Clinical Predictors Of Mortality Research Articles

Machine Learning and Clinical Predictors of Mortality in Cardiac Arrest Patients: A Comprehensive Analysis.

BACKGROUND Cardiac arrest (CA) is a global public health challenge. This study explored the predictors of mortality and their interactions utilizing machine learning algorithms and their related mortality odds among patients following CA. MATERIAL AND METHODS The study retrospectively investigated 161 medical records of CA patients admitted to the Intensive Care Unit (ICU). The random forest classifier algorithm was used to assess the parameters of mortality. The best classification trees were chosen from a set of 100 trees proposed by the algorithm. Conditional mortality odds were investigated with the use of logistic regression models featuring interactions between variables. RESULTS In the logistic regression model, male sex was associated with 5.68-fold higher mortality odds. The mortality odds among the asystole/pulseless electrical activity (PEA) patients were modulated by body mass index (BMI) and among ventricular fibrillation/pulseless ventricular tachycardia (VF/pVT) patients were by serum albumin concentration (decrease by 2.85-fold with 1 g/dl increase). Procalcitonin (PCT) concentration, age, high-sensitivity C-reactive protein (hsCRP), albumin, and potassium were the most influential parameters for mortality prediction with the use of the random forest classifier. Nutritional status-associated parameters (serum albumin concentration, BMI, and Nutritional Risk Score 2002 [NRS-2002]) may be useful in predicting mortality in patients with CA, especially in patients with PCT >0.17 ng/ml, as showed by the decision tree chosen from the random forest classifier based on goodness of fit (AUC score). CONCLUSIONS Mortality in patients following CA is modulated by many co-existing factors. The conclusions refer to sets of conditions rather than universal truths. For individual factors, the 5 most important classifiers of mortality (in descending order of importance) were PCT, age, hsCRP, albumin, and potassium.

SIOG2023-5-P-398 - Clinical predictors of mortality in older people admitted in a virtual oncogeriatric tumor board

SIOG2023-5-P-398 - Clinical predictors of mortality in older people admitted in a virtual oncogeriatric tumor board

Infected pancreatic necrosis: outcomes and clinical predictors of mortality. A post hoc analysis of the MANCTRA-1 international study

The identification of high-risk patients in the early stages of infected pancreatic necrosis (IPN) is critical, because it could help the clinicians to adopt more effective management strategies. We conducted a post hoc analysis of the MANCTRA-1 international study to assess the association between clinical risk factors and mortality among adult patients with IPN. Univariable and multivariable logistic regression models were used to identify prognostic factors of mortality. We identified 247 consecutive patients with IPN hospitalised between January 2019 and December 2020. History of uncontrolled arterial hypertension (p = 0.032; 95% CI 1.135–15.882; aOR 4.245), qSOFA (p = 0.005; 95% CI 1.359–5.879; aOR 2.828), renal failure (p = 0.022; 95% CI 1.138–5.442; aOR 2.489), and haemodynamic failure (p = 0.018; 95% CI 1.184–5.978; aOR 2.661), were identified as independent predictors of mortality in IPN patients. Cholangitis (p = 0.003; 95% CI 1.598–9.930; aOR 3.983), abdominal compartment syndrome (p = 0.032; 95% CI 1.090–6.967; aOR 2.735), and gastrointestinal/intra-abdominal bleeding (p = 0.009; 95% CI 1.286–5.712; aOR 2.710) were independently associated with the risk of mortality. Upfront open surgical necrosectomy was strongly associated with the risk of mortality (p < 0.001; 95% CI 1.912–7.442; aOR 3.772), whereas endoscopic drainage of pancreatic necrosis (p = 0.018; 95% CI 0.138–0.834; aOR 0.339) and enteral nutrition (p = 0.003; 95% CI 0.143–0.716; aOR 0.320) were found as protective factors. Organ failure, acute cholangitis, and upfront open surgical necrosectomy were the most significant predictors of mortality. Our study confirmed that, even in a subgroup of particularly ill patients such as those with IPN, upfront open surgery should be avoided as much as possible. Study protocol registered in ClinicalTrials.Gov (I.D. Number NCT04747990).Graphical abstract

