In-hospital Mortality In Elderly Patients Research Articles

#2561 Association of acute kidney injury stage with in-hospital mortality in elderly patients with hip fractures

Abstract Background and Aims Elderly individuals often experience hip fractures and acute kidney injury (AKI), both of which are associated with higher rates of morbidity and mortality. This study aimed to evaluate whether the severity of AKI is associated with higher mortality rates in elderly patients with hip fractures. Method Patients aged 65 years and older, who underwent surgery for hip fracture between October 2015 and October 2020, were retrospectively evaluated. Those with known chronic kidney disease stages 4, 5, or end-stage kidney disease were excluded. AKI was defined and categorized into stages 1, 2, and 3 according to the Kidney Disease Improving Global Outcomes (KDIGO) guidelines. Results In the study, 780 elderly patients with hip fractures were included, with a median age of 82.0 years (interquartile range 77.0–87.0) and 22.4% of them being men. Of these, 172 patients (22.1%) experienced AKI, with 125 (72.7%), 26 (15.1%), and 21 (12.2%) of these patients categorized as having stage 1, 2, and 3 AKI, respectively. Higher AKI stages were independently associated with increased in-hospital mortality compared to patients without AKI (odds ratio [OR] 1.07, 95% confidence interval [CI] 0.18–6.55, P=0.940 for AKI stage 1; OR 14.09, 95% CI 1.47–135.52, P=0.022 for AKI stage 2; and OR 413.79, 95% CI 45.84–3735.28, P<0.001 for AKI stage 3). Furthermore, compared to patients without AKI, those with AKI stage 3 had a significantly increased risk of mortality (hazard ratio 15.30, 95% CI 2.65–88.48, P=0.002). Conclusion A higher stage of AKI is independently associated with increased in-hospital mortality in elderly patients with hip fractures. Prevention and prompt treatment of AKI, especially higher AKI stages, is important in elderly patients with hip fractures.

Nomogram predictive model for in-hospital mortality risk in elderly ICU patients with urosepsis

BackgroundUrinary tract infection (UTI) is a common cause of sepsis. Elderly patients with urosepsis in intensive care unit (ICU) have more severe conditions and higher mortality rates owing to factors such as advanced age, immunosenescence, and persistent host inflammatory responses. However, comprehensive studies on nomograms to predict the in-hospital mortality risk in elderly patients with urosepsis are lacking. This study aimed to construct a nomogram predictive model to accurately assess the prognosis of elderly patients with urosepsis and provide therapeutic recommendations.MethodsData of elderly patients with urosepsis were extracted from the Medical Information Mart for Intensive Care (MIMIC) IV 2.2 database. Patients were randomly divided into training and validation cohorts. A predictive nomogram model was constructed from the training set using logistic regression analysis, followed by internal validation and sensitivity analysis.ResultsThis study included 1,251 patients. LASSO regression analysis revealed that the Glasgow Coma Scale (GCS) score, red cell distribution width (RDW), white blood count (WBC), and invasive ventilation were independent risk factors identified from a total of 43 variables studied. We then created and verified a nomogram. The area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) of the nomogram were superior to those of the traditional SAPS-II, APACHE-II, and SOFA scoring systems. The Hosmer-Lemeshow test results and calibration curves suggested good nomogram calibration. The IDI and NRI values showed that our nomogram scoring tool performed better than the other scoring systems. The DCA curves showed good clinical applicability of the nomogram.ConclusionsThe nomogram constructed in this study is a convenient tool for accurately predicting in-hospital mortality in elderly patients with urosepsis in ICU. Improving the treatment strategies for factors related to the model could improve the in-hospital survival rates of these patients.

Risk of Bleeding in Elderly Patients Undergoing Transcatheter Aortic Valve Implantation or Surgical Aortic Valve Replacement

