Independent Predictor Of In-hospital Death Research Articles

Characteristics and outcome of hospitalized patients with heart failure stratified for chronic kidney disease.

Heart failure (HF) and chronic kidney disease (CKD) place significant challenges on the healthcare system, and their co-existence is associated with shared adverse outcomes. The multinational CaReMe project was initiated to provide contemporary, real-world epidemiological data on cardiovascular and reno-metabolic diseases. Utilizing data from the German CaReMe cohort, we characterize a multicentric HF population and describe in-hospital outcomes stratified for co-morbid CKD. This retrospective, observational study analysed administrative data from inpatient cases hospitalized in 87 German Helios hospitals between 1 January 2016 and 31 August 2022. The first hospitalization of patients aged ≥18years with a primary discharge diagnosis of HF, based on ICD-10 codes, were considered the index cases, and subsequent hospitalizations were considered as readmissions. Baseline characteristics and outcomes were stratified for co-morbid CKD using ICD-10-encoding from the index cases. Cox regression was utilized for readmission endpoints and in-hospital mortality. In total, 174829 index cases (mean age 79±15years, 49.9% female) were included; of these, 55.0% had coexisting CKD. Patients with CKD were older, suffered from worse HF-related symptoms, had a higher co-morbidity burden, and in-hospital mortality was increased at index and during follow-up. Prevalent CKD was associated with higher rehospitalization rates and was an independent predictor for in-hospital death. Within this HF inpatient cohort from a multicentric German database, CKD was diagnosed in more than half of the patients and was associated with increased in-hospital mortality at baseline and during follow-up. Rehospitalizations were observed earlier and more frequently in patients with HF and co-morbid CKD.

Effect of fasting plasma glucose level in severe fever and thrombocytopenia syndrome patients without diabetes.

Hyperglycemia is correlated with worse in-hospital outcomes in acute infectious diseases such as coronavirus disease 2019 (COVID-19) and severe fever with thrombocytopenia syndrome (SFTS). This study assessed the relationship between fasting plasma glucose (FPG) levels and in-hospital mortality, disease type, and secondary infections among individuals with SFTS without preexisting diabetes. The clinical data and laboratory results upon admission of 560 patients with SFTS without preexisting diabetes meeting the inclusion criteria at Wuhan Union Hospital were collected. FPG levels in surviving patients with SFTS subjects were significantly lower than those in patients with SFTS who had died (P<0.0001). In multivariate Cox regression, high FPG level (≥11.1 mmol/L) was a risk factor independently associated with the in-hospital death of patients with SFTS without preexisting diabetes. Similarly, the FPG levels in general patients with SFTS were significantly lower than those in patients with severe SFTS (P<0.0001). Multivariate logistic regression identified high FPG level (7.0-11.1 mmol/L) as a risk factor independently associated with SFTS severity. While FPG levels were comparable between patients with SFTS with and without secondary infection (P = 0.5521), logistic regression analysis revealed that high FPG levels were not a risk factor for secondary infection in patients with SFTS without preexisting diabetes. High FPG level on admission was an independent predictor of in-hospital death and severe disease in individuals with SFTS without preexisting diabetes. FPG screening upon admission and glycemic control are effective methods for improving the prognosis of patients with SFTS.

Predictors of in-hospital death in children with myocardial injury related to COVID-19.

Myocardial injury due to COVID-19 infection is associated with higher mortality rates, higher intensive care unit admissions, and greater levels of inflammatory and cardiac markers. However, given the smaller number of papers regarding pediatric patients, there is still scarce data about mortality predictors in this population. This study aims to identify independent predictors of in-hospital death among pediatric patients hospitalized with myocardial injury related to COVID-19. This is an observational, prospective, and longitudinal study of pediatric patients who were hospitalized between March 2020 and June 2021 in a tertiary pediatric hospital. A total of 1,103 consecutive pediatric patients tested positive for COVID-19, and 232 were admitted. All patients underwent cardiac evaluation with electrocardiogram, echocardiogram, and cardiac markers. Of these patients, 88 were diagnosed with myocardial injury but 1 patient refused to participate, therefore 87 patients were included in the study. The median age at the time of diagnosis was 2 years (0-18), 38% had pre-existing comorbidities, and the mortality was 12% (n = 11). Cardiac evaluation has revealed high levels of troponin I in 63%, and electrocardiogram and echocardiogram abnormalities in 81% and 22% respectively. We found that high levels of inflammatory markers had higher mortality, but they didn't emerge as significant predictors of mortality when adjusted for other variables. Patients without pre-existing comorbidities were less likely to die in both univariate and multivariate analysis. Our findings may help healthcare providers better understand the cardiac implications of COVID-19 and define the necessity of cardiac surveillance amongst hospitalized pediatric patients.

