Predictor Of Mortality In Patients Research Articles

Abstract 15632: Rv Dysfunction and the Risk of Mortality in Cardiomyopathy Patients Who Suffered a Cardiac Arrest

Background: Right ventricular (RV) dysfunction has been associated with higher mortality in patients who suffer a cardiac arrest. However, the role of RV dysfunction in patients with known cardiomyopathy (CM) (EF<50%) who suffer cardiac arrest from a shockable rhythm is not well examined. Aim: To assess the relationship between RV dysfunction and mortality in patients with cardiac arrest from a shockable rhythm without an ICD. Methods: We included patients with CM of any etiology who were hospitalized for the first time with cardiac arrest at a tertiary care center between 2011 and 2017. We examined in-hospital as well as long-term mortality. RV dysfunction was defined based on standard echo parameters. Results: A total of 208 patients were admitted to the hospital with cardiac arrest from a shockable rhythm (VT/VfIb) and 139 of those had no ICD at the time of the cardiac arrest. Out of those 139 patients, 48 had RV dysfunction. Patients with RV dysfunction had higher in-hospital mortality (44.7% vs 15.1%, p<.001). Over a mean follow-up of 2.9±3.2 years, patients with RV dysfunction had higher long-term mortality rates (72.9 vs 53.8%, p=.029) as well as shorter mean survival time (33.2, CI [20.6-45.8], vs 66.0 [53-78.9] months, p=0.005, Figure). In a Cox regression model, patients with RV dysfunction had higher long-term mortality rates when adjusting for EF, initiation of new AAD, electrolyte abnormalities and new ischemia (HR 1.68, 95% CI [1.05-2.69], p=.028). During hospitalization, patients with RV dysfunction needed advanced HF treatment options (inotropes, OHT) more frequently compared to patients without RV dysfunction (70.8 vs 46.2%, p=.005). Conclusions: RV dysfunction is an independent predictor of mortality in patients with cardiac arrest from a shockable rhythm who do not have an ICD. Further studies should investigate whether the presence of RV dysfunction alone should be taken into consideration for decisions on ICD implantation in patients with CM.

Abstract 11242: Spatial-Temporal Deep Learning for Automatic Detection of Echocardiographic Predictors of Mortality in Patients Hospitalized With Covid-19 - Data From the PROVAR-COVID Study

Background: Cardiac involvement seems to impact the prognosis of COVID-19. Bedside echocardiography (echo) holds promise for the early outcome prediction, and artificial intelligence has shown to be an additional tool to overcome personnel limitations. We propose a spatial-temporal deep learning-based approach for automatic prediction of mortality of inpatients with COVID-19 with echo images. Methods: Patients admitted to two hospitals in Brazil in 90 days with confirmed moderate and severe COVID-19 underwent clinical and laboratory evaluation, and focused bedside echo (GE Vivid IQ), following admission, with remote interpretation. Independent echo predictors of all-cause mortality were assessed, after adjustment for clinical variables. Our image dataset consists of 752 videos (Mpeg) acquired in three different probe positions: apical 4-chamber and parasternal long and short axis. We predicted the patients’ outcome (discharge or death) in a 10-fold cross-validation procedure manner, where we applied a 2-stream deep neural network composed of 2 identical ResNet-18 Convolutional Neural Networks (CNN) - pre-trained and developed for Rheumatic Heart Disease - followed by attention units to extract relevant spatial and temporal features from RGB and Optical Flow frames. A final softmax layer is used for classification. Results: A total of 163 patients were enrolled, the mean age was 64±16 years, 107 (66%) were admitted to intensive care and in-hospital mortality was 34% (N=56). Independent predictors of mortality were age≥63 years (OR=5.53, 95%CI 1.52-20.17), LVEF<64% (OR=7.37, 95%CI 2.10-25.94), and TAPSE<18.5 mm (OR=9.43, 95% CI 2.57-35.03), C-statistic=0.83. During training, utilizing a balanced set of 106 patients, we rescaled videos to 224x224 pixels and used temporal jittering to select a final clip with 32 frames per video in random batches of size 6. Our proposed method achieved an exam-wise accuracy of 62.22% (95%CI 52.40-72,04), sensitivity of 67.52% (95%CI 51.62-83.42), and specificity of 60.45% (95%CI 45.15-75.75) for mortality. Conclusion: Automatic detection of high-risk echo findings in COVID-19 inpatients at the bedside seems feasible and, with more research, can improve mortality prediction at the point of care.

