In-hospital Death In Patients Research Articles

Pulmonary artery elastance as a predictor of hospital mortality in heart failure cardiogenic shock.

The initial bundle of cares strongly affects haemodynamics and outcomes in acute decompensated heart failure cardiogenic shock (ADHF-CS). We sought to characterize whether 24h haemodynamic profiling provides superior prognostic information as compared with admission assessment and which haemodynamic parameters best predict in-hospital death. All patients with ADHF-CS and with available admission and 24h invasive haemodynamic assessment from two academic institutions were considered for this study. The primary endpoint was in-hospital death. Regression analyses were run to identify relevant predictors of study outcome. We included 127 ADHF-CS patients [65 (inter-quartile range 52-72) years, 25.2% female]. Overall, in-hospital mortality occurred in 26.8%. Non-survivors were older, with greater CS severity. Among admission variables, age [odds ratio (OR)=1.06; 95% confidence interval (CI): 1.02-1.11; Padj=0.005] and CPIRAP (OR=0.62 for 0.1 increment; 95% CI: 0.39-0.95; Padj=0.034) were found significantly associated with in-hospital death. Among 24h haemodynamic univariate predictors of in-hospital death, pulmonary elastance (PaE) was the strongest (area under the curve of 0.77; 95% CI: 0.68-0.86). PaE (OR=5.98; 95% CI: 2.29-17.48; Padj<0.001), pulmonary artery pulsatility index (PAPi, OR=0.77; 95% CI: 0.62-0.92; Padj=0.013) and age (OR=1.06; 95% CI: 1.02-1.11; Padj=0.010) were independently associated with in-hospital death. Best cut-off for PaE was 0.85mmHg/mL and for PAPi was 2.95; cohort phenotyping based on these PaE and PAPi thresholds further increased in-hospital death risk stratification; patients with 24h high PaE and low PAPi exhibited the highest in-hospital mortality (56.2%). Pulmonary artery elastance has been found to be the most powerful 24h haemodynamic predictor of in-hospital death in patients with ADHF-CS. Age, 24h PaE, and PAPi are independently associated with hospital mortality. PaE captures right ventriclar (RV) afterload mismatch and PAPi provides a metric of RV adaptation, thus their combination generates four distinct haemodynamic phenotypes, enhancing in-hospital death risk stratification.

Association between the neutrophil-to-lymphocyte ratio and in-hospital mortality in patients with chronic kidney disease and coronary artery disease in the intensive care unit

BackgroundThe objective of this study was to investigate the correlation between neutrophil-to-lymphocyte ratios (NLR) and the risk of in-hospital death in patients admitted to the intensive care unit (ICU) with both chronic kidney disease (CKD) and coronary artery disease (CAD).MethodsData from the MIMIC-IV database, which includes a vast collection of more than 50,000 ICU admissions occurring between 2008 and 2019, was utilized in the study and eICU-CRD was conducted for external verification. The Boruta algorithm was employed for feature selection. Univariable and multivariable logistic regression analyses and multivariate restricted cubic spline regression were employed to scrutinize the association between NLR and in-hospital mortality. The receiver operating characteristic (ROC) curves were conducted to estimate the predictive ability of NLR.ResultsAfter carefully applying criteria to include and exclude participants, a total of 2254 patients with CKD and CAD were included in the research. The findings showed a median NLR of 7.3 (4.4, 12.1). The outcomes of multivariable logistic regression demonstrated that NLR significantly elevated the risk of in-hospital mortality (OR 2.122, 95% confidence interval [CI] 1.542–2.921, P < 0.001) after accounting for all relevant factors. Further insights from subgroup analyses unveiled that age and Sequential Organ Failure Assessment (SOFA) scores displayed an interactive effect in the correlation between NLR and in-hospital deaths. The NLR combined with traditional cardiovascular risk factors showed relatively great predictive value for in-hospital mortality (AUC 0.750).ConclusionThe findings of this research indicate that the NLR can be used as an indicator for predicting the likelihood of death during a patient’s stay in the intensive care unit, particularly for individuals with both CAD and CKD. The results indicate that NLR may serve as a valuable tool for assessing and managing risks in this group at high risk. Further investigation is required to authenticate these findings and investigate the mechanisms that underlie the correlation between NLR and mortality in individuals with CAD and CKD.