Background: Bleeding complications are strong predictors of mortality and major morbidity in elderly patients undergoing surgical aortic valve replacement (SAVR) or transcatheter aortic valve implantation (TAVI). Despite the high prevalence of frailty in this population, little is known about its effects on bleeding risk. Methods: We conducted a retrospective observational study of 502 patients undergoing isolated SAVR or TAVI between January 2015 and February 2022. The occurrence of blood products transfusions and MACEs were the primary endpoints. In-hospital mortality was the secondary endpoint. Results: The Elderly group (age < 80 years old) included 475 subjects, whereas the Very Elderly group (age ≥ 80 years old) included 127 patients. The need for blood product transfusion was similar among the two groups, regardless of the type of procedure. MACEs occurred similarly between groups [SAVR: Elderly group: 7.9% vs. Very Elderly group: 8.6%, p = 0.864; TAVI: Elderly group: 5.5% vs. Very Elderly group: 8.7%, p = 0.378]. The was no difference in in-hospital mortality rate in patients submitted to TAVI, whereas very elderly patients had higher mortality rate compared to the elderly patients submitted to SAVR [SAVR: Elderly group: 0% vs. Very Elderly group: 2.8%, p = 0.024; TAVI: Elderly group: 4,8% vs. Very Elderly group: 8%, p = 0.389]. Conclusions: Age alone should not be considered as a predictive factor for post-operative adverse events or in-hospital mortality in elderly patients with severe symptomatic AS.

The prediction of in-hospital mortality in elderly patients with sepsis-associated acute kidney injury utilizing machine learning models

BackgroundSepsis-associated acute kidney injury (SA-AKI) is a severe complication associated with poorer prognosis and increased mortality, particularly in elderly patients. Currently, there is a lack of accurate mortality risk prediction models for these patients in clinic. ObjectivesThis study aimed to develop and validate machine learning models for predicting in-hospital mortality risk in elderly patients with SA-AKI. MethodsMachine learning models were developed and validated using the public, high-quality Medical Information Mart for Intensive Care (MIMIC)-IV critically ill database. The recursive feature elimination (RFE) algorithm was employed for key feature selection. Eleven predictive models were compared, with the best one selected for further validation. Shapley Additive Explanations (SHAP) values were used for visualization and interpretation, making the machine learning models clinically interpretable. ResultsThere were 16,154 patients with SA-AKI in the MIMIC-IV database, and 8426 SA-AKI patients were included in this study (median age: 77.0 years; female: 45%). 7728 patients excluded based on these criteria. They were randomly divided into a training cohort (5,934, 70%) and a validation cohort (2,492, 30%). Nine key features were selected by the RFE algorithm. The CatBoost model achieved the best performance, with an AUC of 0.844 in the training cohort and 0.804 in the validation cohort. SHAP values revealed that AKI stage, PaO2, and lactate were the top three most important features contributing to the CatBoost model. ConclusionWe developed a model capable of predicting the risk of in-hospital mortality in elderly patients with SA-AKI.

Effect of Comorbid Diseases on Hospitalization and In-Hospital Mortality of Elderly Patients Who Reapplied to the Emergency Department Within 72 Hours.

This study aimed to determine the effects of comorbidities on hospitalization and in-hospital mortality in patients aged 65 years and older who returned to the emergency department within 24, 48, and 72 hours of an initial visit. This study was conducted at the Department of Emergency Medicine, Firat University Faculty of Medicine, Elâzığ, Turkey. It has a retrospective design and received local ethics committee approval from the university. Patients aged 65 years and older who presented to the emergency department within a one-year period (2022) were examined to identify those who returned to the emergency department within 24, 48, and 72 hours of an initial visit. A total of 763 (3.2%) patients >65 years of age returned to the emergency department within the first three days of their initial visit. Of these returning patients, 349 returned within 24 hours (Group 1), 227 within 48 hours (Group 2), and 187 within 72 hours (Group 3). Being female, polypharmacy, the presence of at least one comorbidity, cancer, and chronic renal failure were found to be independent predictors of hospitalization, whereas polypharmacy was found to be an independent predictor of in-hospital mortality. Patients returning to the emergency department shortly after an initial visit should be assessed more diligently due to the risk of mortality.