Abstract 9997: Atrial Fibrillation and Supraventricular Tachycardia Are Predictors of Inpatient Mortality in Mechanically Ventilated Patients in the United States

Objective: Arrhythmias are a significant clinical challenge and can complicate hospital course for mechanically ventilated patients. We aimed to determine the burden of arrhythmias and their effect on inpatient outcomes in mechanically ventilated patients in the US. Methods: The national inpatient sample was retrospectively studied (2015-2019) to identify all mechanically ventilated patients aged >17. We further identified the following arrhythmias within each admission: atrial fibrillation (AF), non-sustained ventricular tachycardia (NSVT), supra-ventricular tachycardia (SVT), atrioventricular block (AVB) and ventricular fibrillation (VF). Primary outcomes were inpatient mortality rate, hospital length of stay (LOS) and total cost for each type of arrythmia. Demographic (age, sex, race) and clinical information (history of hypertension, stroke, myocardial infarction, obesity) was recorded. Data was studied in STATA 15 and multivariate logistic regression was used to estimate adjusted odds ratios (AOR) reported with p-value (significance=<0.05). Results: Our analysis included 1,745,867 weighted discharges of mechanically ventilated patients. Mean age was 65 years (56% male, 69% Caucasian). Most frequent arrythmia was AF (63% of all patients) and least common was AVB (11%). AF, VF, and SVT were associated with an increase in mortality (AOR: 3.52, 2.78, 1.45 respectively, p < 0.001). NSVT, AF, and SVT were associated with an increase in hospital LOS of 4.5, 3.2, and 2.8 days respectively (AOR 3.31, 2.94, 1.75 respectively, p<0.001). The adjusted inpatient cost was highest in patients with NSVT ($21,567, p<0.05). AVB and NSVT were not associated with an increase in mortality (p<0.05). On gender and race stratification, African American males had the highest mortality rate secondary to AF and VF (AOR: 3.54 and 4.21 respectively, p<0.001). Conclusion: We demonstrate that atrial fibrillation, ventricular fibrillation, and SVT are independently associated with increased mortality in mechanically ventilated patients. We also show that NSVT is associated with increase in hospital LOS in these patients. African American mechanically ventilated males had the highest mortality rates secondary to atrial and ventricular fibrillation.

Use of the National Early Warning Score for predicting deterioration of patients with acute pulmonary embolism: a post-hoc analysis of the YEARS Study

BackgroundThe Pulmonary Embolism Severity Index (PESI) and the simplified PESI (sPESI) are validated scores for mortality prediction in patients with pulmonary embolism (PE). National Early Warning Score (NEWS) is a...

THE INITIAL ION SHIFT INDEX AS A PROGNOSTIC INDICATOR TO PREDICT PATIENT SURVIVAL IN TRAUMATIC DAMAGE CONTROL LAPAROTOMY PATIENTS.