Analysis of in-hospital deaths in patients with critical limb ischemia necessitating invasive treatments: based on a Japanese nationwide database.

Critical limb ischemia (CLI) is associated with systemic cardiovascular and non-cardiovascular diseases. Treatments primarily targeting limb-related outcomes may not improve overall life prognosis. We aimed to describe in-hospital mortality and the underlying etiologies in Japanese patients with CLI. We analyzed the Diagnosis Procedure Combination (DPC) database from approximately 1200 Japanese acute-care hospitals between April 2018 and March 2020. The definition of patients with CLI was based on the diagnostic codes listed as the most resource-intensive diagnosis and information regarding invasive procedures (endovascular treatment, bypass, or amputation). The DPC database provides information on whether in-hospital death was caused by the most resource-intensive diagnosis. Among 15,228 distinct patients with CLI, we identified 18,970 records, including 5,378 amputations. In-hospital death occurred in 1238 (6.5%) patients. Among them, 811 (65.5%) were due to causes unrelated to CLI. In patients who underwent amputation (n = 5378), causes unrelated to CLI accounted for 70.0% of in-hospital deaths, whereas among patients who did not undergo amputation (n = 13,592), this proportion was 60.1%. When compared to patients who died due to causes related to CLI, the prevalence of male patients was higher (62.6% vs 52.7%, p = 0.001), and amputation was more frequently performed (58.0% vs 47.1%, p < 0.001) in those who died due to causes unrelated to CLI. The majority of in-hospital deaths among patients with CLI necessitating endovascular treatment, bypass, or amputation were attributable to factors unrelated to the primary condition of CLI. Managing systemic cardiovascular and non-cardiovascular diseases beyond the affected limb is crucial to improve the prognosis of these patients.

The prognostic impact of insulin resistance surrogates in patients with acute myocardial infarction with and without type 2 diabetes.

Cardiovascular disease is the major cause of morbidity and mortality, particularly in type 2 diabetes mellitus (T2DM). Novel markers of insulin resistance and progression of atherosclerosis include the triglycerides and glucose index (TyG index), the triglycerides and body mass index (Tyg-BMI) and the metabolic score for insulin resistance (METS-IR). Establishing independent risk factors for in-hospital death and major adverse cardiac and cerebrovascular events (MACCE) in patients with myocardial infarction (MI) remains critical. The aim of the study was to assess the risk of in-hospital death and MACCE within 12months after ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI) in patients with and without T2DM based on TyG index, Tyg-BMI and METS-IR. Retrospective analysis included 1706 patients with STEMI and NSTEMI hospitalized between 2013 and 2021. We analyzed prognostic value of TyG index, Tyg-BMI and METS-IR for in-hospital death and MACCE as its components (death from any cause, MI, stroke, revascularization) within 12months after STEMI or NSTEMI in patients with and without T2DM. Of 1706 patients, 58 in-hospital deaths were reported (29 patients [4.3%] in the group with T2DM and 29 patients [2.8%] in the group without T2DM; p = 0.1). MACCE occurred in 18.9% of the total study population (25.8% in the group with T2DM and 14.4% in the group without T2DM; p < 0.001). TyG index, Tyg-BMI and METS-IR were significantly higher in the group of patients with T2DM compared to those without T2DM (p < 0.001). Long-term MACCE were more prevalent in patients with T2DM (p < 0.001). The area under the ROC curve (AUC-ROC) for the prediction of in-hospital death and the TyG index was 0.69 (p < 0.001). The ROC curve for predicting in-hospital death based on METS-IR was 0.682 (p < 0.001). The AUC-ROC values for MACCE prediction based on the TyG index and METS-IR were 0.582 (p < 0.001) and 0.57 (p < 0.001), respectively. TyG index was an independent risk factor for in-hospital death in patients with STEMI or NSTEMI. TyG index, TyG-BMI and METS-IR were not independent risk factors for MACCE at 12month follow-up. TyG index and METS-IR have low predictive value in predicting MACCE within 12months after STEMI and NSTEMI.