Clinical Predictors of Mortality and Heart Failure Hospitalization in Patients With Severe Tricuspid Regurgitation

BackgroundThere is an incomplete understanding of the predictors of morbidity and mortality in patients with severe tricuspid regurgitation (TR). This study sought to identify key risk factors for all-cause mortality and heart failure (HF) hospitalization among patients with severe TR. MethodsPatients with severe TR were identified from 2 centers, Oregon Health & Science University and Abrazo Health, from January 01, 2016 to December 31, 2018. Patients with any concomitant severe valvular diseases or prior valvular intervention were excluded. Multivariable regression was utilized to identify demographic, clinical, and echocardiographic variables independently associated with all-cause mortality or HF hospitalization. Results435 patients with severe TR were followed for a median of 2.8 years. The mean age of the population was 66.9 ± 18.5 years and 58% were female. All-cause mortality was identified in 20.5% of the population. Of the cohort, 35.4% of patients were hospitalized for HF. Isolated tricuspid valve intervention was performed in 2.5% of patients. Independent predictors of all-cause mortality included history of solid tumor (odds ratio [OR] 6.6, 95% confidence interval [CI] 2.1-19.1, p = 0.001), history of peripheral artery disease (OR 3.5, 95% CI 1.2-9.4, p = 0.013), and elevated international normalized ratio in the absence of anticoagulation (OR 1.9, 95% CI 1.2-3.2, p = 0.008). Predictors of HF hospitalization included history of diabetes mellitus (OR 2.2, 95% CI 1.1-4.0, p = 0.014) and history of reduced left ventricular ejection fraction (OR 5.7, 95% CI 2.9-11.7, p < 0.0001). ConclusionsSevere untreated TR is associated with high mortality and frequent HF hospitalizations. Understanding predictors of these outcomes is important to identify patients who may benefit from early tricuspid valve intervention to help improve outcomes in this patient population.

Development and validation of machine learning for early mortality in systemic sclerosis

Clinical predictors of mortality in systemic sclerosis (SSc) are diversely reported due to different healthcare conditions and populations. A simple predictive model for early mortality among patients with SSc is needed as a precise referral tool for general practitioners. We aimed to develop and validate a simple predictive model for predicting mortality among patients with SSc. Prognostic research with a historical cohort study design was conducted between January 1, 2013, and December 31, 2020, in adult SSc patients attending the Scleroderma Clinic at a university hospital in Thailand. The data were extracted from the Scleroderma Registry Database. Early mortality was defined as dying within 5 years after the onset of SSc. Deep learning algorithms with Adam optimizer and different machine learning algorithms (including Logistic Regression, Decision tree, AdaBoost, Random Forest, Gradient Boosting, XGBoost, and Autoencoder neural network) were used to classify SSc mortality. In addition, the model’s performance was evaluated using the area under the receiver operating characteristic curve (auROC) and its 95% confidence interval (CI) and values in the confusion matrix. The predictive model development included 528 SSc patients, 343 (65.0%) were females and 374 (70.8%) had dcSSc. Ninety-five died within 5 years after disease onset. The final 2 models with the highest predictive performance comprise the modified Rodnan skin score (mRSS) and the WHO-FC ≥ II for Model 1 and mRSS and WHO-FC ≥ III for Model 2. Model 1 provided the highest predictive performance, followed by Model 2. After internal validation, the accuracy and auROC were good. The specificity was high in Models 1 and 2 (84.8%, 89.8%, and 98.8% in model 1 vs. 84.8%, 85.6%, and 98.8% in model 2). This simplified machine learning model for predicting early mortality among patients with SSc could guide early referrals to specialists and help rheumatologists with close monitoring and management planning. External validation across multi-SSc clinics should be considered for further study.