Age shock index as an early predictor of cardiovascular death in acute coronary syndrome patients.

The shock index (SI), reflecting heart rate (HR) to SBP ratio, is established for predicting adverse outcomes in acute coronary syndrome (ACS) patients. Exploring the age shock index (ASI), obtained by multiplying SI with age, could offer further insights into ACS prognosis. Assess ASI's effectiveness in predicting in-hospital death in individuals with ACS. This study encompassed patients with acute myocardial infarction, drawn from a national registry spanning October 2010 to January 2022. The optimal ASI threshold was established using receiver operating characteristic (ROC) curve analysis. The primary outcome was in-hospital mortality. A total of 27 312 patients were enrolled, exhibiting a mean age of 66 ± 13 years, with 72.3% being male and 47.5% having ST-elevation myocardial infarction. ROC analysis yielded an area under the curve (AUC) of 0.80, identifying the optimal ASI cutoff as 44. Multivariate regression analysis, adjusting for potential confounders, established ASI ≥ 44 as an independent predictor of in-hospital death [hazard ratio: 3.09, 95% confidence interval: 2.56-3.71, P < 0.001]. Furthermore, ASI emerged as a notably superior predictor of in-hospital death compared to the SI (AUC ASI = 0.80 vs. AUC SI = 0.72, P < 0.0001), though it did not outperform the Global Registry of Acute Coronary Events (GRACE) score (AUC ASI = 0.80 vs. AUC GRACE = 0.85, P < 0.001) or thrombolysis in myocardial infarction (TIMI) risk index (AUC ASI = 0.80 vs. AUC TIMI = 0.84, P < 0.001). The ASI offers an expedient mean to promptly identify ACS patients at elevated risk of in-hospital death. Its simplicity and effectiveness could render it a valuable tool for early risk stratification in this population.

A retrospective analysis of 20,178 adult neurological infection admissions to United Kingdom critical care units from 2001 to 2020

BackgroundNeurological infection is an important cause of critical illness, yet little is known on the epidemiology of neurological infections requiring critical care.MethodsWe analysed data on all adults with proven or probable neurological infection admitted to UK (NHS) critical care units between 2001 and 2020 reported to the Intensive Care National Audit and Research Centre. Diagnoses, physiological variables, organ support and clinical outcomes were analysed over the whole period, and for consecutive 5-year intervals within it. Predictors of in-hospital mortality were identified using a backward stepwise regression model.ResultsWe identified 20,178 critical care admissions for neurological infection. Encephalitis was the most frequent presentation to critical care, comprising 6725 (33.3%) of 20,178 cases. Meningitis– bacterial, viral or unspecified cases - accounted for 10,056 (49.8%) of cases. In-hospital mortality was high, at 3945/19,765 (20.0%) overall. Over the four consecutive 5-year periods, there were trends towards higher Glasgow Coma Scale scores on admission, longer critical care admissions (from median 4 [IQR 2–8] to 5 days [IQR 2–10]), and reduced in-hospital mortality (from 24.9 to 18.1%). We identified 12 independent predictors of in-hospital death which when used together showed good discrimination between patients who die and those who survive (AUC = 0.79).ConclusionsAdmissions with neurological infection to UK critical care services are increasing and the mortality, although improving, remains high. To further improve outcomes from severe neurological infection, novel approaches to the evaluation of risk stratification, monitoring and management strategies are required.

Surgical repair of left ventricular free-wall rupture complicating acute myocardial infarction: a single-center 30 years of experience.

Left ventricular free-wall rupture (LVFWR) is a catastrophic complication of acute myocardial infarction (AMI). Historically, cardiac surgery is considered the treatment of choice. However, because of the rarity of this entity, little is known regarding the efficacy and safety of surgical treatment for post-infarction LVFWR. The aim of this study was to report a single-center experience in this field over a period of 30 years. Patients who developed LVFWR following AMI and underwent surgical repair at our Institution from January 1990 to December 2019 were considered. The primary end-point was in-hospital morality rate; secondary outcomes were long-term survival and postoperative complications. Multivariate analysis was carried out by constructing a logistic regression model to identify risk factors for early mortality. A total of 35 patients were enrolled in this study. The mean age was 68.9 years; 65.7% were male. The oozing type of LVFWR was encountered in 29 individuals, and the blowout type in 6 subjects. Sutured repair was used in 77.1% of patients, and sutureless repair in the remaining cases. The in-hospital mortality rate was 28.6%. Low cardiac output syndrome was the main cause of postoperative death. Multivariable analysis identified age >75 years at operation, preoperative cardiac arrest, concurrent ventricular septal rupture (VSR) as independent predictors of in-hospital death. Follow-up was complete in 100% of patients who survived surgery (mean follow-up: 9.3 ± 7.8 years); among the survivors, 16 patients died during the follow-up with a 3-year and 12-year overall survival rate of 82.5% and 55.2%, respectively. Surgical treatment of LVFWR following AMI is possible with acceptable in-hospital mortality and excellent long-term results. Advanced age, concurrent VSR and cardiac arrest at presentation are independent risk factors of poor early outcome.