Objective: The ion shift index (ISI), which considers extracellular fluid ions such as phosphate, calcium, and magnesium, represents the ion shift following ischemia; concentrations of these ions are maintained within narrow normal ranges by adenosine triphosphate-dependent homeostasis. The ISI is defined as follows: {potassium (mmol/L-1) + phosphate (mmol/L-1) + Mg (mmol/L-1)}/calcium (mmol/L-1). This study investigated the possibility of predicting the 30-day survival rate of patients who underwent traumatic damage control laparotomy by comparing ISI and other laboratory findings, as well as the initial Trauma and Injury Severity Score (TRISS) and shock indices. Methods: Among the 134 patients who underwent damage control surgery between November 2012 and December 2021, 115 patients were enrolled in this study. Data regarding injury mechanism, age, sex, laboratory findings, vital signs, Glasgow Coma Scale score, Injury Severity Score, Abbreviated Injury Scale score, blood component transfusion, type of surgery, postoperative laboratory outcomes, morbidity, mortality rates, fluids administered, and volume of transfusions were collected and analyzed. Results: In univariate analysis, the odds ratio of the initial ISI was 2.875 (95% confidence interval, 1.52-5.43; P = 0.04), which showed a higher correlation with mortality compared with other indices. The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were derived from different multivariable logistic regression models. The initial ISI had high sensitivity and specificity in predicting patient mortality (AUC, 0.7378). In addition, in the model combining the initial ISI, crystalloids, and TRISS, the AUC showed a high value (AUC, 0.8227). Conclusion: The ISI evaluated using electrolytes immediately after admission in patients undergoing traumatic damage control surgery may be a predictor of patient mortality.

Hyperlactatemia Is a Predictor of Mortality in Patients Undergoing Continuous Renal Replacement Therapy for AKI

Hyperlactatemia Is a Predictor of Mortality in Patients Undergoing Continuous Renal Replacement Therapy for AKI

Dynamic Risk Prediction of 30-Day Mortality in Patients With Advanced Lung Cancer: Comparing Five Machine Learning Approaches

Administering systemic anticancer treatment (SACT) to patients near death can negatively affect their health-related quality of life. Late SACT administrations should be avoided in these cases. Machine learning techniques could be used to build decision support tools leveraging registry data for clinicians to limit late SACT administration. Patients with advanced lung cancer who were treated at the Department of Oncology, Aalborg University Hospital and died between 2010 and 2019 were included (N = 2,368). Diagnoses, treatments, biochemical data, and histopathologic results were used to train predictive models of 30-day mortality using logistic regression with elastic net penalty, random forest, gradient tree boosting, multilayer perceptron, and long short-term memory network. The importance of the variables and the clinical utility of the models were evaluated. The random forest and gradient tree boosting models outperformed other models, whereas the artificial neural network-based models underperformed. Adding summary variables had a modest effect on performance with an increase in average precision from 0.500 to 0.505 and from 0.498 to 0.509 for the gradient tree boosting and random forest models, respectively. Biochemical results alone contained most of the information with a limited degradation of the performances when fitting models with only these variables. The utility analysis showed that by applying a simple threshold to the predicted risk of 30-day mortality, 40% of late SACT administrations could have been prevented at the cost of 2% of patients stopping their treatment 90 days before death. This study demonstrates the potential of a decision support tool to limit late SACT administration in patients with cancer. Further work is warranted to refine the model, build an easy-to-use prototype, and conduct a prospective validation study.

Predictors of Death in Patients with Neonatal Sepsis in a Peruvian Hospital.

Reducing neonatal mortality is a global challenge. This study's objective was to determine the predictors of mortality in patients with neonatal sepsis. The study was a retrospective cohort study in a Peruvian hospital from January 2014 to April 2022. Neonates diagnosed with sepsis were included. To find predictors of mortality, we used Cox proportional regression models. We evaluated 288 neonates with sepsis; the median birth weight and hospitalization time were 3270 g and seven days, respectively. During follow-up, 18.4% did not survive, and the most common complications were jaundice (35.42%), respiratory distress syndrome (29.51%), and septic shock (12.5%). The most isolated bacteria were Klebsiella pneumoniae. The risk factors associated with higher mortality were prematurity (aHR = 13.92; 95% CI: 1.71-113.51), platelets <150,000 (aHR = 3.64; 1.22-10.88), creatinine greater than 1.10 (aHR = 3.03; 1.09-8.45), septic shock (aHR = 4.41; 2.23-8.74), and admission to IMV (aHR = 5.61; 1.86-16.88), On the other hand, breastfeeding was associated with a lower risk of death (aHR = 0.25; 0.13-0.48). In conclusion, we report a high incidence of death and identify clinical (prematurity, septic shock, admission to IMV) and laboratory characteristics (elevated creatinine and thrombocytopenia) associated with higher mortality in patients with neonatal sepsis. Breastfeeding was a factor associated with survival in these patients.