Cardiovascular features and risk of mortality in COVID-19 hospitalized patients in Spain during 2020. A nationwide study from the Minimum Basic Data Set

Abstract Introduction During the last two years scientific evidence has been gathered regarding the cardiovascular complications of Covid-19. Nevertheless nationwide studies are still required to better understand both the incidence of less frequent clinical findings, and the prognostic implications of cardiovascular COVID-19 complications. Purpose The aim of this study was to estimate the incidence of cardiovascular diseases among COVID-19 hospitalized patients in Spain during 2020, as well as their association with mortality, besides other clinical and epidemiological factors. Methods We used the Minimum Basic Data Set from the Spanish Ministry of Health (RAE-CMBD) to analyze the data of all COVID-19 hospitalized patients in Spain during 2020. This national database includes concurrent diagnostics of all studied patients codified according to the Tenth International Classification of Diseases (ICD-10). Logistic regression analysis was performed to evaluate the influence of the different clinical and epidemiological variables in the evolution of COVID-19 hospitalized patients. Odds ratios were obtained for each variable adjusting by age and sex, and also adjusting by the rest of clinical factors. The software used for analysis was STATA v 16.1. Results 75585 men (55.15%) and 61468 women (44.85%) were hospitalized due to COVID-19 during 2020 in Spain. The median age was 66 in men and 71 in women. Mortality was 14.92% in males, and 13.81% in females. 9.62% of patients were admitted to intensive care unit (ICU). Mortality in ICU was 29.13%. Heart Failure (7.8%), Atrial Fibrillation (7.7%), Pulmonary Embolism (3.46%), Supraventricular Arrythmias (1.18%), Cardiomyopathy (1.06%), Acute Coronary Syndrome (0.87%), Ischemic Stroke (0.33%), Myocarditis (0.12%) Pericarditis (0.06%), or Takotsubo Disease (0.05%), were relevant cardiovascular findings in COVID-19 hospitalized patients (Table 1). In the logistic regression multivariate analysis in COVID-19 patients we found epidemiological predictors of in-hospital mortality such as age (OR 2.38 for each decade), or male sex (OR 1.39). Among the clinical predictors of mortality we differentiated cardiovascular ones as Acute Coronary Syndrome (OR 1.51), Ischemic Stroke (OR 1.46), or Heart Failure (OR 1.43); and non cardiovascular ones such as admission to ICU (OR 3.12), Adult Respiratory Distress Syndrome (OR 2.74), need for Mecanical Ventilation (OR 2.52), Acute Kidney Failure (OR 2.07), Liver damage (OR 1.67), or Dementia (OR 1.66), (Table 2) Conclusion(s) Heart Failure, Pulmonary Embolism, Ischaemic Heart Disease, Atrial fibrillation, Ischemic Stroke, or Cardiomyopathy were among the main cardiovascular diseases associated to COVID-19. They increased in a different measure the risk of mortality in COVID-19, together with factors such as Mecanical ventilation, ICU admission, Acute kidney failure, Dementia, Liver damage, Adult Respiratory Distress Syndrome, older age, or male sex. Funding Acknowledgement Type of funding sources: None.

The COVIDTW study: Clinical predictors of COVID-19 mortality and a novel AI prognostic model using chest X-ray

