In-hospital Death In Patients Research Articles

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.

Association of Blood Pressure Variability With Death and Discharge Destination Among Critically Ill Patients With and Without Stroke.

It is unclear whether blood pressure variability's (BPV) association with worse outcomes is unique to patients with stroke or a risk factor among all critically ill patients. We (1) determined whether BPV differed between patients with stroke and nonstroke patients, (2) examined BPV's associations with in-hospital death and favorable discharge destination in patients with stroke and nonstroke patients, and (3) assessed how minimum mean arterial pressure (MAP)-a correlate of illness severity and cerebral perfusion-affects these associations. This is a retrospective analysis of adult intensive care unit patients hospitalized between 2001 and 2012 from the Medical Information Mart for Intensive Care III database. Confounder-adjusted logistic regressions determined associations between BPV, measured as SD and average real variability (ARV), and (1) in-hospital death and (2) favorable discharge, with testing of minimum MAP for effect modification. BPV was higher in patients with stroke (N = 2,248) compared with nonstroke patients (N = 9,085) (SD mean difference 2.3, 95% CI 2.1-2.6, p < 0.01). After adjusting for minimum tertile of MAP and other confounders, higher SD remained significantly associated (p < 0.05) with higher odds of in-hospital death for patients with acute ischemic strokes (AISs, odds ratio [OR] 2.7, 95% CI 1.5-4.8), intracerebral hemorrhage (ICH, OR 2.6, 95% CI 1.6-4.3), subarachnoid hemorrhage (SAH, OR 3.4, 95% CI 1.2-9.3), and pneumonia (OR 1.9, 95% CI 1.1-3.3) and lower odds of favorable discharge destination in patients with ischemic stroke (OR 0.3, 95% CI 0.2-0.6) and ICH (OR 0.4, 95% CI 0.3-0.6). No interaction was found between minimum MAP tertile with SD (p > 0.05). Higher ARV was not significantly associated with increased risk of death in any condition when adjusting for illness severity but portended worse discharge destination in those with AIS (OR favorable discharge 0.4, 95% CI 0.3-0.7), ICH (OR favorable discharge 0.5, 95% CI 0.3-0.7), sepsis (OR favorable discharge 0.8, 95% CI 0.6-1.0), and pneumonia (OR favorable discharge 0.5, 95% CI 0.4-0.8). BPV is higher and generally associated with worse outcomes among patients with stroke compared with nonstroke patients. BPV in patients with AIS and patients with ICH may be a marker of central autonomic network injury, although clinician-driven blood pressure goals likely contribute to the association between BPV and outcomes.

[Construction of a predictive model for in-hospital mortality of sepsis patients in intensive care unit based on machine learning].

To analyze the risk factors of in-hospital death in patients with sepsis in the intensive care unit (ICU) based on machine learning, and to construct a predictive model, and to explore the predictive value of the predictive model. The clinical data of patients with sepsis who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to April 2021 were retrospectively analyzed,including demographic information, vital signs, complications, laboratory examination indicators, diagnosis, treatment, etc. Patients were divided into death group and survival group according to whether in-hospital death occurred. The cases in the dataset (70%) were randomly selected as the training set for building the model, and the remaining 30% of the cases were used as the validation set. Based on seven machine learning models including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), a prediction model for in-hospital mortality of sepsis patients was constructed. The receiver operator characteristic curve (ROC curve), calibration curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the seven models from the aspects of identification, calibration and clinical application, respectively. In addition, the predictive model based on machine learning was compared with the sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation II (APACHE II) models. A total of 741 patients with sepsis were included, of which 390 were discharged after improvement, 351 died in hospital, and the in-hospital mortality was 47.4%. There were significant differences in gender, age, APACHE II score, SOFA score, Glasgow coma score (GCS), heart rate, oxygen index (PaO2/FiO2), mechanical ventilation ratio, mechanical ventilation time, proportion of norepinephrine (NE) used, maximum NE, lactic acid (Lac), activated partial thromboplastin time (APTT), albumin (ALB), serum creatinine (SCr), blood urea nitrogen (BUN), blood uric acid (BUA), pH value, base excess (BE), and K+ between the death group and the survival group. ROC curve analysis showed that the area under the curve (AUC) of RF, XGBoost, LR, ANN, DT, SVM, KNN models, SOFA score, and APACHE II score for predicting in-hospital mortality of sepsis patients were 0.871, 0.846, 0.751, 0.747, 0.677, 0.657, 0.555, 0.749 and 0.760, respectively. Among all the models, the RF model had the highest precision (0.750), accuracy (0.785), recall (0.773), and F1 score (0.761), and best discrimination. The calibration curve showed that the RF model performed best among the seven machine learning models. DCA curve showed that the RF model exhibited greater net benefit as well as threshold probability compared to other models, indicating that the RF model was the best model with good clinical utility. The machine learning model can be used as a reliable tool for predicting in-hospital mortality in sepsis patients. RF models has the best predictive performance, which is helpful for clinicians to identify high-risk patients and implement early intervention to reduce mortality.