The course of the pulmonary embolism in patients with oncological diseases

The aim – to identify factors associated with in-hospital mortality among patients with acute pulmonary embolism (PE) and oncological diseases. Materials and methods. A retrospective analysis of 647 medical records of patients with acute PE treated at one center from January 1, 2018, to January 1, 2020, was conducted. Among them, 115 patients had verified oncological diseases. PE diagnosis in all patients was confirmed by computed tomography and/or autopsy results. Alongside laboratory studies, patients underwent ultrasound examination. Results and discussion. A comparative assessment of clinical and anamnestic parameters in patients with PE, depending on the presence of oncological diseases, was conducted. The analysis revealed that patients with PE associated with oncological process were on average 5 years older, with higher proportion of elderly patients, and less frequently had anamnestic episodes of venous thrombosis (13.9 % vs. 24.6 %), but predictably more often had additional risk factors, such as blood transfusions, central venous catheters, and laparoscopic interventions. According to the results of the multifactorial regression analysis, factors independently influencing the risk of in-hospital death in patients with acute PE and oncological diseases included decrease of blood pressure, saturation, an increase of the pulmonary artery pressure, leukocytosis and presence of atrial fibrillation. The ROC analysis results allowed determining threshold values of parametric indicators associated with the risk of death during the hospital period in a patient with PE and oncological diseases, i.e. blood pressure ≤ 105 mm Hg, SpO2 ≤ 87 %, pulmonary artery pressure &gt; 48 mm Hg, and leukocyte count &gt; 9.8  109/l. Conclusion. The risk factors of in-hospital death in patients with PE associated with oncological disease are atrial fibrillation, increased pulmonary artery pressure, leukocyte count, low blood pressure and saturation.

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.

A new nomogram to predict in-hospital mortality in patients with acute decompensated chronic heart failure and diabetes after 48 Hours of Intensive Care Unit

BackgroundThe study set out to develop an accurate and clinically valuable prognostic nomogram to assess the risk of in-hospital death in patients with acute decompensated chronic heart failure (ADCHF) and diabetes.MethodsWe extracted clinical data of patients diagnosed with ADCHF and diabetes from the Medical Information Mart for Intensive Care III database. Risk variables were selected utilizing least absolute shrinkage and selection operator regression analysis, and were included in multivariate logistic regression and presented in nomogram. bootstrap was used for internal validation. The discriminative power and predictive accuracy of the nomogram were estimated using the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA).ResultsAmong 867 patients with ADCHF and diabetes, In-hospital death occurred in 81 (9.3%) patients. Age, heart rate, systolic blood pressure, red blood cell distribution width, shock, β-blockers, angiotensin converting enzyme inhibitors or angiotensin receptor blockers, assisted ventilation, and blood urea nitrogen were brought into the nomogram model. The calibration curves suggested that the nomogram was well calibrated. The AUC of the nomogram was 0.873 (95% CI: 0.834–0.911), which was higher that of the Simplified Acute Physiology Score II [0.761 (95% CI: 0.711–0.810)] and sequential organ failure assessment score [0.699 (95% CI: 0.642–0.756)], and Guidelines-Heart Failure score [0.782 (95% CI: 0.731–0.835)], indicating that the nomogram had better ability to predict in-hospital mortality. In addition, the internally validated C-index was 0.857 (95% CI: 0.825–0.891), which again verified the validity of this model.ConclusionsThis study constructed a simple and accurate nomogram for predicting in-hospital mortality in patients with ADCHF and diabetes, especially in those who admitted to the intensive care unit for more than 48 hours, which contributed clinicians to assess the risk and individualize the treatment of patients, thereby reducing in-hospital mortality.

[Risk analysis of gastrointestinal bleeding in patients with acute ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention].