BackgroundThere is a lack of published research on the impact of the first wave of the COVID-19 pandemic in Taiwan. We investigated the mortality risk factors among critically ill patients with COVID-19 in Taiwan during the initial wave. Furthermore, we aim to develop a novel AI mortality prediction model using chest X-ray (CXR) alone.MethodWe retrospectively reviewed the medical records of patients with COVID-19 at Taipei Tzu Chi Hospital from May 15 to July 15 2021. We enrolled adult patients who received invasive mechanical ventilation. The CXR images of each enrolled patient were divided into 4 categories (1st, pre-ETT, ETT, and WORST). To establish a prediction model, we used the MobilenetV3-Small model with “Imagenet” pretrained weights, followed by high Dropout regularization layers. We trained the model with these data with Five-Fold Cross-Validation to evaluate model performance.ResultA total of 64 patients were enrolled. The overall mortality rate was 45%. The median time from symptom onset to intubation was 8 days. Vasopressor use and a higher BRIXIA score on the WORST CXR were associated with an increased risk of mortality. The areas under the curve of the 1st, pre-ETT, ETT, and WORST CXRs by the AI model were 0.87, 0.92, 0.96, and 0.93 respectively.ConclusionThe mortality rate of COVID-19 patients who receive invasive mechanical ventilation was high. Septic shock and high BRIXIA score were clinical predictors of mortality. The novel AI mortality prediction model using CXR alone exhibited a high performance.

Long-Term Outcomes and Clinical Predictors of Mortality Following Occurrence of Stent Thrombosis.

BackgroundStent thrombosis (ST) remains a significant medical issue. In particular, longer‐term mortality and clinical predictors after ST occurrence have yet to be elucidated.Methods and ResultsThis was a multicenter, retrospective, observational study. A total of 187 definite ST cases from January 2008 to December 2017 were enrolled, and the long‐term clinical outcomes were investigated. The primary outcome measure was the cumulative mortality after ST occurrence. In addition, independent predictors of mortality were assessed. Among the stent types causing ST, bare‐metal stent, first‐generation drug‐eluting stent, second‐generation drug‐eluting stent, and third‐generation drug‐eluting stent comprised 31.0%, 19.3%, 36.9%, and 6.4% of cases, respectively. Median duration from stent implantation to ST was 680.5 (interquartile range, 33.8–2450.5) days. Cumulative mortality was 14.6%, 17.4%, 21.2%, 24.4%, and 33.8% at 1, 2, 3, 5 and 10 years, respectively. The cumulative mortality did not significantly differ by type of stent, and mortality of late ST was higher than that of early ST and very late ST; however, it did not reach statistical significance after the multivariate analysis. Independent predictors of mortality were hemodialysis (hazard ratio [HR], 7.80; 95% CI, 3.07–19.81; P<0.001), culprit lesions in the left main trunk (HR, 8.14; 95% CI, 1.71–38.75; P=0.008), culprit lesions in the left coronary artery (HR, 2.77; 95% CI, 1.10–6.96; P=0.030), and peak creatine kinase (HR, 1.017; 95% CI, 1.011–1.022; P<0.001).ConclusionsThe 10‐year cumulative mortality after ST reached 33.8%. Close follow‐up is thus mandatory for patients with ST, especially with hemodialysis, culprit lesions in the left main trunk and left coronary artery, and high peak creatine kinase.

Clinical Characteristics and Predictors of Mortality in Obese African-Americans with COVID-19: a Single-Center Retrospective Study.

Background This study aims to add to the body of evidence linking obesity as an established risk factor for COVID-19 infection and also look at predictors of mortality for COVID-19 in the African-Americans (AA) population.MethodsA retrospective cohort study of patients with confirmed COVID-19 infection was done in a community hospital in New York City. The cohort was divided into two groups, with the non-obese group having a BMI < 30 kg/m2 and the obese group with a BMI ≥ 30 kg/m2. Clinical predictors of mortality were assessed using multivariate regression analysis.ResultsAmong the 469 (AA) patients included in the study, 56.3% (n = 264) had a BMI < 30 kg/m2 and 43.7% (n = 205) had a BMI ≥ 30 kg/m2. Most common comorbidities were hypertension (n = 304, 64.8%), diabetes (n = 200, 42.6%), and dyslipidemia (n = 74, 15.8%). Cough, fever/chills, and shortness of breath had a higher percentage of occurring in the obese group (67.8 vs. 55.7%, p = 0.008; 58.0 vs. 46.2%, p = 0.011; 72.2 vs. 59.8%, p = 0.005, respectively). In-hospital mortality (41.5 vs. 25.4%, p < 0.001) and mechanical ventilation rates (34.6 vs. 22.7%, p = 0.004) were also greater for the obese group. Advanced age (p = 0.034), elevated sodium levels (p = 0.04), and elevated levels of AST (0.012) were associated with an increase in likelihood of in-hospital mortality in obese group.ConclusionsOur results show that having a BMI that is ≥ 30 kg/m2 is a significant risk factor in COVID-19 morbidity and mortality. These results highlight the need for caution when managing obese individuals.