Delayed gastric emptying is associated with increased risk of mortality in patients undergoing pancreaticoduodenectomy for pancreatic adenocarcinoma

Delayed gastric emptying (DGE) is common in patients undergoing pancreaticoduodenectomy (PD). The effect of DGE on mortality is less clear. We sought to identify predictors of mortality in patients undergoing PD for pancreatic adenocarcinoma hypothesizing DGE to independently increase risk of 30-day mortality. The ACS-NSQIP targeted pancreatectomy database (2014–2017) was queried for patients with pancreatic adenocarcinoma undergoing PD. A multivariable logistic regression analysis was performed. Separate sensitivity analyses were performed adjusting for postoperative pancreatic fistula (POPF) grades A–C. Out of 8011 patients undergoing PD, 1246 had DGE (15.6%). About 8.5% of patients with DGE had no oral intake by postoperative day-14. The DGE group had a longer median operative duration (373 vs. 362 min, p = 0.019), and a longer hospital length of stay (16.5 vs. 8 days, p < 0.001). After adjusting for age, gender, comorbidities, preoperative chemotherapy, preoperative radiation, open versus laparoscopic approach, vascular resection, deep surgical space infection (DSSI), postoperative percutaneous drain placement, and development of a POPF, DGE was associated with an increased risk for 30-day mortality (OR 3.25, 2.16–4.88, p < 0.001). On sub-analysis, grades A and B POPF were not associated with risk of mortality while grade C POPF was associated with increased risk of mortality (OR 5.64, 2.24–14.17, p < 0.001). The rate of DGE in patients undergoing PD in this large database was over 15%. DGE is associated with greater than three times the increased associated risk of mortality, even when controlling for POPF, DSSI, and other known predictors of mortality.

Power of mortality prediction in patients awaiting liver transplantation according to the Global Leadership Initiative on Malnutrition criteria and Subjective Global Assessment and Royal Free Hospital Global Assessment scores

ObjectivesThis study aimed to analyze the performance of the Global Leadership Initiative on Malnutrition (GLIM) criteria and Subjective Global Assessment (SGA) and Royal Free Hospital Global Assessment (RFH-GA) scores in predicting 12-mo mortality in patients awaiting liver transplantation. MethodsThis is a longitudinal observational study, carried out between March 2019 and November 2021. Clinical data were collected and nutritional assessment was performed through anthropometry and application of validated instruments, such as the SGA, GLIM criteria, and RFH-GA. A Cox regression model was carried out, in which the dependent variable was mortality in 1 y, and the independent variables were the classifications of nutritional status by the different methods. ResultsThe sample consisted of 126 patients, most of them male (56.35%). Malnutrition was diagnosed in 85.71% of the patients according to the RFH-GA, 62.70% according to the SGA, and 56.31% according to the GLIM criteria. Malnutrition assessed by GLIM was related to a 3.79-fold increase in the chance of mortality over time in patients awaiting liver transplantation. Moreover, the GLIM criteria had good discriminatory power in identifying mortality in patients awaiting liver transplantation, compared with the initial and final SGA and RFH-GA scores and the Model for End-stage Liver Disease–Sodium (MELD-Na) index. ConclusionsThe GLIM criteria were a good predictor of increased risk of mortality in malnourished patients with chronic liver disease awaiting liver transplantation, compared with the SGA and RFH-GA scores and the MELD-Na index.