Dose-Response Relationship between C-Reactive Protein/Albumin Ratio and In-Hospital Mortality in Elderly Patients with Acute Ischemic Stroke

Introduction: The C-reactive protein/albumin ratio is a reliable indicator of outcome risk in several diseases. This study aims to evaluate prognostic power of the C-reactive protein/albumin ratio for in-hospital mortality and the dose-response relationship between the two in the oldest-old patients with acute ischemic stroke. Methods: A longitudinal observational study was conducted on patients with acute ischemic stroke (aged ≥80 years) from two tertiary hospitals between January 1, 2014, and January 31, 2020. Based on the tertiles of the C-reactive protein/albumin ratio, the patients were divided into three groups. Restrictive cubic spline and robust locally weighted regression analysis were performed on continuous variables to examine the dose-response relationship between the C-reactive protein/albumin ratio and in-hospital mortality risk. All-cause mortality during hospitalization was the outcome for this study. Results: The study included 584 patients (mean age = 84.6 ± 3.1 years; 59.6% men). The C-reactive protein/albumin ratio was divided into three groups, namely, T1 of <0.73, T2 of 0.73–2.03, and T3: >2.03. After adjusting for demographic and clinical characteristics, a higher C-reactive protein/albumin ratio was independently associated with in-hospital mortality. The hazard ratio for this association was 2.01 (95% confidence interval: 1.12–3.60, p = 0.019). A dose-response relationship between the C-reactive protein/albumin ratio and in-hospital mortality risk was observed. Sensitivity analysis found no attenuation in the hazard ratio in uninfected individuals, whereas no difference in the hazard ratio was noted in individuals with infections. Conclusions: When predicting in-hospital mortality in the oldest-old patients with ischemic stroke, the C-reactive protein/albumin ratio might be a helpful and convenient metric.

Abstract 11810: Is the Presence of Statin Use Before Onset of Disease Associated With In-Hospital Mortality in Elderly Patients With Acute Myocardial Infarction?

Introduction: The effect of statin on preventing cardiovascular events is clear, and aggressive lipid-lowering therapy with statins is strongly recommended for patients with coronary artery disease including of acute myocardial infarction (AMI). Hypothesis: The relationship between in-hospital mortality of AMI and statin use before onset of AMI in the elderly patients is unknown, we thus assessed. Methods: The subjects were 207 consecutive AMI patients aged 65 years or older who underwent successful percutaneous coronary intervention (PCI) at Kagoshima university hospital between January 2015 and March 2021. We investigated the association between in-hospital mortality after PCI and baseline characteristics including Global Registry of Acute Coronary Events (GRACE) risk score, which is widely used for estimating mortality in AMI patients. Results: Twenty-three patients (11%) died in-hospital. The mortality rate was significantly lower in patients who were taking statins at presentation compared to those who were not (3% vs. 16%, p=0.002). Univariate logistic regression analysis of risk factors for in-hospital mortality revealed that statin use before onset of AMI, level of serum albumin, level of high sensitivity C-reactive protein (hs-CRP) and GRACE risk score were associated with in-hospital mortality. Multivariate logistic analysis models adjusted for age, statin use, level of serum albumin, level of hs-CRP, and GRACE risk score showed that statin use [Odd's ratio (OR): 0.13, 95% confidence interval (CI): 0.02-0.81, p=0.029] and GRACE risk score (OR: 1.04, 95% CI: 1.02-1.06, p &lt;0.001) were independently associated with in-hospital mortality. The optimal cut-off on the receiver-operating characteristic curve of GRACE risk score for predicting in-hospital mortality was 160. In addition, multivariate logistic analysis models using GRACE risk score ≥160 and non-use of statin before the onset of AMI showed greater risk of in-hospital mortality (HR 19.24; 95% CI 6.62-55.94; p &lt;0.001) after PCI as compared to patients with GRACE risk score &lt;160 and statins use. Conclusions: In elderly AMI patients undergoing PCI, statin use before the onset of AMI was an independent risk factor for in-hospital mortality, as was the GRACE risk score.

Elevated ApoB/apoA-1 is Associated with in-Hospital Mortality in Elderly Patients with Acute Myocardial Infarction.