MiRNAs, from Evolutionary Junk to Possible Prognostic Markers and Therapeutic Targets in COVID-19.

The COVID-19 pandemic has been a public health issue around the world in the last few years. Currently, there is no specific antiviral treatment to fight the disease. Thus, it is essential to highlight possible prognostic predictors that could identify patients with a high risk of developing complications. Within this framework, miRNA biomolecules play a vital role in the genetic regulation of various genes, principally, those related to the pathophysiology of the disease. Here, we review the interaction of host and viral microRNAs with molecular and cellular elements that could potentiate the main pulmonary, cardiac, renal, circulatory, and neuronal complications in COVID-19 patients. miR-26a, miR-29b, miR-21, miR-372, and miR-2392, among others, have been associated with exacerbation of the inflammatory process, increasing the risk of a cytokine storm. In addition, increased expression of miR-15b, -199a, and -491 are related to the prognosis of the disease, and miR-192 and miR-323a were identified as clinical predictors of mortality in patients admitted to the intensive care unit. Finally, we address miR-29, miR-122, miR-155, and miR-200, among others, as possible therapeutic targets. However, more studies are required to confirm these findings.

Clinical Predictors of Mortality in Prehospital Distress Calls by Emergency Medical Service Subscribers.

(1) Introduction: Most studies rely on in-hospital data to predict cardiovascular risk and do not include prehospital information that is substantially important for early decision making. The aim of the study was to define clinical parameters in the prehospital setting, which may affect clinical outcomes. (2) Methods: In this population-based study, we performed a retrospective analysis of emergency calls that were made by patients to the largest private emergency medical services (EMS) in Israel, SHL Telemedicine Ltd., who were treated on-site by the EMS team. Demographics, clinical characteristics, and clinical outcomes were analyzed. Mortality was evaluated at three time points: 1, 3, and 12 months’ follow-up. The first EMS prehospital measurements of the systolic blood pressure (SBP) were recorded and analyzed. Logistic regression analyses were performed. (3) Results: A total of 64,320 emergency calls were included with a follow-up of 12 months post index EMS call. Fifty-five percent of patients were men and the mean age was 70.2 ± 13.1 years. During follow-up of 12 months, 7.6% of patients died. Age above 80 years (OR 3.34; 95% CI 3.03–3.69, p < 0.005), first EMS SBP ≤ 130 mm Hg (OR 2.61; 95% CI 2.36–2.88, p < 0.005), dyspnea at presentation (OR 2.55; 95% CI 2.29–2.83, p < 0001), and chest pain with ischemic ECG changes (OR 1.95; 95% CI 1.71–2.23, p < 0.001) were the highest predictors of 1 month mortality and remained so for mortality at 3 and 12 months. In contrast, history of hypertension and first EMS prehospital SBP ≥ 160 mm Hg were significantly associated with decreased mortality at 1, 3 and 12 months. (4) Conclusions: We identified risk predictors for all-cause mortality in a large cohort of patients during prehospital EMS calls. Age over 80 years, first EMS-documented prehospital SBP < 130 mm Hg, and dyspnea at presentation were the most profound risk predictors for short- and long-term mortality. The current study demonstrates that in prehospital EMS call settings, several parameters can be used to improve prioritization and management of high-risk patients.