Association of cardiovascular disease with hospital mortality in COVID-19 patients

Aim. To evaluate the relationship between the in-hospital mortality of patients with COVID-19 and the history of cardiovascular disease (CVD) using data from the Russian registry of patients with COVID-19.Material and methods. This study included 758 patients with COVID-19 (403 men, 355 women) aged from 18 to 95 years (median, 61 years), successively hospitalized in the COVID hospital of the Chazov National Medical Research Center of Cardiology from April through June 2020. Death predictors were studied using single- and multivariate regression analyses with the SPSS Statistics, Version 23.0 software.Results. During the stay in the hospital, 59 (7.8 %) patients with COVID-19 died, 677 (89.3 %) were discharged, and 22 (2.9 %) were transferred to other hospitals. The univariate regression analysis showed that the increase in age per decade was associated with a 92% increase in the risk of death [relative risk (RR), 1.92; 95% confidence interval (CI), 1.58-2.34; p &lt;0.001], and an increase in the number of CVDs increases the risk of death by 71% (RR 1.71; 95% CI 1.42–2.07; p&lt;0.001). The presence of one or more CVDs or specific diseases [atrial fibrillation, chronic heart failure (CHF), ischemic heart disease, myocardial infarction, history of cerebrovascular accidents], as well as diabetes mellitus were associated with a higher risk of fatal outcome during the hospitalization for COVID-19. The presence of any CVD increased the risk of in-hospital death by 3.2 times. However, when the model was adjusted for age and sex, this association lost its strength, and only the presence of CHF was associated with a 3-fold increase in the risk of death (RR, 3.16; 95 % CI, 1.64-6.09; p=0.001). Age was another independent predictor of death (RR, 1.05; 95 % CI, 1.03-1.08; p &lt; 0.001).Conclusion. A history of CVD and the CVD number and severity are associated with a higher risk of death during the hospitalization for COVID-19; the independent predictors of in-hospital death are an age of 80 years and older and CHF.

Traumatic Brain Injury: in-hospital Survival Rates and the Main Predictors of in-hospital Mortality in Northeastern Brazil*

Abstract Background Upper middle-income countries have epidemiological peculiarities that should be considered to identify the main predictive factors of intrahospital mortality regarding traumatic brain injury (TBI) to address modifiable problems. Objective To assess the in-hospital survival of patients with TBI and to identify the predictors of in-hospital death. Methods This is a retrospective dynamic cohort study of victims of TBI who were admitted to the Hospital de Urgência de Sergipe (HUSE, in the Portuguese acronym) between March 1, 2017 and April 29, 2018. The outcome considered was in-hospital death from any cause. Cox regression was used to assess predictors of in-hospital mortality. Results The sample comprised 596 patients, with a median age of 31.0 (12–94) years old, 504 (84%) of whom were men. Regarding TBI severity, 250 had mild TBI; 121 had moderate TBI; and 225 had severe TBI. The average follow-up was 20.6 ± 4.0 days, with 60 in-hospital deaths and a 30-day mortality of 22.9%. Four independent predictors of in-hospital death were identified: acute subdural hemorrhage (ASDH) (risk ratio [RR] = 1.926; 95% confidence interval [CI] = 1.15–3.22; p = 0.013), swelling (risk ratio [RR] = 3.706; 95%CI = 2.21–6.19; p &lt; 0.001), skull fracture (RR = 2.551; 95%CI = 1.36–4.75; p = 0.003), and severe TBI (RR = 2.039; 95%CI = 1.29–4.12; p = 0.005). Conclusions Acute subdural hemorrhage, swelling, skull cap fracture, and a Glasgow Coma Scale score of &lt; 9 at admission were independent predictors of in-hospital mortality in patients with TBI.

Establishment and evaluation of early in-hospital death prediction model for patients with acute pancreatitis in intensive care unit

