiovascular mortality over 20 years. Hypertension. 2020; 75: 302-308 Crossref PubMed Scopus (62) Google Scholar ]. Several other observations have also found that UA enhances the CV mortality and morbidity risk stratification across the totality of the cardiometabolic spectrum using the same lower cut-offs [ [6] Pugliese N.R. Mengozzi A. Virdis A. et al. The importance of including uric acid in the definition of metabolic syndrome when assessing the mortality risk. Clin Res Cardiol. 2021; 110: 1073-1082 Crossref PubMed Scopus (7) Google Scholar , [7] Muiesan M.L. Salvetti M. Virdis A. et al. Serum uric acid, predicts heart failure in a large Italian cohort: search for a cut-off value the uric acid right for heart health study. J Hypertens. 2021; 39: 62-69 Crossref PubMed Scopus (12) Google Scholar ]. However, whilst in the current literature the relationship between UA in CV disease has been explored mainly in a chronic context (i.e. diabetes, hypertension, chronic kidney disease), little evidence is available to help us in defining whether the predictive power of UA holds true also for the acute setting, such as the acute coronary syndrome (ACS) [ [8] Maloberti A. Biolcati M. Ruzzenenti G. et al. The role of uric acid in acute and chronic coronary syndromes. J Clin Med. 2021; 10 Crossref Scopus (2) Google Scholar ].

Circulating miR-320b and miR-483-5p levels are associated with COVID-19 in-hospital mortality

The stratification of mortality risk in COVID-19 patients remains extremely challenging for physicians, especially in older patients. Innovative minimally invasive molecular biomarkers are needed to improve the prediction of mortality risk and better customize patient management. In this study, aimed at identifying circulating miRNAs associated with the risk of COVID-19 in-hospital mortality, we analyzed serum samples of 12 COVID-19 patients by small RNA-seq and validated the findings in an independent cohort of 116 COVID-19 patients by qRT-PCR. Thirty-four significantly deregulated miRNAs, 25 downregulated and 9 upregulated in deceased COVID-19 patients compared to survivors, were identified in the discovery cohort. Based on the highest fold-changes and on the highest expression levels, 5 of these 34 miRNAs were selected for the analysis in the validation cohort. MiR-320b and miR-483-5p were confirmed to be significantly hyper-expressed in deceased patients compared to survived ones. Kaplan-Meier and Cox regression models, adjusted for relevant confounders, confirmed that patients with the 20% highest miR-320b and miR-483-5p serum levels had three-fold increased risk to die during in-hospital stay for COVID-19. In conclusion, high levels of circulating miR-320b and miR-483-5p can be useful as minimally invasive biomarkers to stratify older COVID-19 patients with an increased risk of in-hospital mortality.

Frailty in the oldest old: is the current level or the rate of change more predictive of mortality?

It is unclear whether frailty index (FI) change captures mortality risk better than and independently of the current FI level, i.e. whether a regular FI assessment among older adults provides additional insights for mortality risk stratification or not. We used data from the LASA 75-PLUS-study, which monitored health among 508 older adults (75+) between 2016 and 2019 every 9months. Joint models for longitudinal and time-to-event data were used to assess the impact of both current FI and within-person FI change during the last year on mortality risk. Twenty percent of the participants died during 4.5years of follow-up. Adding within-person FI change to the current FI model improved model fit and it showed that FI increases during the last year were associated with an increase in mortality risk. Consequently, the effect of the current FI decreased considerably and became statistically non-significant. The rate of FI change was more important than the current FI level for short-term mortality prediction among the oldest old, which highlights the benefits of regular frailty assessments.

Validation of cardiogenic shock phenotypes in a mixed cardiac intensive care unit population.