Objective: Of all spontaneous bleeding complications in patients with acute ST-elevation myocardial infarction (STEMI), upper gastrointestinal bleeding (UGIB) is the most common and of specific interest, because it can be prevented by several prophylactic measures. The purpose of this study was to investigate the in-hospital incidence, associated outcomes, and predictors of UGIB after STEMI. Methods: In this retrospective study, we analyzed the records of 2 791 patients with acute STEMI admitted to Beijing Anzhen Hospital Affiliated to Capital Medical University between January 2018 and January 2022. The patients were divided into the UGIB group (n=61) and non-UGIB group (n=2 730) according to the presence or absence of upper gastrointestinal hemorrhage, respectively. Baseline clinical conditions, coronary lesions, in-hospital deaths, and in-hospital adverse events were compared between the two groups. Logistic regression analysis was also performed for risk factors that could lead to UGIB. Results: The in-hospital incidence of UGIB after STEMI was 2.2% (61/2 791). Hospital stay was significantly longer in the UGIB group [8(6, 12) days vs. 5 (4, 7) days, Z=3.28, P<0.001] and in-hospital mortality was significantly higher in the UGIB group than in the non-UGIB group (9.8% vs. 0.8%, χ2=0.63, P=0.001). Patients with UGIB were significantly older than those without UGIB (63±11 years vs. 58±11 years, t=-3.75, P<0.001). The serum creatinine level of UGIB patients was significantly higher than that of non-UGIB patients [(80(62, 98) mmol/L vs. 73(64, 84) mmol/L, Z=1.68, P=0.007], the red blood cell count of UGIB patients was significantly lower than that of non-UGIB patients [4.1(3.8, 4.6)×1012/L vs. 4.6(4.2, 4.9)×1012/L, Z=2.61,P<0.001], and the hemoglobin concentration of UGIB patients was significantly lower than that of non-UGIB patients [129(109, 141) g/L vs. 141(130, 152) g/L, Z=2.52,P<0.001]. Brain natriuretic peptide levels were significantly higher in UGIB patients than in non-UGIB patients [331(165, 644) ng/L vs. 181(89, 333) ng/L, Z=2.42,P<0.001]. Logistic regression analysis showed that age (OR=1.045, 95%CI 1.009-1.082, P=0.013); hemoglobin (OR=1.594, 95%CI 1.150-2.210, P=0.005); hematocrit (OR=0.181, 95%CI 0.060-0.546, P=0.002); and mean hemoglobin concentration (OR=0.845, 95%CI 0.752-0.951, P=0.005) were independent risk factors for UGIB in patients with STEMI. Logistic regression analysis of risk factors for in-hospital death revealed that concurrent UGIB was an independent risk factor for in-hospital death in patients with STEMI (OR=2.954, 95%CI 0.635-13.751, P=0.024). Conclusions: The incidence of in-hospital UGIB in STEMI patients was 2.2%, and the in-hospital mortality rate of STEMI complicated with UGIB increased to 9.8%. Concurrent UGIB was an independent risk factor for in-hospital death in patients with STEMI. The most important predictors of in-hospital UGIB in patients with STEMI were age, hemoglobin, hematocrit, and mean hemoglobin concentration.

Development and validation of a nomogram for predicting in-hospital mortality in patients with nonhip femoral fractures

BackgroundThe incidence of nonhip femoral fractures is gradually increasing, but few studies have explored the risk factors for in-hospital death in patients with nonhip femoral fractures in the ICU or developed mortality prediction models. Therefore, we chose to study this specific patient group, hoping to help clinicians improve the prognosis of patients.MethodsThis is a retrospective study based on the data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Least absolute shrinkage and selection operator (LASSO) regression was used to screen risk factors. The receiver operating characteristic (ROC) curve was drawn, and the areas under the curve (AUC), net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated to evaluate the discrimination of the model. The consistency between the actual probability and the predicted probability was assessed by the calibration curve and Hosmer–Lemeshow goodness of fit test (HL test). Decision curve analysis (DCA) was performed, and the nomogram was compared with the scoring system commonly used in clinical practice to evaluate the clinical net benefit.ResultsThe LASSO regression analysis showed that heart rate, temperature, red blood cell distribution width, blood urea nitrogen, Glasgow Coma Scale (GCS), Simplified Acute Physiology Score II (SAPSII), Charlson comorbidity index and cerebrovascular disease were independent risk factors for in-hospital death in patients with nonhip femoral fractures. The AUC, IDI and NRI of our model in the training set and validation set were better than those of the GCS and SAPSII scoring systems. The calibration curve and HL test results showed that our model prediction results were in good agreement with the actual results (P = 0.833 for the HL test of the training set and P = 0.767 for the HL test of the validation set). DCA showed that our model had a better clinical net benefit than the GCS and SAPSII scoring systems.ConclusionIn this study, the independent risk factors for in-hospital death in patients with nonhip femoral fractures were determined, and a prediction model was constructed. The results of this study may help to improve the clinical prognosis of patients with nonhip femoral fractures.