To investigate the death risk prediction factors of acute pancreatitis (AP) patients in intensive care unit (ICU), and to establish a death prediction model and evaluate its efficacy. A retrospective cohort study was conducted using the data in the Medical Information Mart for Intensive Care-III (MIMIC-III). The clinical data of 285 AP patients admitted to the ICU in the database were collected, including age, gender, blood routine and blood biochemical indicators, comorbidities, simplified acute physiology score III (SAPS III) and hospital prognosis. By using univariate analysis, the differences in the clinical data of the patients were compared between the two groups. Binary multivariate Logistic regression analysis was used to screen out independent predictors of in-hospital death in AP patients. A death prediction model was established, and the nomogram was drawn. The receiver operator characteristic curve (ROC curve) was plotted, and the area under the ROC curve (AUC) was used to test the discrimination of the prediction model. In addition, the prediction model was compared with the SAPS III score in predicting in-hospital death. The calibration ability of the prediction model was evaluated by the Hosmer-Lemeshow goodness of fit test, and a calibration map was drawn to show the calibration degree of the prediction model. Among 285 patients with AP, 29 patients died in the hospital and 256 patients survived. Univariate analysis showed that the patients in the death group were older than those in the survival group (years old: 70±17 vs. 58±16), and had higher white blood cell count (WBC), total bilirubin (TBil), serum creatinine (SCr), blood urea nitrogen (BUN), red blood cell volume distribution width (RDW), proportion of congestive heart failure and SAPS III score [WBC (×109/L): 18.5 (13.9, 24.3) vs. 13.2 (9.3, 17.9), TBil (μmol/L): 29.1 (15.4, 66.7) vs. 16.2 (10.3, 29.1), SCr (μmol/L): 114.9 (88.4, 300.6) vs. 79.6 (53.0, 114.9), BUN (mmol/L): 13.9 (9.3, 17.8) vs. 6.1 (3.7, 9.6), RDW: 0.152 (0.141, 0.165) vs. 0.141 (0.134, 0.150), congestive heart failure: 34.5% vs. 14.8%, SAPS III score: 66 (52, 90) vs. 39 (30, 48), all P < 0.05]. Multivariate Logistic regression analysis showed that age [odds ratio (OR) = 1.038, 95% confidence interval (95%CI) was 1.005-1.073], WBC (OR = 1.103, 95%CI was 1.038-1.172), TBil (OR = 1.247, 95%CI was 1.066-1.459), BUN (OR = 1.034, 95%CI was 1.014-1.055) and RDW (OR = 1.344, 95%CI was 1.024-1.764) were independent risk predictors of in-hospital death in patients with AP. Logistic regression model was established: Logit(P) = 0.037×age+0.098×WBC+0.221×TBil+0.033×BUN+0.296×RDW-12.133. ROC curve analysis showed that the AUC of the Logistic regression model for predicting the in-hospital death of patients with AP was 0.870 (95%CI was 0.794-0.946), the sensitivity was 86.2%, and the specificity was 78.5%, indicating that the model had good predictive performance, and it was superior to the SAPS III score [AUC was 0.831 (95%CI was 0.754-0.907), the sensitivity was 82.8%, and the specificity was 75.4%]. A nomogram model was established based on the result of multivariate Logistic regression analysis. The calibration map showed that the calibration curve of the nomogram model was very close to the standard curve, with the goodness of fit test: χ2 = 6.986, P = 0.538, indicating that the consistency between the predicted death risk of the nomogram model and the actual occurrence risk was relatively high. The older the AP patient is, the higher the WBC, TBil, BUN, and RDW, and the greater the risk of hospital death. The death prediction Logistic regression model and nomogram model constructed based on the above indicators have good discrimination ability and high accuracy for high-risk patients with hospital death, which can accurately predict the probability of death in AP patients and provide a basis for prognosis judgment and clinical treatment of AP patients.

Factors Affecting Hospitalization Length and in-Hospital Death Due to COVID-19 Infection in Saudi Arabia: A Single-Center Retrospective Analysis.

The emerging COVID-19 coronavirus disease has widely spread, causing a serious worldwide pandemic. Disease severity and mortality risk can be predicted using an analysis of COVID-19 clinical characteristics. Finding out what influences patients' hospitalization length and in-hospital mortality is crucial for decision-making and planning for emergencies. The goal of this study is to identify the factors that influence hospital stay length and in-hospital death due to COVID-19 infection. This cross-sectional study was conducted from August to October 2020 and included 630 patients with a confirmed diagnosis of COVID-19 infection. Using odds ratios (OR) and 95% confidence intervals (CI), a multivariable logistic regression model was used to assess the variables that are linked to longer hospital stays and in-hospital deaths. Most patients were male (64.3%), and most were older than 40 years (81.4%). The mean length of hospital stay (LoHS) was 10.4±11.6 days. The overall death rate among these COVID-19 cases was 14.3%. Non-survivors were older, had more comorbidities, had prolonged LoHS with increased ICU admission rates and mechanical ventilation usage, and had a more severe condition than survivors. ICU admission, low serum albumin, and elevated LDH levels were associated with longer LoHS, while ICU admission, DM, and respiratory diseases as comorbidities, total leukocytic count, and serum albumin were predictors of mortality. Longer LoHS due to COVID-19 infection was linked to ICU admission, low serum albumin, and elevated LDH levels, while the independent predictors of in-hospital death were ICU admission, DM, and respiratory diseases as comorbidities, total leukocytic count, and serum albumin.