Proposed phenotypes have recently been identified in cardiogenic shock (CS) populations using unsupervised machine learning clustering methods. We sought to validate these phenotypes in a mixed cardiac intensive care unit (CICU) population of patients with CS. We included Mayo Clinic CICU patients admitted from 2007 to 2018 with CS. Agnostic K means clustering was used to assign patients to threeclusters based on admission values of estimated glomerular filtration rate, bicarbonate, alanine aminotransferase, lactate, platelets, and white blood cell count. In-hospital mortality and 1-year mortality were analyzed using logistic regression and Cox proportional-hazards models, respectively. We included 1498 CS patients with a mean age of 67.8 ± 13.9 years, and 37.1% were females. The acute coronary syndrome was present in 57.3%, and cardiac arrest was present in 34.0%. Patients were assigned to clusters as follows: Cluster 1 (noncongested), 603 (40.2%); Cluster 2 (cardiorenal), 452 (30.2%); and Cluster 3 (hemometabolic), 443 (29.6%). Clinical, laboratory, and echocardiographic characteristics differed across clusters, with the greatest illness severity in Cluster 3. Cluster assignment was associated with in-hospital mortality across subgroups. In-hospital mortality was higher in Cluster 3 (adjusted odds ratio [OR]: 2.6 vs. Cluster 1 and adjusted OR: 2.0 vs. Cluster 2, both p < 0.001). Adjusted 1-year mortality was incrementally higher in Cluster 3 versus Cluster 2 versus Cluster 1 (all p < 0.01). We observed similar phenotypes in CICU patients with CS as previously reported, identifying a gradient in both in-hospital and 1-year mortality by cluster. Identifying these clinical phenotypes can improve mortality risk stratification for CS patients beyond standard measures.

Risk Stratification for In-Hospital Mortality in Adult Patients with COVID-19

Background: Since the outbreak of coronavirus 2019 (COVID-19), identifying risk factors associated with in-hospital mortality has been a global priority. In this study, the purpose was to evaluate the clinical, laboratory, and radiological characteristics of hospitalized patients with COVID-19 to develop a predictive model and scoring system for in-hospital mortality. Methods: In this retrospective cohort study, 611 adult patients with COVID-19, admitted to Sina hospital were enrolled and followed up. Results: Out of the total number of 611 patients, 104 patients (17%) deceased during hospitalization, including 75 (12.2%) deaths in ICU and 29 (4.7%) deaths in the wards. After multivariate logistic regression analysis, several characteristics including age &gt;55 years, previous history of malignancy, history of cerebrovascular accident, tachypnea on admission, CRP&gt;54 on admission, D-dimer&gt;1300, and bilateral pulmonary consolidation on chest Computed Tomography (CT) were shown to be the main determinants for stratifying the risk for in-hospital death. The factors were finally considered for introducing a new predictive scoring system for COVID-19 related death. Conclusion: In-hospital mortality rate in patients with COVID-19 is estimated to be 17%. A new scoring system for predicting in-hospital mortality in such patients was structured based on determinant factors of advanced age, history of malignancy, cerebrovascular accident, tachypnea, raised CRP, raised D-dimer on admission, and bilateral pulmonary consolidation on chest CT scan.

Red Cell Distribution Width in Acute Pulmonary Embolism Patients Improves 30-Day Mortality Risk Stratification Based on the Pulmonary Embolism Severity Index

To validate red cell distribution width (RDW) as an improvement in 30-day mortality risk stratification based on the Pulmonary Embolism Severity Index (PESI) in acute pulmonary embolism (PE). Prospective observational analysis of consecutive adult acute PE patients. Among 731 patients, 30-day mortality was 11.9%. With adjustment for the PESI score and number of covariates, higher RDW was associated with higher mortality (RDW continuous: OR 1.21, 95% CI 1.06-1.38; Bayesian OR 1.22, 1.07-1.40; RDW 'high' [>14.5% in men >16.1% in women] vs normal: OR 3.83, 1.98-7.46; Bayesian OR 3.98, 2.04-7.68]. Crude mortality was 3.6% if PESI 86-105 (intermediate risk), but 1.2% if RDW normal and 7.1% if RDW high; 11.8% if PESI 106-125 (high risk), but 3.6% if RDW normal and 18.8% if RDW high. Adjusted probabilities showed higher mortality (ORs between 3.5-5.8) if RDW was high in any PESI risk subgroup. Crude mortality rates in two random-split subsets (n=365 and n=366) again showed the same patterns. On-admission RDW above the normal range improves 30-day mortality risk stratification based on PESI score in acute PE. Particularly, it corrects PESI-based intermediate-risk or high-risk allocation by reclassification into very low-risk (<3.5%) or very high-risk (>11.0%).

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.