Triglyceride glucose index is a significant predictor of severe disturbance of consciousness and all-cause mortality in critical cerebrovascular disease patients

ObjectiveThe association of the triglyceride-glucose (TyG) index with severe consciousness disturbance and in-hospital mortality in patients with cerebrovascular disease in the intensive care unit (ICU) is unclear. This study aimed to investigate the TyG index’s predictive ability on the severity of impaired consciousness and in-hospital mortality in patients with cerebrovascular disease in the ICU.MethodPatients diagnosed with non-traumatic cerebral hemorrhage and cerebral infarction were extracted from the MIMIC-IV database and analyzed as two cohorts. The association between the TyG index and the severity of patients’ impairment of consciousness and in-hospital mortality was analyzed using logistic regression models. Using restricted cubic spline curves, we analyzed potential nonlinear relationships between TyG indices and outcome indicators. receiver operating characteristic (ROC) curves were utilized to evaluate the predictive ability of the TyG index for outcome indicators.ResultThe study’s last two cohorts comprised 537 patients with traumatic cerebral hemorrhage and 872 patients with cerebral infarction. TyG index was a significant predictor of the severity of impaired consciousness and in-hospital mortality in patients with cerebrovascular disease, as determined by logistic regression. The risk of severe consciousness impairment and in-hospital mortality increased roughly linearly with increasing TyG index.ConclusionThe TyG index was found to be a significant predictor for severe impairment of consciousness and in-hospital death in patients with cerebrovascular disease in the ICU, and it provides some predictive value for the severity of consciousness disturbances and in-hospital mortality in cerebrovascular disease patients.

Elevated Serum Urea-to-Creatinine Ratio and In-Hospital Death in Patients with Hyponatremia Hospitalized for COVID-19.

Hyponatremia is associated with adverse outcomes in hospitalized patients. An elevated value of the serum urea-to-creatinine ratio (UCR) has been proposed as a proxy of hypovolemia. The aim of this study was to investigate the relationship between the UCR and in-hospital death in patients hospitalized with COVID-19 and hyponatremia. We studied 258 patients admitted for COVID-19 between January 2020 and May 2021 with serum sodium at < 135 mmol/L. The primary end-point was all-cause mortality. A 5-unit increase in the serum UCR during hospital stays was associated with an 8% increase in the hazard of all-cause death (HR = 1.08, 95% CI: 1.03-1.14, p = 0.001) after adjusting for potential confounders. In patients with a UCR > 40 at baseline, a > 10 mmol/L increase in serum sodium values within the first week of hospitalization was associated with higher odds of in-hospital death (OR = 2.93, 95% CI: 1.03-8.36, p = 0.044) compared to patients who experienced a < 10 mmol/L change. This was not observed in patients with a UCR < 40. Hypovolemia developing during hospital stays in COVID-19 patients with hyponatremia detected at hospital admission bears an adverse prognostic impact. Moreover, in hypovolemic patients, a > 10 mmol/L increase in serum sodium within the first week of hospital stays may further worsen the in-hospital prognosis.

Development and validation of a prediction model based on comorbidities to estimate the risk of in-hospital death in patients with COVID-19.

Most existing prognostic models of COVID-19 require imaging manifestations and laboratory results as predictors, which are only available in the post-hospitalization period. Therefore, we aimed to develop and validate a prognostic model to assess the in-hospital death risk in COVID-19 patients using routinely available predictors at hospital admission. We conducted a retrospective cohort study of patients with COVID-19 using the Healthcare Cost and Utilization Project State Inpatient Database in 2020. Patients hospitalized in Eastern United States (Florida, Michigan, Kentucky, and Maryland) were included in the training set, and those hospitalized in Western United States (Nevada) were included in the validation set. Discrimination, calibration, and clinical utility were evaluated to assess the model's performance. A total of 17 954 in-hospital deaths occurred in the training set (n = 168 137), and 1,352 in-hospital deaths occurred in the validation set (n = 12 577). The final prediction model included 15 variables readily available at hospital admission, including age, sex, and 13 comorbidities. This prediction model showed moderate discrimination with an area under the curve (AUC) of 0.726 (95% confidence interval [CI]: 0.722-0.729) and good calibration (Brier score = 0.090, slope = 1, intercept = 0) in the training set; a similar predictive ability was observed in the validation set. An easy-to-use prognostic model based on predictors readily available at hospital admission was developed and validated for the early identification of COVID-19 patients with a high risk of in-hospital death. This model can be a clinical decision-support tool to triage patients and optimize resource allocation.

Prognostic Value of Serum Cholinesterase Levels for In-Hospital Mortality among Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease

Cholinesterase (ChE) is associated with the pathogenesis of chronic obstructive pulmonary disease (COPD), including chronic airway inflammation and oxidation/antioxidant imbalance. However, the relationship between serum ChE levels and survival outcomes of patients hospitalized with acute exacerbations of COPD (AECOPD) is unknown. In this retrospective single-center study, we investigated the ability of the serum ChE level to predict in-hospital death in patients hospitalized with AECOPD. The clinicopathological data, including serum ChE levels as well as clinical and biochemical indicators were extracted for 477 patients from the hospital records and analyzed. Our results demonstrated that AECOPD patients with lower serum ChE levels were associated with increased mortality, frequent hospitalization due to acute exacerbations (AE) in the past year, and longer hospital stay. The optimal cutoff value for the serum ChE level was 4323 U/L. The area under the ROC curve (AUC) values for predicting in-hospital mortality based on the serum ChE level was 0.79 (95% confidence interval (CI), 0.72–0.85). Multivariate logistic regression analysis demonstrated that serum ChE level ≤ 4323 U/L (odds ratio (OR) 9.09, 95% CI 3.43–28.3, p < 0.001), age-adjusted Charlson comorbidity index (aCCI), and the number of hospitalizations due to AE in the past year were independent risk factors for predicting the in-hospital mortality of AECOPD patients. In conclusion, our study demonstrated that low serum ChE levels were associated with significantly higher in-hospital mortality rates of patients hospitalized with AECOPD. Therefore, serum ChE level is a promising prognostic predictor of hospitalized AECOPD patients.