Apolipoprotein B/apolipoprotein A-1 (apoB/apoA-1) has been shown to be strongly associated with the risk of future cardiovascular disease, but the association between apoB/apoA-1 and the risk of in-hospital death in elderly patients with acute myocardial infarction(AMI) is inconclusive. To investigate the association between apoB/apoA-1 and the risk of in-hospital death in elderly patients with AMI. From December 2015 to December 2021, a total of 1495 elderly AMI patients (aged ≥ 60 years) with complete clinical history data were enrolled in the Second Hospital of Dalian Medical University. Outcome was defined as all-cause mortality during hospitalization. Multivariate logistic regression and restricted spline cubic (RCS) models were used to evaluate the association between apoB/apoA-1 and in-hospital mortality risk, respectively. Receiver operating characteristic(ROC) curves were used to evaluate the predictive value of apoB/apoA-1 for in-hospital mortality events. Discordance analysis was performed when apoB/apoA-1 and LDL-C/HDL-C were not in concordance. (1) A total of 128 patients (8.6%) died during hospitalization. Patients in the death group had higher apoB/apoA-1 than those in the non-death group, but lower apoA-1 levels than those in the non-death group, and the difference was statistically significant (P < 0.05); (2) Multivariate logistic regression analysis showed that apoB/apoA-1 was associated with the risk of in-hospital death in elderly AMI patients [Model 3 OR = 3.524 (1.622-7.659), P = 0.001]; (3) ROC curve analysis showed that apoB/apoA-1 (AUC = 0.572, P = 0.011) had some predictive value for the risk of in-hospital death in elderly AMI patients; (4) RCS models showed a linear dose-response relationship between apoB/apoA-1 and in-hospital death after adjusting for confounders (P for non-linearity = 0.762). ApoB/apoA-1 is associated with the risk of in-hospital death in elderly patients with AMI, and is superior to other blood lipid parameters and blood lipid ratio.

Risk factors for early mortality in elderly patients with unstable isolated C2 odontoid fracture treated with halo-vest or surgery

The aim of this study was to compare in-hospital mortality of three procedures –halo-vest immobilization, anterior spinal fixation (ASF), and posterior spinal fixation (PSF)– in the treatment of elderly patients with isolated C2 odontoid fracture. We extracted data for elderly patients who were admitted with C2 odontoid fracture and treated with at least one of the three procedures (halo-vest immobilization, ASF, or PSF) during hospitalization. We conducted a generalized propensity score-based matching weight analysis to compare in-hospital mortality among the three procedures. We further investigated independent risk factors for in-hospital death. The study involved 891 patients (halo-vest, n = 463; ASF, n = 74; and PSF, n = 354) with a mean age of 78 years. In-hospital death occurred in 45 (5.1%) patients. Treatment type was not significantly associated with in-hospital mortality. Male sex (odds ratio 2.98; 95% confidence interval 1.32–6.73; p = 0.009) and a Charlson comorbidity index of ≥ 3 (odds ratio 9.18; 95% confidence interval 3.25–25.92; p < 0.001) were independent risk factors for in-hospital mortality. In conclusion, treatment type was not significantly associated with in-hospital mortality in elderly patients with isolated C2 odontoid fracture. Halo-vest immobilization can help to avoid adverse events in patients with C2 odontoid fracture who are considered less suitable for surgical treatment.

An Easy-to-Use Nomogram Based on SII and SIRI to Predict in-Hospital Mortality Risk in Elderly Patients with Acute Myocardial Infarction.

Inflammatory response is closely associated with poor prognosis in elderly patients with acute myocardial infarction (AMI). The aim of this study was to develop an easy-to-use predictive model based on medical history data at admission, systemic immune inflammatory index (SII), and systemic inflammatory response index (SIRI) to predict the risk of in-hospital mortality in elderly patients with AMI. We enrolled 1550 elderly AMI patients (aged ≥60 years) with complete medical history data and randomized them 5:5 to the training and validation cohorts. Univariate and multivariate logistic regression analyses were used to screen risk factors associated with outcome events (in-hospital death) and to establish a nomogram. The discrimination, calibration, and clinical application value of nomogram were evaluated based on receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), respectively. The results of multivariate logistic regression showed that age, body mass index (BMI), previous stroke, diabetes, SII, and SIRI were associated with in-hospital death, and these indicators will be included in the final prediction model, which can be obtained by asking the patient's medical history and blood routine examination in the early stage of admission and can improve the utilization rate of the prediction model. The areas under the ROC curve for the training and validation cohorts nomogram were 0.824 (95% CI 0.796 to 0.851) and 0.809 (95% CI 0.780 to 0.836), respectively. Calibration curves and DCA showed that nomogram could better predict the risk of in-hospital mortality in elderly patients with AMI. The nomogram constructed by combining SII, SIRI, and partial medical history data (age, BMI, previous stroke, and diabetes) at admission has a good predictive effect on the risk of in-hospital death in elderly patients with AMI.

The Association between Emergency Department Length of Stay and In-Hospital Mortality in Older Patients Using Machine Learning: An Observational Cohort Study.