Clinical Predictors of Mortality in Patients with Moderate to Severe Mitral Regurgitation

BackgroundMitral regurgitation is the most common form of valvular heart disease worldwide, however, there is an incomplete understanding of predictors of mortality in this population. This study sought to identify risk factors of mortality in a real-world population with mitral regurgitation. MethodsAll patients with moderate or severe mitral regurgitation were identified at a single center from January 1, 2016 to August 31, 2017. Multivariate regression was performed to evaluate variables independently associated with all-cause mortality. ResultsA total of 490 patients with moderate (76.3%) or severe (23.7%) mitral regurgitation due to primary (20.8%) or secondary (79.2%) etiology were identified. The mean age was 66.7 years; 50% were male. At a median follow-up of 3.1 years, the incidence of all-cause mortality was 30.1%, heart failure hospitalization 23.1%, and mitral valve intervention 11.6%. Of 117 variables, multivariate analysis demonstrated 5 that were independently predictive of mortality: baseline creatinine (hazard ratio [HR] 1.2; 95% CI, 1.0-1.3; P = .02), right atrial pressure by echocardiogram (HR 1.3; 95% CI, 1.07-1.55; P = .008), hemoglobin (HR 0.65; 95% CI, 0.52-0.83; P = .001), hospitalization for heart failure (HR 1.6; 95% CI, 1.1-2.4; P = .015), and mitral valve intervention (HR 0.40; 95% CI, 0.16-0.83; P = .049). ConclusionIn this retrospective, pragmatic analysis of patients with moderate or severe mitral regurgitation, admission for heart failure exacerbation, elevated right atrial pressure, renal dysfunction, anemia, and lack of mitral valve intervention were independently associated with increased risk of all-cause mortality. Whether these risk factors may better identify select patients who may benefit from more intensive monitoring or earlier intervention should be considered in future studies.

Clinical Predictors of Mortality and Critical Illness in Patients with COVID-19 Pneumonia.

Early identification of patients with COVID-19 who will develop severe or critical disease symptoms is important for delivering proper and early treatment. We analyzed demographic, clinical, immunological, hematological, biochemical and radiographic findings that may be of utility to clinicians in predicting COVID-19 severity and mortality. Electronic medical record data from patients diagnosed with COVID-19 from November 2020 to June 2021 in the COVID-19 Department in the Galilee Medical Center, Nahariya, Israel, were collected. Epidemiologic, clinical, laboratory and imaging variables were analyzed. Multivariate stepwise regression analyses and discriminant analyses were used to identify and validate powerful predictors. The main outcome measure was invasive ventilation, or death. The study population included 390 patients, with a mean age of 61 ± 18, and 51% were male. The non-survivors were mostly male, elderly and overweight and significantly suffered from hypertension, diabetes mellitus type 2, lung disease, hemodialysis and past use of aspirin. Four predictive factors were found that associated with increased disease severity and/or mortality: age, NLR, BUN, and use of high flow oxygen therapy (HFNC). The AUC or diagnostic accuracy was 87%, with a sensitivity of 97%, specificity of 60%, PPV of 87% and NPP of 91%. The cytokine levels of CXCL-10, GCSF, IL-2 and IL-6 were significantly reduced upon the discharge of severely ill COVID-19 patients. The predictive factors associated with increased mortality include age, NLR, BUN, and use of HFNC upon admission. Identifying those with higher risks of mortality could help in early interventions to reduce the risk of death.

Thromboinflammatory findings and clinical predictors of mortality in vaccine-induced immune thrombotic thrombocytopenia

Thromboinflammatory findings and clinical predictors of mortality in vaccine-induced immune thrombotic thrombocytopenia

1726P The optimal care study: Outcomes of patients with solid malignancies admitted to intensive care unit

New cancer treatments have improved life prognosis in oncology, leading to an increasing demand for critical care attention and complexity in clinical decisions. We aim to assess the outcomes and prognosis of cancer patients (pts) admitted to the Intensive Care Unit (ICU) since there is limited data about clinical predictors for mortality in this setting.