Abstract 12495: Modes of In-Hospital Death in Patients With Cardiogenic Shock (CS): An Analysis From the Critical Care Cardiology Trials Network (CCCTN)

Introduction: Mortality from cardiogenic shock (CS), a syndrome of multi-organ system dysfunction, remains high. However, data regarding how patients with CS expire are limited and may have implications for designing clinical trials, identifying therapeutic strategies, and improving outcomes. Aim: We sought to describe primary modes of in-hospital death among patients admitted with CS to cardiac intensive care units (CICUs). Methods: CCCTN is a multi-national research network of advanced CICUs coordinated by the TIMI Study Group (Boston, MA). Using standardized definitions, site investigators classified the direct mode of in-hospital death for patients with CS. Categories included 8 possible modes of cardiovascular (CV) and non-CV death ( Fig ). Subgroups defined by cardiac arrest prior to CICU admission and use of mechanical circulatory support (MCS) were explored. Results: Among 1098 CS cases captured from 10/2021-09/2022, 358 (32.6%) died during the index hospitalization with a median time to death of 4.3 (Q1-Q3, 1.1-10.9) days from CICU admission. Among the deaths, 50%, 41%, and 9% occurred in the context of HF-CS, AMI-CS, and secondary CS, respectively. Overall, mode of death was CV in 82% of cases ( Fig A ). Refractory CS was the dominant mode of death (66%), followed by arrhythmia (13%), anoxic brain injury (6%), and respiratory failure (5%; Fig A ). Compared to patients without preceding cardiac arrest, those with cardiac arrest prior to CICU admission were more likely to die from anoxic brain injury (10% vs. 1%; p&lt;0.01; Fig B ). Compared to patients managed without MCS, those with MCS were more likely to die from refractory shock (p&lt;0.01), either cardiogenic (74% vs. 61%) or non-cardiogenic (6% vs. 3%; Fig C ). Conclusions: Most in-hospital deaths in patients with CS are related to direct CV causes, particularly refractory CS. However, there is potentially important heterogeneity in mode of death across subgroups defined by preceding cardiac arrest and use of MCS.

Prediction models for in-hospital deaths of patients with COVID-19 using electronic healthcare data

Objective Many models for predicting various disease prognoses have achieved high performance without laboratory test results. However, whether laboratory test results can improve performance remains unclear. This study aimed to investigate whether laboratory test results improve the model performance for coronavirus disease 2019 (COVID-19). Methods Prediction models were developed using data from the electronic healthcare record database in Japan. Patients aged ≥18 years hospitalized for COVID-19 after February 11, 2020, were included. Their age, sex, comorbidities, laboratory test results, and number of days from February 11, 2020, were collected. We developed a logistic regression, XGBOOST, random forest, and neural network analysis and compared the performance with and without laboratory test results. The performance of predicting in-hospital death was evaluated using the area under the curve (AUC). Results Data from 8,288 hospitalized patients (females, 46.5%) were analyzed. The median patient age was 71 years. A total of 6,630 patients were included in the training dataset, and 312 (4.7%) died. In the logistic regression model, the area under the curve was 0.88 (95% confidence interval [CI] = 0.83–0.93) and 0.75 (95% CI = 0.68–0.81) with and without laboratory test results, respectively. The performance was not fundamentally different between the model types, and the laboratory test results improved the performance in all cases. The variables useful for prediction were blood urea nitrogen, albumin, and lactate dehydrogenase. Conclusions Laboratory test results, such as blood urea nitrogen, albumin, and lactate dehydrogenase levels, along with background information, helped estimate the prognosis of patients hospitalized for COVID-19.