Machine learning models for 180-day mortality prediction of patients with advanced cancer using patient-reported symptom data

PurposeThe objective of the current study was to develop and test the performances of different ML algorithms which were trained using patient-reported symptom severity data to predict mortality within 180 days for patients with advanced cancer.MethodsWe randomly selected 630 of 689 patients with advanced cancer at our institution who completed symptom PRO measures as part of routine care between 2009 and 2020. Using clinical, demographic, and PRO data, we trained and tested four ML algorithms: generalized regression with elastic net regularization (GLM), extreme gradient boosting (XGBoost) trees, support vector machines (SVM), and a single hidden layer neural network (NNET). We assessed the performance of algorithms individually as well as part of an unweighted voting ensemble on the hold-out testing sample. Performance was assessed using area under the receiver-operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).ResultsThe starting cohort of 630 patients was randomly partitioned into training (n = 504) and testing (n = 126) samples. Of the four ML models, the XGBoost algorithm demonstrated the best performance for 180-day mortality prediction in testing data (AUROC = 0.69, sensitivity = 0.68, specificity = 0.62, PPV = 0.66, NPV = 0.64). Ensemble of all algorithms performed worst (AUROC = 0.65, sensitivity = 0.65, specificity = 0.62, PPV = 0.65, NPV = 0.62). Of individual PRO symptoms, shortness of breath emerged as the variable of highest impact on the XGBoost 180-mortality prediction (1-AUROC = 0.30).ConclusionOur findings support ML models driven by patient-reported symptom severity as accurate predictors of short-term mortality in patients with advanced cancer, highlighting the opportunity to integrate these models prospectively into future studies of goal-concordant care.

The Caprini Risk Score for Early Prediction of Mortality in Patients With Acute Coronary Syndrome.

The Caprini Risk Score (CRS) is a validated predictive instrument for venous thrombosis. Previous investigators have shown that a high CRS is associated with a higher risk of mortality from thrombotic diseases. The aim of this study was to assess the association between the CRS and prognosis of patients with acute coronary syndrome (ACS). Secondary analysis of data from a retrospective cohort study was conducted. Patients were classified into 3 CRS-based categories (CRS ≤ 2, CRS = 3-4, and CRS ≥ 5, indicating low, medium, and high, respectively). Kaplan-Meier curves and Cox regression models were used to assess the prognosis of patients with ACS. All-cause mortality and cardiac mortality were the end points. Two hundred fifty-four patients (12.8%) died during follow-up. Multivariate Cox regression models identified CRS as an independent risk factor for all-cause mortality among patients with ACS (CRS = 3-4 vs CRS ≤ 2, hazard ratio: 3.268, 95% confidence interval: 1.396-7.647, P = .006; CRS ≥ 5 vs CRS ≤ 2, hazard ratio: 4.099, 95% confidence interval: 1.708-9.841, P = .002). Pearson correlation analysis showed a positive correlation between CRS and fibrinogen level ( r = 0.486, R2 = 0.765, P < .001) as well as D-dimer level ( r = 0.480, R2 = 0.465, P < .001). The CRS is a useful prognostic assessment instrument for patients with ACS, and the risk stratification of patients with ACS can be achieved based on their CRS at admission.

Bradykinin and Galectin-3 in Survived and Deceased Patients with COVID-19 Pneumonia: An Increasingly Promising Biochemical Target.

There are still no definite curative or preventive strategies for COVID-19 disease. It is crucial to fully comprehend the pathogenesis of COVID-19 infection so that we can develop expedient pharmacological protocols. While the impact of cytokine storm on COVID-19 severity has been one of the most tested hypotheses, the role of bradykinin and various other oxidative stress markers has been relatively under-researched. Their levels can be determined immediately after a hospital admission so they could be used as early predictors of the further development of the disease. The study aims at evaluating the possibility of using bradykinin and galectin-3 levels as early predictors that COVID-19 disease will progress into a severe case. Material and methods. The study was conducted as a prospective cross-sectional study. It included 47 consecutive adult patients with confirmed SARS-CoV-2 infection and COVID-19 pneumonia. All study subjects were admitted for a hospital treatment to the tertiary Clinical Hospital Center Bezanijska kosa, Belgrade, Serbia on June 2021. The blood samples were collected at the patients' admission. The analyses of demographic, radiological, and clinical data were later conducted for both groups (the deceased patients and those who survived). In addition, we analyzed the potential relations between the outcome and the levels of bradykinin and galectin-3 measured immediately after the patients were admitted to the hospital. The patients who passed away were predominantly older men with comorbidities. We recorded higher CT scores in the deceased patients and the significantly higher levels of urea, creatinine, CK, troponine, CRP, and other laboratory markers. They stayed at the ICU unit longer and required mechanical ventilation more frequently than the patients who survived. On the other hand, no differences were recorded in the time periods passing from the onset of the systems to the hospital admissions. Finally, we can highlight several independent predictors of mortality in patients with COVID-19 pneumonia, including the following: (1) patients who are 50 or more years old, (2) with in-hospital stays are longer that 4 days, (3) bradykinin levels surpass 220000 pg/ml, (4) D-dimer, creatinine, and CRP are elevated, and (5) comorbidities were present (such as hypertension and diabetes). The present study strongly supports the bradykinin storm hypothesis. Since elevated bradykinin levels have been found in most COVID-19 cases with fatal outcomes, the future therapeutical strategies for COVID-19 have to be focused on reducing bradykinin serum concentrations.