Clinical characteristics and clinical predictors of mortality in hospitalised patients of COVID 19 : An Indian study

BackgroundThe rapid spread of the coronavirus disease 2019 (COVID-19) with high mortality rate necessitates disease characterization and accurate prognostication for prompt clinical decision-making. The aim of this study is to study clinical characteristics and predictors of mortality in hospitalized patients with COVID-19 in India. MethodsRetrospective cohort study was conducted in a tertiary care hospital in northern India. All consecutive confirmed hospitalized COVID-19 cases aged 15 years and older from 13 Apr till 31 Aug 2020 are included. Primary end point was 30-day mortality. ResultsOf 1622 patients ,1536 cases were valid. Median age was 36 years, 88.3% were men and 58.1% were symptomatic. Fever (37.6%) was commonest presenting symptom. Dyspnea was reported by 15.4%. Primary hypertension (8.5%) was commonest comorbidity, followed by diabetes mellitus (6.7%). Mild, moderate, and severe hypoxemia were seen in 3.4%, 4.3%, and 0.8% respectively. Logistic regression showed greater odds of moderate/severe disease in patients with dyspnea, hypertension, Chronic Kidney Disease (CKD), and malignancy. Seventy six patients died (4.9%). In adjusted Cox proportional hazards model for mortality, patients with dyspnea (hazard ratio [HR]: 14.449 [5.043-41.402]), altered sensorium (HR: 2.762 [1.142-6.683]), Diabetes Mellitus (HR: 1.734 [1.001-3.009]), malignancy (HR:10.443 [4.396-24.805]) and Chronic Liver Disease (CLD) (HR: 14.432 [2.321-89.715]) had higher risk. Rising respiratory rate (HR: 1.098 [1.048-1.150]), falling oxygen saturation (HR: 1.057 per unit change 95% CI: 1.028-1.085) were significant predictors. ConclusionAnalysis suggests that age, dyspnea, and malignancy were associated with both severe disease and mortality. Diabetes Mellitus and Chronic Liver Disease were associated with increased the risk of fatal outcome. Simple clinical parameters such as respiratory rate and oxygen saturation are strong predictors and with other risk factors at admission can be effectively used to triage patients.

Socioeconomic and Clinical Predictors of Mortality in Patients with Acute Dyspnea.

BackgroundFactors predicting long-term prognosis in patients with acute dyspnea may guide both acute management and follow-up. The aim of this study was to identify socioeconomic and clinical risk factors for all-cause mortality among acute dyspnea patients admitted to an Emergency Department.MethodsWe included 798 patients with acute dyspnea admitted to the ED of Skåne University Hospital, Malmö, Sweden from 2013 to 2016. Exposures were living in the immigrant-dense urban part of Malmö (IDUD), country of birth, annual income, comorbidities, smoking habits, medical triage priority and severity of dyspnea. Mean follow-up time was 2.2 years. Exposures were related to risk of all-cause mortality using Cox proportional hazard model.ResultsDuring follow-up 40% died. In models adjusted for age and gender, low annual income, previous or ongoing smoking, certain comorbidities, high medical triage priority and severe dyspnea were all significantly associated with increased mortality. After adjusting for age, gender and all significant exposures, the lowest quintile of income, ongoing or previous smoking, history of serious infection, anemia, hip fracture, high medical triage priority and severe dyspnea significantly and independently predicted mortality. In contrast, neither country of birth nor living in IDUD predicted a mortality risk.ConclusionApart from several clinical risk factors, low annual income predicts two-year mortality risk in patients with acute dyspnea. This is not the case for country of birth and living in IDUD. Our results underline the wide range of mortality risk factors in acute dyspnea patients. Knowledge of patients’ annual income as well as certain clinical features may aid risk stratification and determining the need of follow-up both in hospital and after discharge from an ED.

Role of demographic and clinical factors in survival of HIV patients on antiretroviral therapy.