A dynamic online nomogram for predicting death in hospital after aneurysmal subarachnoid hemorrhage

BackgroundThis study aimed to validate the efficacy the multiplication of neutrophils and monocytes (MNM) and a novel dynamic nomogram for predicting in-hospital death in patients with aneurysmal subarachnoid hemorrhage (aSAH).MethodsRetrospective study was done on 986 patients with endovascular coiling for aSAH. Independent risk factors associated with in-hospital death were identified using both univariate and multivariate logistic regression analysis. In the development cohort, a dynamic nomogram of in-hospital deaths was introduced and made available online as a straightforward calculator. To predict the in-hospital death from the external validation cohort by nomogram, calibration analysis, decision curve analysis, and receiver operating characteristic analysis were carried out.Results72/687 patients (10.5%) in the development cohort and 31/299 patients (10.4%) in the validation cohort died. MNM was linked to in-hospital death in univariate and multivariate regression studies. In the development cohort, a unique nomogram demonstrated a high prediction ability for in-hospital death. According to the calibration curves, the nomogram has a reliable degree of consistency and calibration. With threshold probabilities between 10% and 90%, the nomogram’s net benefit was superior to the basic model. The MNM and nomogram also exhibited good predictive values for in-hospital death in the validation cohort.ConclusionsMNM is a novel predictor of in-hospital mortality in patients with aSAH. For aSAH patients, a dynamic nomogram is a useful technique for predicting in-hospital death.

Development and validation of a prediction model for in-hospital death in patients with heart failure and atrial fibrillation

BackgroundTo develop a prediction model for in-hospital mortality of patients with heart failure (HF) and atrial fibrillation (AF).MethodsThis cohort study extracted the data of 10,236 patients with HF and AF upon intensive care unit (ICU) from the Medical Information Mart for Intensive Care (MIMIC). The subjects from MIMIC-IV were divided into the training set to construct the prediction model, and the testing set to verify the performance of the model. The samples from MIMIC-III database and eICU-CRD were included as the internal and external validation set to further validate the predictive value of the model, respectively. Univariate and multivariable Logistic regression analyses were used to explore predictors for in-hospital death in patients with HF and AF. The receiver operator characteristic (ROC), calibration curves and the decision curve analysis (DCA) curves were plotted to evaluate the predictive values of the model.ResultsThe mean survival time of participants from MIMIC-III was 11.29 ± 10.05 days and the mean survival time of participants from MIMIC-IV was 10.56 ± 9.19 days. Simplified acute physiology score (SAPSII), red blood cell distribution width (RDW), beta-blocker, race, respiratory rate, urine output, coronary artery bypass grafting (CABG), Charlson comorbidity index, renal replacement therapies (RRT), antiarrhythmic, age, and anticoagulation were predictors finally included in the prediction model. The AUC of our prediction model was 0.810 (95%CI: 0.791–0.828) in the training set, 0.757 (95%CI: 0.729–0.786) in the testing set, 0.792 (95%CI: 0.774–0.810) in the internal validation set, and 0.724 (95%CI: 0.687–0.762) in the external validation set. The calibration curves of revealed that the predictive probabilities of our model for the in-hospital death in patients with HF and AF deviated slightly from the ideal model. The DCA curves revealed that the use of our prediction model increased the net benefit than use no model.ConclusionThe prediction model had good discriminative ability, and might provide a tool to timely identify patients with HF complicated with AF who were at high risk of in-hospital mortality.

Analysis of Risk Factors for in-hospital Death in Elderly Patients with TEXAS Stage 3 and 4 Diabetic Foot Ulcers after Tibial Transverse Translation: A Case-Control Study.