Epidemiology of Community-Acquired Sepsis: Data from an E-Sepsis Registry of a Tertiary Care Center in South India.

The study aims to characterize community-acquired sepsis patients admitted to our 1300-bedded tertiary care hospital in South India from the Surviving Sepsis Campaign (SSC) guideline-compliant e-sepsis registry stratified by focus of infection. The prospective observational study recruited 1009 adult sepsis patients presenting to the emergency department at the center based on Sepsis-2 criteria for a period of three years. Of the patients, 41% were between 61 and 80 years with a mean age of 57.37 ± 13.5%. A total of 13.5% (136) was under septic shock and in-hospital mortality for the study cohort was 25%. The 3 h and 6 h bundle compliance rates observed were 37% and 49%, respectively, without significant survival benefits. Predictors of mortality among patients with bloodstream infections were septic shock (p = 0.01, OR 2.4, 95% CI 1.23-4.79) and neutrophil-to-lymphocyte ratio (p = 0.008, OR 1.01, 95% CI 1.009-1.066). The presence of Acinetobacter (p = 0.005, OR 4.07, 95% CI 1.37-12.09), Candida non-albicans (p = 0.001, OR16.02, 95% CI 3.0-84.2) and septic shock (p = 0.071, OR 2.5, 95% CI 0.97-6.6) were significant predictors of mortality in patients with community-acquired pneumonia. The registry has proven to be a key data source detailing regional microbial etiology and clinical outcomes of adult sepsis patients, enabling comprehensive evaluation of regional community-acquired sepsis to tailor institutional sepsis treatment protocols.

COVID-19-related acute invasive fungal rhinosinusitis: risk factors associated with mortality

BackgroundAcute invasive fungal rhinosinusitis (AIFRS) is a rare aggressive life-threatening infection that affects immunocompromised individuals. Recently, an increase in the incidence of this infection has been reported in patients who have SARS-CoV-2 infection or recently recovered. This study was to assess the outcome and define risk factors that might affect the outcome in SARS-CoV-2-related AIFRS. A prospective observational study included 54 patients diagnosed with SARS-CoV-2-related AIFRS. Controlling the predisposing factors, systemic antifungal, and early surgical debridement was performed. The mortality rate was calculated. Age, sex, underlying risk factors, the extent of the disease, debridement technique, and other biochemical variables were evaluated regarding their impact on survival. Patients were followed up for 3 months.ResultsFifty-four patients with a mean age of 48.1 years. Diabetes mellitus was the most common comorbidity affecting 52 patients (96.3%). Intracranial and intraorbital extension had a predictive value for mortality (P value 0.050 and 0.049 respectively). However, only intracranial extension was the independent predictor of mortality. Biochemical variables were higher than the normal range, but only serum ferritin level above 165 ng/ml was an independent predictor of mortality in patients with AIFR. The mortality rate was 38.9%.ConclusionThe extent of the disease has a major impact on survival, so early diagnosis of AIFRS within patients infected with SARS-CoV-2 or recently recovered is essential to reduce mortality.