The association between emergency department (ED) length of stay (EDLOS) with in-hospital mortality (IHM) in older patients remains unclear. This retrospective study aims to delineate the relationship between EDLOS and IHM in elderly patients. From the ED patients (n = 383,586) who visited an urban academic tertiary care medical center from January 2010 to December 2016, 78,478 older patients (age ≥60 years) were identified and stratified into three age subgroups: 60-74 (early elderly), 75-89 (late elderly), and ≥90 years (longevous elderly). We applied multiple machine learning approaches to identify the risk correlation trends between EDLOS and IHM, as well as boarding time (BT) and IHM. The incidence of IHM increased with age: 60-74 (2.7%), 75-89 (4.5%), and ≥90 years (6.3%). The best area under the receiver operating characteristic curve was obtained by Light Gradient Boosting Machine model for age groups 60-74, 75-89, and ≥90 years, which were 0.892 (95% CI, 0.870-0.916), 0.886 (95% CI, 0.861-0.911), and 0.838 (95% CI, 0.782-0.887), respectively. Our study showed that EDLOS and BT were statistically correlated with IHM (p < 0.001), and a significantly higher risk of IHM was found in low EDLOS and high BT. The flagged rate of quality assurance issues was higher in lower EDLOS ≤1 h (9.96%) vs. higher EDLOS 7 h <t≤ 8 h (1.84%). Special attention should be given to patients admitted after a short stay in the ED and a long BT, and new concepts of ED care processes including specific areas and teams dedicated to older patients care could be proposed to policymakers.

Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio are Important Indicators for Predicting in-Hospital Death in Elderly AMI Patients.

To investigate the role of neutrophil-to-lymphocyte ratio(NLR) and platelet-to-lymphocyte(PLR) in predicting the risk of in-hospital mortality in elderly acute myocardial infarction(AMI) patients. This study was a single-center, retrospective and observational study. From December 2015 to December 2021, a total of 1550 elderly patients (age ≥ 60 years) with AMI with complete clinical history data were enrolled in the Second Hospital of Dalian Medical University. Routine blood tests were performed on admission, and NLR and PLR were calculated based on neutrophil, platelet, and lymphocyte counts. Outcome was defined as all-cause mortality during hospitalization. Cox regression and restricted spline cubic(RCS) models were used to evaluate the association of NLR and in-hospital mortality risk and the association of PLR with in-hospital mortality risk, respectively. (1) A total of 132 (8.5%) patients died during hospitalization. From the results of blood routine, the white blood cell, neutrophil, NLR and PLR in the death group were higher than those in the non-death group, while the lymphocyte was lower than that in the non-death group, and the difference was statistically significant (P < 0.05). (2) The results of receiver operating characteristic(ROC) curves analysis showed that the predictive ability of NLR (AUC = 0.790) for in-hospital death was better than that of PLR (AUC = 0.637). (3) Multivariate Cox proportional regression hazard models showed that high NLR was associated with the risk of in-hospital mortality in elderly AMI patients (HR = 3.091, 95% CI 2.097-4.557, P < 0.001), while high PLR was not. (4) RCS models showed a nonlinear dose-response relationship between NLR and in-hospital death (P for nonlinear = 0.0007). High NLR (> 6.69) is associated with the risk of in-hospital mortality in elderly patients with AMI and can be an independent predictor of poor short-term prognosis in elderly patients with AMI.

Predictors of in-hospital mortality in elderly patients with heart failure treated with tolvaptan.

We conducted an analysis of first-time tolvaptan users (≥80 years old) to determine the factors associated with the prognosis of elderly patients with heart failure. We retrospectively analyzed 66 consecutive patients with worsening heart failure (aged ≥80 years) who were admitted to Fujita Health University Bantane Hospital from 2011 to 2016 and treated with tolvaptan. Differences between the in-hospital death and survival groups were evaluated. Multivariate logistic regression analysis was also performed to identify the risk factors for mortality. Sixty-six patients were included, and 26 patients died during the index hospitalization. The patients who died had a significantly higher prevalence of ischemic heart disease; a higher heart rate; higher levels of plasma C-reactive protein, blood urea nitrogen (BUN), and creatinine; a lower serum albumin level; and a lower estimated glomerular filtration rate than surviving patients. The proportion of patients requiring early initiation of tolvaptan treatment (within 3 days of admission) was significantly higher in surviving patients. On the basis of multivariate logistic regression analysis, although a high heart rate and high BUN levels were independent factors for in-hospital prognosis, they were not significantly associated with the early use of tolvaptan (≤3 days vs. ≥4 days; odds ratio=0.39; 95% confidence interval=0.07-2.21; p=0.29). This study revealed that a higher heart rate and higher BUN levels were independent factors for in-hospital prognosis in elderly patients who received tolvaptan and that early tolvaptan use may not always be effective in elderly patients.