Our retrospective cohort study assesses the survival probability and identifies the demographic and clinical predictors of mortality in HIV patients taking antiretroviral therapy using an antiretroviral therapy centre data in Western India. Secondary data on 7532 registered HIV-infected individuals between September 2006 and January 2013 were analysed. The probability of survival at 75months was 84.9%. Significant indicators of poor chances of survival were greater age, lower occupation class, lower CD4 count, poor functional status; higher stage of disease, lower weight, the presence and type of opportunistic infections, co-trimoxazole therapy and poor adherence to antiretroviral therapy. We thus find that, in addition to pre-ART, antiretroviral therapy clinical status and treatment adherence, socioeconomic status plays an important influence on ultimate survival of HIV patients on antiretroviral therapy.

Clinical Predictors of mortality in Acute Mesenteric Ischemia

Background: Acute mesenteric ischemia (AMI) is an emergency condition, which is accompanied by fatal complications. Clinical signs and symptoms are often unspecific. In addition, there is no single laboratory test to diagnose acute mesenteric ischemia. Early and accurate diagnosis of intestinal ischemia is important to provide rapid and correct treatment and reduce morbidity and mortality rates. Objective: To determine the clinical predictors of outcome in patients with acute mesenteric ischemia at Emergency Hospital, Mansoura University. Materials and Methods: This was a prospective study, which was conducted on patients that were admitted to Emergency Hospital, Mansoura University over a year from December 2019 to December 2020. All patients above 18 years old with acute mesenteric ischemia were included while whom less than 18 years old were excluded. Results: this study had 50 patients (21 males and 29 females) with average age of 66 years old. The results showed that majority of the studied cases died, while only 42% (21 cases) of AMI cases survived. Hypercholesterolemia was considered the most frequently reported Co-morbidity followed by HTN, AF and lastly DM and cardiomegaly. There were statistically significant differences as regards co-morbidities (presence of 2 or more co-morbidities). Conclusion: Acute Mesenteric Ischemia is a serious condition with high mortality rate especially in advanced age and in cases associated with comorbidities, presence of 2 or more co-morbidities, hypertension, hypercholesterolemia, past surgical history increase the incidence of death.

Abstract 15087: Identifying Clinical Predictors of Mortality After PCI and the Impact of Complete Revascularization in Patients With Multi-vessel Coronary Artery Disease

Coronary revascularization for multi-vessel coronary artery disease (mvCAD) can be established through PCI or CABG. The benefit of surgical intervention over PCI has been attributed, in part, to more complete revascularization. However, among patients treated with PCI in the present era of drug-eluting stents, it is important to understand whether the completeness of revascularization is associated with mortality. Further, it is important to investigate this relationship in specific patient populations to determine which patients, if any, derive a survival benefit from complete revascularization. Moreover, it is critical to identify which factors are associated with higher mortality in this population to pinpoint potential areas of intervention to mitigate risk. In this propensity-score adjusted analysis, outcomes among 1,580 mvCAD patients who underwent PCI were analyzed and stratified by complete versus incomplete revascularization. The primary outcome was all-cause mortality. Complete revascularization was achieved in 28.4% of the cohort. Complete revascularization was not independently associated with survival compared with incomplete revascularization in the overall cohort (p=0.16). The predictors of increased all-cause mortality included advanced age, chronic lung disease, cardiogenic shock, diabetes, renal insufficiency, reduced left ventricular function, and left main stenosis. Complete revascularization was associated with reduced all-cause mortality in specific patient subsets including those with congestive heart failure and chronic lung disease. Our analysis elucidated numerous clinical predictors of all-cause mortality for mvCAD patients undergoing PCI. Complete revascularization was not among the independent predictors of mortality in the overall population and was associated with reduced mortality in patients with chronic lung disease, history of congestive heart failure and those who did not have previous PCI. While this study was not designed to test whether complete revascularization was superior to incomplete revascularization, the results suggest that when PCI is pursued among patients with mvCAD, achieving complete revascularization does not contribute to increased mortality.