Financial toxicity in lung cancer.

In the United States, lung cancer is the third most common cancer and the overall leading cause of cancer death. Due to advances in immunotherapy and targeted therapy, 5-year survival is increasing. The growing population of patients with lung cancer and cancer survivors highlights the importance of comprehensive cancer care, including recognizing and addressing financial toxicity. Financial toxicity is a term used to contextualize the negative effects of the costs of cancer treatment in terms of patient quality of life. The American Society of Clinical Oncology (ASCO) Value Framework places emphasis on high-value care as it evaluates cancer treatments "based on clinical benefit, side effects, and improvements in patient symptoms or quality of life in the context of cost". Prior studies have shown that risk factors for financial toxicity in patients with lung cancer include lower household income or savings, inability to afford basic necessities, higher than anticipated out of pocket expenses, and taking sick leave. Among lung cancer survivors, patients experience increased unemployment and lower wages compared to the general population underscoring the lasting effects of financial toxicity. Financial toxicity is associated with increased psychosocial distress and decreased quality of life, and bankruptcy is an independent predictor of mortality in patients with cancer. Despite the negative implications of financial toxicity on patients, standardized screening practices and evidence-based interventions are lacking. The "COmphrensive Score for financial Toxicity (COST)" tool has been validated for assessing financial toxicity with correlation with health-related quality of life. Further research is needed to understand the utility of incorporating routine screening for financial toxicity into clinical practice and the efficacy of interventions. Understanding the relationship between financial toxicity and quality of life and survival is critical to providing high-value cancer care and survivorship care.

Prediction of long-term mortality for patients with severe asymptomatic de novo carotid stenosis undergoing carotid endarterectomy (PREMY2SE-CEA): Derivation and validation of a novel risk score

ObjectiveThe aim of the present study was to develop and validate a risk prediction model for the prediction of long-term mortality for patients with severe asymptomatic de novo carotid stenosis undergoing carotid endarterectomy (PREMY2SE-CEA). MethodsData were collected retrospectively from a dedicated database of consecutive patients who had undergone elective CEA for severe (>70% using the NASCET [North American Symptomatic Carotid Endarterectomy Trial] criteria) asymptomatic carotid stenosis at two Italian University Hospitals from 2008 through 2016. Internal validation of the score was performed after random sampling in a 3:1 fashion. The primary end point of the PREMY2SE-CEA risk score was the 5-year mortality. ResultsOf the 1214 patients, 901 were included in the derivation cohort and 313 in the validation cohort. Using multivariable logistic regression with backward elimination, a parsimonious model was derived. A risk score incorporating eight risk factors was generated and found to be highly predictive of long-term mortality in the derivation (odds ratio [OR], 1.38; 95% confidence interval [CI], 1.28-1.41; P < .001) and validation (OR, 1.29; 95% CI, 1.21-1.37; P <.001) cohorts. The discrimination power in the receiver operating characteristic curve analysis was C = 0.775 (95% CI, 0.74-.80), and the optimism-corrected area under the curve in the bootstrapped samples was 0.761 (P < .001). A strong correlation was found between the predicted and actual mortality rates in the validation cohort (r = 0.71; P < .001). ConclusionsIn the present study, we have described the development, evaluation, and validation of a risk prediction model (PREMY2SE-CEA) for long-term mortality after CEA in asymptomatic patients. Physicians could use the PREMY2SE-CEA risk scoring tool to complement their estimates of life expectancy and prompt selective consideration of prophylactic CEA to improve the long-term benefits of interventions.

Usefulness of SMART-COP score in prognosis of aspiration pneumonia

IntroductionAspiration pneumonia is becoming a common syndrome in the elderly in aging societies such as Japan. Although a number of tools have been validated for prediction of mortality in patients with community-acquired pneumonia, none have been established for aspiration pneumonia. The purpose of this study was to access the correlations of the A-DROP, CURB-65 and SMART-COP scores at the emergency visit with the 30-day mortality risk in patients with aspiration pneumonia. MethodsWe Titleretrospectively investigated 210 patients who presented to the emergency department at Mishuku Hospital in Tokyo, Japan. ResultsThe areas under the curve for the ability of A-DROP, Curb-65 and SMART-COP scores to predict the 30-day mortality risk were 0.6359, 0.6468 and 0.7594, respectively. Among the parameters of SMART-COP, involvement of multiple lobes on chest radiographs is the best predictor of the mortality. ConclusionsThe SMART-COP score can be a better predictor of the 30-day mortality risk.