Characteristics and in-hospital mortality of elderly patients with heart failure in Spanish hospitals.

The prevalence of heart failure (HF) increases with age, and it is one of the leading causes of hospitalization and death in older patients. However, there are little data on in-hospital mortality in patients with HF ≥ 75 years in Spain. A retrospective analysis of the Spanish Minimum Basic Data Set was performed, including all HF episodes discharged from public hospitals in Spain between 2016 and 2019. Coding was performed using the International Classification of Diseases, 10th Revision. Patients ≥ 75 years with HF as the principal diagnosis were selected. We calculated: (1) the crude in-hospital mortality rate and its distribution according to age and sex; (2) the risk-standardized in-hospital mortality ratio; and (3) the association between in-hospital mortality and the availability of an intensive cardiac care unit (ICCU) in the hospital. We included 354,792 HF episodes of patients over 75 years. The mean age was 85.2 ± 5.5 years, and 59.2% of patients were women. The most frequent comorbidities were renal failure (46.1%), diabetes mellitus (35.5%), valvular disease (33.9%), cardiorespiratory failure (29.8%), and hypertension (26.9%). In-hospital mortality was 12.7%, and increased with age [odds ratio (OR) = 1.07, 95% CI: 1.07-1.07, P < 0.001] and was lower in women (OR = 0.96, 95% CI: 0.92-0.97, P < 0.001). The main predictors of mortality were the presence of cardiogenic shock (OR = 19.5, 95% CI: 16.8-22.7, P < 0.001), stroke (OR = 3.5, 95% CI: 3.0-4.0, P < 0.001) and advanced cancer (OR = 2.6, 95% CI: 2.5-2.8, P < 0.001). In hospitals with ICCU, the in-hospital risk-adjusted mortality tended to be lower (OR = 0.85, 95% CI: 0.72-1.00, P = 0.053). In-hospital mortality in patients with HF ≥ 75 years between 2016 and 2019 was 12.7%, higher in males and elderly patients. The main predictors of mortality were cardiogenic shock, stroke, and advanced cancer. There was a trend toward lower mortality in centers with an ICCU.

Development and validation of a nomogram for predicting in-hospital mortality of elderly patients with persistent sepsis-associated acute kidney injury in intensive care units: a retrospective cohort study using the MIMIC-IV database

ObjectivesTo identify the clinical risk factors that influence in-hospital mortality in elderly patients with persistent sepsis-associated acute kidney injury (S-AKI) and to establish and validate a nomogram to predict in-hospital...

An In-Hospital Mortality Risk Model for Elderly Patients Undergoing Cardiac Valvular Surgery Based on LASSO-Logistic Regression and Machine Learning.

To preferably evaluate and predict the risk for in-hospital mortality in elderly patients receiving cardiac valvular surgery, we developed a new prediction model using least absolute shrinkage and selection operator (LASSO)-logistic regression and machine learning (ML) algorithms. Clinical data including baseline characteristics and peri-operative data of 7163 elderly patients undergoing cardiac valvular surgery from January 2016 to December 2018 were collected at 87 hospitals in the Chinese Cardiac Surgery Registry (CCSR). Patients were divided into training (N = 5774 [80%]) and testing samples (N = 1389 [20%]) according to their date of operation. LASSO-logistic regression models and ML models were used to analyze risk factors and develop the prediction model. We compared the discrimination and calibration of each model and EuroSCORE II. A total of 7163 patients were included in this study, with a mean age of 69.8 (SD 4.5) years, and 45.0% were women. Overall, in-hospital mortality was 4.05%. The final model included seven risk factors: age, prior cardiac surgery, cardiopulmonary bypass duration time (CPB time), left ventricular ejection fraction (LVEF), creatinine clearance rate (CCr), combined coronary artery bypass grafting (CABG) and New York Heart Association (NYHA) class. LASSO-logistic regression, linear discriminant analysis (LDA), support vector classification (SVC) and logistic regression (LR) models had the best discrimination and calibration in both training and testing cohorts, which were superior to the EuroSCORE II. The mortality rate for elderly patients undergoing cardiac valvular surgery was relatively high. LASSO-logistic regression, LDA, SVC and LR can predict the risk for in-hospital mortality in elderly patients receiving cardiac valvular surgery well.