Association of age with death and withdrawal of life-sustaining therapy after severe traumatic brain injury.

Compared to younger age, older age (≥ 65 yr) is associated with worse outcomes after severe traumatic brain injury (TBI). We sought to describe the association of older age with in-hospital death and aggressiveness of intervention. We conducted a retrospective cohort study of adult (age ≥ 16 yr) patients with severe TBI admitted to a single academic tertiary care neurotrauma centre between January 2014 and December 2015. We collected data through chart review as well as from our institutional administrative database. We provided descriptive statistics and used multivariable logistic regression to evaluate the independent association of age with the primary outcome, in-hospital death. The secondary outcome was early withdrawal of life-sustaining therapy. There were 126 adult patients (median age 67 yr [Q1-Q3, 33-80 yr]) with severe TBI during the study period who met our eligibility criteria. The most common mechanism was high-velocity blunt injury (55 patients [43.6%]). The median Marshall score was 4 (Q1-Q3, 2-6), and the median Injury Severity Score was 26 (Q1-Q3, 25-35). After controlling for confounders including clinical frailty, pre-existing comorbidity, injury severity, Marshall score and neurologic examination at admission, we observed that older patients were more likely than younger patients to die in hospital (odds ratio 5.10, 95% confidence interval 1.65-15.78). Older patients were also more likely to experience early withdrawal of life-sustaining therapy and less likely to receive invasive interventions. After controlling for confounding factors relevant to older patients, we observed that age was an important and independent predictor of in-hospital death and early withdrawal of life-sustaining therapy. The mechanism by which age influences clinical decision-making independent of global and neurologic injury severity, clinical frailty and comorbidities remains unclear.

After an acute coronary syndrome: levosimendan or dobutamine? this is our impression

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Inotropic support is frequently needed after acute coronary syndromes(ACS) at coronary care units(CCU). Despite it has being long time since approval of levosimendan(LV) as inotropic support to cardiogenic shock, dobutamine(DB) still being the basis of this treatment. We think levosimendan is a secure and effective alternative. Purpose To analyse the use of levosimendan and dobutamine done in our ACS patients to determine if we have followed some clear criteria of selection, and try to draw a defined pattern for future use. Method Retrospective, analytic, observational study of all ACS admitted to our CCU between 08/2011- 02/2022. We confront patients treated with dobutamine versus levosimendan. X2, U-Mann Whitney and T-Student were used when necessary. Statistical significance: p-value&amp;lt;0.05. Result 2881patients were admitted with ACS, 6.49%of them(n: 187) needed ionotropic support. Out of these, 60.96%(n: 114) were supported with dobutamine, 28.88%(n: 54) with levosimedan and 10.16%(n: 19)received in some time both drugs, so were excluded from the analysis. Mean age: 68.43±11.48years old. 66.66%(n: 112) men. 54.76%(n: 92)with persistent ST-segment elevation. No difference was found between groups, neither in past medical history, including cardiovascular risk factors, history of arrhythmias(11.3% of patients), previous use of betablockers(30.4%), antiarrhythmic drugs(0.6%) or having pacemaker(1.2%). 114 patients(67.86%)needed norepinephrine(70.17%with DB and 62.96% LV, p:0.35); 29(17.26%) were supported with intra-aortic balloon pump(20.18% in DB group and 11.11% in LV group, p:0.134), without differences by drug. 62 patients(36.9%) presented atrial fibrillation(33.33% in DB and 44.44% in LV, p:0.166), 58(34.52%)ventricular arrhythmias[sustained ventricular tachycardia/ventricular fibrillation](33.33% in DB and 37.04% in LV, p:0.638). In-hospital mortality of these patients was 29.17%(n:49), 29.82% in DB and 27.77% in LV, p:0.785. In multivariate analysis adjusted by clinic and epidemiologic factors, only cardiogenic shock resulted an independent predictor of in-hospital death. Pattern of use we use DB in those patients with worst Killip-kimball(KK) state(73.54%in DB group with KK-IV vs53.70%in LV´s) and LV in those in intermediate states of instability(38.88%LV in KK-II/III vs 8.84%DB in KK-II/III),p&amp;lt;0.001. We chose DB in those who became unstable in CCU and LV in those who became unstable in hospitalization room, p:0.049. Finally, we noted a significant change in trend of use during the period of study, falling use of DB in favour of LV(p&amp;lt;0.001). Conclusion In our experience, dobutamine has been the most frequent ionotropic drug used in last 11 years after an acute coronary syndrome. Nevertheless, a substitution by levosimendan trend is evolving. We did not detect differences in complications or prognosis dependent on the inotropic drug used, but the use of dobutamine were predominant in those patients with more severe situation.