The Prognostic Accuracy Evaluation of mNUTRIC, APACHE II, SOFA, and SAPS 2 Scores for Mortality Prediction in Patients with Sepsis.

Background The modified Nutrition Risk in the Critically Ill (mNUTRIC) score is a helpful tool to evaluate nutritional risk in critically ill patients. However, there is a lack of data on the relationship between mNUTRIC score and septic patients' outcomes. So, this study aims to validate the prognostic role of the mNUTRIC score and to compare the performances of mNUTRIC, APACHE II, SOFA, and SAPS 2 scores for mortality prediction in patients with sepsis. Methods This prospective observational study was performed on 194 septic patients admitted to the Intensive Care Unit (ICU) of 108 Military Central Hospital. Sepsis was defined based on the sepsis-3 definition. The mNUTRIC score was used to evaluate the nutritional status within 24 h of ICU admission. Baseline characteristics and clinical information were collected to calculate the mNUTRIC, APACHE II, SOFA, and SAPS 2 scores. The outcome was in-hospital mortality from all causes. Results Nonsurvivors patients had a significantly higher median mNUTRIC score (6 vs. 4, P < 0.001). The mortality rate in the group with a NUTRIC score ≥5 was significantly higher than in the group with a NUTRIC score <5 (56.0% vs 10.2%; P < 0.001). The area under the ROC curves (AUC) for predicting the mortality of mNUTRIC was 0.79 (sensitivity 67.1% and specificity 81.0% (P < 0.001)). Compared with other severity scores in mortality prediction, AUC was 0.78 for APACHE II (sensitivity 84.9% and specificity 67.7%), 0.77 for SOFA score (sensitivity 76.7% and specificity 65.3%), and 0.73 for SAPS 2 (sensitivity 66.1%, specificity 77.7%). In the multivariate analysis, mNUTRIC score was associated with in-hospital mortality (HR, 2.00; 95% CI, 1.54 to 2.58; P < 0.001). Conclusions Our study showed that the mNUTRIC score was similar to severity scores (APACHE II, SOFA, SAPS 2) in mortality prediction and was the independent mortality predictor in patients with sepsis.

Staphylococcus aureus bacteraemia and mortality: a population-based study in Olmsted County, Minnesota, from 2006 to 2020

Objectives To provide an evaluation of predictors of 6-month mortality in incident Staphylococcus aureus bacteraemia cases. Methods A retrospective population-based study of 541 adult residents of Olmsted County, MN with monomicrobial S. aureus bacteraemia from 1 January 2006 through 31 December 2020. Multivariable Cox regression was used to investigate risk factors of 6-month mortality. Results The median (interquartile range [IQR]) age of 541 patients with S. aureus bacteraemia was 66.8 (54.4–78.5) years and 39.6% were female. The median (IQR) Charlson Comorbidity Index was 6 (3–9). Overall, 144 patients died during the six-month period following their initial episode (30-day and 6-month mortality = 16.5% and 26.7%, respectively). In a multivariable analysis, older age, ICU admission, and unknown source of infection were significant predictors of increased 6-month mortality. In contrast, having an infectious diseases (ID) consultation was associated with reduced mortality in the first 2 weeks of follow-up. Secondary analyses revealed an early benefit of ID consultation during the first 30 days of follow-up and that infective endocarditis was an additional predictor of 6-month mortality. Conclusions To our knowledge, this investigation represents the only US population-based study evaluating predictors of mortality in patients with S. aureus bacteraemia. The finding of a short-term survival benefit associated with early ID consultation may be due to more extensive diagnostic efforts.