Chinese physicians developed the Tibial Transverse Transport (TTT) technique to treat diabetic foot ulcers with more than 90% effective rate. But this method still could not avoid the in-hospital death of patients. This study adopted a case-control study to explore the risk factors of in-hospital death in elderly patients with chronic ischemic diabetic foot after receiving TTT treatment. A total of 54 patients were included in the study from January 1, 2017 to April 30, 2021, by being paired with the cases in case group with their demographic data and results of blood routine, liver and kidney function. There were nine patients in case group with six male and three male. Forty-five patients were selected in control group according to gender and diabetes type with 30 male and 15 female. Single factor logics regression analysis was used to explore the risk factors and odd ratios (OR) of in-hospital death in patients. The nomogram and decision curve analysis (DCA) had been done by R Studio software. The study found that age, course of diabetic foot, small dense low-density Lipoprotein (smLDL), homocysteine (Hcy), superoxide dismutase (SOD), and prealbumin (PA) were risk factors for in-hospital death of patients. The smLDL had the highest risk. The nomogram showed that PA accounted for the largest proportion in the death risk factors. The results of DCA proved that above six risk factors were the risk factors for patients with TEXAS Stage 3 and 4 diabetic foot ulcers. In the future diagnosis and TTT treatment for diabetic foot ulcers, doctors need to pay close attention to age, course of diabetic foot, smLDL, Hcy, SOD, and PA.

213.1: Estimating the donor potential in Germany: Systematic evaluation of all in-hospital deaths of patients with primary or secondary brain damage.

213.1: Estimating the donor potential in Germany: Systematic evaluation of all in-hospital deaths of patients with primary or secondary brain damage.

Machine learning-based prediction of in-hospital death for patients with takotsubo syndrome: The InterTAK-ML model.

Takotsubo syndrome (TTS) is associated with a substantial rate of adverse events. We sought to design a machine learning (ML)-based model to predict the risk of in-hospital death and to perform a clustering of TTS patients to identify different risk profiles. A ridge logistic regression-based ML model for predicting in-hospital death was developed on 3482 TTS patients from the International Takotsubo (InterTAK) Registry, randomly split in a train and an internal validation cohort (75% and 25% of the sample size, respectively) and evaluated in an external validation cohort (1037 patients). Thirty-one clinically relevant variables were included in the prediction model. Model performance represented the primary endpoint and was assessed according to area under the curve (AUC), sensitivity and specificity. As secondary endpoint, a K-medoids clustering algorithm was designed to stratify patients into phenotypic groups based on the 10 most relevant features emerging from the main model. The overall incidence of in-hospital death was 5.2%. The InterTAK-ML model showed an AUC of 0.89 (0.85-0.92), a sensitivity of 0.85 (0.78-0.95) and a specificity of 0.76 (0.74-0.79) in the internal validation cohort and an AUC of 0.82 (0.73-0.91), a sensitivity of 0.74 (0.61-0.87) and a specificity of 0.79 (0.77-0.81) in the external cohort for in-hospital death prediction. By exploiting the 10 variables showing the highest feature importance, TTS patients were clustered into six groups associated with different risks of in-hospital death (28.8% vs. 15.5% vs. 5.4% vs. 1.0.8% vs. 0.5%) which were consistent also in the external cohort. A ML-based approach for the identification of TTS patients at risk of adverse short-term prognosis is feasible and effective. The InterTAK-ML model showed unprecedented discriminative capability for the prediction of in-hospital death.

Influence of Impaired Hydration Status on Postoperative in-Hospital Death in Patients with Acute Type A Aortic Dissection.

The hydration state of the body is getting more and more attention from researchers. The purpose of this study is to investigate the relationship between impaired hydration status and postoperative hospitalization death in patients with A AAD. From January 2019 to October 2021, the clinical data of 299 patients undergoing A AAD surgery were retrospectively analyzed. Patients were divided into normal hydration group, imminent dehydration group and current dehydration group according to the dehydration standard at admission. Univariate and multivariate logistic regression analysis were used to determine the independent risk factors for in-hospital death of patients with A AAD. Postoperative in-hospital death in A AAD patients was significantly more common in the imminent and current dehydration groups (>295mmol/L) (26.7% vs 11.9%; P=0.001). The length of ICU stay was significantly longer in the impending and current dehydration groups (P<0.05). After controlling for other factors by multivariate logistic regression analysis, the results showed that the group of impending and current dehydration (>295) (OR=3.61, 95% confidence interval [CI]: 1.61-8.06; P=0.002), CRRT (OR=10.55, 95%[CI]: 3.59-31.01; P<0.001), lactic acid (OR=1.25, 95%[CI]: 1.13-1.38; P<0.001), CAD (OR=5.27, 95%[CI]: 1.12-24.80; P=0.035) was an independent risk factor for in-hospital death in A AAD patients. Albumin (OR=0.92, 95%[CI]: 0.85-0.99; P=0.040) is a protective factor. The presence of high serum osmotic pressure on admission of A AAD patients can independently predict postoperative death, and the impaired body hydration status should be paid attention to.