The arrival ward requiring help by wheelchair or medical cart, arterial oxygenation index, age, albumin and neutrophil count score: Predicting in-hospital mortality in Chinese patients with acute exacerbations of chronic obstructive pulmonary disease.

In this study, we will derive and validate a prognostic tool to predict in-hospital death based on Chinese acute exacerbation of chronic obstructive pulmonary disease (AECOPD) patients. Independent predictors of in-hospital death were identified by logistic regression analysis and incorporated into a clinical prediction tool. The clinical prediction model was developed with data from 1121 patients and validated with data from 245 patients. The five predictors of in-hospital death from the development cohort (Arrival ward requiring help by wheelchair or medical cart, Arterial oxygenation index, Age, Albumin and Neutrophil count) were combined to form the AAAAN Score. The AAAAN Score achieved good discrimination (AUC = 0.85, 95% CI 0.81-0.89) and calibration (Hosmer-Lemeshow chi-square value was 3.33, p = 0.65). The AAAAN Score, which underwent internal bootstrap validation, also showed excellent discrimination for mortality (AUC = 0.85, 95% CI 0.81 to 0.89) and performed more strongly than other clinical prediction tools. Patients were categorized into 3 risk groups based on the scores: low risk (0-2 points, 0.7% in-hospital mortality), intermediate risk (3-4 points, 4.1% in-hospital mortality), and high risk (5-7 points, 23.4% in-hospital mortality). Predictive performance was confirmed by external validation. The AAAAN Score is a prognostic tool to predict in-hospital death in Chinese AECOPD patients.

Biomarkers of cell damage, neutrophil and macrophage activation associated with in-hospital mortality in geriatric COVID-19 patients

BackgroundThe risk for symptomatic COVID-19 requiring hospitalization is higher in the older population. The course of the disease in hospitalised older patients may show significant variation, from mild to severe illness, ultimately leading to death in the most critical cases. The analysis of circulating biomolecules involved in mechanisms of inflammation, cell damage and innate immunity could lead to identify new biomarkers of COVID-19 severity, aimed to improve the clinical management of subjects at higher risk of severe outcomes.In a cohort of COVID-19 geriatric patients (n= 156) who required hospitalization we analysed, on-admission, a series of circulating biomarkers related to neutrophil activation (neutrophil elastase, LL-37), macrophage activation (sCD163) and cell damage (nuclear cfDNA, mithocondrial cfDNA and nuclear cfDNA integrity). The above reported biomarkers were tested for their association with in-hospital mortality and with clinical, inflammatory and routine hematological parameters. Aim of the study was to unravel prognostic parameters for risk stratification of COVID-19 patients.ResultsLower n-cfDNA integrity, higher neutrophil elastase and higher sCD163 levels were significantly associated with an increased risk of in-hospital decease. Median (IQR) values observed in discharged vs. deceased patients were: 0.50 (0.30-0.72) vs. 0.33 (0.22-0.62) for n-cfDNA integrity; 94.0 (47.7-154.0) ng/ml vs. 115.7 (84.2-212.7) ng/ml for neutrophil elastase; 614.0 (370.0-821.0) ng/ml vs. 787.0 (560.0-1304.0) ng/ml for sCD163. The analysis of survival curves in patients stratified for tertiles of each biomarker showed that patients with n-cfDNA integrity < 0.32 or sCD163 in the range 492-811 ng/ml had higher risk of in-hospital decease than, respectively, patients with higher n-cfDNA integrity or lower sCD163. These associations were further confirmed in multivariate models adjusted for age, sex and outcome-related clinical variables. In these models also high levels of neutrophil elastase (>150 ng/ml) appeared to be independent predictor of in-hospital death. An additional analysis of neutrophil elastase in patients stratified for n-cfDNA integrity levels was conducted to better describe the association of the studied parameters with the outcome.ConclusionsOn the whole, biomarkers of cell-free DNA integrity, neutrophil and macrophage activation might provide a valuable contribution to identify geriatric patients with high risk of COVID-19 in-hospital mortality.

Association between serum calcium level and in-hospital mortality in patients with acute myocardial infarction: a retrospective cohort study