Risk factors associated with mortality among elderly patients with COVID-19: Data from 55 intensive care units in Spain

Introduction and objectivesCritically-ill elderly ICU patients with COVID-19 have poor outcomes. We aimed to compare the rates of in-hospital mortality between non-elderly and elderly critically-ill COVID-19 ventilated patients, as well as to analyze the characteristics, secondary outcomes and independent risk factors associated with in-hospital mortality of elderly ventilated patients. Patients and MethodsWe conducted a multicentre, observational cohort study including consecutive critically-ill patients admitted to 55 Spanish ICUs due to severe COVID-19 requiring mechanical ventilation (non-invasive respiratory support [NIRS; include non-invasive mechanical ventilation and high-flow nasal cannula] and invasive mechanical ventilation [IMV]) between February 2020 and October 2021. ResultsOut of 5,090 critically-ill ventilated patients, 1,525 (27%) were aged ≥70 years (554 [36%] received NIRS and 971 [64%] received IMV. In the elderly group, median age was 74 years (interquartile range 72–77) and 68% were male. Overall in-hospital mortality was 31% (23% in patients <70 years and 50% in those ≥70 years; p<0.001). In-hospital mortality in the group ≥70 years significantly varied according to the modality of ventilation (40% in NIRS vs. 55% in IMV group; p<0.001). Factors independently associated with in-hospital mortality in elderly ventilated patients were age (sHR 1.07 [95%CI 1.05–1.10], p<0.001); previous admission within the last 30 days (sHR 1.40 [95%CI 1.04–1.89], p = 0.027); chronic heart disease (sHR 1.21 [95%CI 1.01–1.44], p = 0.041); chronic renal failure (sHR 1.43 [95%CI 1.12- 1.82], p = 0.005); platelet count (sHR 0.98 [95% CI 0.98–0.99], p<0.001); IMV at ICU admission (sHR 1.41 [95% CI 1.16- 1.73], p<0.001); and systemic steroids (sHR 0.61 [95%CI 0.48- 0.77], p<0.001). ConclusionsAmongst critically-ill COVID-19 ventilated patients, those aged ≥70 years presented significantly higher rates of in-hospital mortality than younger patients. Increasing age, previous admission within the last 30 days, chronic heart disease, chronic renal failure, platelet count, IMV at ICU admission and systemic steroids (protective) all comprised independent factors for in-hospital mortality in elderly patients

The nomogram to predict the occurrence of sepsis-associated encephalopathy in elderly patients in the intensive care units: A retrospective cohort study.

Sepsis-associated encephalopathy (SAE) is a critical and common problem in elderly patients with sepsis, which is still short of efficient predictive tools. Therefore, this study aims to screen the risk factors and establish a useful predictive nomogram for SAE in elderly patients with sepsis in the intensive care unit (ICU). Elderly patients (age ≥ 65 years) with sepsis were selected from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Data from demographics and laboratory examinations were collected on the first day of admission to the ICU. SAE was defined by two criteria in the presence of sepsis: ① a Glasgow Coma Scale (GCS) score of < 15 or ② delirium. Differences in demographics and laboratory tests were calculated between SAE and non-SAE groups. Participants were randomly divided into a training set and a validation set without replacement at a ratio of 6:4. A predictive nomogram was constructed in the training set by logistic regression analysis and then validated. The predictive capability of the nomogram was demonstrated by receiver operating characteristic (ROC) analysis and calibration curve analysis. A total of 22,361 patients were selected, of which 2,809 patients (12.7%) died in the hospital and 8,290 patients (37.1%) had SAE. In-hospital mortality in the SAE group was higher than that in the non-SAE group (18.8 vs. 8.9%, p < 0.001). Based on the results of logistic regression analysis, a nomogram integrating age, Na+, Sequential Organ Failure Assessment (SOFA) score, heart rate, and body temperature were constructed. The area under the curve (AUC) of the nomogram was 80.2% in the training set and 80.9% in the validation set. Calibration curve analysis showed a good predictive capacity of the nomogram. SAE is an independent risk of in-hospital mortality in elderly patients in the intensive care unit. The nomogram has an excellent predictive capability of SAE and helps in clinical practice.