Predictors of In-Hospital Death in Patients With Acute Myocardial Infarction.

Factors such as age, vital signs, renal function, Killip class, cardiac arrest, elevated cardiac biomarker levels, and ST deviation predict survival in patients with acute myocardial infarction (AMI). However, the existing risk assessment tools lack comprehensive consideration of catheter-related factors, and short-term prognostic predictors are unknown. This study aimed to clarify in-hospital prognostic predictors in hospitalized patients with AMI. Five hundred and thirty-six patients who underwent percutaneous coronary intervention (PCI) for AMI were divided into non-survivor (n = 36) and survivor (n = 500) groups. Coronary risk factors, laboratory findings, angiographic findings, and clinical courses were compared between the two groups. Multiple logistic regression was used to analyze in-hospital death in pre- and post-PCI phases. In the pre-PCI phase, multiple logistic regression analysis revealed several predictors of in-hospital death, including systolic blood pressure [odds ratio (OR) = 0.985, p = 0.023)], Killip class ≥2 (OR = 14.051, p <0.001), and chronic kidney disease (OR = 4.859, p = 0.040). In the post-PCI phase, multiple logistic regression analysis revealed additional predictors of in-hospital death, including Killip class ≥2 (OR = 5.982, p = 0.039), presence of lesions in the left main trunk (OR = 51.381, p = 0.044), utilization of intra-aortic balloon pumps and percutaneous cardiopulmonary support (OR = 6.141, p = 0.016), and presence of multi-vessel disease (OR = 6.323, p = 0.022). Predictors of in-hospital death in AMI extend beyond conventional risk factors to include culprit lesions, mechanical support, and multi-vessel disease that manifest post-PCI.

[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.

Postoperative Lactate Predicts In-Hospital Death in Patients with Acute Type A Aortic Dissection.

Several studies have found that lactate correlates with surgical outcomes in patients with heart disease. However, the prognostic value of postoperative lactate in patients with acute type A aortic dissection (AAAD) remains unclear. This study aimed to investigate the relationship between postoperative lactate and in-hospital mortality in patients with AAAD. Patients who underwent AAAD surgery at Fujian Cardiac Medical Center from February 2020 to January 2022 were enrolled in this retrospective study. Correlations between in-hospital mortality and various parameters, including lactate, were investigated. A total of 357 patients were included in this study, 58 of which died. Multivariate logistic regression analysis revealed that body mass index (BMI) (odds ratio [OR] = 1.099, 95% confidence interval [CI]: 1.017-1.188, P = 0.017), cardiopulmonary bypass (CPB) time (OR = 1.005; 95% CI: 1.000-1.010, P = 0.039), and lactate (OR = 1.291, 95% CI: 1.182-1.409, P < 0.001) were independent risk factors for in-hospital mortality in AAAD patients. Receiver operating characteristic (ROC) curve analysis showed that lactate had a moderate power for in-hospital mortality (area under the curve [AUC] = 0.729, 95% CI: 0.647-0.810, P < 0.001). Furthermore, the combination of lactate, BMI, and CPB time showed better performance (AUC = 0.780; 95% CI: 0.706-0.854, P < 0.001) in predicting in-hospital mortality than in using these variables independently. Among patients undergoing AAAD surgery, postoperative lactate was significantly associated with in-hospital mortality. Lactate can be used as a potential predictor of in-hospital mortality. The combination of lactate, BMI, and CPB time showed better performance in predicting in-hospital mortality than using single one.