The association between serum calcium levels and the prognosis of patients with acute myocardial infarction (AMI) remains controversial. This study aimed to explore the association between serum calcium and in-hospital mortality in patients with AMI. The data of this study were extracted from the Philips eICU Collaborative Research Database. A total of 7284 patients were eventually enrolled in this study, of which 799 (10.97%) died during hospitalization. For each patient, serum calcium, corrected to albumin, was calculated and categorized into four groups: Q1 ≤ 8.5, Q2 8.5–9.5, Q3 9.5–10.5, and Q4 > 10.5 mg/dL. Multivariate analysis demonstrated that corrected sCa was an independent predictor of in-hospital death (Q2 vs. Q1, OR 0.5, 95% CI 0.4–0.7, P < 0.001; Q3 vs. Q1, OR 0.8, 95% CI 0.6–1.0, P = 0.035; Q4 vs. Q1, OR 1.6, 95% CI 1.1–2.3, P = 0.008). The association remained stable in the fully adjusted model. A significant U-shaped association between corrected serum calcium and in-hospital mortality was observed in piecewise linear regression model (Corrected sCa < 9.4 mg/dL, OR 0.8, 95% CI 0.7–0.9, P < 0.001; corrected sCa > 9.4 mg/dL, OR 1.5, 95% CI 1.3–1.8, P < 0.001). In conclusion, both decreased and increased corrected serum calcium is associated with increased in-hospital mortality in patients with AMI, and patients may have the lowest risk of in-hospital death when corrected serum calcium is 9.4 mg/dL (2.35 mmol/L).

Prognostic Factors of In-Hospital Mortality in Patients with Acute Myocardial Infarction Complicated by Cardiogenic Shock.

Among patients with acute myocardial infarction (MI) complicated by cardiogenic shock (CS), in-hospital mortality remains high. In the present study, we aimed to identify factors associated with clinical outcomes of acute MI patients with CS in a contemporary setting. A total of 1102 patients with acute MI undergoing primary percutaneous coronary intervention were included, among whom 196 (17.8%) were complicated by CS. The primary outcome was all-cause death during hospitalization, and factors associated with in-hospital mortality were explored in patients with acute MI and CS. Of the 196 patients with acute MI complicated by CS, 77 (39.3%) died during hospitalization. The rates of non-ST-segment elevation MI (NSTEMI) (33.8% vs. 19.3%, p = 0.02) and culprit lesion in the left main or left anterior descending coronary artery (68.8% vs. 47.9%, p = 0.004) were higher, while left ventricular ejection fraction (LVEF) was lower (24.4 ± 11.7% vs. 39.7 ± 13.8%, p < 0.001) in non-survivors than in survivors. Multivariable analysis identified NSTEMI presentation and lower LVEF as independent predictors of in-hospital death. In conclusion, NSTEMI and low LVEF were identified as factors associated with higher in-hospital mortality. The identification of even higher-risk subsets and targeted therapeutic strategies may be warranted to improve survival of patients with acute MI and CS.

The prognostic value of speckle tracking echocardiography in patients hospitalized with COVID-19

Abstract Introduction In COVID-19 patients, both preexisting cardiovascular disease as well as cardiac injury resulting from SARS-CoV-2 infection are associated with increased mortality. We hypothesized that novel parameters of myocardial function may be useful in the assessment of in-hospital and long-term prognosis. Aim The aim of study was to determine the prevalence of myocardial dysfunction revealed by speckle tracking echocardiography and its association with in-hospital and one-year mortality. Methods The study group comprised 192 patients hospitalized in the cardiology department due to COVID-19. All patients underwent transthoracic echocardiographic examination with off-line analysis. Using speckle tracking technique, we measured the following parameters: left ventricular global longitudinal strain (GLS), right ventricular global longitudinal strain (RV-GLS), right ventricular free wall strain (RV-FWS) and myocardial work parameters – global work index (GWI), global wasted work (GWW), global constructive work (GCW) and global work efficiency (GWE). The primary outcome was in-hospital and one-year mortality. Results 112 patients (mean age 68±14 years, 76 (68%) male) had adequate image quality to evaluate strain-derived parameters. 27 patients died during hospitalization and 44 patients died within one-year after discharge. In-hospital non-survivors were older, had lower baseline oxygen saturation (SpO2) and had higher NTproBNP (Table 1). In non-survivors speckle-tracking echocardiography revealed significant impairment of left and right ventricular function compared to the group of survivors (Table 1). The independent predictors of in-hospital death were GWE (OR 0.85; 95% CI 0.78–0.93) and SpO2 on admission (OR 0.91; 95% CI 0.86–0.96). Based on the ROC curve analysis, the optimal cut-off points for predicting in-hospital death were identified: GWE ≤87% (sensitivity 63%, specificity 89%) and baseline SpO2 value ≤88% (sensitivity 81%, specificity 71%). The independent predictors of one-year mortality were: age (OR 1.28 [1.13–1.46]), NTproBNP (OR 1.002 [1.001–1.003]), baseline SpO2 (OR 0.71 [0.59–0.86]) and RV-GLS (OR 1.32 [1.12–1.55]). Based on the ROC curve analysis, the cut-off points optimal for predicting death within 12 months after COVID-19 were also identified: baseline SpO2 value ≤88% (sensitivity 69.8%, specificity 77.3%), age &amp;gt;60 years (sensitivity 90%, specificity 43%), NTproBNP &amp;gt;500 pg/ml (sensitivity 95%, specificity 41.8%), RV-GLS &amp;gt;−18.5 (sensitivity 93%, specificity 64.2%). Conclusions Two-dimensional speckle tracking echocardiography is a useful technique to evaluate myocardial function in COVID-19 patients and provides good prognostic value for identifying patients at risk of death during hospitalization and in long term follow-up. Funding Acknowledgement Type of funding sources: None.