Twenty-eight-day in-hospital mortality prediction for elderly patients with ischemic stroke in the intensive care unit: Interpretable machine learning models.

Risk stratification of elderly patients with ischemic stroke (IS) who are admitted to the intensive care unit (ICU) remains a challenging task. This study aims to establish and validate predictive models that are based on novel machine learning (ML) algorithms for 28-day in-hospital mortality in elderly patients with IS who were admitted to the ICU. Data of elderly patients with IS were extracted from the electronic intensive care unit (eICU) Collaborative Research Database (eICU-CRD) records of those elderly patients admitted between 2014 and 2015. All selected participants were randomly divided into two sets: a training set and a validation set in the ratio of 8:2. ML algorithms, such as Naïve Bayes (NB), eXtreme Gradient Boosting (xgboost), and logistic regression (LR), were applied for model construction utilizing 10-fold cross-validation. The performance of models was measured by the area under the receiver operating characteristic curve (AUC) analysis and accuracy. The present study uses interpretable ML methods to provide insight into the model's prediction and outcome using the SHapley Additive exPlanations (SHAP) method. As regards the population demographics and clinical characteristics, the analysis in the present study included 1,236 elderly patients with IS in the ICU, of whom 164 (13.3%) died during hospitalization. As regards feature selection, a total of eight features were selected for model construction. In the training set, both the xgboost and NB models showed specificity values of 0.989 and 0.767, respectively. In the internal validation set, the xgboost model identified patients who died with an AUC value of 0.733 better than the LR model which identified patients who died with an AUC value of 0.627 or the NB model 0.672. The xgboost model shows the best predictive performance that predicts mortality in elderly patients with IS in the ICU. By making the ML model explainable, physicians would be able to understand better the reasoning behind the outcome.

In-hospital mortality and cardiovascular treatment during hospitalization for heart failure among patients with schizophrenia: a nationwide cohort study.

Schizophrenia is associated with cardiovascular disease (CVD) risk, and patients with schizophrenia are more likely to receive suboptimal care for CVD. However, there is limited knowledge regarding in-hospital prognosis and quality of care for patients with schizophrenia hospitalized for heart failure (HF). This study sought to elucidate the association between schizophrenia and in-hospital mortality, as well as cardiovascular treatment in patients hospitalized with HF. Using the nationwide cardiovascular registry data in Japan, a total of 704,193 patients hospitalized with HF from 2012 to 2019 were included and stratified by age: young age, > 18 to 45years (n=20,289); middle age, >45 to 65years (n=114,947); and old age, >65 to 85years (n=568,957). All and 30-day in-hospital mortality as well as prescription of cardiovascular medications were assessed. After multiple imputation for missing values, mixed-effect multivariable logistic regression analysis was performed using patient and hospital characteristics with hospital identifier as a variable with random effects. Patients with schizophrenia were more likely to experience prolonged hospital stays, and incur higher hospitalization costs. In-hospital mortality for non-elderly patients with schizophrenia was significantly worse than for those without schizophrenia: the mortality rate was 7.6% vs 3.5% and the adjusted odds ratio (OR) was 1.96 (95% confidence interval (CI): 1.24-3.10, P=0.0037) in young adult patients; 6.2% vs 4.0% and 1.49 (95% CI: 1.17-1.88, P<0.001) in middle-aged patients. Thirty-day in-hospital mortality was significantly worse in middle-aged patients: the mortality rate was 4.7% vs 3.0% and an adjusted OR was 1.40 (95% CI: 1.07-1.83, P=0.012). In-hospital mortality in elderly patients did not differ between those with and without schizophrenia. Prescriptions of beta-blockers and angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers were significantly lower in patients with schizophrenia across all age groups. Schizophrenia was identified as a risk factor for in-hospital mortality and reduced prescription of cardioprotective medications in non-elderly patients hospitalized with HF. These findings highlight the necessity for differentiated care and management of HF in patients with severe mental illnesses.