Vaccination saves lives: a real-time study of patients with chronic diseases and severe COVID-19 infection.

ObjectivesThis study aims to describe the demographic and clinical profile and ascertain the determinants of outcome among hospitalised COVID-19 adult patients enrolled in the National Clinical Registry for COVID-19 (NCRC).MethodsNCRC is an on-going data collection platform operational in 42 hospitals across India. Data of hospitalized COVID-19 patients enrolled in NCRC between 1st September 2020 to 26th October 2021 were examined.ResultsAnalysis of 29,509 hospitalised, adult COVID-19 patients [mean (SD) age: 51.1 (16.2) year; male: 18752 (63.6%)] showed that 15678 (53.1%) had at least one comorbidity. Among 25715 (87.1%) symptomatic patients, fever was the commonest symptom (72.3%) followed by shortness of breath (48.9%) and dry cough (45.5%). In-hospital mortality was 14.5% (n = 3957). Adjusted odds of dying were significantly higher in age-group ≥60 years, males, with diabetes, chronic kidney diseases, chronic liver disease, malignancy, and tuberculosis, presenting with dyspnea and neurological symptoms. WHO ordinal scale 4 or above at admission carried the highest odds of dying [5.6 (95% CI: 4.6, 7.0)]. Patients receiving one [OR: 0.5 (95% CI: 0.4, 0.7)] or two doses of anti-SARS CoV-2 vaccine [OR: 0.4 (95% CI: 0.3, 0.7)] were protected from in-hospital mortality.ConclusionsWHO ordinal scale at admission is the most important independent predictor for in-hospital death in COVID-19 patients. Anti-SARS-CoV2 vaccination provides significant protection against mortality.

Inferior ST-Segment Elevation Can Predict In-Hospital Mortality in Patients with Anterior Myocardial Infarction Complicated by Ventricular Septal Rupture.

Background Electrocardiogram (ECG) findings in patients with anterior wall myocardial infarction (MI) complicated by ventricular septal rupture (VSR) have rarely been studied. Methods We conducted a single-center retrospective study among patients with anterior MI complicated by VSR over the past ten years. The presence of ST-segment elevation (STE) in inferior leads and new onset of complete right bundle branch block (RBBB) on ECG were studied for the prediction of in-hospital mortality. Results Among the 85 patients enrolled, 45 (52.9%) were male, with a median age of 70 years. Sixty-five patients (76.5%) died in the hospital, and the remaining 20 patients (23.5%) had improved conditions and were discharged. Inferior STE was present in 35 patients (41.2%), including 32 patients in the death group and 3 patients in the survival group (P = 0.005). New-onset RBBB was present in 25 patients (29.4%), with 22 patients in the death group and 3 patients in the survival group (P = 0.103). Multivariate logistic regression showed that inferior STE was an independent predictor of in-hospital death in patients with anterior MI and VSR (OR: 14.488; 95% CI: 1.708-122.887; P = 0.014). Conclusions In patients with anterior MI complicated by VSR, inferior STE was associated with a higher risk of in-hospital mortality.

A Model to Predict in-Hospital Death in Patients with Type B Acute Aortic Dissection: A Single-Center Retrospective Observational Study.

Acute type B aortic dissection (BAAD), as a catastrophic disease, is linked to high morbidity and mortality. The current research is to create a simple risk model to predict in-hospital mortality in BAAD patients based on laboratory results. Patients with BAAD were included from April 1, 2017, to November 30, 2019, in the hospital. Clinical features and laboratory results were collected. Logistic regression analyses and ROC were applied to the evaluation. Hemoglobin (HB) (114.88 ± 28.42 (nonsurvivor) vs. 134.95 ± 17.88 (survivor), P < 0.001) and UREA (10.93 ± 7.02 (nonsurvivor) vs. 7.17 ± 3.77 (survivor), P = 0.001) were significantly different. In multivariate analysis, HB (hazard ratio (HR): 0.124; 95% confidence interval (CI) 0.025 - 0.627; P = 0.012) and UREA (HR: 8.765; 95% CI 2.022 - 37.993; P = 0.004) were independent predictors of in-hospital death. Then, a model with good performance (AUC 0.761 (0.677 - 0.832) was developed. A simple model with good prediction value was developed. With this model, physicians quickly can identify high-risk patients, determine the best treatment strategies, and improve prognosis.